release: alpha bump prebuilt 1.1.0a1, langgraph 1.2.0a4 (#7679)

## Summary
- Bumps `langgraph` 1.2.0a1 → 1.2.0a3
- Bumps `langgraph-checkpoint` 4.1.0a1 → 4.1.0a3
- Bumps `langgraph-checkpoint-postgres` 3.1.0a1 → 3.1.0a3
- Bumps min `langgraph-checkpoint` constraint in `langgraph` to
`>=4.1.0a3`
- Refreshes uv locks across the workspace

(Note: `a2` was already cut from another branch.)

## Test plan
- [ ] CI passes

.github/workflows/_integration_test.yml

@@ -121,8 +121,8 @@ jobs:
             exit 1
           fi
           LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
-          if [ "$LANGCHAIN_OPENAI_VERSION" != "1.0.1" ]; then
-            echo "LANGCHAIN_OPENAI_VERSION != 1.0.1; $LANGCHAIN_OPENAI_VERSION"
+          if [ "$LANGCHAIN_OPENAI_VERSION" != "1.1.14" ]; then
+            echo "LANGCHAIN_OPENAI_VERSION != 1.1.14; $LANGCHAIN_OPENAI_VERSION"
             exit 1
           fi
           LANGCHAIN_ANTHROPIC_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-anthropic'); print(v);")

README.md

@@ -16,7 +16,7 @@
   <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
   <a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
   <a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
-  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
+  <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
 </div>
 
 <br>

docs/superpowers/specs/2026-04-17-diff-channel-design.md

@@ -1,405 +0,0 @@
-# DiffChannel: Incremental Checkpoint Storage for Append-Style Reducers
-
-**Date:** 2026-04-17  
-**Status:** Approved for implementation  
-**Scope:** `libs/checkpoint`, `libs/langgraph`, `libs/checkpoint-postgres`
-
----
-
-## Motivation
-
-LangGraph checkpoints today store the **full accumulated value** of every channel on every step. For a `messages` channel backed by `add_messages`, this means each checkpoint blob contains the entire conversation history. Storage cost grows O(N²) in the number of turns: step 1 stores 1 message, step 100 stores 100 messages, step 1000 stores 1000 messages. For long-running agentic conversations with high-token messages this is untenable.
-
-The fix is to store only the **delta** (new writes) per step, reconstructing the full accumulated value at load time by replaying the chain. This is an opt-in mechanism — existing graphs are unaffected.
-
----
-
-## Non-Goals
-
-- **Compaction / materialized snapshots**: deferred. Load cost stays O(N) blob fetches but those fetches are batched into a single query — acceptable for now.
-- **SQLite saver support**: SQLite stores all channel values inline in one row (no per-channel blob table). Deferred to a follow-up.
-- **Automatic migration** of existing `BinaryOperatorAggregate` channels: users opt in explicitly. Old checkpoints load correctly via the backwards-compatibility path in `from_checkpoint`.
-
----
-
-## Architecture Overview
-
-```
-User state definition
-  └── Annotated[list[AnyMessage], DiffChannel(add_messages)]
-
-Write path (per superstep)
-  DiffChannel.update()       — apply operator, accumulate writes in _pending
-  DiffChannel.checkpoint()   — return DiffDelta(delta=_pending, prev_version=_base_version)
-  serde.dumps_typed()        — serialize DiffDelta as ("diff", msgpack_bytes)
-  saver.put()                — store blob at (thread_id, ns, "messages", version_N)
-  DiffChannel.after_checkpoint(version_N) — advance _base_version, clear _pending
-
-Read path (on graph load or time-travel)
-  saver.get_tuple()          — fetch current-version blob per channel
-  saver._load_blobs()        — detect "diff" type → follow chain to reconstruct DiffChainValue
-  DiffChannel.from_checkpoint(DiffChainValue) — replay deltas with operator → full list
-  DiffChannel.after_checkpoint(version_N)     — set _base_version for next write
-```
-
-The pregel layer (`_checkpoint.py`, `_loop.py`) is unchanged except for two small additions to call the new `after_checkpoint` hook. The saver public interface (`BaseCheckpointSaver`) gains no new methods. All chain-following logic lives inside each saver's private `_load_blobs`.
-
----
-
-## New Protocol Types
-
-**Location:** `libs/checkpoint/langgraph/checkpoint/base/__init__.py`
-
-Two dataclasses form the contract between `DiffChannel` and savers:
-
-```python
-@dataclass
-class DiffDelta:
-    """Returned by DiffChannel.checkpoint(). Written to the blob store."""
-    delta: list[Any]           # raw writes passed to update() this step
-    prev_version: str | None   # version of the previous diff blob; None = chain root
-```
-
-```python
-@dataclass
-class DiffChainValue:
-    """Passed to DiffChannel.from_checkpoint(). Assembled by _load_blobs()."""
-    base: list[Any] | None     # starting accumulated value (None = empty start)
-    deltas: list[list[Any]]    # write-sets ordered oldest → newest
-```
-
-`DiffDelta` lives in the checkpoint base package (not the channel module) so savers can import it without creating a circular dependency. `DiffChainValue` is there for the same reason.
-
----
-
-## `BaseChannel.after_checkpoint()` Hook
-
-**Location:** `libs/langgraph/langgraph/channels/base.py`
-
-```python
-def after_checkpoint(self, version: Any) -> None:
-    """Called after checkpoint() (with the new version) and after from_checkpoint()
-    (with the current version). No-op by default; DiffChannel overrides."""
-    pass
-```
-
-This is a **non-abstract, no-op default** — fully backwards compatible. All existing channels inherit it silently. It is NOT in the abstract interface.
-
----
-
-## `DiffChannel[V]`
-
-**Location:** `libs/langgraph/langgraph/channels/diff.py` (new file)
-
-### Internal state
-
-| Attribute | Type | Description |
-|---|---|---|
-| `value` | `list[V]` | Full accumulated value (the reconstructed list) |
-| `operator` | `Callable` | The binary reducer (e.g. `add_messages`) |
-| `_pending` | `list[Any]` | Raw writes accumulated since last `after_checkpoint` call |
-| `_base_version` | `str \| None` | Version this channel was last checkpointed at (= `prev_version` for next delta) |
-| `_overwritten` | `bool` | True if an `Overwrite` was applied since last `after_checkpoint`; makes next blob a chain root |
-
-### `update(values)`
-
-Mirrors `BinaryOperatorAggregate.update()` with two additions:
-
-1. For each non-Overwrite value: apply `self.operator(self.value, value)` as before; **also append the raw incoming value to `self._pending`**.
-2. For an `Overwrite(v)` value: set `self.value = v`; set `self._pending = list(v)` (full value becomes the new delta); set `self._overwritten = True`.
-
-The key: `_pending` stores the **incoming writes** (what was passed to `update()`), not the diff of `self.value`. This is important because `add_messages` handles removal and update-by-ID — replaying the writes with `operator` during reconstruction applies that logic correctly.
-
-### `checkpoint()`
-
-```python
-def checkpoint(self) -> DiffDelta:
-    return DiffDelta(
-        delta=self._pending[:],
-        prev_version=None if self._overwritten else self._base_version,
-    )
-```
-
-- Normal step: `prev_version = self._base_version` → chain link
-- After Overwrite: `prev_version = None` → chain root (reconstruction stops here and uses `delta` as the full base value)
-
-Returns `DiffDelta`, never the raw accumulated list. The serde handles serialization.
-
-### `from_checkpoint(checkpoint)`
-
-```python
-def from_checkpoint(self, checkpoint) -> Self:
-    new = DiffChannel(self.typ, self.operator)
-    new.key = self.key
-    if checkpoint is MISSING:
-        new.value = []
-    elif isinstance(checkpoint, DiffChainValue):
-        accumulated = checkpoint.base or []
-        for step_writes in checkpoint.deltas:
-            # Mirror update() exactly: apply each write individually so operator
-            # semantics (e.g. add_messages ID-based removal) are respected.
-            for write in step_writes:
-                accumulated = new.operator(accumulated, write)
-        new.value = accumulated
-    elif isinstance(checkpoint, DiffDelta):
-        # Unsupported saver: _load_blobs returned a raw DiffDelta instead of
-        # assembling a DiffChainValue. Raise rather than silently losing history.
-        raise ValueError(
-            "DiffChannel received a raw DiffDelta from the checkpoint saver. "
-            "Your saver does not support incremental channel storage. "
-            "Use InMemorySaver or PostgresSaver."
-        )
-    else:
-        # Backwards compat: plain list from old BinaryOperatorAggregate checkpoint.
-        new.value = checkpoint
-    new._pending = []
-    new._base_version = None  # set by the subsequent after_checkpoint() call
-    return new
-```
-
-The operator is available on `self` (the channel spec) so reconstruction is correct for any reducer — the saver never needs to know about `add_messages`.
-
-`_pending` stores **individual writes** (each `value` from `update()`'s `values` sequence), so each `step_writes` list in `DiffChainValue.deltas` is replayed write-by-write — identical to the `update()` loop.
-
-### `after_checkpoint(version)`
-
-```python
-def after_checkpoint(self, version: Any) -> None:
-    if version != self._base_version:
-        self._base_version = version
-        self._pending = []
-        self._overwritten = False
-```
-
-No-op when `version == self._base_version` (channel wasn't updated this step — blob was not written). Clears `_pending` and advances `_base_version` when the channel was actually checkpointed.
-
-### Opt-in API
-
-```python
-from langgraph.channels.diff import DiffChannel
-
-class State(TypedDict):
-    messages: Annotated[list[AnyMessage], DiffChannel(add_messages)]
-```
-
-`StateGraph` already handles `BaseChannel` instances as annotation metadata — `DiffChannel` inherits this without any changes to `StateGraph`.
-
----
-
-## Serde Extension
-
-**Location:** `libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py`
-
-Add one branch to `dumps_typed` (before the `else` msgpack fallback), using the existing module-level `_msgpack_enc` so message ext-types (Pydantic v2, etc.) are handled correctly:
-
-```python
-elif isinstance(obj, DiffDelta):
-    return "diff", _msgpack_enc({"d": obj.delta, "p": obj.prev_version})
-```
-
-Add one branch to `loads_typed` so savers can decode diff blobs without importing `ormsgpack` directly:
-
-```python
-elif type_ == "diff":
-    return ormsgpack.unpackb(
-        data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-    )
-    # returns {"d": [writes...], "p": prev_version_str_or_none}
-```
-
-Savers call `serde.loads_typed(("diff", raw_bytes))` to decode a diff blob into `{"d": ..., "p": ...}`, then check `type_tag == "diff"` to trigger chain traversal. The serde layer is the only place that knows about `ormsgpack`.
-
----
-
-## Saver Changes
-
-### InMemorySaver
-
-**`put()` — `libs/checkpoint/langgraph/checkpoint/memory/__init__.py`**
-
-No change needed. The existing `self.serde.dumps_typed(values[k])` call already handles `DiffDelta` via the new serde branch above, storing it as `("diff", bytes)`.
-
-**`_load_blobs()` — same file**
-
-After checking `vv[0] != "empty"`, add a branch for `"diff"` before calling `serde.loads_typed`:
-
-```python
-def _load_blobs(self, thread_id, checkpoint_ns, versions):
-    channel_values = {}
-    diff_channels = {}  # channel_name -> current_version for diff channels
-
-    for k, v in versions.items():
-        kk = (thread_id, checkpoint_ns, k, v)
-        if kk not in self.blobs:
-            continue
-        type_tag, blob_bytes = self.blobs[kk]
-        if type_tag == "diff":
-            diff_channels[k] = v  # handle below
-        elif type_tag != "empty":
-            channel_values[k] = self.serde.loads_typed((type_tag, blob_bytes))
-
-    for k, current_version in diff_channels.items():
-        # Follow chain: newest → oldest, then reverse
-        chain_deltas = []
-        base = None
-        version = current_version
-        while version is not None:
-            kk = (thread_id, checkpoint_ns, k, version)
-            if kk not in self.blobs:
-                break
-            type_tag, blob_bytes = self.blobs[kk]
-            if type_tag == "diff":
-                # Use serde so we don't need to import ormsgpack directly
-                payload = self.serde.loads_typed((type_tag, blob_bytes))
-                chain_deltas.append(payload["d"])
-                version = payload["p"]  # prev_version; None = root
-            else:
-                # Old non-diff blob encountered: treat as base accumulated value
-                base = self.serde.loads_typed((type_tag, blob_bytes))
-                break
-        chain_deltas.reverse()
-        channel_values[k] = DiffChainValue(base=base, deltas=chain_deltas)
-
-    return channel_values
-```
-
-Each blob lookup is O(1) on the dict. Total: N dict lookups for a chain of depth N. Memory usage is identical to loading a single full-list blob (same total bytes, split across N entries).
-
-### PostgresSaver
-
-**`_load_blobs()` — `libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py`**
-
-The existing `SELECT_SQL` fetches one blob per channel via a JOIN. After running that query, detect any `"diff"` channels in the result and issue one additional range query:
-
-```python
-def _load_blobs(self, blob_values):
-    if not blob_values:
-        return {}
-
-    result = {}
-    diff_channels = {}  # channel_name -> current_version (as str)
-
-    for k, t, v in blob_values:
-        channel = k.decode()
-        type_tag = t.decode()
-        if type_tag == "diff":
-            # Decode via serde — no direct ormsgpack import needed
-            payload = self.serde.loads_typed((type_tag, v))
-            diff_channels[channel] = payload  # store for chain fetch
-        elif type_tag != "empty":
-            result[channel] = self.serde.loads_typed((type_tag, v))
-
-    if diff_channels:
-        result.update(self._load_diff_chains(diff_channels))
-
-    return result
-```
-
-`_load_diff_chains` issues one SQL query per diff channel (typically just `messages`):
-
-```sql
-SELECT version, type, blob
-FROM checkpoint_blobs
-WHERE thread_id = %s
-  AND checkpoint_ns = %s
-  AND channel = %s
-  AND version <= %s
-ORDER BY version ASC
-```
-
-In Python, iterate rows in ascending version order: if `type = "diff"`, accumulate the delta; if any other type is encountered, treat it as the base accumulated value and stop. Return `DiffChainValue(base=..., deltas=[...])`.
-
-This results in **at most 2 queries total** for a graph with one `DiffChannel` — existing behaviour for all other channels is unchanged.
-
-**`put()` / `_dump_blobs()`**
-
-No change needed. `_dump_blobs` calls `self.serde.dumps_typed(v)` for each channel value in `new_versions`. When `v` is a `DiffDelta`, the serde produces `("diff", bytes)` which is stored as `type = "diff"` in `checkpoint_blobs`. The `ON CONFLICT DO NOTHING` semantics are preserved.
-
-### SQLite
-
-Deferred. `SqliteSaver` stores the entire checkpoint as a single serialized row — it has no per-channel blob table. Supporting `DiffChannel` on SQLite would require adding a new blobs table, which is a separate migration tracked separately.
-
----
-
-## Pregel Layer Changes
-
-### `channels_from_checkpoint` — `libs/langgraph/langgraph/pregel/_checkpoint.py`
-
-After constructing each channel from its checkpoint value, call `after_checkpoint` so the channel records its current version:
-
-```python
-channels = {}
-for k, v in channel_specs.items():
-    ch = v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
-    ch.after_checkpoint(checkpoint["channel_versions"].get(k))
-    channels[k] = ch
-return channels, managed_specs
-```
-
-Existing channels get the no-op `after_checkpoint`. `DiffChannel` uses it to set `_base_version`.
-
-### `PregelLoop._put_checkpoint` — `libs/langgraph/langgraph/pregel/_loop.py`
-
-After `create_checkpoint(self.checkpoint, self.channels, self.step, ...)` returns and `do_checkpoint is True` and `self.channels is not None`, iterate channels and notify:
-
-```python
-if do_checkpoint and self.channels:
-    for k, ch in self.channels.items():
-        ch.after_checkpoint(self.checkpoint["channel_versions"].get(k))
-```
-
-This is called after `create_checkpoint` updates `self.checkpoint["channel_versions"]`, so `get(k)` returns the new version for updated channels and the old version for unchanged ones. `DiffChannel.after_checkpoint` only clears `_pending` when `version != _base_version`, so unchanged channels are no-ops.
-
----
-
-## Backwards Compatibility
-
-| Scenario | Behaviour |
-|---|---|
-| Existing graph using `add_messages` (BinaryOperatorAggregate) | Unaffected — no code changes, no data migration |
-| New graph with `DiffChannel`, loading old checkpoint blobs | `from_checkpoint` receives a plain `list` → used directly as accumulated value |
-| `DiffChannel` with `InMemorySaver` or `PostgresSaver` | Fully supported |
-| `DiffChannel` with `SqliteSaver` | `from_checkpoint` receives a raw `DiffDelta` (SqliteSaver stores channel_values inline), raises `ValueError` with a clear message pointing to supported savers |
-| Time-travel / fork to past checkpoint | Chain traversal uses the version at that checkpoint → reconstruction is correct |
-| `update_state` | Treated as a normal step: writes are deltas chained to history |
-| `Overwrite` value | Resets chain: next blob has `prev_version=None`; reconstruction starts fresh |
-
----
-
-## Testing Strategy
-
-1. **Unit tests for `DiffChannel`** (`libs/langgraph/tests/`):
-   - `update` → `checkpoint` → `after_checkpoint` → `checkpoint` lifecycle (2 steps, verify delta isolation)
-   - `from_checkpoint(DiffChainValue)` correctly replays multi-step chains using the operator
-   - `from_checkpoint(plain_list)` backwards-compat path
-   - `Overwrite` creates a root blob (`prev_version=None`) and reconstruction ignores prior chain
-   - `after_checkpoint` no-ops when version is unchanged
-
-2. **Integration tests with `InMemorySaver`** (`libs/langgraph/tests/`):
-   - 10-step conversation: verify final loaded state equals full accumulated messages
-   - Time-travel: fork to step 5, verify only messages 1–5 are present
-   - Mixed graph: some channels `BinaryOperatorAggregate`, one `DiffChannel` — both reconstruct correctly
-
-3. **Serde tests** (`libs/checkpoint/tests/`):
-   - `DiffDelta` round-trips through `dumps_typed` / saver storage
-   - Old `"msgpack"` blob for a channel → `DiffChannel.from_checkpoint` handles it
-
-4. **Postgres integration tests** (`libs/checkpoint-postgres/tests/`):
-   - Range query reconstructs correct full list after N steps
-   - Time-travel to checkpoint M reconstructs correct list of M messages
-
----
-
-## Files Changed
-
-| File | Change |
-|---|---|
-| `libs/checkpoint/langgraph/checkpoint/base/__init__.py` | Add `DiffDelta`, `DiffChainValue` dataclasses |
-| `libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py` | Add `"diff"` branch in `dumps_typed` |
-| `libs/checkpoint/langgraph/checkpoint/memory/__init__.py` | Chain traversal in `_load_blobs` |
-| `libs/langgraph/langgraph/channels/base.py` | Add no-op `after_checkpoint` method |
-| `libs/langgraph/langgraph/channels/diff.py` | **New file** — `DiffChannel` implementation |
-| `libs/langgraph/langgraph/channels/__init__.py` | Export `DiffChannel` |
-| `libs/langgraph/langgraph/pregel/_checkpoint.py` | Call `after_checkpoint` in `channels_from_checkpoint` |
-| `libs/langgraph/langgraph/pregel/_loop.py` | Call `after_checkpoint` after `create_checkpoint` |
-| `libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py` | Range-query chain reconstruction in `_load_blobs` |

libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

@@ -4,26 +4,35 @@ import threading
 from collections import defaultdict
 from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
 
 from langgraph.checkpoint.postgres import _internal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_STAGE1_SQL,
+    SELECT_DELTA_STAGE2_SQL,
+    BasePostgresSaver,
+    _DeltaStage1Row,
+    _DeltaStage2Row,
+)
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 
 Conn = _internal.Conn  # For backward compatibility
@@ -302,7 +311,12 @@ class PostgresSaver(BasePostgresSaver):
         # others are stored in blobs table
         blob_values = {}
         for k, v in checkpoint["channel_values"].items():
-            if v is None or isinstance(v, (str, int, float, bool)):
+            if v is DELTA_SENTINEL:
+                copy["channel_values"].pop(k)
+            elif isinstance(v, _DeltaSnapshot):
+                blob_values[k] = copy["channel_values"].pop(k)
+                copy["channel_values"][k] = True
+            elif v is None or isinstance(v, (str, int, float, bool)):
                 pass
             else:
                 blob_values[k] = copy["channel_values"].pop(k)
@@ -430,42 +444,55 @@ class PostgresSaver(BasePostgresSaver):
                 with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
-    def get_channel_blob(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-    ) -> Any:
-        """Look up a channel blob by checkpoint ID + channel via checkpoint_blobs."""
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._get_channel_writes_history`.
+
+        Two-stage query: stage 1 scans checkpoint metadata to walk the parent
+        chain and locate the nearest snapshot; stage 2 fetches only the
+        chain-limited writes and single seed blob.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = self.get_tuple(config)
+            if target is None:
+                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
         with self._cursor() as cur:
             cur.execute(
-                """
-                SELECT cb.type, cb.blob
-                FROM checkpoint_blobs cb
-                WHERE cb.thread_id = %s
-                  AND cb.checkpoint_ns = %s
-                  AND cb.channel = %s
-                  AND cb.version = (
-                      SELECT checkpoint->'channel_versions'->>%s
-                      FROM checkpoints
-                      WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s
-                  )
-                """,
+                SELECT_DELTA_STAGE1_SQL,
+                (channel, channel, thread_id, checkpoint_ns),
+            )
+            stage1_rows = cur.fetchall()
+        chain_cids, seed_version = self._walk_stage1(
+            cast("list[_DeltaStage1Row]", stage1_rows), checkpoint_id
+        )
+        seed_versions = [seed_version] if seed_version else []
+        with self._cursor() as cur:
+            cur.execute(
+                SELECT_DELTA_STAGE2_SQL,
                 (
                     thread_id,
                     checkpoint_ns,
                     channel,
-                    channel,
+                    chain_cids,
                     thread_id,
                     checkpoint_ns,
-                    checkpoint_id,
+                    channel,
+                    seed_versions,
                 ),
             )
-            row = cur.fetchone()
-        if row is None:
-            return NotImplemented
-        return self.serde.loads_typed((row["type"], row["blob"]))
+            stage2_rows = cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            chain_cids=chain_cids,
+            seed_version=seed_version,
+            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
+        )
 
     def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
@@ -479,13 +506,6 @@ class PostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
-        with self._cursor() as cur:
-            channel_values = self._load_blobs(
-                value["channel_values"],
-                thread_id=value["thread_id"],
-                checkpoint_ns=value["checkpoint_ns"],
-                cur=cur,
-            )
         return CheckpointTuple(
             {
                 "configurable": {
@@ -498,7 +518,7 @@ class PostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **channel_values,
+                    **self._load_blobs(value["channel_values"]),
                 },
             },
             value["metadata"],

libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py

@@ -4,26 +4,35 @@ import asyncio
 from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool
 
 from langgraph.checkpoint.postgres import _ainternal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_STAGE1_SQL,
+    SELECT_DELTA_STAGE2_SQL,
+    BasePostgresSaver,
+    _DeltaStage1Row,
+    _DeltaStage2Row,
+)
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 
 Conn = _ainternal.Conn  # For backward compatibility
@@ -261,7 +270,12 @@ class AsyncPostgresSaver(BasePostgresSaver):
         # others are stored in blobs table
         blob_values = {}
         for k, v in checkpoint["channel_values"].items():
-            if v is None or isinstance(v, (str, int, float, bool)):
+            if v is DELTA_SENTINEL:
+                copy["channel_values"].pop(k)
+            elif isinstance(v, _DeltaSnapshot):
+                blob_values[k] = copy["channel_values"].pop(k)
+                copy["channel_values"][k] = True
+            elif v is None or isinstance(v, (str, int, float, bool)):
                 pass
             else:
                 blob_values[k] = copy["channel_values"].pop(k)
@@ -391,42 +405,55 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
-    async def aget_channel_blob(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-    ) -> Any:
-        """Async look up of a channel blob by checkpoint ID + channel name."""
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._aget_channel_writes_history`.
+
+        Two-stage query: stage 1 scans checkpoint metadata to walk the parent
+        chain and locate the nearest snapshot; stage 2 fetches only the
+        chain-limited writes and single seed blob.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = await self.aget_tuple(config)
+            if target is None:
+                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
         async with self._cursor() as cur:
             await cur.execute(
-                """
-                SELECT cb.type, cb.blob
-                FROM checkpoint_blobs cb
-                WHERE cb.thread_id = %s
-                  AND cb.checkpoint_ns = %s
-                  AND cb.channel = %s
-                  AND cb.version = (
-                      SELECT checkpoint->'channel_versions'->>%s
-                      FROM checkpoints
-                      WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s
-                  )
-                """,
+                SELECT_DELTA_STAGE1_SQL,
+                (channel, channel, thread_id, checkpoint_ns),
+            )
+            stage1_rows = await cur.fetchall()
+        chain_cids, seed_version = self._walk_stage1(
+            cast("list[_DeltaStage1Row]", stage1_rows), checkpoint_id
+        )
+        seed_versions = [seed_version] if seed_version else []
+        async with self._cursor() as cur:
+            await cur.execute(
+                SELECT_DELTA_STAGE2_SQL,
                 (
                     thread_id,
                     checkpoint_ns,
                     channel,
-                    channel,
+                    chain_cids,
                     thread_id,
                     checkpoint_ns,
-                    checkpoint_id,
+                    channel,
+                    seed_versions,
                 ),
             )
-            row = await cur.fetchone()
-        if row is None:
-            return NotImplemented
-        return self.serde.loads_typed((row["type"], row["blob"]))
+            stage2_rows = await cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            chain_cids=chain_cids,
+            seed_version=seed_version,
+            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
+        )
 
     async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
@@ -440,19 +467,11 @@ class AsyncPostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
-        thread_id = value["thread_id"]
-        checkpoint_ns = value["checkpoint_ns"]
-        blob_values = value["channel_values"]
-
-        channel_values: dict[str, Any] = {}
-        if blob_values:
-            channel_values = self._load_blobs(blob_values)
-
         return CheckpointTuple(
             {
                 "configurable": {
-                    "thread_id": thread_id,
-                    "checkpoint_ns": checkpoint_ns,
+                    "thread_id": value["thread_id"],
+                    "checkpoint_ns": value["checkpoint_ns"],
                     "checkpoint_id": value["checkpoint_id"],
                 }
             },
@@ -460,15 +479,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **channel_values,
+                    **self._load_blobs(value["channel_values"]),
                 },
             },
             value["metadata"],
             (
                 {
                     "configurable": {
-                        "thread_id": thread_id,
-                        "checkpoint_ns": checkpoint_ns,
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
                         "checkpoint_id": value["parent_checkpoint_id"],
                     }
                 }

libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py

@@ -4,13 +4,16 @@ import random
 import warnings
 from collections.abc import Sequence
 from importlib.metadata import version as get_version
-from typing import Any, cast
+from typing import Any, TypedDict, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
+    PendingWrite,
+    _ChannelWritesHistory,
     get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
@@ -153,6 +156,62 @@ INSERT_CHECKPOINT_WRITES_SQL = """
 """
 
 
+class _DeltaStage2Row(TypedDict, total=False):
+    """One row from `SELECT_DELTA_STAGE2_SQL` (a UNION ALL of writes and blobs)."""
+
+    _kind: str  # "w" or "b"
+    checkpoint_id: str | None  # "w" rows only
+    type: str | None
+    blob: bytes | None
+    task_id: str | None  # "w" rows only
+    idx: int | None  # "w" rows only
+    version: str | None  # "b" rows only
+
+
+# Two-stage DeltaChannel reconstruction.  Stage 1 scans checkpoint
+# metadata (no blob bytes) to walk the parent chain and locate the
+# nearest snapshot marker.  Stage 2 fetches only the chain-limited
+# writes and the single seed snapshot blob.
+#
+# Parameter order:
+#   stage1: (channel, channel, thread_id, checkpoint_ns)
+#   stage2: (thread_id, checkpoint_ns, channel, chain_cids[],
+#            thread_id, checkpoint_ns, channel, seed_versions[])
+
+SELECT_DELTA_STAGE1_SQL = """
+    SELECT checkpoint_id,
+           parent_checkpoint_id,
+           checkpoint -> 'channel_versions' ->> %s AS ver,
+           (checkpoint -> 'channel_values' -> %s) IS NOT NULL AS has_snapshot
+    FROM checkpoints
+    WHERE thread_id = %s AND checkpoint_ns = %s
+"""
+
+SELECT_DELTA_STAGE2_SQL = """
+    SELECT 'w'::text AS _kind,
+           checkpoint_id,
+           type, blob, task_id, idx, NULL::text AS version
+    FROM checkpoint_writes
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+      AND checkpoint_id = ANY(%s)
+    UNION ALL
+    SELECT 'b', NULL,
+           type, blob, NULL, NULL, version
+    FROM checkpoint_blobs
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+      AND version = ANY(%s)
+"""
+
+
+class _DeltaStage1Row(TypedDict):
+    """One row from `SELECT_DELTA_STAGE1_SQL`."""
+
+    checkpoint_id: str
+    parent_checkpoint_id: str | None
+    ver: str | None
+    has_snapshot: bool
+
+
 class BasePostgresSaver(BaseCheckpointSaver[str]):
     SELECT_SQL = SELECT_SQL
     SELECT_PENDING_SENDS_SQL = SELECT_PENDING_SENDS_SQL
@@ -185,22 +244,97 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         )
 
     def _load_blobs(
-        self,
-        blob_values: list[tuple[bytes, bytes, bytes]],
-        *,
-        thread_id: str = "",
-        checkpoint_ns: str = "",
-        cur: Any = None,
+        self, blob_values: list[tuple[bytes, bytes, bytes]]
     ) -> dict[str, Any]:
         if not blob_values:
             return {}
-        result: dict[str, Any] = {}
-        for k, t, v in blob_values:
-            channel = k.decode()
-            type_tag = t.decode()
-            if type_tag != "empty":
-                result[channel] = self.serde.loads_typed((type_tag, v))
-        return result
+        return {
+            k.decode(): self.serde.loads_typed((t.decode(), v))
+            for k, t, v in blob_values
+            if t.decode() != "empty"
+        }
+
+    @staticmethod
+    def _walk_stage1(
+        stage1_rows: Sequence[_DeltaStage1Row],
+        target_id: str,
+    ) -> tuple[list[str], str | None]:
+        """Walk the parent chain from stage 1 metadata rows.
+
+        Returns (chain_cids, seed_version):
+          chain_cids: ancestor checkpoint IDs from target's parent down to
+                      the seed (or root), in newest-first order.
+          seed_version: the channel blob version at the nearest ancestor
+                        with has_snapshot=True, or None if pure delta.
+        """
+        parent_of: dict[str, str | None] = {}
+        ver_of: dict[str, str | None] = {}
+        snapshot_of: dict[str, bool] = {}
+        for r in stage1_rows:
+            cid = r["checkpoint_id"]
+            parent_of[cid] = r["parent_checkpoint_id"]
+            ver_of[cid] = r["ver"]
+            snapshot_of[cid] = r["has_snapshot"]
+
+        chain_cids: list[str] = []
+        seed_version: str | None = None
+        cur_cid: str | None = parent_of.get(target_id)
+        while cur_cid is not None:
+            chain_cids.append(cur_cid)
+            if snapshot_of.get(cur_cid, False):
+                seed_version = ver_of.get(cur_cid)
+                break
+            cur_cid = parent_of.get(cur_cid)
+        return chain_cids, seed_version
+
+    def _build_delta_channel_writes_history(
+        self,
+        *,
+        channel: str,
+        chain_cids: list[str],
+        seed_version: str | None,
+        stage2_rows: Sequence[_DeltaStage2Row],
+    ) -> _ChannelWritesHistory:
+        """Reconstruct delta channel history from two-stage query results.
+
+        chain_cids are in newest-first order (target's parent first).
+        stage2_rows contain only writes for chain_cids and the single
+        seed blob at seed_version.
+        """
+        writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
+        seed_blob: tuple[str, bytes] | None = None
+
+        for r in stage2_rows:
+            kind = r["_kind"]
+            if kind == "w":
+                cid = cast(str, r["checkpoint_id"])
+                writes_by_cid.setdefault(cid, []).append(
+                    cast(
+                        "tuple[str, bytes, str, int]",
+                        (r["type"], r["blob"], r["task_id"], r["idx"]),
+                    )
+                )
+            else:  # kind == "b"
+                seed_blob = cast("tuple[str, bytes]", (r["type"], r["blob"]))
+
+        for ws in writes_by_cid.values():
+            ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
+
+        if not chain_cids:
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        collected: list[PendingWrite] = []
+        for cid in chain_cids:
+            for type_tag, write_blob, task_id, _idx in writes_by_cid.get(cid, []):
+                val = self.serde.loads_typed((type_tag, write_blob))
+                collected.append((task_id, channel, val))
+
+        seed: Any = DELTA_SENTINEL
+        if seed_blob is not None and seed_blob[0] != "empty":
+            seed = self.serde.loads_typed(seed_blob)
+
+        collected.reverse()
+        return _ChannelWritesHistory(seed=seed, writes=collected)
 
     def _dump_blobs(
         self,

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.5"
+version = "3.1.0a3"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -12,7 +12,7 @@ readme = "README.md"
 license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
-  "langgraph-checkpoint>=2.1.2,<5.0.0",
+  "langgraph-checkpoint>=4.1.0a3,<5.0.0",
   "orjson>=3.11.5",
   "psycopg>=3.2.0",
   "psycopg-pool>=3.2.0",
@@ -20,7 +20,7 @@ dependencies = [
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint-postgres/tests/test_async.py

@@ -361,9 +361,9 @@ async def test_get_checkpoint_no_channel_values(
 
         load_checkpoint_tuple = saver._load_checkpoint_tuple
 
-        def patched_load_checkpoint_tuple(value):
+        async def patched_load_checkpoint_tuple(value):
             value["checkpoint"].pop("channel_values", None)
-            return load_checkpoint_tuple(value)
+            return await load_checkpoint_tuple(value)
 
         monkeypatch.setattr(
             saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
@@ -385,11 +385,11 @@ async def test_delta_channel_chain_reconstruction(saver_name: str) -> None:
     from langchain_core.messages import AIMessage, HumanMessage
     from langgraph.channels.delta import DeltaChannel
     from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
+    from langgraph.graph.message import _messages_delta_reducer
     from typing_extensions import TypedDict
 
     class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
 
     def respond(state: State) -> dict:
         n = len(state["messages"])

libs/checkpoint-postgres/uv.lock

@@ -259,7 +259,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -307,7 +307,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.5"
+version = "3.1.0a3"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },

libs/checkpoint-sqlite/pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-sqlite"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint-sqlite/uv.lock

@@ -268,7 +268,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },

libs/checkpoint/langgraph/checkpoint/base/__init__.py

@@ -1,10 +1,9 @@
 from __future__ import annotations
 
 import copy
-import dataclasses
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
-from typing import (  # noqa: UP035
+from typing import (
     Any,
     Generic,
     Literal,
@@ -19,6 +18,9 @@ from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.types import (
+    DELTA_SENTINEL as DELTA_SENTINEL,
+)
 from langgraph.checkpoint.serde.types import (
     ERROR,
     INTERRUPT,
@@ -31,24 +33,6 @@ V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
 
 
-@dataclasses.dataclass
-class DeltaValue:
-    """Returned by DeltaChannel.checkpoint(). Represents one step's writes."""
-
-    delta: list[Any]
-    prev_checkpoint_id: (
-        str | None
-    )  # ID of checkpoint containing previous blob; None = chain root
-
-
-@dataclasses.dataclass
-class DeltaChainValue:
-    """Passed to DeltaChannel.from_checkpoint(). Assembled by the pregel layer."""
-
-    base: list[Any] | None  # starting accumulated value; None = start from empty
-    deltas: list[list[Any]]  # per-step write-sets, ordered oldest → newest
-
-
 logger = logging.getLogger(__name__)
 
 
@@ -140,6 +124,30 @@ class CheckpointTuple(NamedTuple):
     pending_writes: list[PendingWrite] | None = None
 
 
+class _ChannelWritesHistory(NamedTuple):
+    """Result of `BaseCheckpointSaver._get_channel_writes_history`.
+
+    Storage-level view of what one channel wrote across the ancestor chain
+    of a target checkpoint:
+
+      * `seed` — the nearest ancestor's stored blob value for this channel,
+        or `DELTA_SENTINEL` if the walk reached the root without finding a
+        stored value. A non-sentinel seed typically indicates a pre-delta
+        snapshot preserved across a channel-type migration (e.g.
+        `BinaryOperatorAggregate` storage extended under `DeltaChannel`).
+      * `writes` — on-path deltas oldest→newest, one `PendingWrite` per
+        step that wrote to this channel. Writes stored at the target
+        checkpoint itself are pending for the next super-step and are
+        excluded.
+
+    Experimental: method surface may change; the NamedTuple shape is the
+    contract.
+    """
+
+    seed: Any
+    writes: list[PendingWrite]
+
+
 class BaseCheckpointSaver(Generic[V]):
     """Base class for creating a graph checkpointer.
 
@@ -478,41 +486,103 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
-    def get_channel_blob(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-    ) -> Any:
-        """Look up a single channel blob by checkpoint ID + channel name.
+    def _get_tuple_raw(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Pure storage read used by `_get_channel_writes_history`.
 
-        Returns NotImplemented if this saver does not support efficient
-        per-channel-version blob lookup. The pregel layer will fall back to
-        get_tuple() traversal in that case.
-
-        Savers with a dedicated blob store (InMemorySaver, PostgresSaver)
-        should override this for O(1) performance.
+        Must return the same value as `get_tuple` but must NOT trigger channel
+        reconstruction; otherwise the channel-hydration path would re-enter
+        `_get_channel_writes_history`. Override only if `get_tuple` itself
+        performs channel hydration.
         """
-        return NotImplemented
+        return self.get_tuple(config)
 
-    async def aget_channel_blob(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-    ) -> Any:
-        """Look up a single channel blob by checkpoint ID + channel name (async).
+    async def _aget_tuple_raw(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Async version of `_get_tuple_raw`. See docstring there."""
+        return await self.aget_tuple(config)
 
-        Returns NotImplemented if this saver does not support efficient
-        per-channel-version blob lookup. The pregel layer will fall back to
-        aget_tuple() traversal in that case.
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """**Experimental.** Query one channel's writes along the parent chain.
 
-        Savers with a dedicated blob store (InMemorySaver, PostgresSaver)
-        should override this for O(1) performance.
+        Storage-level query, not channel semantics: returns `(seed, writes)`
+        reflecting what storage knows about a single channel across the
+        ancestor chain of the target checkpoint identified by `config`.
+
+        * `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
+          Writes stored at the target `checkpoint_id` itself are pending
+          for the next super-step and are excluded.
+        * `seed` — the nearest ancestor's stored blob value for this
+          channel; `DELTA_SENTINEL` if the walk reached the root without
+          finding a stored value. A non-sentinel seed typically indicates
+          a pre-delta snapshot preserved across a channel-type migration.
+
+        Walks the **parent chain** (not `list(before=...)`): for forked
+        threads, only on-path ancestors contribute.
+
+        Reference implementation walks `get_tuple` + `parent_config`,
+        inspecting each ancestor's `channel_values[channel]` for the seed
+        terminator. Savers with direct storage access (`InMemorySaver`,
+        `PostgresSaver`) override for performance; the return contract is
+        fixed here.
+
+        Underscore-prefixed because the method surface is experimental.
         """
-        return NotImplemented
+        collected: list[PendingWrite] = []  # newest first; reversed at the end
+        target_tuple = self._get_tuple_raw(config)
+        cursor_config: RunnableConfig | None = (
+            target_tuple.parent_config if target_tuple else None
+        )
+        while cursor_config is not None:
+            tup = self._get_tuple_raw(cursor_config)
+            if tup is None:
+                break
+            # Collect this ancestor's writes FIRST — they encode the
+            # transition from this ancestor's state to its child's, so
+            # they must be included whether or not this ancestor is the
+            # seed terminator.
+            if tup.pending_writes:
+                # Within a superstep, pending_writes are oldest→newest;
+                # reverse to scan newest-first.
+                for write in reversed(tup.pending_writes):
+                    if write[1] != channel:
+                        continue
+                    collected.append(write)
+            # Seed terminator: any non-sentinel blob on an ancestor
+            # establishes the reconstruction base. Stop here.
+            ancestor_value = tup.checkpoint["channel_values"].get(channel)
+            if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                collected.reverse()
+                return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+            cursor_config = tup.parent_config
+        collected.reverse()
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Async version of `_get_channel_writes_history`. See docstring there."""
+        collected: list[PendingWrite] = []
+        target_tuple = await self._aget_tuple_raw(config)
+        cursor_config: RunnableConfig | None = (
+            target_tuple.parent_config if target_tuple else None
+        )
+        while cursor_config is not None:
+            tup = await self._aget_tuple_raw(cursor_config)
+            if tup is None:
+                break
+            if tup.pending_writes:
+                for write in reversed(tup.pending_writes):
+                    if write[1] != channel:
+                        continue
+                    collected.append(write)
+            ancestor_value = tup.checkpoint["channel_values"].get(channel)
+            if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                collected.reverse()
+                return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+            cursor_config = tup.parent_config
+        collected.reverse()
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
 
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -14,16 +14,20 @@ from typing import Any
 from langchain_core.runnables import RunnableConfig
 
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    PendingWrite,
     SerializerProtocol,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_checkpoint_metadata,
 )
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 
 logger = logging.getLogger(__name__)
 
@@ -121,50 +125,114 @@ class InMemorySaver(
         return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
     def _load_blobs(
-        self, thread_id: str, checkpoint_ns: str, versions: ChannelVersions
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        versions: ChannelVersions,
     ) -> dict[str, Any]:
-        channel_values: dict[str, Any] = {}
-        for k, v in versions.items():
-            kk = (thread_id, checkpoint_ns, k, v)
+        result: dict[str, Any] = {}
+        for k, ver in versions.items():
+            kk = (thread_id, checkpoint_ns, k, ver)
             if kk not in self.blobs:
                 continue
             vv = self.blobs[kk]
-            if vv[0] != "empty":
-                channel_values[k] = self.serde.loads_typed(vv)
-        return channel_values
+            if vv[0] == "empty":
+                continue
+            result[k] = self.serde.loads_typed(vv)
+        return result
 
-    def get_channel_blob(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-    ) -> Any:
-        """Fast-path blob lookup: checkpoint → channel version → blob."""
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = config["configurable"].get("checkpoint_id", "")
         ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
-        entry = ns_storage.get(checkpoint_id)
-        if entry is None:
-            return NotImplemented
-        checkpoint = self.serde.loads_typed(entry[0])
-        version = checkpoint["channel_versions"].get(channel)
-        if version is None:
-            return NotImplemented
-        kk = (thread_id, checkpoint_ns, channel, version)
-        if kk not in self.blobs:
-            return NotImplemented
-        vv = self.blobs[kk]
-        if vv[0] == "empty":
-            return NotImplemented
-        return self.serde.loads_typed(vv)
+        # Walk the parent chain newest→oldest. Skip the target itself —
+        # writes stored AT `checkpoint_id` are pending for the next step
+        # (pregel applies them via `apply_writes`; they aren't part of the
+        # snapshot value AT `checkpoint_id`).
+        chain: list[str] = []
+        target_entry = ns_storage.get(checkpoint_id)
+        current: str | None = target_entry[2] if target_entry is not None else None
+        while current is not None:
+            entry = ns_storage.get(current)
+            if entry is None:
+                break
+            chain.append(current)
+            _, _, parent = entry
+            current = parent
+        # Scan newest→oldest. A pre-delta blob on an ancestor terminates the
+        # walk and is bound as `seed`; without this, a thread migrated from
+        # pre-delta storage would replay ancestor writes all the way to the
+        # root AND miss any value that lived only in the old blob (e.g. from
+        # `update_state`).
+        #
+        # At each ancestor, check the blob BEFORE processing its pending
+        # writes: a pre-delta blob represents the state AT that ancestor,
+        # which already subsumes any writes stored under it. Processing
+        # those writes first would fold them into the reconstructed value
+        # twice (once via the blob, once via replay).
+        collected: list[PendingWrite] = []  # newest first
+        for cp_id in chain:  # newest → oldest
+            entry = ns_storage.get(cp_id)
+            if entry is not None:
+                ckpt = self.serde.loads_typed(entry[0])
+                ver = ckpt.get("channel_versions", {}).get(channel)
+                if ver is not None:
+                    blob_entry = self.blobs.get(
+                        (thread_id, checkpoint_ns, channel, ver)
+                    )
+                    if blob_entry is not None and blob_entry[0] != "empty":
+                        blob_value = self.serde.loads_typed(blob_entry)
+                        if blob_value is not DELTA_SENTINEL:
+                            if isinstance(blob_value, _DeltaSnapshot):
+                                # Step-based snapshot: the blob is state AT this
+                                # ancestor, but the ancestor's pending_writes
+                                # encode the NEXT step's transition and are NOT
+                                # subsumed by the snapshot — collect them first.
+                                step_writes = self.writes.get(
+                                    (thread_id, checkpoint_ns, cp_id), {}
+                                )
+                                for (_task_id, _idx), (
+                                    tid,
+                                    ch,
+                                    serialized,
+                                    _,
+                                ) in sorted(step_writes.items(), reverse=True):
+                                    if ch != channel:
+                                        continue
+                                    collected.append(
+                                        (tid, ch, self.serde.loads_typed(serialized))
+                                    )
+                                collected.reverse()
+                                return _ChannelWritesHistory(
+                                    seed=blob_value, writes=collected
+                                )
+                            # Pre-delta blob: state AT this ancestor already
+                            # subsumes its pending_writes — skip them.
+                            collected.reverse()
+                            return _ChannelWritesHistory(
+                                seed=blob_value, writes=collected
+                            )
 
-    async def aget_channel_blob(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-    ) -> Any:
-        return self.get_channel_blob(thread_id, checkpoint_ns, checkpoint_id, channel)
+            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
+            # Within a superstep, sorted by (task_id, idx) = oldest → newest;
+            # reverse for newest-first scan.
+            for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
+                step_writes.items(), reverse=True
+            ):
+                if ch != channel:
+                    continue
+                val = self.serde.loads_typed(serialized)
+                collected.append((tid, ch, val))
+        collected.reverse()
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        return self._get_channel_writes_history(config, channel)
 
     def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the in-memory storage.
@@ -384,7 +452,9 @@ class InMemorySaver(
         values: dict[str, Any] = c.pop("channel_values")  # type: ignore[misc]
         for k, v in new_versions.items():
             self.blobs[(thread_id, checkpoint_ns, k, v)] = (
-                self.serde.dumps_typed(values[k]) if k in values else ("empty", b"")
+                self.serde.dumps_typed(values[k])
+                if k in values and values[k] is not DELTA_SENTINEL
+                else ("empty", b"")
             )
         self.storage[thread_id][checkpoint_ns].update(
             {

libs/checkpoint/langgraph/checkpoint/serde/_msgpack.py

@@ -73,6 +73,7 @@ SAFE_MSGPACK_TYPES: frozenset[tuple[str, ...]] = frozenset(
         ("langchain_core.documents.base", "Document"),
         # langgraph
         ("langgraph.types", "Send"),
+        ("langgraph.types", "TimeoutPolicy"),
         ("langgraph.types", "Interrupt"),
         ("langgraph.types", "Command"),
         ("langgraph.types", "StateSnapshot"),
@@ -80,8 +81,6 @@ SAFE_MSGPACK_TYPES: frozenset[tuple[str, ...]] = frozenset(
         ("langgraph.types", "Overwrite"),
         ("langgraph.store.base", "Item"),
         ("langgraph.store.base", "GetOp"),
-        # DeltaChannel checkpoint value type
-        ("langgraph.checkpoint.base", "DeltaValue"),
     }
 )
 

libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py

@@ -33,24 +33,50 @@ from langchain_core.load.load import Reviver
 from langgraph.checkpoint.serde import _msgpack as _lg_msgpack
 from langgraph.checkpoint.serde.base import SerializerProtocol
 from langgraph.checkpoint.serde.event_hooks import emit_serde_event
-from langgraph.checkpoint.serde.types import SendProtocol
+from langgraph.checkpoint.serde.types import (
+    SendProtocol,
+    _DeltaSnapshot,
+)
 from langgraph.store.base import Item
 
 if TYPE_CHECKING:
     from langgraph.checkpoint.serde._msgpack import (
         AllowedMsgpackModules,
     )
-    from langgraph.checkpoint.serde.types import SendProtocol
 
 LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
 logger = logging.getLogger(__name__)
 
+# Dedup log warnings across process lifetime; cap bounds state if types are
+# dynamically generated (also acts as a circuit breaker on warning volume).
+# Dedup is best-effort: racing threads may each emit once for the same key,
+# and warnings are silently dropped once _MAX_WARNED_TYPES is reached.
+_MAX_WARNED_TYPES = 1000
+_warned_unregistered_types: set[tuple[str, str]] = set()
+_warned_blocked_types: set[tuple[str, str]] = set()
 
-def _is_delta_value(obj: Any) -> bool:
-    from langgraph.checkpoint.base import DeltaValue  # lazy import avoids circular dep
 
-    return isinstance(obj, DeltaValue)
+def _is_safe_json_type(id_list: list[str]) -> bool:
+    """Return True if an lc=2 id refers to a type in SAFE_MSGPACK_TYPES.
+
+    Safe types bypass the ``allowed_json_modules`` gate so that old "json" format
+    checkpoints (written before the msgpack migration) can be resumed without
+    requiring users to configure an explicit allowlist.
+    """
+    if len(id_list) < 2:
+        return False
+    module_name = ".".join(id_list[:-1])
+    return (module_name, id_list[-1]) in _lg_msgpack.SAFE_MSGPACK_TYPES
+
+
+def _warn_once(
+    seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
+) -> None:
+    if key in seen or len(seen) >= _MAX_WARNED_TYPES:
+        return
+    seen.add(key)
+    logger.warning(msg, *args)
 
 
 class JsonPlusSerializer(SerializerProtocol):
@@ -153,19 +179,23 @@ class JsonPlusSerializer(SerializerProtocol):
         return out
 
     def _reviver(self, value: dict[str, Any]) -> Any:
-        if self._allowed_json_modules and (
+        if (
             value.get("lc", None) == 2
             and value.get("type", None) == "constructor"
             and value.get("id", None) is not None
         ):
-            try:
-                return self._revive_lc2(value)
-            except InvalidModuleError as e:
-                logger.warning(
-                    "Object %s is not in the deserialization allowlist.\n%s",
-                    value["id"],
-                    e.message,
-                )
+            id_list = value["id"]
+            is_safe = _is_safe_json_type(id_list)
+            if self._allowed_json_modules or is_safe:
+                try:
+                    return self._revive_lc2(value)
+                except InvalidModuleError as e:
+                    if not is_safe:
+                        logger.warning(
+                            "Object %s is not in the deserialization allowlist.\n%s",
+                            value["id"],
+                            e.message,
+                        )
 
         return LC_REVIVER(value)
 
@@ -213,6 +243,13 @@ class JsonPlusSerializer(SerializerProtocol):
             method_display = "<init>"
 
         dotted = ".".join(needed)
+        # Safe types (the same set already allowed for msgpack deserialization) are
+        # permitted without an explicit allowlist — they are known-safe LangGraph and
+        # LangChain types.  This restores backwards-compat for old "json" checkpoints
+        # that pre-date the msgpack migration without reopening the broader security gate.
+        if _is_safe_json_type(list(needed)):
+            return
+
         if not self._allowed_json_modules:
             raise InvalidModuleError(
                 f"Refused to deserialize JSON constructor: {dotted} (method: {method_display}). "
@@ -245,8 +282,6 @@ class JsonPlusSerializer(SerializerProtocol):
             return "bytes", obj
         elif isinstance(obj, bytearray):
             return "bytearray", obj
-        elif _is_delta_value(obj):
-            return "delta", _msgpack_enc({"d": obj.delta, "c": obj.prev_checkpoint_id})
         else:
             try:
                 return "msgpack", _msgpack_enc(obj)
@@ -269,13 +304,6 @@ class JsonPlusSerializer(SerializerProtocol):
             return ormsgpack.unpackb(
                 data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
             )
-        elif type_ == "delta":
-            from langgraph.checkpoint.base import DeltaValue  # lazy import
-
-            raw = ormsgpack.unpackb(
-                data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-            )
-            return DeltaValue(delta=raw["d"], prev_checkpoint_id=raw.get("c"))
         elif self.pickle_fallback and type_ == "pickle":
             return pickle.loads(data_)
         else:
@@ -291,10 +319,13 @@ EXT_METHOD_SINGLE_ARG = 3
 EXT_PYDANTIC_V1 = 4
 EXT_PYDANTIC_V2 = 5
 EXT_NUMPY_ARRAY = 6
+EXT_DELTA_SNAPSHOT = 7
 
 
 def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
-    if hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
+    if isinstance(obj, _DeltaSnapshot):
+        return ormsgpack.Ext(EXT_DELTA_SNAPSHOT, _msgpack_enc(obj.value))
+    elif hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
         return ormsgpack.Ext(
             EXT_PYDANTIC_V2,
             _msgpack_enc(
@@ -466,10 +497,13 @@ def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
             ),
         )
     elif isinstance(obj, SendProtocol):
+        args: tuple[Any, ...] = (obj.node, obj.arg)
+        if (timeout := getattr(obj, "timeout", None)) is not None:
+            args = (obj.node, obj.arg, timeout)
         return ormsgpack.Ext(
             EXT_CONSTRUCTOR_POS_ARGS,
             _msgpack_enc(
-                (obj.__class__.__module__, obj.__class__.__name__, (obj.node, obj.arg)),
+                (obj.__class__.__module__, obj.__class__.__name__, args),
             ),
         )
     elif dataclasses.is_dataclass(obj):
@@ -520,6 +554,15 @@ def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
         raise TypeError(f"Object of type {obj.__class__.__name__} is not serializable")
 
 
+def _send_from_args(args: Sequence[Any]) -> Any:
+    # ya we have a cyclic import here ¯\_(ツ)_/¯
+    from langgraph.types import Send  # type: ignore
+
+    if len(args) == 2:
+        return Send(*args)
+    return Send(args[0], args[1], timeout=args[2])
+
+
 def _create_msgpack_ext_hook(
     allowed_modules: set[tuple[str, ...]] | Literal[True] | None,
 ) -> Callable[[int, bytes], Any]:
@@ -549,7 +592,9 @@ def _create_msgpack_ext_hook(
                     "name": name,
                 }
             )
-            logger.warning(
+            _warn_once(
+                _warned_unregistered_types,
+                key,
                 "Deserializing unregistered type %s.%s from checkpoint. "
                 "This will be blocked in a future version. "
                 "Set LANGGRAPH_STRICT_MSGPACK=true to block now, or add "
@@ -571,7 +616,9 @@ def _create_msgpack_ext_hook(
                 "name": name,
             }
         )
-        logger.warning(
+        _warn_once(
+            _warned_blocked_types,
+            key,
             "Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
             "Add to allowed_msgpack_modules to allow: [(%r, %r)]",
             module,
@@ -604,7 +651,13 @@ def _create_msgpack_ext_hook(
         return False
 
     def ext_hook(code: int, data: bytes) -> Any:
-        if code == EXT_CONSTRUCTOR_SINGLE_ARG:
+        if code == EXT_DELTA_SNAPSHOT:
+            return _DeltaSnapshot(
+                ormsgpack.unpackb(
+                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+                )
+            )
+        elif code == EXT_CONSTRUCTOR_SINGLE_ARG:
             try:
                 tup = ormsgpack.unpackb(
                     data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
@@ -625,6 +678,8 @@ def _create_msgpack_ext_hook(
                 )
                 if not _check_allowed(tup[0], tup[1]):
                     return tup[2]
+                if tup[0] == "langgraph.types" and tup[1] == "Send":
+                    return _send_from_args(tup[2])
                 # module, name, args
                 return getattr(importlib.import_module(tup[0]), tup[1])(*tup[2])
             except Exception:
@@ -738,9 +793,7 @@ def _msgpack_ext_hook_to_json(code: int, data: bytes) -> Any:
                 option=ormsgpack.OPT_NON_STR_KEYS,
             )
             if tup[0] == "langgraph.types" and tup[1] == "Send":
-                from langgraph.types import Send  # type: ignore
-
-                return Send(*tup[2])
+                return _send_from_args(tup[2])
             # module, name, args
             return tup[2]
         except Exception:

libs/checkpoint/langgraph/checkpoint/serde/types.py

@@ -1,6 +1,7 @@
 from collections.abc import Sequence
 from typing import (
     Any,
+    NamedTuple,
     Protocol,
     TypeVar,
     runtime_checkable,
@@ -14,6 +15,37 @@ INTERRUPT = "__interrupt__"
 RESUME = "__resume__"
 TASKS = "__pregel_tasks"
 
+
+class _DeltaSentinel:
+    """In-memory marker for a DeltaChannel field with no snapshot.
+
+    Never serialized to storage — checkpointers strip it before writing.
+    Compare with `is DELTA_SENTINEL`; always the same module-level instance.
+    """
+
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "DELTA_SENTINEL"
+
+
+DELTA_SENTINEL = _DeltaSentinel()
+
+
+class _DeltaSnapshot(NamedTuple):
+    """Snapshot blob for a DeltaChannel with finite snapshot_frequency.
+
+    Stored in checkpoint_blobs via the `EXT_DELTA_SNAPSHOT` msgpack ext code.
+    The ancestor walk in `_get_channel_writes_history` terminates when it
+    encounters this type (any non-sentinel blob stops the walk).
+
+    `from_checkpoint` reconstructs the channel value directly from `.value`
+    without replaying writes — the snapshot IS the accumulated state.
+    """
+
+    value: Any
+
+
 Value = TypeVar("Value", covariant=True)
 Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"
@@ -18,7 +18,7 @@ dependencies = [
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint/tests/test_encrypted.py

@@ -29,6 +29,8 @@ from langgraph.checkpoint.serde.jsonplus import (
     EXT_METHOD_SINGLE_ARG,
     JsonPlusSerializer,
     _msgpack_enc,
+    _warned_blocked_types,
+    _warned_unregistered_types,
 )
 
 
@@ -102,6 +104,13 @@ def test_msgpack_method_pathlib_blocked_encrypted_strict(
 class TestEncryptedSerializerMsgpackAllowlist:
     """Test msgpack allowlist behavior through EncryptedSerializer."""
 
+    @pytest.fixture(autouse=True)
+    def _reset_warned_types(self) -> None:
+        # Warning dedup state is process-global; reset per-test so each case
+        # sees a fresh slate and assertions about warning emission are stable.
+        _warned_unregistered_types.clear()
+        _warned_blocked_types.clear()
+
     def test_safe_types_no_warning(self, caplog: pytest.LogCaptureFixture) -> None:
         """Test safe types deserialize without warnings through encryption."""
         serde = _make_encrypted_serde()

libs/checkpoint/tests/test_jsonplus.py

@@ -35,6 +35,8 @@ from langgraph.checkpoint.serde.jsonplus import (
     JsonPlusSerializer,
     _msgpack_enc,
     _msgpack_ext_hook_to_json,
+    _warned_blocked_types,
+    _warned_unregistered_types,
 )
 from langgraph.store.base import Item
 
@@ -331,6 +333,57 @@ def test_serde_jsonplus_bytes() -> None:
     assert serde.loads_typed(dumped) == some_bytes
 
 
+def test_lc2_json_safe_type_revives_without_allowlist() -> None:
+    """Old 'json' blobs with lc=2 for safe types must revive without an explicit allowlist.
+
+    Regression test for: https://github.com/langchain-ai/langgraph/issues/7498
+    Threads checkpointed before v1.0.1 (pre-msgpack) stored messages as lc=2 JSON
+    constructor dicts. Resuming those threads must reconstruct proper BaseMessage objects
+    rather than returning raw dicts that cause MESSAGE_COERCION_FAILURE in add_messages.
+    """
+    from langchain_core.messages import AIMessage
+
+    serde = JsonPlusSerializer()  # default: _allowed_json_modules=None
+
+    human_blob = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["langchain_core", "messages", "human", "HumanMessage"],
+        "kwargs": {"content": "hello", "type": "human"},
+    }
+    ai_blob = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["langchain_core", "messages", "ai", "AIMessage"],
+        "kwargs": {"content": "hi there", "type": "ai"},
+    }
+    result = serde.loads_typed(("json", json.dumps([human_blob, ai_blob]).encode()))
+
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage), (
+        f"Expected HumanMessage, got {type(result[0])}: {result[0]!r}\n"
+        "lc=2 JSON blobs for safe types must deserialize without an explicit allowlist"
+    )
+    assert result[0].content == "hello"
+    assert isinstance(result[1], AIMessage)
+    assert result[1].content == "hi there"
+
+
+def test_lc2_json_unknown_type_stays_blocked_without_allowlist() -> None:
+    """lc=2 JSON blobs for types NOT in SAFE_MSGPACK_TYPES still require an allowlist."""
+    serde = JsonPlusSerializer()
+    load = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["pprint", "pprint"],
+        "kwargs": {"object": "HELLO"},
+    }
+    # No allowlist configured → raw dict returned (not raised, not reconstructed)
+    result = serde.loads_typed(("json", json.dumps(load).encode()))
+    assert isinstance(result, dict), "Unknown lc=2 type must stay as raw dict"
+    assert result.get("lc") == 2
+
+
 def test_deserde_invalid_module() -> None:
     serde = JsonPlusSerializer()
     load = {
@@ -580,6 +633,14 @@ def test_msgpack_safe_types_no_warning(caplog: pytest.LogCaptureFixture) -> None
         assert result is not None
 
 
+@pytest.fixture(autouse=True)
+def _reset_warned_types() -> None:
+    # Warning dedup state is process-global; reset per-test so each case sees
+    # a fresh slate and assertions about warning emission are stable.
+    _warned_unregistered_types.clear()
+    _warned_blocked_types.clear()
+
+
 def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) -> None:
     """Pydantic models not in allowlist should log warning but still deserialize."""
     current = _lg_msgpack.STRICT_MSGPACK_ENABLED
@@ -595,6 +656,12 @@ def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) ->
     assert "unregistered type" in caplog.text.lower()
     assert "allowed_msgpack_modules" in caplog.text
     assert result == obj
+
+    # Second deserialization of the same type should NOT produce another warning
+    caplog.clear()
+    result2 = serde.loads_typed(dumped)
+    assert "unregistered type" not in caplog.text.lower()
+    assert result2 == obj
     _lg_msgpack.STRICT_MSGPACK_ENABLED = current
 
 
@@ -639,7 +706,6 @@ def test_msgpack_allowlist_silences_warning(caplog: pytest.LogCaptureFixture) ->
 
 def test_msgpack_none_blocks_unregistered(caplog: pytest.LogCaptureFixture) -> None:
     """allowed_msgpack_modules=None should block unregistered types."""
-
     serde = JsonPlusSerializer(allowed_msgpack_modules=None)
 
     obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
@@ -657,7 +723,6 @@ def test_msgpack_allowlist_blocks_non_listed(
     caplog: pytest.LogCaptureFixture,
 ) -> None:
     """Allowlists should block unregistered types even if msgpack is enabled."""
-
     serde = JsonPlusSerializer(
         allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
     )
@@ -983,31 +1048,3 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
     # No blocking should occur - inner is serialized as dict, not ext
     assert "blocked" not in caplog.text.lower()
     assert result == obj
-
-
-def test_delta_value_serde_round_trip() -> None:
-    from langgraph.checkpoint.base import DeltaValue
-    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
-    serde = JsonPlusSerializer()
-    original = DeltaValue(
-        delta=[{"type": "human", "content": "hi"}], prev_checkpoint_id="abc-123"
-    )
-    type_tag, blob = serde.dumps_typed(original)
-    assert type_tag == "delta"
-    loaded = serde.loads_typed((type_tag, blob))
-    assert isinstance(loaded, DeltaValue)
-    assert loaded.delta == original.delta
-    assert loaded.prev_checkpoint_id == "abc-123"
-
-
-def test_delta_value_serde_chain_root() -> None:
-    from langgraph.checkpoint.base import DeltaValue
-    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
-    serde = JsonPlusSerializer()
-    original = DeltaValue(delta=[], prev_checkpoint_id=None)
-    type_tag, blob = serde.dumps_typed(original)
-    loaded = serde.loads_typed((type_tag, blob))
-    assert isinstance(loaded, DeltaValue)
-    assert loaded.prev_checkpoint_id is None

libs/checkpoint/tests/test_memory.py

@@ -6,19 +6,32 @@ from langchain_core.runnables import RunnableConfig
 from pydantic import BaseModel
 
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     Checkpoint,
     CheckpointMetadata,
     create_checkpoint,
     empty_checkpoint,
 )
 from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.jsonplus import (
+    JsonPlusSerializer,
+    _warned_blocked_types,
+    _warned_unregistered_types,
+)
 
 
 class MemoryPydantic(BaseModel):
     foo: str
 
 
+@pytest.fixture(autouse=True)
+def _reset_warned_types() -> None:
+    # Warning dedup state is process-global; reset per-test so each case sees
+    # a fresh slate and assertions about warning emission are stable.
+    _warned_unregistered_types.clear()
+    _warned_blocked_types.clear()
+
+
 class TestMemorySaver:
     @pytest.fixture(autouse=True)
     def setup(self) -> None:
@@ -196,8 +209,6 @@ class TestMemorySaver:
 
 
 async def test_memory_saver() -> None:
-    from langgraph.checkpoint.memory import InMemorySaver
-
     memory_saver = InMemorySaver()
     assert isinstance(memory_saver, InMemorySaver)
 
@@ -311,33 +322,335 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
 
 
 class TestInMemorySaverDeltaChannel:
-    def test_get_channel_blob(self) -> None:
-        """get_channel_blob returns the deserialized blob for a checkpoint+channel."""
-        from langgraph.checkpoint.base import DeltaValue, empty_checkpoint
+    def test_load_blobs_omits_delta_channel(self) -> None:
+        """_load_blobs omits delta channels (stored as 'empty'); reconstruction deferred."""
+        saver = InMemorySaver()
 
+        thread_id, ns, channel = "t1", "", "messages"
+        v1 = "00000000000000000000000000000001.0000000000000000"
+
+        saver.blobs[(thread_id, ns, channel, v1)] = ("empty", b"")
+
+        result = saver._load_blobs(thread_id, ns, {channel: v1})
+        assert channel not in result
+
+    def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
+        """_get_channel_writes_history collects ancestor writes oldest→newest,
+        and excludes writes stored at the target checkpoint itself (those are
+        pending writes for the next step, applied separately by pregel)."""
         saver = InMemorySaver()
         serde = JsonPlusSerializer()
 
         thread_id, ns, channel = "t1", "", "messages"
-        version = "00000000000000000000000000000001.0000000000000000"
-        delta = DeltaValue(delta=[{"content": "hi"}], prev_checkpoint_id=None)
-        saver.blobs[(thread_id, ns, channel, version)] = serde.dumps_typed(delta)
 
-        cp = empty_checkpoint()
-        cp["id"] = "cp1"
-        cp["channel_versions"][channel] = version
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
         saver.storage[thread_id][ns] = {
-            "cp1": (serde.dumps_typed(cp), serde.dumps_typed({}), None)
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+        }
+        # Writes stored at cp1 produced the cp1 snapshot; part of history.
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "hi"}),
+            "",
+        )
+        # Writes stored at cp2 are pending — they will produce cp3 when the
+        # step that loaded cp2 completes. They MUST NOT appear in the
+        # reconstructed snapshot value at cp2.
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp2",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "hi"}]
+
+    def test_get_channel_writes_at_root_returns_empty(self) -> None:
+        """Reconstructing the root checkpoint's state: no ancestors → []."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp1",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        assert result.writes == []
+
+
+class TestBaseFallbackGetChannelWrites:
+    """Exercises the `BaseCheckpointSaver._get_channel_writes_history` default
+    implementation — the path third-party savers inherit when they don't
+    override `_get_channel_writes_history` themselves.
+
+    Regression guard for a bug where the fallback passed the caller's config
+    (with `checkpoint_id`) straight to `self.list()`, which most savers
+    collapse to a single row — causing the fallback to return `[]`.
+    """
+
+    def _build_saver_with_chain(self) -> tuple[InMemorySaver, str, str]:
+        """Build an InMemorySaver with a 3-checkpoint chain and per-step writes
+        for a `messages` channel.
+
+        Returns `(saver, thread_id, namespace)`. The saver subclass deletes the
+        InMemorySaver override so the base class fallback is exercised.
+        """
+
+        class _ThirdPartyStyleSaver(InMemorySaver):
+            _get_channel_writes_history = (
+                InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+            )
+            _aget_channel_writes_history = (
+                InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+            )
+
+        saver = _ThirdPartyStyleSaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp0 = empty_checkpoint()
+        cp0["id"] = "00000000000000000000000000000001.0000000000000000"
+        cp1 = empty_checkpoint()
+        cp1["id"] = "00000000000000000000000000000002.0000000000000000"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "00000000000000000000000000000003.0000000000000000"
+        saver.storage[thread_id][ns] = {
+            cp0["id"]: (serde.dumps_typed(cp0), serde.dumps_typed({}), None),
+            cp1["id"]: (serde.dumps_typed(cp1), serde.dumps_typed({}), cp0["id"]),
+            cp2["id"]: (serde.dumps_typed(cp2), serde.dumps_typed({}), cp1["id"]),
+        }
+        # Writes under cp0 produced cp1's state; writes under cp1 produced cp2's.
+        saver.writes[(thread_id, ns, cp0["id"])][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "first"}),
+            "",
+        )
+        saver.writes[(thread_id, ns, cp1["id"])][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "second"}),
+            "",
+        )
+        return saver, thread_id, ns
+
+    def test_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
         }
 
-        result = saver.get_channel_blob(thread_id, ns, "cp1", channel)
-        assert isinstance(result, DeltaValue)
-        assert result.delta == [{"content": "hi"}]
-        assert result.prev_checkpoint_id is None
+        result = saver._get_channel_writes_history(config, "messages")
 
-    def test_get_channel_blob_missing(self) -> None:
-        """get_channel_blob returns NotImplemented when checkpoint or channel not found."""
-        saver = InMemorySaver()
-        assert (
-            saver.get_channel_blob("t1", "", "no-such-cp", "messages") is NotImplemented
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = await saver._aget_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_concurrent_tasks_do_not_interfere(self) -> None:
+        """Regression: the re-entrancy guard must be task-local, not thread-local.
+
+        Two concurrent `_aget_channel_writes_history` calls on the same
+        event-loop thread must each see their full reconstructed writes. A
+        `threading.local()` guard would let whichever task set it first
+        short-circuit the other to `writes=[]`.
+        """
+        import asyncio
+
+        saver, thread_id, ns = self._build_saver_with_chain()
+
+        # Force the two tasks to interleave across the `set(True)` boundary:
+        # each `aget_tuple` yields control, so if the guard were thread-local
+        # the second task would observe `active=True` set by the first.
+        orig_aget_tuple = saver.aget_tuple
+
+        async def slow_aget_tuple(config: RunnableConfig) -> Any:
+            await asyncio.sleep(0)
+            return await orig_aget_tuple(config)
+
+        saver.aget_tuple = slow_aget_tuple  # type: ignore[method-assign]
+
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        results = await asyncio.gather(
+            saver._aget_channel_writes_history(config, "messages"),
+            saver._aget_channel_writes_history(config, "messages"),
         )
+
+        expected_values = [{"content": "first"}, {"content": "second"}]
+        for result in results:
+            assert result.seed is DELTA_SENTINEL
+            values = [v for _, _, v in result.writes]
+            assert values == expected_values
+
+
+class TestPreDeltaBlobTerminator:
+    """Verify the pre-delta blob terminator: when the ancestor walk hits a
+    checkpoint whose blob for the channel is a real value (not
+    DELTA_SENTINEL), reconstruction seeds from it and stops. This guards
+
+      * back-compat: a thread written by pre-delta code, then extended under
+        delta — reconstruction must return the correct value without walking
+        past the last pre-delta ancestor;
+      * perf: without the terminator, every reconstruct-after-migration would
+        walk all the way to the thread root.
+    """
+
+    def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
+        """Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
+        write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
+        seed=[A] + writes=[B, C].
+
+        Returns `(saver, thread_id, ns, channel, cp3_id)`.
+        """
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        v1 = "00000000000000000000000000000001.0"
+        v2 = "00000000000000000000000000000002.0"
+        v3 = "00000000000000000000000000000003.0"
+
+        # Pre-delta: cp1 stored a real blob for the channel.
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
+        # Delta-era: cp2 and cp3 store "empty"; real writes in checkpoint_writes.
+        saver.blobs[(thread_id, ns, channel, v2)] = ("empty", b"")
+        saver.blobs[(thread_id, ns, channel, v3)] = ("empty", b"")
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        cp2["channel_versions"][channel] = v2
+        cp3 = empty_checkpoint()
+        cp3["id"] = "cp3"
+        cp3["channel_versions"][channel] = v3
+
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+            "cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
+        }
+        # Write under cp1 would be from the pre-delta era and MUST be ignored
+        # (the blob already captures it). We add one and assert it is not
+        # folded into the reconstructed result.
+        saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
+            "task0",
+            channel,
+            serde.dumps_typed("PRE-DELTA-WRITE"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed("B"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
+            "task3",
+            channel,
+            serde.dumps_typed("PENDING-AT-TARGET"),
+            "",
+        )
+        return saver, thread_id, ns, channel, "cp3"
+
+    def test_seed_from_pre_delta_ancestor_blob(self) -> None:
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        # Seed came from the pre-delta blob at cp1.
+        assert result.seed == ["A"]
+        # Delta-era writes from cp2 replay through the reducer on top of seed.
+        # cp3 is the target — its own write is pending for the NEXT step and
+        # must be excluded.
+        values = [v for _, _, v in result.writes]
+        assert values == ["B"]
+
+    def test_pre_delta_blob_terminates_walk_before_older_writes(self) -> None:
+        """Writes stored at the pre-delta ancestor itself must not be replayed
+        (the blob subsumes them)."""
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        values = [v for _, _, v in result.writes]
+        # The pre-delta write under cp1 must not appear (the blob subsumes it).
+        assert "PRE-DELTA-WRITE" not in values
+        # And the pending write at the target is never folded in.
+        assert "PENDING-AT-TARGET" not in values

libs/checkpoint/uv.lock

@@ -286,7 +286,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },

libs/cli/examples/graph_prerelease_reqs/deps/zuper_deps/pyproject.toml

@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==1.0.1"
+  "langchain-openai==1.1.14"
 ]

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==1.0.0a2",
+  "langchain-openai==1.1.14",
   "langchain-anthropic==1.0.0a5",
   "langgraph==1.1.5"
 ]

libs/cli/examples/graph_prerelease_reqs_fail/pyproject.toml

@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==1.0.0a2",
+  "langchain-openai==1.1.14",
   "langgraph==1.1.2",
   "langchain_community>=0.3.0",
 ]

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.22"
+__version__ = "0.4.24"

libs/cli/langgraph_cli/_ignore.py

@@ -0,0 +1,124 @@
+"""Shared ignore-file handling for local source filtering."""
+
+import pathlib
+from dataclasses import dataclass
+
+import pathspec
+
+_ALWAYS_EXCLUDE = [
+    "__pycache__/",
+    ".git/",
+    ".venv/",
+    "venv/",
+    "node_modules/",
+    ".tox/",
+    ".mypy_cache/",
+]
+_ALWAYS_EXCLUDE_NAMES = frozenset(
+    pattern.rstrip("/").split("/")[-1] for pattern in _ALWAYS_EXCLUDE
+)
+_GLOB_CHARS = frozenset("*?[")
+
+
+@dataclass(frozen=True, slots=True)
+class _NegatedDockerignoreHints:
+    exact_dirs: frozenset[pathlib.PurePosixPath] = frozenset()
+    wildcard_prefixes: frozenset[pathlib.PurePosixPath] = frozenset()
+    recurse_all: bool = False
+
+    def requires_dir_walk(self, path: pathlib.PurePosixPath) -> bool:
+        if self.recurse_all or path in self.exact_dirs:
+            return True
+        return any(
+            path == prefix or path in prefix.parents or prefix in path.parents
+            for prefix in self.wildcard_prefixes
+        )
+
+
+def _build_ignore_spec(
+    directory: pathlib.Path, *, include_gitignore: bool = True
+) -> pathspec.PathSpec:
+    """Build a PathSpec combining built-in exclusions with ignore files.
+
+    Always excludes common non-source directories (`_ALWAYS_EXCLUDE`). On top
+    of that, patterns from `.dockerignore` are merged in. `.gitignore` patterns
+    are optional because some callers need Docker build-context semantics,
+    while archive creation wants both files.
+    """
+    lines: list[str] = list(_ALWAYS_EXCLUDE)
+    ignore_files = [".dockerignore"]
+    if include_gitignore:
+        ignore_files.append(".gitignore")
+    for name in ignore_files:
+        ignore_file = directory / name
+        if ignore_file.is_file():
+            lines.extend(ignore_file.read_text(encoding="utf-8").splitlines())
+    return pathspec.PathSpec.from_lines("gitwildmatch", lines)
+
+
+def _is_always_excluded(path: pathlib.PurePosixPath, *, is_dir: bool) -> bool:
+    """Whether `path` lives inside a built-in excluded directory."""
+    parent_parts = path.parts if is_dir else path.parts[:-1]
+    return any(part in _ALWAYS_EXCLUDE_NAMES for part in parent_parts)
+
+
+def _build_dockerignore_negation_hints(
+    directory: pathlib.Path,
+) -> _NegatedDockerignoreHints:
+    """Summarize which ignored directories must still be traversed.
+
+    Most negations only require walking a small, concrete chain of parent
+    directories (for example `!assets/keep.txt` requires entering `assets/`).
+    Broader glob negations may force a wider walk.
+    """
+    ignore_file = directory / ".dockerignore"
+    if not ignore_file.is_file():
+        return _NegatedDockerignoreHints()
+
+    exact_dirs: set[pathlib.PurePosixPath] = set()
+    wildcard_prefixes: set[pathlib.PurePosixPath] = set()
+    recurse_all = False
+
+    for raw_line in ignore_file.read_text(encoding="utf-8").splitlines():
+        line = raw_line.strip()
+        if not line or line.startswith("#") or line.startswith("\\!"):
+            continue
+        if line.startswith("\\#"):
+            line = line[1:]
+        if not line.startswith("!"):
+            continue
+
+        pattern = line[1:].lstrip("/")
+        while pattern.startswith("./"):
+            pattern = pattern[2:]
+        pattern = pattern.rstrip("/")
+        parts = [part for part in pattern.split("/") if part and part != "."]
+        if not parts:
+            recurse_all = True
+            continue
+
+        wildcard_index = next(
+            (
+                idx
+                for idx, part in enumerate(parts)
+                if any(char in part for char in _GLOB_CHARS)
+            ),
+            None,
+        )
+        if wildcard_index is not None:
+            literal_parts = parts[:wildcard_index]
+            if not literal_parts:
+                recurse_all = True
+                continue
+            wildcard_prefixes.add(pathlib.PurePosixPath(*literal_parts))
+            continue
+
+        parent_parts = parts[:-1]
+        for idx in range(1, len(parent_parts) + 1):
+            exact_dirs.add(pathlib.PurePosixPath(*parent_parts[:idx]))
+
+    return _NegatedDockerignoreHints(
+        exact_dirs=frozenset(exact_dirs),
+        wildcard_prefixes=frozenset(wildcard_prefixes),
+        recurse_all=recurse_all,
+    )

libs/cli/langgraph_cli/archive.py

@@ -9,35 +9,12 @@ from contextlib import contextmanager
 import click
 import pathspec
 
+from langgraph_cli._ignore import _build_ignore_spec
 from langgraph_cli.config import Config, _assemble_local_deps
 
 _WARN_SIZE = 50 * 1024 * 1024  # 50 MB
 _MAX_SIZE = 200 * 1024 * 1024  # 200 MB
 
-_ALWAYS_EXCLUDE = [
-    "__pycache__/",
-    ".git/",
-    ".venv/",
-    "venv/",
-    "node_modules/",
-    ".tox/",
-    ".mypy_cache/",
-]
-
-
-def _build_ignore_spec(directory: pathlib.Path) -> pathspec.PathSpec:
-    """Build a PathSpec combining built-in exclusions with .dockerignore and .gitignore.
-
-    Always excludes common non-source directories (_ALWAYS_EXCLUDE).  On top of
-    that, patterns from .dockerignore and .gitignore (if present) are merged in.
-    """
-    lines: list[str] = list(_ALWAYS_EXCLUDE)
-    for name in (".dockerignore", ".gitignore"):
-        ignore_file = directory / name
-        if ignore_file.is_file():
-            lines.extend(ignore_file.read_text(encoding="utf-8").splitlines())
-    return pathspec.PathSpec.from_lines("gitwildmatch", lines)
-
 
 def _tar_filter(tarinfo: tarfile.TarInfo) -> tarfile.TarInfo | None:
     """Strip symlinks, hardlinks, and traversal paths from archive."""

libs/cli/langgraph_cli/uv_lock.py

@@ -10,7 +10,13 @@ except ModuleNotFoundError:  # pragma: no cover - exercised on Python 3.10.
     import tomli as tomllib
 
 import click
+import pathspec
 
+from langgraph_cli._ignore import (
+    _build_dockerignore_negation_hints,
+    _build_ignore_spec,
+    _is_always_excluded,
+)
 from langgraph_cli.schemas import Config
 
 
@@ -440,16 +446,32 @@ def _container_root_for_uv_lock_package(
 
 
 def _uv_lock_package_copy_items(
-    package: UvLockPackage, plan: UvLockPlan
+    package: UvLockPackage,
+    plan: UvLockPlan,
+    ignore_spec: pathspec.PathSpec,
 ) -> tuple[tuple[pathlib.PurePosixPath, pathlib.PurePosixPath], ...]:
+    # Skip entries that .dockerignore / built-in exclusions would strip from
+    # the build context. Emitting `ADD <path>` for a file that Docker has
+    # filtered out causes the build to fail with
+    # "failed to compute cache key: <path> not found".
     if package.root != plan.project_root:
         relative_root = pathlib.PurePosixPath(
             *package.root.relative_to(plan.project_root).parts
         )
+        if _is_always_excluded(relative_root, is_dir=True) or ignore_spec.match_file(
+            f"{relative_root.as_posix()}/"
+        ):
+            raise click.UsageError(
+                f"Workspace member '{package.name}' at {relative_root} is "
+                "excluded from the Docker build context, but uv.lock requires "
+                "it to be copied into the build context. Remove the matching "
+                "pattern or drop the member from [tool.uv.workspace].members."
+            )
         return ((relative_root, plan.container_roots[package.root]),)
 
     root_container = plan.container_roots[package.root]
     workspace_member_roots = plan.all_workspace_roots - {plan.project_root}
+    negated_dockerignore_hints = _build_dockerignore_negation_hints(plan.project_root)
 
     def iter_entries(
         current_dir: pathlib.Path,
@@ -461,18 +483,32 @@ def _uv_lock_package_copy_items(
                 # and excluded entirely otherwise.
                 continue
 
-            descendant_member_roots = [
-                ws_root
-                for ws_root in workspace_member_roots
-                if child in ws_root.parents
-            ]
-            if child.is_dir() and descendant_member_roots:
-                entries.extend(iter_entries(child))
-                continue
-
             relative_child = pathlib.PurePosixPath(
                 *child.relative_to(plan.project_root).parts
             )
+            is_dir = child.is_dir()
+            if _is_always_excluded(relative_child, is_dir=is_dir):
+                continue
+            ignored = ignore_spec.match_file(
+                f"{relative_child.as_posix()}/" if is_dir else relative_child.as_posix()
+            )
+            is_workspace_parent = is_dir and any(
+                child in ws_root.parents for ws_root in workspace_member_roots
+            )
+
+            if is_workspace_parent:
+                entries.extend(iter_entries(child))
+                continue
+            if (
+                is_dir
+                and ignored
+                and negated_dockerignore_hints.requires_dir_walk(relative_child)
+            ):
+                entries.extend(iter_entries(child))
+                continue
+            if ignored:
+                continue
+
             entries.append(
                 (relative_child, root_container.joinpath(*relative_child.parts))
             )
@@ -956,10 +992,13 @@ def python_config_to_docker_uv_lock(
     docker_plan.add_raw("# -- End of uv.lock dependencies install --")
     docker_plan.add_blank()
 
+    ignore_spec = _build_ignore_spec(plan.project_root, include_gitignore=False)
     for package in plan.install_order:
         package_label = package.root.relative_to(plan.project_root).as_posix() or "."
         docker_plan.add_raw(f"# -- Adding workspace package {package_label} --")
-        for source, destination in _uv_lock_package_copy_items(package, plan):
+        for source, destination in _uv_lock_package_copy_items(
+            package, plan, ignore_spec
+        ):
             docker_plan.add_raw(copy_from_project_root(source, destination.as_posix()))
         docker_plan.add_instruction(
             "WORKDIR", plan.container_roots[package.root].as_posix()

libs/cli/pyproject.toml

@@ -23,13 +23,13 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.5.35,<0.9.0 ; python_version >= '3.11'",
     "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
 ]
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/cli"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/cli/tests/unit_tests/test_archive.py

@@ -99,6 +99,13 @@ class TestBuildIgnoreSpec:
         assert spec.match_file("app.log")
         assert spec.match_file("mod.pyc")
 
+    def test_can_skip_gitignore(self, tmp_path):
+        (tmp_path / ".dockerignore").write_text("*.log\n")
+        (tmp_path / ".gitignore").write_text("*.pyc\n")
+        spec = _build_ignore_spec(tmp_path, include_gitignore=False)
+        assert spec.match_file("app.log")
+        assert not spec.match_file("mod.pyc")
+
     def test_no_ignore_files_only_builtins(self, tmp_path):
         spec = _build_ignore_spec(tmp_path)
         assert spec.match_file("__pycache__/")

libs/cli/tests/unit_tests/test_config.py

@@ -4,6 +4,7 @@ import os
 import pathlib
 import tempfile
 import textwrap
+from unittest.mock import patch
 
 import click
 import pytest
@@ -1855,6 +1856,364 @@ def test_config_to_docker_uv_lock_supports_single_uv_project_root():
         assert additional_contexts == {}
 
 
+def test_config_to_docker_uv_lock_skips_dockerignore_entries():
+    """Entries filtered by .dockerignore / built-in excludes must not appear
+    as ADD lines. Docker fails to compute the cache key for paths that the
+    build context has stripped."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root = tmpdir_path / "single"
+        project_root.mkdir()
+        (project_root / "uv.lock").write_text("# uv lock file\n")
+        (project_root / "pyproject.toml").write_text(
+            textwrap.dedent(
+                """
+                [project]
+                name = "single-app"
+                version = "0.1.0"
+                dependencies = ["httpx>=0.28"]
+
+                [build-system]
+                requires = ["setuptools>=61"]
+                build-backend = "setuptools.build_meta"
+                """
+            ).strip()
+            + "\n"
+        )
+        (project_root / "langgraph.json").write_text("{}\n")
+        (project_root / "src").mkdir()
+        (project_root / "src" / "agent.py").write_text("graph = object()\n")
+        (project_root / "README.md").write_text("# hi\n")
+
+        # Built-in exclusions — must never appear as ADD lines.
+        (project_root / ".git").mkdir()
+        (project_root / ".git" / "HEAD").write_text("ref: refs/heads/main\n")
+        (project_root / ".venv").mkdir()
+        (project_root / ".venv" / "pyvenv.cfg").write_text("home = /usr\n")
+        (project_root / "__pycache__").mkdir()
+        (project_root / "__pycache__" / "x.cpython-311.pyc").write_bytes(b"\x00")
+
+        # .dockerignore excludes .gitignore and a custom path.
+        (project_root / ".dockerignore").write_text(".gitignore\nsecrets.env\n")
+        (project_root / ".gitignore").write_text("*.pyc\n")
+        (project_root / "secrets.env").write_text("TOKEN=abc\n")
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {"agent": "./src/agent.py:graph"},
+                "source": {"kind": "uv"},
+            }
+        )
+        docker, _ = config_to_docker(
+            project_root / "langgraph.json",
+            config,
+            base_image="langchain/langgraph-api:0.2.47",
+        )
+
+        for excluded in (
+            "ADD .git ",
+            "ADD .gitignore ",
+            "ADD .venv ",
+            "ADD __pycache__ ",
+            "ADD secrets.env ",
+        ):
+            assert excluded not in docker, (
+                f"{excluded!r} should be filtered out of Dockerfile:\n{docker}"
+            )
+
+        # The .dockerignore itself is still part of the context and should be
+        # ADDed (Docker needs it at build time, and archive.py includes it).
+        assert "ADD .dockerignore /deps/workspace/.dockerignore" in docker
+        assert "ADD src /deps/workspace/src" in docker
+        assert "ADD README.md /deps/workspace/README.md" in docker
+
+
+def test_config_to_docker_uv_lock_does_not_apply_gitignore():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root = tmpdir_path / "single"
+        project_root.mkdir()
+        (project_root / "uv.lock").write_text("# uv lock file\n")
+        (project_root / "pyproject.toml").write_text(
+            textwrap.dedent(
+                """
+                [project]
+                name = "single-app"
+                version = "0.1.0"
+                dependencies = ["httpx>=0.28"]
+
+                [build-system]
+                requires = ["setuptools>=61"]
+                build-backend = "setuptools.build_meta"
+                """
+            ).strip()
+            + "\n"
+        )
+        (project_root / "langgraph.json").write_text("{}\n")
+        (project_root / "src").mkdir()
+        (project_root / "src" / "agent.py").write_text("graph = object()\n")
+        (project_root / "README.md").write_text("# hi\n")
+        (project_root / ".gitignore").write_text("README.md\n")
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {"agent": "./src/agent.py:graph"},
+                "source": {"kind": "uv"},
+            }
+        )
+        docker, _ = config_to_docker(
+            project_root / "langgraph.json",
+            config,
+            base_image="langchain/langgraph-api:0.2.47",
+        )
+
+        assert "ADD README.md /deps/workspace/README.md" in docker
+
+
+def test_config_to_docker_uv_lock_skips_dockerignore_entries_in_workspace():
+    """Multi-member workspace: ignore patterns must filter root-level entries
+    AND entries encountered while recursing into directories that contain
+    workspace members (the `descendant_member_roots` branch)."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root, config_path = _write_uv_lock_workspace(
+            tmpdir_path,
+            agent_dependencies=["workspace-root", "shared", "httpx>=0.28"],
+            root_sources="[tool.uv.sources]\nshared = { workspace = true }\nworkspace-root = { workspace = true }",
+            agent_sources="[tool.uv.sources]\nshared = { workspace = true }\nworkspace-root = { workspace = true }",
+        )
+        root_src = project_root / "src" / "workspace_root"
+        root_src.mkdir(parents=True)
+        (root_src / "__init__.py").write_text("__all__ = []\n")
+        (project_root / "README.md").write_text("workspace root package\n")
+
+        # A non-member sibling of the `apps/agent` member that should be
+        # filtered out via .dockerignore. This exercises the recursion into
+        # `apps/` where `apps/agent` is kept (it's a member) but its sibling is
+        # filtered.
+        (project_root / "apps" / "scratch.txt").write_text("scratch\n")
+        # A root-level path that .dockerignore excludes.
+        (project_root / "secrets.env").write_text("TOKEN=abc\n")
+        (project_root / ".dockerignore").write_text("secrets.env\napps/scratch.txt\n")
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {
+                    "agent": "../../apps/agent/src/agent/graph.py:graph",
+                },
+                "source": {"kind": "uv", "root": "../..", "package": "agent"},
+            }
+        )
+        docker, _ = config_to_docker(
+            config_path, config, base_image="langchain/langgraph-api:0.2.47"
+        )
+
+        assert "COPY --from=uv-workspace-root src /deps/workspace/src" in docker
+        assert (
+            "COPY --from=uv-workspace-root README.md /deps/workspace/README.md"
+            in docker
+        )
+        assert (
+            "COPY --from=uv-workspace-root .dockerignore /deps/workspace/.dockerignore"
+            in docker
+        )
+        assert "secrets.env" not in docker
+        assert "apps/scratch.txt" not in docker
+        # Workspace members themselves are still copied via their own per-member
+        # COPY line — the sibling filter must not disturb this.
+        assert (
+            "COPY --from=uv-workspace-root apps/agent /deps/workspace/apps/agent"
+            in docker
+        )
+
+
+def test_config_to_docker_uv_lock_preserves_negated_dockerignore_descendants():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root = tmpdir_path / "single"
+        project_root.mkdir()
+        (project_root / "uv.lock").write_text("# uv lock file\n")
+        (project_root / "pyproject.toml").write_text(
+            textwrap.dedent(
+                """
+                [project]
+                name = "single-app"
+                version = "0.1.0"
+                dependencies = ["httpx>=0.28"]
+
+                [build-system]
+                requires = ["setuptools>=61"]
+                build-backend = "setuptools.build_meta"
+                """
+            ).strip()
+            + "\n"
+        )
+        (project_root / "langgraph.json").write_text("{}\n")
+        (project_root / "src").mkdir()
+        (project_root / "src" / "agent.py").write_text("graph = object()\n")
+        (project_root / "assets").mkdir()
+        (project_root / "assets" / "keep.txt").write_text("keep\n")
+        (project_root / "assets" / "drop.txt").write_text("drop\n")
+        (project_root / ".dockerignore").write_text("assets/\n!assets/keep.txt\n")
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {"agent": "./src/agent.py:graph"},
+                "source": {"kind": "uv"},
+            }
+        )
+        docker, _ = config_to_docker(
+            project_root / "langgraph.json",
+            config,
+            base_image="langchain/langgraph-api:0.2.47",
+        )
+
+        assert "ADD assets /deps/workspace/assets" not in docker
+        assert "ADD assets/keep.txt /deps/workspace/assets/keep.txt" in docker
+        assert "assets/drop.txt" not in docker
+
+
+def test_config_to_docker_uv_lock_prunes_unrelated_ignored_subtrees():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root = tmpdir_path / "single"
+        project_root.mkdir()
+        (project_root / "uv.lock").write_text("# uv lock file\n")
+        (project_root / "pyproject.toml").write_text(
+            textwrap.dedent(
+                """
+                [project]
+                name = "single-app"
+                version = "0.1.0"
+                dependencies = ["httpx>=0.28"]
+
+                [build-system]
+                requires = ["setuptools>=61"]
+                build-backend = "setuptools.build_meta"
+                """
+            ).strip()
+            + "\n"
+        )
+        (project_root / "langgraph.json").write_text("{}\n")
+        (project_root / "src").mkdir()
+        (project_root / "src" / "agent.py").write_text("graph = object()\n")
+        (project_root / "assets").mkdir()
+        (project_root / "assets" / "keep.txt").write_text("keep\n")
+        (project_root / "vendor").mkdir()
+        (project_root / "vendor" / "huge.txt").write_text("large\n")
+        (project_root / ".dockerignore").write_text(
+            "vendor/\nassets/\n!assets/keep.txt\n"
+        )
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {"agent": "./src/agent.py:graph"},
+                "source": {"kind": "uv"},
+            }
+        )
+
+        original_iterdir = pathlib.Path.iterdir
+
+        def guarded_iterdir(self):
+            if self == project_root / "vendor":
+                raise AssertionError("should not walk unrelated ignored subtree")
+            return original_iterdir(self)
+
+        with patch.object(
+            pathlib.Path, "iterdir", autospec=True, side_effect=guarded_iterdir
+        ):
+            docker, _ = config_to_docker(
+                project_root / "langgraph.json",
+                config,
+                base_image="langchain/langgraph-api:0.2.47",
+            )
+
+        assert "ADD assets/keep.txt /deps/workspace/assets/keep.txt" in docker
+        assert "vendor/huge.txt" not in docker
+
+
+def test_config_to_docker_uv_lock_never_reincludes_always_excluded_subtrees():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root = tmpdir_path / "single"
+        project_root.mkdir()
+        (project_root / "uv.lock").write_text("# uv lock file\n")
+        (project_root / "pyproject.toml").write_text(
+            textwrap.dedent(
+                """
+                [project]
+                name = "single-app"
+                version = "0.1.0"
+                dependencies = ["httpx>=0.28"]
+
+                [build-system]
+                requires = ["setuptools>=61"]
+                build-backend = "setuptools.build_meta"
+                """
+            ).strip()
+            + "\n"
+        )
+        (project_root / "langgraph.json").write_text("{}\n")
+        (project_root / "src").mkdir()
+        (project_root / "src" / "agent.py").write_text("graph = object()\n")
+        (project_root / ".venv" / "pkg").mkdir(parents=True)
+        (project_root / ".venv" / "pkg" / "keep.txt").write_text("keep\n")
+        (project_root / "node_modules" / "pkg").mkdir(parents=True)
+        (project_root / "node_modules" / "pkg" / "package.json").write_text("{}\n")
+        (project_root / ".dockerignore").write_text(
+            "!.venv/pkg/keep.txt\n!node_modules/pkg/package.json\n"
+        )
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {"agent": "./src/agent.py:graph"},
+                "source": {"kind": "uv"},
+            }
+        )
+        docker, _ = config_to_docker(
+            project_root / "langgraph.json",
+            config,
+            base_image="langchain/langgraph-api:0.2.47",
+        )
+
+        assert ".venv/pkg/keep.txt" not in docker
+        assert "node_modules/pkg/package.json" not in docker
+        assert "ADD src /deps/workspace/src" in docker
+
+
+def test_config_to_docker_uv_lock_rejects_ignored_workspace_member():
+    """A workspace member matched by .dockerignore cannot be copied into the
+    build context — uv.lock requires it, so fail loudly with a clear message."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmpdir_path = pathlib.Path(tmpdir)
+        project_root, config_path = _write_uv_lock_workspace(
+            tmpdir_path,
+            agent_sources="[tool.uv.sources]\nshared = { workspace = true }",
+        )
+        (project_root / ".dockerignore").write_text("libs/shared\n")
+
+        config = validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": {"agent": "../../apps/agent/src/agent/graph.py:graph"},
+                "source": {"kind": "uv", "root": "../..", "package": "agent"},
+                "auth": {"path": "../../libs/shared/src/shared/auth.py:create_auth"},
+            }
+        )
+        with pytest.raises(
+            click.UsageError, match=r"Workspace member 'shared' at libs/shared"
+        ):
+            config_to_docker(
+                config_path, config, base_image="langchain/langgraph-api:0.2.47"
+            )
+
+
 def test_config_to_docker_uv_lock_rejects_invalid_source_package_type():
     with tempfile.TemporaryDirectory() as tmpdir:
         tmpdir_path = pathlib.Path(tmpdir)

libs/langgraph/README.md

@@ -18,7 +18,7 @@
   <a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
   <a href="https://github.com/langchain-ai/langgraph/issues" target="_blank"><img src="https://img.shields.io/github/issues-raw/langchain-ai/langgraph" alt="Open Issues"></a>
   <a href="https://docs.langchain.com/oss/python/langgraph/overview" target="_blank"><img src="https://img.shields.io/badge/docs-latest-blue" alt="Docs"></a>
-  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
+  <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
 </div>
 
 <br>

libs/langgraph/langgraph/_internal/_constants.py

@@ -12,6 +12,9 @@ RESUME = sys.intern("__resume__")
 # for values passed to resume a node after an interrupt
 ERROR = sys.intern("__error__")
 # for errors raised by nodes
+ERROR_SOURCE_NODE = sys.intern("__error_source_node__")
+# failed source node name for node-level error handlers
+# value format in pending writes: `(task_id, ERROR_SOURCE_NODE, node_name: str)`
 NO_WRITES = sys.intern("__no_writes__")
 # marker to signal node didn't write anything
 TASKS = sys.intern("__pregel_tasks")
@@ -56,6 +59,8 @@ CONFIG_KEY_CHECKPOINT_NS = sys.intern("checkpoint_ns")
 # holds the current checkpoint_ns, "" for root graph
 CONFIG_KEY_NODE_FINISHED = sys.intern("__pregel_node_finished")
 # holds a callback to be called when a node is finished
+CONFIG_KEY_TIMED_ATTEMPT_OBSERVER = sys.intern("__pregel_timed_attempt_observer")
+# holds a callback to be called when an idle-timed node attempt starts or finishes
 CONFIG_KEY_SCRATCHPAD = sys.intern("__pregel_scratchpad")
 # holds a mutable dict for temporary storage scoped to the current task
 CONFIG_KEY_RUNNER_SUBMIT = sys.intern("__pregel_runner_submit")
@@ -66,6 +71,13 @@ CONFIG_KEY_RUNTIME = sys.intern("__pregel_runtime")
 # holds a `Runtime` instance with context, store, stream writer, etc.
 CONFIG_KEY_RESUME_MAP = sys.intern("__pregel_resume_map")
 # holds a mapping of task ns -> resume value for resuming tasks
+CONFIG_KEY_STREAM_MESSAGES_V2 = sys.intern("__pregel_stream_messages_v2")
+# when True, attach StreamMessagesHandlerV2 so content-block (v2) events
+# flow through stream_mode="messages"; set by StreamingHandler only.
+CONFIG_KEY_NODE_ERROR = sys.intern("__pregel_node_error")
+# holds a `NodeError` (failed source node + exception) for the current
+# node-level error handler invocation, injected when handler signature
+# requests `error: NodeError`
 
 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")
@@ -93,6 +105,7 @@ RESERVED = {
     INTERRUPT,
     RESUME,
     ERROR,
+    ERROR_SOURCE_NODE,
     NO_WRITES,
     # reserved config.configurable keys
     CONFIG_KEY_SEND,
@@ -106,7 +119,9 @@ RESERVED = {
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_ID,
     CONFIG_KEY_CHECKPOINT_NS,
+    CONFIG_KEY_TIMED_ATTEMPT_OBSERVER,
     CONFIG_KEY_RESUME_MAP,
+    CONFIG_KEY_STREAM_MESSAGES_V2,
     # other constants
     PUSH,
     PULL,

libs/langgraph/langgraph/_internal/_runnable.py

@@ -51,9 +51,11 @@ from langgraph._internal._config import (
 )
 from langgraph._internal._constants import (
     CONF,
+    CONFIG_KEY_NODE_ERROR,
     CONFIG_KEY_RUNTIME,
 )
 from langgraph._internal._typing import MISSING
+from langgraph.errors import NodeError
 from langgraph.types import StreamWriter
 
 try:
@@ -117,6 +119,19 @@ def set_config_context(
         ctx.run(_unset_config_context, config_token, run)
 
 
+def create_task_in_config_context(
+    coro_factory: Callable[[], Coroutine[Any, Any, Any]], config: RunnableConfig
+) -> asyncio.Task[Any]:
+    """Create an asyncio.Task that inherits `config` as the child runnable context.
+
+    `asyncio.create_task` snapshots the current contextvars onto the new task,
+    so calling `create_task` while the config context is set ensures the task
+    sees `config` via `var_child_runnable_config` and any tracing parent.
+    """
+    with set_config_context(config) as context:
+        return context.run(lambda: asyncio.create_task(coro_factory()))
+
+
 # Before Python 3.11 native StrEnum is not available
 class StrEnum(str, enum.Enum):
     """A string enum."""
@@ -181,6 +196,15 @@ KWARGS_CONFIG_KEYS: tuple[tuple[str, tuple[Any, ...], str, Any], ...] = (
         "N/A",
         inspect.Parameter.empty,
     ),
+    (
+        "error",
+        (NodeError, "NodeError"),
+        # we never hit this block, we read directly from configurable
+        "N/A",
+        # default to None so non-handler nodes that happen to type a parameter
+        # `error: NodeError` don't blow up; handlers always receive a NodeError.
+        None,
+    ),
 )
 """List of kwargs that can be passed to functions, and their corresponding
 config keys, default values and type annotations.
@@ -354,6 +378,8 @@ class RunnableCallable(Runnable):
             kw_value: Any = MISSING
             if kw == "config":
                 kw_value = config
+            elif kw == "error":
+                kw_value = config.get(CONF, {}).get(CONFIG_KEY_NODE_ERROR, MISSING)
             elif runtime:
                 if kw == "runtime":
                     kw_value = runtime
@@ -426,6 +452,8 @@ class RunnableCallable(Runnable):
             kw_value: Any = MISSING
             if kw == "config":
                 kw_value = config
+            elif kw == "error":
+                kw_value = config.get(CONF, {}).get(CONFIG_KEY_NODE_ERROR, MISSING)
             elif runtime:
                 if kw == "runtime":
                     kw_value = runtime

libs/langgraph/langgraph/_internal/_timeout.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from datetime import timedelta
+from typing import Literal
+
+from langgraph.types import TimeoutPolicy
+
+_SYNC_TIMEOUT_PREFIX = (
+    "Node timeouts are only supported for async nodes because sync Python "
+    "execution cannot be safely cancelled in-process."
+)
+
+
+def coerce_timeout_policy(
+    value: float | timedelta | TimeoutPolicy | None,
+) -> TimeoutPolicy | None:
+    """Normalize a timeout value to positive-second policy fields."""
+    return TimeoutPolicy.coerce(value)
+
+
+def sync_timeout_unsupported(
+    name: str, *, kind: Literal["Node", "Task"] = "Node"
+) -> ValueError:
+    """Build the canonical error for using `timeout` with a sync target."""
+    return ValueError(f"{_SYNC_TIMEOUT_PREFIX} {kind} {name!r} is sync.")

libs/langgraph/langgraph/callbacks.py

@@ -245,15 +245,6 @@ class _GraphCallbackManager(BaseCallbackManager):
             run_id=run_id,
         )
 
-    def add_handler(
-        self,
-        handler: BaseCallbackHandler,
-        inherit: bool = True,  # noqa: FBT001,FBT002
-    ) -> None:
-        if not isinstance(handler, GraphCallbackHandler):
-            raise TypeError("handlers must inherit GraphCallbackHandler")
-        super().add_handler(handler, inherit=inherit)
-
     def copy(
         self,
         *,
@@ -321,15 +312,6 @@ class _AsyncGraphCallbackManager(BaseCallbackManager):
             run_id=run_id,
         )
 
-    def add_handler(
-        self,
-        handler: BaseCallbackHandler,
-        inherit: bool = True,  # noqa: FBT001,FBT002
-    ) -> None:
-        if not isinstance(handler, GraphCallbackHandler):
-            raise TypeError("handlers must inherit GraphCallbackHandler")
-        super().add_handler(handler, inherit=inherit)
-
     def copy(
         self,
         *,

libs/langgraph/langgraph/channels/base.py

@@ -119,12 +119,3 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
         Returns `True` if the channel was updated, `False` otherwise.
         """
         return False
-
-    def after_checkpoint(self, version: Any, checkpoint_id: str | None = None) -> None:
-        """Called after checkpoint() with the assigned version, and after
-        from_checkpoint() with the current channel version.
-
-        No-op by default. Override in channels that track their own version
-        for incremental checkpointing (e.g. DeltaChannel).
-        """
-        pass

libs/langgraph/langgraph/channels/binop.py

@@ -22,10 +22,9 @@ __all__ = ("BinaryOperatorAggregate",)
 def _strip_extras(t):  # type: ignore[no-untyped-def]
     """Strips Annotated, Required and NotRequired from a given type."""
     if hasattr(t, "__origin__"):
+        if t.__origin__ in (Required, NotRequired):
+            return _strip_extras(t.__args__[0])
         return _strip_extras(t.__origin__)
-    if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
-        return _strip_extras(t.__args__[0])
-
     return t
 
 
@@ -33,11 +32,22 @@ def _get_overwrite(value: Any) -> tuple[bool, Any]:
     """Inspects the given value and returns (is_overwrite, overwrite_value)."""
     if isinstance(value, Overwrite):
         return True, value.value
-    if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
+    if isinstance(value, dict) and len(value) == 1 and OVERWRITE in value:
         return True, value[OVERWRITE]
     return False, None
 
 
+def _operators_equal(a: Callable, b: Callable) -> bool:
+    """Return True if two reducer operators should be considered equal.
+
+    Lambdas all share the name '<lambda>' so identity comparison is
+    unreliable; treat any pairing that includes a lambda as equal.
+    """
+    if a.__name__ == "<lambda>" or b.__name__ == "<lambda>":
+        return True
+    return a is b
+
+
 class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
     """Stores the result of applying a binary operator to the current value and each new value.
 
@@ -68,11 +78,8 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
             self.value = MISSING
 
     def __eq__(self, value: object) -> bool:
-        return isinstance(value, BinaryOperatorAggregate) and (
-            value.operator is self.operator
-            if value.operator.__name__ != "<lambda>"
-            and self.operator.__name__ != "<lambda>"
-            else True
+        return isinstance(value, BinaryOperatorAggregate) and _operators_equal(
+            self.operator, value.operator
         )
 
     @property

libs/langgraph/langgraph/channels/delta.py

@@ -1,175 +1,183 @@
 from __future__ import annotations
 
 import collections.abc
+import copy as _copy
 from collections.abc import Callable, Sequence
 from typing import Any, Generic
 
-from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
+from langgraph.checkpoint.base import DELTA_SENTINEL, PendingWrite
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 from typing_extensions import Self
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.base import BaseChannel, Value
-from langgraph.channels.binop import _get_overwrite, _strip_extras
-from langgraph.errors import EmptyChannelError
+from langgraph.channels.binop import _get_overwrite, _operators_equal, _strip_extras
+from langgraph.errors import (
+    EmptyChannelError,
+    ErrorCode,
+    InvalidUpdateError,
+    create_error_message,
+)
 
 __all__ = ("DeltaChannel",)
 
 
-class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
-    """A channel that stores only per-step write deltas in checkpoints.
+class DeltaChannel(Generic[Value], BaseChannel[Any, Any, Any]):
+    """Reducer channel that stores only a sentinel in checkpoint blobs and
+    reconstructs state by replaying ancestor writes through the reducer.
 
-    Reconstructs the full accumulated list at load time by replaying the
-    chain of deltas through the operator. Use with append-style reducers
-    (e.g. `add_messages`) on long-running threads to reduce checkpoint
-    storage from O(N²) to O(N).
+    The reducer receives the current accumulated value and a batch of writes
+    in one call: `reducer(state, [write1, write2, ...]) -> new_state`.
 
-    Works with all checkpointers. Savers with a dedicated blob store
-    (InMemorySaver, PostgresSaver) use an O(1) fast-path per chain step;
-    all others (SQLite, MongoDB, etc.) fall back to get_tuple traversal.
+    Reducers must be deterministic and batching-invariant (associative across
+    folds): applying two consecutive write batches separately must produce the
+    same state as applying their concatenation once:
 
-    Use `snapshot_every=N` to cap chain traversal depth at N steps. Every N
-    steps a full snapshot is written as the chain root; subsequent deltas
-    chain back to it, so `get_state` / reload never traverses more than N
-    checkpoints regardless of thread length. Recommended for savers without
-    a dedicated blob store.
+        reducer(reducer(state, xs), ys) == reducer(state, xs + ys)
 
-    Usage::
+    This lets LangGraph replay checkpointed writes in larger batches than they
+    were originally produced without changing reconstructed state.
 
-        class State(TypedDict):
-            messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
-            # Cap reconstruction depth (recommended for SQLite / MongoDB savers):
-            messages: Annotated[list[AnyMessage], DeltaChannel(add_messages, snapshot_every=50)]
+    `snapshot_frequency=None` (default): pure delta; stores only
+    `DELTA_SENTINEL` in checkpoint blobs; reads replay all ancestor writes.
+
+    `snapshot_frequency=N`: `create_checkpoint` writes a full `_DeltaSnapshot`
+    blob every N steps, bounding replay depth to N.
+
+    Parameters:
+        reducer: `(state, list[writes]) -> new_state`. Must be deterministic
+            and batching-invariant as described above.
+        typ: The value type (e.g. `list`, `dict`). Inferred automatically
+            from the outer type when used inside `Annotated[T, DeltaChannel(...)]`.
+        snapshot_frequency: Every Nth pregel step writes a snapshot blob.
+            `None` (default) = pure delta, never snapshot.
     """
 
-    __slots__ = (
-        "value",
-        "operator",
-        "snapshot_every",
-        "_pending",
-        "_base_version",
-        "_last_checkpoint_id",
-        "_overwritten",
-        "_steps_since_snapshot",
-    )
+    __slots__ = ("value", "reducer", "snapshot_frequency")
+    value: Value | Any
 
     def __init__(
         self,
-        operator: Callable[[list[Value], Any], list[Value]],
-        typ: type = list,
+        reducer: Callable[[Any, Sequence[Any]], Any],
+        typ: type[Value] | None = None,
         *,
-        snapshot_every: int | None = None,
+        snapshot_frequency: int | None = None,
     ) -> None:
-        typ = _strip_extras(typ)
-        if typ in (
-            collections.abc.Sequence,
-            collections.abc.MutableSequence,
-        ):
-            typ = list
+        if typ is None:
+            typ = list  # type: ignore[assignment]  # placeholder; overridden by _is_field_channel
         super().__init__(typ)
-        self.operator = operator
-        self.snapshot_every = snapshot_every
-        try:
-            self.value: list[Value] = typ()
-        except Exception:
-            self.value = []
-        self._pending: list[Any] = []
-        self._base_version: str | None = None
-        self._last_checkpoint_id: str | None = None
-        self._overwritten: bool = False
-        self._steps_since_snapshot: int = 0
+        self.reducer = reducer
+        self.snapshot_frequency = snapshot_frequency
+        typ = _strip_extras(typ)
+        if typ in (collections.abc.Sequence, collections.abc.MutableSequence):
+            typ = list
+        if typ in (collections.abc.Set, collections.abc.MutableSet):
+            typ = set
+        if typ in (collections.abc.Mapping, collections.abc.MutableMapping):
+            typ = dict
+        self.typ = typ
+        self.value: Any = MISSING
 
     def __eq__(self, other: object) -> bool:
         if not isinstance(other, DeltaChannel):
             return False
-        if self.snapshot_every != other.snapshot_every:
+        if self.snapshot_frequency != other.snapshot_frequency:
             return False
-        if (
-            self.operator.__name__ != "<lambda>"
-            and other.operator.__name__ != "<lambda>"
-        ):
-            return self.operator is other.operator
-        return True
+        return _operators_equal(self.reducer, other.reducer)
 
     @property
     def ValueType(self) -> Any:
-        return list[self.typ]  # type: ignore[name-defined]
+        return self.typ
 
     @property
     def UpdateType(self) -> Any:
-        return self.typ | list[self.typ]  # type: ignore[name-defined]
+        return self.typ
+
+    def is_snapshot_step(self, step: int) -> bool:
+        """True if pregel should write a snapshot blob at this step."""
+        return (
+            self.snapshot_frequency is not None
+            and step > 0
+            and step % self.snapshot_frequency == 0
+        )
 
     def copy(self) -> Self:
-        new = DeltaChannel(self.operator, self.typ, snapshot_every=self.snapshot_every)
+        new = self.__class__(
+            self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
+        )
         new.key = self.key
-        new.value = self.value[:]
-        new._pending = self._pending[:]
-        new._base_version = self._base_version
-        new._last_checkpoint_id = self._last_checkpoint_id
-        new._overwritten = self._overwritten
-        new._steps_since_snapshot = self._steps_since_snapshot
+        new.value = self.value if self.value is MISSING else _copy.copy(self.value)
         return new
 
     def from_checkpoint(self, checkpoint: Any) -> Self:
-        new = DeltaChannel(self.operator, self.typ, snapshot_every=self.snapshot_every)
+        """Initialize from a stored blob or sentinel.
+
+        Blob types (dispatched via serde ext code, not dict key inspection):
+          * `DELTA_SENTINEL` / `MISSING`: start empty; caller replays writes.
+          * `_DeltaSnapshot(value)`: restore value directly from snapshot.
+          * plain value (migration from old BinOp blobs): use directly.
+        """
+        new = self.__class__(
+            self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
+        )
         new.key = self.key
-        if checkpoint is MISSING:
-            new.value = []
-        elif isinstance(checkpoint, DeltaChainValue):
-            accumulated: list[Value] = list(checkpoint.base) if checkpoint.base else []
-            for step_writes in checkpoint.deltas:
-                for write in step_writes:
-                    accumulated = new.operator(accumulated, write)
-            new.value = accumulated
-            # Seed the counter from actual chain depth so rehydration fires at
-            # the right time regardless of how many prior invocations there were.
-            new._steps_since_snapshot = len(checkpoint.deltas)
-        elif isinstance(checkpoint, DeltaValue):
-            # Should never reach here — the pregel layer assembles DeltaValues
-            # into DeltaChainValue before calling from_checkpoint.
-            raise AssertionError(
-                "DeltaChannel.from_checkpoint received a raw DeltaValue. "
-                "This is a bug in the pregel layer — chain assembly should have "
-                "occurred before from_checkpoint was called."
-            )
+        if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
+            new.value = self.typ()
+        elif isinstance(checkpoint, _DeltaSnapshot):
+            new.value = checkpoint.value
         else:
-            # Backwards compat: plain list from old BinaryOperatorAggregate checkpoint.
-            new.value = list(checkpoint)
-        new._pending = []
-        new._base_version = None  # set by the subsequent after_checkpoint() call
-        new._overwritten = False
+            new.value = checkpoint
         return new
 
+    def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
+        """Apply ancestor writes oldest-to-newest via a single reducer call.
+
+        If any write is an Overwrite, the last one in the sequence acts as
+        the reset point: its value becomes the new base and only writes
+        after it are passed to the reducer.
+        """
+        values = [v for _, _, v in writes]
+        if not values:
+            return
+        base = self.value
+        start = 0
+        for i, v in enumerate(values):
+            is_ow, ow_value = _get_overwrite(v)
+            if is_ow:
+                base = _copy.copy(ow_value) if ow_value is not None else self.typ()
+                start = i + 1
+        remaining = values[start:]
+        self.value = self.reducer(base, remaining) if remaining else base
+
     def update(self, values: Sequence[Any]) -> bool:
         if not values:
             return False
-        seen_overwrite = False
-        for value in values:
-            is_overwrite, overwrite_value = _get_overwrite(value)
-            if is_overwrite:
-                if seen_overwrite:
-                    from langgraph.errors import (
-                        ErrorCode,
-                        InvalidUpdateError,
-                        create_error_message,
-                    )
-
+        overwrite_idx: int | None = None
+        for i, v in enumerate(values):
+            is_ow, _ = _get_overwrite(v)
+            if is_ow:
+                if overwrite_idx is not None:
                     msg = create_error_message(
                         message="Can receive only one Overwrite value per super-step.",
                         error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
                     )
                     raise InvalidUpdateError(msg)
-                self.value = (
-                    list(overwrite_value) if overwrite_value is not None else []
-                )
-                self._pending = list(self.value)
-                self._overwritten = True
-                seen_overwrite = True
-            elif not seen_overwrite:
-                self.value = self.operator(self.value, value)
-                self._pending.append(value)
+                overwrite_idx = i
+        if overwrite_idx is not None:
+            _, overwrite_value = _get_overwrite(values[overwrite_idx])
+            base = (
+                _copy.copy(overwrite_value)
+                if overwrite_value is not None
+                else self.typ()
+            )
+            remaining = [v for i, v in enumerate(values) if i != overwrite_idx]
+            self.value = self.reducer(base, remaining) if remaining else base
+            return True
+        base = self.typ() if self.value is MISSING else self.value
+        self.value = self.reducer(base, list(values))
         return True
 
-    def get(self) -> list[Value]:
+    def get(self) -> Any:
         if self.value is MISSING:
             raise EmptyChannelError()
         return self.value
@@ -178,29 +186,12 @@ class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
         return self.value is not MISSING
 
     def checkpoint(self) -> Any:
-        if (
-            self.snapshot_every is not None
-            and self._steps_since_snapshot >= self.snapshot_every
-        ):
-            # Emit a full snapshot to cap chain depth at snapshot_every.
-            # The saver stores this as a plain (non-diff) blob, so future
-            # deltas will chain back to it and traversal depth resets to 1.
-            return list(self.value)
-        return DeltaValue(
-            delta=self._pending[:],
-            prev_checkpoint_id=None if self._overwritten else self._last_checkpoint_id,
-        )
+        """Return stored representation: always `DELTA_SENTINEL`.
 
-    def after_checkpoint(self, version: Any, checkpoint_id: str | None = None) -> None:
-        if version != self._base_version:
-            if self._base_version is None:
-                pass  # First call after from_checkpoint — anchor without counting a step.
-            elif self.snapshot_every is not None:
-                if self._steps_since_snapshot >= self.snapshot_every:
-                    self._steps_since_snapshot = 0
-                else:
-                    self._steps_since_snapshot += 1
-            self._base_version = version
-            self._last_checkpoint_id = checkpoint_id
-            self._pending = []
-            self._overwritten = False
+        Snapshot decisions are made by `create_checkpoint` in pregel (which
+        has the step number) via `is_snapshot_step`. `checkpoint()` is only
+        called for non-snapshot steps or when no checkpointer is available.
+        """
+        if self.value is MISSING:
+            return MISSING
+        return DELTA_SENTINEL

libs/langgraph/langgraph/errors.py

@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
+from dataclasses import dataclass
 from enum import Enum
-from typing import Any
+from typing import Any, Literal
 from warnings import warn
 
 # EmptyChannelError is re-exported from langgraph.channels.base
@@ -15,11 +16,14 @@ from langgraph.warnings import LangGraphDeprecatedSinceV10
 __all__ = (
     "EmptyChannelError",
     "ErrorCode",
+    "GraphDrained",
     "GraphRecursionError",
     "InvalidUpdateError",
     "GraphBubbleUp",
     "GraphInterrupt",
+    "NodeError",
     "NodeInterrupt",
+    "NodeTimeoutError",
     "ParentCommand",
     "EmptyInputError",
     "TaskNotFound",
@@ -42,6 +46,23 @@ def create_error_message(*, message: str, error_code: ErrorCode) -> str:
     )
 
 
+class GraphBubbleUp(Exception):
+    pass
+
+
+class GraphDrained(GraphBubbleUp):
+    """Raised when a graph run exits early due to a drain request.
+
+    This indicates the graph stopped cooperatively at a superstep boundary
+    because `RunControl.request_drain()` was called (e.g., in response to
+    SIGTERM). The checkpoint is saved and the run can be resumed later.
+    """
+
+    def __init__(self, reason: str = "shutdown") -> None:
+        self.reason = reason
+        super().__init__(f"Graph drained: {reason}")
+
+
 class GraphRecursionError(RecursionError):
     """Raised when the graph has exhausted the maximum number of steps.
 
@@ -77,10 +98,6 @@ class InvalidUpdateError(Exception):
     pass
 
 
-class GraphBubbleUp(Exception):
-    pass
-
-
 class GraphInterrupt(GraphBubbleUp):
     """Raised when a subgraph is interrupted, suppressed by the root graph.
     Never raised directly, or surfaced to the user."""
@@ -125,3 +142,77 @@ class TaskNotFound(Exception):
     """Raised when the executor is unable to find a task (for distributed mode)."""
 
     pass
+
+
+@dataclass(frozen=True, slots=True)
+class NodeError:
+    """Failure context passed to a node-level error handler.
+
+    Inject by adding a parameter typed `NodeError` to a handler registered via
+    `StateGraph.add_node(..., error_handler=...)`:
+
+    ```python
+    def handler(state: State, error: NodeError) -> Command:
+        return Command(update={"status": f"recovered from {error.node}: {error.error}"})
+    ```
+    """
+
+    node: str
+    """Name of the node whose execution failed."""
+
+    error: BaseException
+    """Exception raised by the failed node."""
+
+
+class NodeTimeoutError(Exception):
+    """Raised when a node invocation exceeds one of its configured timeouts.
+
+    Does **not** inherit from the built-in `TimeoutError` (a subclass of
+    `OSError`) so that the default `RetryPolicy` treats it as retryable.
+
+    Both `idle_timeout` and `run_timeout` reflect the configured policy at the
+    time of the failure (each is `None` if not configured). `kind` and
+    `timeout` identify which one fired.
+    """
+
+    node: str
+    timeout: float
+    run_timeout: float | None
+    idle_timeout: float | None
+    elapsed: float
+    kind: Literal["idle", "run"]
+
+    def __init__(
+        self,
+        node: str,
+        elapsed: float,
+        *,
+        kind: Literal["idle", "run"],
+        idle_timeout: float | None = None,
+        run_timeout: float | None = None,
+    ) -> None:
+        if kind == "idle":
+            if idle_timeout is None:
+                raise ValueError("idle_timeout is required when kind='idle'")
+            message = (
+                f"Node '{node}' exceeded its idle timeout of "
+                f"{idle_timeout:.3f}s without making progress "
+                f"(elapsed: {elapsed:.3f}s)."
+            )
+            self.timeout = idle_timeout
+        elif kind == "run":
+            if run_timeout is None:
+                raise ValueError("run_timeout is required when kind='run'")
+            message = (
+                f"Node '{node}' exceeded its run timeout of "
+                f"{run_timeout:.3f}s (elapsed: {elapsed:.3f}s)."
+            )
+            self.timeout = run_timeout
+        else:
+            raise ValueError("kind must be 'idle' or 'run'")
+        super().__init__(message)
+        self.node = node
+        self.elapsed = elapsed
+        self.kind = kind
+        self.idle_timeout = idle_timeout
+        self.run_timeout = run_timeout

libs/langgraph/langgraph/func/__init__.py

@@ -5,6 +5,7 @@ import inspect
 import warnings
 from collections.abc import Awaitable, Callable, Sequence
 from dataclasses import dataclass
+from datetime import timedelta
 from typing import (
     Any,
     Generic,
@@ -22,6 +23,11 @@ from typing_extensions import Unpack
 
 from langgraph._internal import _serde
 from langgraph._internal._constants import CACHE_NS_WRITES, PREVIOUS
+from langgraph._internal._runnable import is_async_callable
+from langgraph._internal._timeout import (
+    coerce_timeout_policy,
+    sync_timeout_unsupported,
+)
 from langgraph._internal._typing import MISSING, DeprecatedKwargs
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
@@ -31,13 +37,19 @@ from langgraph.pregel._call import (
     P,
     SyncAsyncFuture,
     T,
-    call,
+    _call_with_options,
     get_runnable_for_entrypoint,
     identifier,
 )
 from langgraph.pregel._read import PregelNode
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
-from langgraph.types import _DC_KWARGS, CachePolicy, RetryPolicy, StreamMode
+from langgraph.types import (
+    _DC_KWARGS,
+    CachePolicy,
+    RetryPolicy,
+    StreamMode,
+    TimeoutPolicy,
+)
 from langgraph.typing import ContextT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
 
@@ -51,6 +63,7 @@ class _TaskFunction(Generic[P, T]):
         *,
         retry_policy: Sequence[RetryPolicy],
         cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+        timeout: TimeoutPolicy | None = None,
         name: str | None = None,
     ) -> None:
         if name is not None:
@@ -67,15 +80,17 @@ class _TaskFunction(Generic[P, T]):
         self.func = func
         self.retry_policy = retry_policy
         self.cache_policy = cache_policy
+        self.timeout = timeout
         functools.update_wrapper(self, func)
 
     def __call__(self, *args: P.args, **kwargs: P.kwargs) -> SyncAsyncFuture[T]:
-        return call(
+        return _call_with_options(
             self.func,
+            args,
+            kwargs,
             retry_policy=self.retry_policy,
             cache_policy=self.cache_policy,
-            *args,
-            **kwargs,
+            timeout=self.timeout,
         )
 
     def clear_cache(self, cache: BaseCache) -> None:
@@ -98,6 +113,7 @@ def task(
     name: str | None = None,
     retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
     cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+    timeout: float | timedelta | TimeoutPolicy | None = None,
     **kwargs: Unpack[DeprecatedKwargs],
 ) -> Callable[
     [Callable[P, Awaitable[T]] | Callable[P, T]],
@@ -119,6 +135,7 @@ def task(
     name: str | None = None,
     retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
     cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+    timeout: float | timedelta | TimeoutPolicy | None = None,
     **kwargs: Unpack[DeprecatedKwargs],
 ) -> (
     Callable[[Callable[P, Awaitable[T]] | Callable[P, T]], _TaskFunction[P, T]]
@@ -142,6 +159,14 @@ def task(
         name: An optional name for the task. If not provided, the function name will be used.
         retry_policy: An optional retry policy (or list of policies) to use for the task in case of a failure.
         cache_policy: An optional cache policy to use for the task. This allows caching of the task results.
+        timeout: Timeout for each task attempt. A number or `timedelta` is a hard
+            wall-clock cap and is not refreshed. Use `TimeoutPolicy` to configure
+            both a wall-clock `run_timeout` and an `idle_timeout` refreshed by
+            progress signals. For long-running work that doesn't naturally emit
+            progress, call `runtime.heartbeat()` from inside the task. When the
+            timeout fires, `NodeTimeoutError` is raised and the retry policy (if
+            any) decides whether to retry. Supported only for async tasks; sync
+            tasks cannot be safely cancelled in-process.
 
     Returns:
         A callable function when used as a decorator.
@@ -196,6 +221,7 @@ def task(
         )
         if retry_policy is None:
             retry_policy = retry  # type: ignore[assignment]
+    timeout_policy = coerce_timeout_policy(timeout)
 
     retry_policies: Sequence[RetryPolicy] = (
         ()
@@ -208,8 +234,15 @@ def task(
     def decorator(
         func: Callable[P, Awaitable[T]] | Callable[P, T],
     ) -> Callable[P, SyncAsyncFuture[T]]:
+        if timeout_policy is not None and not is_async_callable(func):
+            name_ = name or getattr(func, "__name__", func.__class__.__name__)
+            raise sync_timeout_unsupported(str(name_), kind="Task")
         return _TaskFunction(
-            func, retry_policy=retry_policies, cache_policy=cache_policy, name=name
+            func,
+            retry_policy=retry_policies,
+            cache_policy=cache_policy,
+            timeout=timeout_policy,
+            name=name,
         )
 
     if __func_or_none__ is not None:
@@ -268,6 +301,15 @@ class entrypoint(Generic[ContextT]):
             passed to the workflow.
         cache_policy: A cache policy to use for caching the results of the workflow.
         retry_policy: A retry policy (or list of policies) to use for the workflow in case of a failure.
+        timeout: Timeout for each workflow attempt. A number or `timedelta` is a
+            hard wall-clock cap and is not refreshed. Use `TimeoutPolicy` to
+            configure both a wall-clock `run_timeout` and an `idle_timeout`
+            refreshed by progress signals. For long-running work that doesn't
+            naturally emit progress, call `runtime.heartbeat()` from inside the
+            workflow. When the timeout fires, `NodeTimeoutError` is raised and
+            the retry policy (if any) decides whether to retry. Supported only
+            for async workflows; sync workflows cannot be safely cancelled
+            in-process.
 
     !!! warning "`config_schema` Deprecated"
         The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0.
@@ -400,6 +442,7 @@ class entrypoint(Generic[ContextT]):
         context_schema: type[ContextT] | None = None,
         cache_policy: CachePolicy | None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> None:
         """Initialize the entrypoint decorator."""
@@ -426,6 +469,7 @@ class entrypoint(Generic[ContextT]):
         self.cache = cache
         self.cache_policy = cache_policy
         self.retry_policy = retry_policy
+        self.timeout = coerce_timeout_policy(timeout)
         self.context_schema = context_schema
 
     @dataclass(**_DC_KWARGS)
@@ -535,6 +579,7 @@ class entrypoint(Generic[ContextT]):
                     bound=bound,
                     triggers=[START],
                     channels=START,
+                    timeout=self.timeout,
                     writers=[
                         ChannelWrite(
                             [

libs/langgraph/langgraph/graph/_node.py

@@ -9,7 +9,7 @@ from langgraph.store.base import BaseStore
 
 from langgraph._internal._typing import EMPTY_SEQ
 from langgraph.runtime import Runtime
-from langgraph.types import CachePolicy, RetryPolicy, StreamWriter
+from langgraph.types import CachePolicy, RetryPolicy, StreamWriter, TimeoutPolicy
 from langgraph.typing import ContextT, NodeInputT, NodeInputT_contra
 
 
@@ -88,5 +88,8 @@ class StateNodeSpec(Generic[NodeInputT, ContextT]):
     input_schema: type[NodeInputT]
     retry_policy: RetryPolicy | Sequence[RetryPolicy] | None
     cache_policy: CachePolicy | None
+    is_error_handler: bool = False
+    error_handler_node: str | None = None
     ends: tuple[str, ...] | dict[str, str] | None = EMPTY_SEQ
     defer: bool = False
+    timeout: TimeoutPolicy | None = None

libs/langgraph/langgraph/graph/message.py

@@ -244,6 +244,52 @@ def add_messages(
     return merged
 
 
+def _messages_delta_reducer(
+    state: list[AnyMessage], writes: list[list[AnyMessage]]
+) -> list[AnyMessage]:
+    """**Experimental.** Batch reducer for use with `DeltaChannel`.
+
+    Processes all writes in one pass — dedup by ID, `RemoveMessage`
+    tombstoning — without calling `add_messages`. Assumes writes contain
+    already-typed `BaseMessage` objects (no raw-dict coercion).
+
+    This reducer is batching-invariant, as required by `DeltaChannel`:
+    `reducer(reducer(state, xs), ys) == reducer(state, xs + ys)`.
+
+    Use `add_messages` as the reducer for `BinaryOperatorAggregate` or
+    anywhere raw message dicts / strings need to be coerced first.
+
+    Example::
+
+        from typing import Annotated
+        from langgraph.channels.delta import DeltaChannel
+        from langgraph.graph.message import _messages_delta_reducer
+
+        class State(TypedDict):
+            messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+    """
+    from itertools import chain
+
+    index: dict[str, int] = {m.id: i for i, m in enumerate(state) if m.id is not None}
+    result: list[AnyMessage | None] = list(state)
+    for msg in chain.from_iterable(
+        [w] if isinstance(w, BaseMessage) else w for w in writes
+    ):
+        mid = msg.id
+        if mid is None:
+            result.append(msg)
+        elif isinstance(msg, RemoveMessage):
+            if mid in index:
+                result[index[mid]] = None
+                del index[mid]
+        elif mid in index:
+            result[index[mid]] = msg
+        else:
+            index[mid] = len(result)
+            result.append(msg)
+    return [m for m in result if m is not None]
+
+
 @deprecated(
     "MessageGraph is deprecated in langgraph 1.0.0, to be removed in 2.0.0. Please use StateGraph with a `messages` key instead.",
     category=None,

libs/langgraph/langgraph/graph/state.py

@@ -7,6 +7,7 @@ import warnings
 from collections import defaultdict
 from collections.abc import Awaitable, Callable, Hashable, Sequence
 from dataclasses import is_dataclass
+from datetime import timedelta
 from functools import partial
 from inspect import isclass, isfunction, ismethod, signature
 from types import FunctionType
@@ -45,9 +46,11 @@ from langgraph._internal._fields import (
 )
 from langgraph._internal._pydantic import create_model
 from langgraph._internal._runnable import coerce_to_runnable
+from langgraph._internal._timeout import coerce_timeout_policy
 from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -81,6 +84,7 @@ from langgraph.types import (
     Command,
     RetryPolicy,
     Send,
+    TimeoutPolicy,
     ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
@@ -299,7 +303,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         input_schema: None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
         destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
@@ -366,7 +372,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         input_schema: type[NodeInputT],
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
         destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the `StateGraph` where input schema is specified.
@@ -438,7 +446,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         input_schema: None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
         destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
@@ -505,7 +515,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         input_schema: type[NodeInputT],
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
         destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the `StateGraph`, input schema is specified.
@@ -579,7 +591,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         input_schema: type[NodeInputT] | None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
         destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the `StateGraph`.
@@ -598,6 +612,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
 
                 If a sequence is provided, the first matching policy will be applied.
             cache_policy: The cache policy for the node.
+            error_handler: Optional node-level error handler callable for this node.
             destinations: Destinations that indicate where a node can route to.
 
                 Useful for edgeless graphs with nodes that return `Command` objects.
@@ -609,6 +624,14 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 !!! warning
 
                     This is only used for graph rendering and doesn't have any effect on the graph execution.
+            timeout: Timeout for each node attempt. A number or `timedelta` is
+                a hard wall-clock cap and is not refreshed. Use `TimeoutPolicy`
+                to configure both a wall-clock `run_timeout` and an
+                `idle_timeout` refreshed by progress signals. When exceeded, a
+                [`NodeTimeoutError`][langgraph.errors.NodeTimeoutError] is raised
+                and the retry policy (if any) decides whether to retry. Timeouts
+                are supported only for async nodes; sync nodes cannot be safely
+                cancelled in-process.
 
         Example:
             ```python
@@ -662,6 +685,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             )
             if input_schema is None:
                 input_schema = cast(type[NodeInputT] | None, input_)
+        timeout = coerce_timeout_policy(timeout)
 
         if not isinstance(node, str):
             action = node
@@ -748,6 +772,25 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         if destinations is not None:
             ends = destinations
 
+        resolved_input_schema: type[Any] = (
+            input_schema or inferred_input_schema or self.state_schema
+        )
+        handler_node_name: str | None = None
+        if error_handler is not None:
+            handler_node_name = f"__error_handler__{node}"
+            if handler_node_name in self.nodes:
+                raise ValueError(
+                    f"Auto-generated error handler node `{handler_node_name}` already exists."
+                )
+            self.nodes[handler_node_name] = StateNodeSpec[Any, ContextT](
+                coerce_to_runnable(error_handler, name=handler_node_name, trace=False),  # type: ignore[arg-type]
+                metadata=None,
+                input_schema=resolved_input_schema,
+                retry_policy=None,
+                cache_policy=None,
+                is_error_handler=True,
+            )
+
         if input_schema is not None:
             self.nodes[node] = StateNodeSpec[NodeInputT, ContextT](
                 coerce_to_runnable(action, name=node, trace=False),  # type: ignore[arg-type]
@@ -755,8 +798,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 input_schema=input_schema,
                 retry_policy=retry_policy,
                 cache_policy=cache_policy,
+                error_handler_node=handler_node_name,
                 ends=ends,
                 defer=defer,
+                timeout=timeout,
             )
         elif inferred_input_schema is not None:
             self.nodes[node] = StateNodeSpec(
@@ -765,8 +810,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 input_schema=inferred_input_schema,
                 retry_policy=retry_policy,
                 cache_policy=cache_policy,
+                error_handler_node=handler_node_name,
                 ends=ends,
                 defer=defer,
+                timeout=timeout,
             )
         else:
             self.nodes[node] = StateNodeSpec[StateT, ContextT](
@@ -775,8 +822,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 input_schema=self.state_schema,
                 retry_policy=retry_policy,
                 cache_policy=cache_policy,
+                error_handler_node=handler_node_name,
                 ends=ends,
                 defer=defer,
+                timeout=timeout,
             )
 
         input_schema = input_schema or inferred_input_schema
@@ -1031,7 +1080,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             for node in interrupt:
                 if node not in self.nodes:
                     raise ValueError(f"Interrupt node `{node}` not found")
-
         self.compiled = True
         return self
 
@@ -1045,6 +1093,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         interrupt_after: All | list[str] | None = None,
         debug: bool = False,
         name: str | None = None,
+        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
     ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
         """Compiles the `StateGraph` into a `CompiledStateGraph` object.
 
@@ -1077,11 +1126,19 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             interrupt_after: An optional list of node names to interrupt after.
             debug: A flag indicating whether to enable debug mode.
             name: The name to use for the compiled graph.
+            transformers: Optional sequence of `StreamTransformer` classes or
+                configured factories. Classes and factories are instantiated
+                per run whenever `stream_events(version="v3")` / `astream_events(version="v3")` is called and are
+                propagated to subgraph scopes. Custom factories should follow
+                the standard `StreamTransformer` constructor shape by
+                accepting `scope` as their first argument. Appended after the
+                built-in stream transformers.
 
         Returns:
             CompiledStateGraph: The compiled `StateGraph`.
         """
         checkpointer = ensure_valid_checkpointer(checkpointer)
+
         serde_allowlist: set[tuple[str, ...]] | None = None
         if _serde.STRICT_MSGPACK_ENABLED:
             schema_types: list[type[Any]] = [
@@ -1136,6 +1193,11 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 key for key, val in self.channels.items() if not is_managed_value(val)
             ]
         )
+        node_error_handler_map = {
+            node_name: spec.error_handler_node
+            for node_name, spec in self.nodes.items()
+            if spec.error_handler_node is not None
+        }
 
         compiled = CompiledStateGraph[StateT, ContextT, InputT, OutputT](
             builder=self,
@@ -1158,7 +1220,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             debug=debug,
             store=store,
             cache=cache,
+            node_error_handler_map=node_error_handler_map,
             name=name or "LangGraph",
+            stream_transformers=transformers,
         )
         compiled._serde_allowlist = serde_allowlist
 
@@ -1331,7 +1395,10 @@ class CompiledStateGraph(
                 metadata=node.metadata,
                 retry_policy=node.retry_policy,
                 cache_policy=node.cache_policy,
+                is_error_handler=node.is_error_handler,
+                error_handler_node=node.error_handler_node,
                 bound=node.runnable,  # type: ignore[arg-type]
+                timeout=node.timeout,
             )
         else:
             raise RuntimeError
@@ -1667,6 +1734,20 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
         # Search through all annotated medata to find channel annotations
         for item in meta:
             if isinstance(item, BaseChannel):
+                if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
+                    origin = typ.__origin__
+                    # Unwrap parameterized Required[X]/NotRequired[X] to X
+                    # (e.g. Annotated[NotRequired[dict[...]], ...]).
+                    if hasattr(origin, "__origin__") and origin.__origin__ in (
+                        Required,
+                        NotRequired,
+                    ):
+                        origin = origin.__args__[0]
+                    item = item.__class__(
+                        item.reducer,
+                        origin,
+                        snapshot_frequency=item.snapshot_frequency,
+                    )
                 return item
             elif isclass(item) and issubclass(item, BaseChannel):
                 # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]

libs/langgraph/langgraph/pregel/_algo.py

@@ -39,6 +39,7 @@ from langgraph._internal._constants import (
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_NS,
     CONFIG_KEY_CHECKPOINTER,
+    CONFIG_KEY_NODE_ERROR,
     CONFIG_KEY_READ,
     CONFIG_KEY_RESUME_MAP,
     CONFIG_KEY_RUNTIME,
@@ -47,6 +48,7 @@ from langgraph._internal._constants import (
     CONFIG_KEY_TASK_ID,
     CONFIG_KEY_THREAD_ID,
     ERROR,
+    ERROR_SOURCE_NODE,
     INTERRUPT,
     NO_WRITES,
     NS_END,
@@ -66,6 +68,7 @@ from langgraph.channels.base import BaseChannel
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.constants import TAG_HIDDEN
+from langgraph.errors import NodeError
 from langgraph.managed.base import ManagedValueMapping
 from langgraph.pregel._call import get_runnable_for_task, identifier
 from langgraph.pregel._io import read_channels
@@ -80,6 +83,7 @@ from langgraph.types import (
     PregelTask,
     RetryPolicy,
     Send,
+    TimeoutPolicy,
 )
 
 GetNextVersion = Callable[[V | None, None], V]
@@ -114,13 +118,21 @@ class PregelTaskWrites(NamedTuple):
 
 
 class Call:
-    __slots__ = ("func", "input", "retry_policy", "cache_policy", "callbacks")
+    __slots__ = (
+        "func",
+        "input",
+        "retry_policy",
+        "cache_policy",
+        "callbacks",
+        "timeout",
+    )
 
     func: Callable
     input: tuple[tuple[Any, ...], dict[str, Any]]
     retry_policy: Sequence[RetryPolicy] | None
     cache_policy: CachePolicy | None
     callbacks: Callbacks
+    timeout: TimeoutPolicy | None
 
     def __init__(
         self,
@@ -130,12 +142,14 @@ class Call:
         retry_policy: Sequence[RetryPolicy] | None,
         cache_policy: CachePolicy | None,
         callbacks: Callbacks,
+        timeout: TimeoutPolicy | None = None,
     ) -> None:
         self.func = func
         self.input = input
         self.retry_policy = retry_policy
         self.cache_policy = cache_policy
         self.callbacks = callbacks
+        self.timeout = timeout
 
 
 def should_interrupt(
@@ -281,7 +295,15 @@ def apply_writes(
     pending_writes_by_channel: dict[str, list[Any]] = defaultdict(list)
     for task in tasks:
         for chan, val in task.writes:
-            if chan in (NO_WRITES, PUSH, RESUME, INTERRUPT, RETURN, ERROR):
+            if chan in (
+                NO_WRITES,
+                PUSH,
+                RESUME,
+                INTERRUPT,
+                RETURN,
+                ERROR,
+                ERROR_SOURCE_NODE,
+            ):
                 pass
             elif chan in channels:
                 pending_writes_by_channel[chan].append(val)
@@ -733,11 +755,48 @@ def prepare_single_task(
                         task_path[:3],
                         writers=proc.flat_writers,
                         subgraphs=proc.subgraphs,
+                        timeout=proc.timeout,
                     )
             else:
                 return PregelTask(task_id, name, task_path[:3])
 
 
+def _coerce_pending_error(value: Any) -> BaseException:
+    if isinstance(value, BaseException):
+        return value
+    return Exception(str(value))
+
+
+def _read_errors_from_pending_writes(
+    pending_writes: list[PendingWrite],
+) -> list[BaseException]:
+    errors: list[BaseException] = []
+    for _, channel, value in pending_writes:
+        if channel == ERROR:
+            errors.append(_coerce_pending_error(value))
+    return errors
+
+
+def _read_error_for_task_id_from_pending_writes(
+    pending_writes: list[PendingWrite], task_id: str
+) -> BaseException | None:
+    for pending_task_id, channel, value in reversed(pending_writes):
+        if pending_task_id == task_id and channel == ERROR:
+            return _coerce_pending_error(value)
+    return None
+
+
+def _read_error_source_node_from_pending_writes(
+    pending_writes: list[PendingWrite], task_id: str
+) -> str | None:
+    for pending_task_id, channel, value in reversed(pending_writes):
+        if pending_task_id == task_id and channel == ERROR_SOURCE_NODE:
+            if isinstance(value, str):
+                return value
+            return str(value)
+    return None
+
+
 def prepare_push_task_functional(
     task_path: tuple[str, tuple, int, str, Call],
     # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
@@ -870,6 +929,7 @@ def prepare_push_task_functional(
             cache_key,
             task_id,
             in_progress_task_path,
+            timeout=call.timeout,
         )
     else:
         return PregelTask(task_id, name, in_progress_task_path)
@@ -1041,11 +1101,153 @@ def prepare_push_task_send(
             translated_task_path,
             writers=proc.flat_writers,
             subgraphs=proc.subgraphs,
+            timeout=packet.timeout if packet.timeout is not None else proc.timeout,
         )
     else:
         return PregelTask(task_id, packet.node, translated_task_path)
 
 
+def prepare_node_error_handler_task(
+    failed_task: PregelExecutableTask,
+    *,
+    handler_node_name: str,
+    failed_error: BaseException,
+    checkpoint: Checkpoint,
+    pending_writes: list[PendingWrite],
+    processes: Mapping[str, PregelNode],
+    channels: Mapping[str, BaseChannel],
+    managed: ManagedValueMapping,
+    config: RunnableConfig,
+    step: int,
+    stop: int,
+    store: BaseStore | None = None,
+    checkpointer: BaseCheckpointSaver | None = None,
+    manager: None | ParentRunManager | AsyncParentRunManager = None,
+    cache_policy: CachePolicy | None = None,
+    retry_policy: Sequence[RetryPolicy] = (),
+) -> PregelExecutableTask | None:
+    """Prepare an immediate node-level error handler task for a failed task."""
+    if handler_node_name not in processes:
+        return None
+    proc = processes[handler_node_name]
+    proc_node = proc.node
+    if proc_node is None:
+        return None
+
+    checkpoint_id_bytes = binascii.unhexlify(checkpoint["id"].replace("-", ""))
+    task_id_func = _xxhash_str if checkpoint["v"] > 1 else _uuid5_str
+    configurable = config.get(CONF, {})
+    parent_ns = configurable.get(CONFIG_KEY_CHECKPOINT_NS, "")
+    checkpoint_ns = (
+        f"{parent_ns}{NS_SEP}{handler_node_name}" if parent_ns else handler_node_name
+    )
+    task_id = task_id_func(
+        checkpoint_id_bytes,
+        checkpoint_ns,
+        str(step),
+        handler_node_name,
+        PUSH,
+        "node_error_handler",
+        failed_task.id,
+    )
+    task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+    translated_task_path = (*failed_task.path[:3], "node_error_handler", False)
+    metadata = {
+        "langgraph_step": step,
+        "langgraph_node": handler_node_name,
+        "langgraph_triggers": PUSH_TRIGGER,
+        "langgraph_path": translated_task_path,
+        "langgraph_checkpoint_ns": task_checkpoint_ns,
+    }
+    if proc.metadata:
+        metadata.update(proc.metadata)
+    writes: deque[tuple[str, Any]] = deque()
+
+    effective_retry_policy = proc.retry_policy or retry_policy
+    effective_cache_policy = proc.cache_policy or cache_policy
+    if effective_cache_policy:
+        args_key = effective_cache_policy.key_func(failed_task.input)
+        cache_key = CacheKey(
+            (
+                CACHE_NS_WRITES,
+                (identifier(proc) or "__dynamic__"),
+                handler_node_name,
+            ),
+            xxh3_128_hexdigest(
+                args_key.encode() if isinstance(args_key, str) else args_key
+            ),
+            effective_cache_policy.ttl,
+        )
+    else:
+        cache_key = None
+
+    scratchpad = _scratchpad(
+        config[CONF].get(CONFIG_KEY_SCRATCHPAD),
+        pending_writes,
+        task_id,
+        xxh3_128_hexdigest(task_checkpoint_ns.encode()),
+        config[CONF].get(CONFIG_KEY_RESUME_MAP),
+        step,
+        stop,
+    )
+    runtime = cast(Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME))
+    runtime = runtime.override(
+        store=store, previous=checkpoint["channel_values"].get(PREVIOUS, None)
+    )
+    additional_config: RunnableConfig = {
+        "metadata": metadata,
+        "tags": proc.tags,
+    }
+    return PregelExecutableTask(
+        handler_node_name,
+        failed_task.input,
+        proc_node,
+        writes,
+        patch_config(
+            merge_configs(config, additional_config),
+            run_name=handler_node_name,
+            callbacks=manager.get_child(f"graph:step:{step}") if manager else None,
+            configurable={
+                CONFIG_KEY_TASK_ID: task_id,
+                CONFIG_KEY_SEND: writes.extend,
+                CONFIG_KEY_READ: partial(
+                    local_read,
+                    scratchpad,
+                    channels,
+                    managed,
+                    PregelTaskWrites(
+                        translated_task_path,
+                        handler_node_name,
+                        writes,
+                        PUSH_TRIGGER,
+                    ),
+                ),
+                CONFIG_KEY_CHECKPOINTER: (
+                    checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
+                ),
+                CONFIG_KEY_CHECKPOINT_MAP: {
+                    **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
+                    parent_ns: checkpoint["id"],
+                },
+                CONFIG_KEY_CHECKPOINT_ID: None,
+                CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
+                CONFIG_KEY_SCRATCHPAD: scratchpad,
+                CONFIG_KEY_RUNTIME: runtime,
+                CONFIG_KEY_NODE_ERROR: NodeError(
+                    node=failed_task.name, error=failed_error
+                ),
+            },
+        ),
+        PUSH_TRIGGER,
+        effective_retry_policy,
+        cache_key,
+        task_id,
+        translated_task_path,
+        writers=proc.flat_writers,
+        subgraphs=proc.subgraphs,
+    )
+
+
 def checkpoint_null_version(
     checkpoint: Checkpoint,
 ) -> V | None:
@@ -1255,4 +1457,4 @@ def sanitize_untracked_values_in_send(
         for k, v in packet.arg.items()
         if not isinstance(channels.get(k), UntrackedValue)
     }
-    return Send(node=packet.node, arg=sanitized_arg)
+    return Send(node=packet.node, arg=sanitized_arg, timeout=packet.timeout)

libs/langgraph/langgraph/pregel/_call.py

@@ -8,6 +8,7 @@ import inspect
 import sys
 import types
 from collections.abc import Awaitable, Callable, Generator, Sequence
+from datetime import timedelta
 from typing import Any, Generic, TypeVar, cast
 
 from langchain_core.runnables import Runnable
@@ -20,9 +21,13 @@ from langgraph._internal._runnable import (
     is_async_callable,
     run_in_executor,
 )
+from langgraph._internal._timeout import (
+    coerce_timeout_policy,
+    sync_timeout_unsupported,
+)
 from langgraph.config import get_config
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
-from langgraph.types import CachePolicy, RetryPolicy
+from langgraph.types import CachePolicy, RetryPolicy, TimeoutPolicy
 
 ##
 # Utilities borrowed from cloudpickle.
@@ -255,8 +260,31 @@ def call(
     *args: Any,
     retry_policy: Sequence[RetryPolicy] | None = None,
     cache_policy: CachePolicy | None = None,
+    timeout: float | timedelta | TimeoutPolicy | None = None,
     **kwargs: Any,
 ) -> SyncAsyncFuture[T]:
+    return _call_with_options(
+        func,
+        args,
+        kwargs,
+        retry_policy=retry_policy,
+        cache_policy=cache_policy,
+        timeout=coerce_timeout_policy(timeout),
+    )
+
+
+def _call_with_options(
+    func: Callable[P, Awaitable[T]] | Callable[P, T],
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+    *,
+    retry_policy: Sequence[RetryPolicy] | None = None,
+    cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
+) -> SyncAsyncFuture[T]:
+    if timeout is not None and not is_async_callable(func):
+        name = getattr(func, "__name__", func.__class__.__name__)
+        raise sync_timeout_unsupported(name, kind="Task")
     config = get_config()
     impl = config[CONF][CONFIG_KEY_CALL]
     fut = impl(
@@ -265,5 +293,6 @@ def call(
         retry_policy=retry_policy,
         cache_policy=cache_policy,
         callbacks=config["callbacks"],
+        timeout=timeout,
     )
     return fut

libs/langgraph/langgraph/pregel/_checkpoint.py

@@ -1,189 +1,22 @@
 from __future__ import annotations
 
-import logging
-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from datetime import datetime, timezone
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
-from langgraph.checkpoint.base import (
-    BaseCheckpointSaver,
-    Checkpoint,
-    DeltaChainValue,
-    DeltaValue,
-)
+from langgraph.checkpoint.base import DELTA_SENTINEL, BaseCheckpointSaver, Checkpoint
 from langgraph.checkpoint.base.id import uuid6
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.base import BaseChannel
+from langgraph.channels.delta import DeltaChannel
 from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec
 
 LATEST_VERSION = 4
 
-logger = logging.getLogger(__name__)
-
-_MISSING_SENTINEL = object()
-
-
-def _assemble_delta_channels(
-    checkpoint: Checkpoint,
-    config: RunnableConfig,
-    checkpointer: BaseCheckpointSaver,
-) -> dict[str, Any]:
-    """Resolve any DeltaValue entries in checkpoint channel_values to DeltaChainValue.
-
-    Returns a dict of only the channels that needed assembly (others are untouched).
-    Tries get_channel_blob fast-path first; falls back to get_tuple traversal.
-    """
-    thread_id = str(config["configurable"]["thread_id"])
-    checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-    current_checkpoint_id = checkpoint.get("id")
-    assembled: dict[str, Any] = {}
-
-    for channel, value in checkpoint["channel_values"].items():
-        if not isinstance(value, DeltaValue):
-            continue
-
-        chain_deltas: list[list[Any]] = []
-        base: list[Any] | None = None
-        cursor: DeltaValue = value
-        # Pre-seed with current checkpoint ID to guard against self-referential chains.
-        visited: set[str] = {current_checkpoint_id} if current_checkpoint_id else set()
-
-        while True:
-            chain_deltas.append(cursor.delta)
-            prev_id = cursor.prev_checkpoint_id
-            if prev_id is None:
-                break  # chain root
-            if prev_id in visited:
-                logger.warning(
-                    "DeltaChannel chain cycle at checkpoint %r for channel %r; breaking",
-                    prev_id,
-                    channel,
-                )
-                break
-            visited.add(prev_id)
-
-            # Fast path: saver has a dedicated blob store.
-            blob = checkpointer.get_channel_blob(
-                thread_id, checkpoint_ns, prev_id, channel
-            )
-            if blob is not NotImplemented:
-                if isinstance(blob, DeltaValue):
-                    cursor = blob
-                    continue
-                else:
-                    base = blob  # plain list = snapshot root
-                    break
-
-            # Fallback: load the full checkpoint and extract channel value.
-            parent_config: RunnableConfig = {
-                "configurable": {
-                    "thread_id": thread_id,
-                    "checkpoint_ns": checkpoint_ns,
-                    "checkpoint_id": prev_id,
-                }
-            }
-            parent_tuple = checkpointer.get_tuple(parent_config)
-            if parent_tuple is None:
-                logger.warning(
-                    "DeltaChannel chain broken: checkpoint %r not found for channel %r",
-                    prev_id,
-                    channel,
-                )
-                break
-            prev_val = parent_tuple.checkpoint["channel_values"].get(
-                channel, _MISSING_SENTINEL
-            )
-            if prev_val is _MISSING_SENTINEL:
-                break
-            elif isinstance(prev_val, DeltaValue):
-                cursor = prev_val
-            else:
-                base = prev_val
-                break
-
-        chain_deltas.reverse()
-        assembled[channel] = DeltaChainValue(base=base, deltas=chain_deltas)
-
-    return assembled
-
-
-async def _aassemble_delta_channels(
-    checkpoint: Checkpoint,
-    config: RunnableConfig,
-    checkpointer: BaseCheckpointSaver,
-) -> dict[str, Any]:
-    """Async version of _assemble_delta_channels."""
-    thread_id = str(config["configurable"]["thread_id"])
-    checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-    current_checkpoint_id = checkpoint.get("id")
-    assembled: dict[str, Any] = {}
-
-    for channel, value in checkpoint["channel_values"].items():
-        if not isinstance(value, DeltaValue):
-            continue
-
-        chain_deltas: list[list[Any]] = []
-        base: list[Any] | None = None
-        cursor: DeltaValue = value
-        visited: set[str] = {current_checkpoint_id} if current_checkpoint_id else set()
-
-        while True:
-            chain_deltas.append(cursor.delta)
-            prev_id = cursor.prev_checkpoint_id
-            if prev_id is None:
-                break
-            if prev_id in visited:
-                logger.warning(
-                    "DeltaChannel chain cycle at checkpoint %r for channel %r; breaking",
-                    prev_id,
-                    channel,
-                )
-                break
-            visited.add(prev_id)
-
-            blob = await checkpointer.aget_channel_blob(
-                thread_id, checkpoint_ns, prev_id, channel
-            )
-            if blob is not NotImplemented:
-                if isinstance(blob, DeltaValue):
-                    cursor = blob
-                    continue
-                else:
-                    base = blob
-                    break
-
-            parent_config: RunnableConfig = {
-                "configurable": {
-                    "thread_id": thread_id,
-                    "checkpoint_ns": checkpoint_ns,
-                    "checkpoint_id": prev_id,
-                }
-            }
-            parent_tuple = await checkpointer.aget_tuple(parent_config)
-            if parent_tuple is None:
-                logger.warning(
-                    "DeltaChannel chain broken: checkpoint %r not found for channel %r",
-                    prev_id,
-                    channel,
-                )
-                break
-            prev_val = parent_tuple.checkpoint["channel_values"].get(
-                channel, _MISSING_SENTINEL
-            )
-            if prev_val is _MISSING_SENTINEL:
-                break
-            elif isinstance(prev_val, DeltaValue):
-                cursor = prev_val
-            else:
-                base = prev_val
-                break
-
-        chain_deltas.reverse()
-        assembled[channel] = DeltaChainValue(base=base, deltas=chain_deltas)
-
-    return assembled
+GetNextVersion = Callable[[Any, None], Any]
 
 
 def empty_checkpoint() -> Checkpoint:
@@ -204,35 +37,87 @@ def create_checkpoint(
     *,
     id: str | None = None,
     updated_channels: set[str] | None = None,
+    get_next_version: GetNextVersion | None = None,
+    force_delta_snapshot: bool = False,
 ) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
+    """Create a checkpoint for the given channels.
+
+    For `DeltaChannel` with `snapshot_frequency=N`, snapshot steps write a
+    `_DeltaSnapshot` blob rather than `DELTA_SENTINEL`, bounding the ancestor
+    walk to at most N steps. Snapshots are eager: even if the channel had no
+    write this step, a version bump is forced (via `get_next_version`) so the
+    blob is stored by `put()`. Without `get_next_version` (e.g. static
+    contexts), snapshot steps gracefully fall back to sentinel.
+
+    `force_delta_snapshot` writes available `DeltaChannel` values as snapshots
+    regardless of `snapshot_frequency`. This is used by `durability="exit"`,
+    where intermediate writes are not stored as ancestor `checkpoint_writes`.
+    """
     ts = datetime.now(timezone.utc).isoformat()
     if channels is None:
         values = checkpoint["channel_values"]
+        channel_versions = checkpoint["channel_versions"]
     else:
         values = {}
+        channel_versions = dict(checkpoint["channel_versions"])
         for k in channels:
-            if k not in checkpoint["channel_versions"]:
+            if k not in channel_versions:
                 continue
-            v = channels[k].checkpoint()
-            if v is not MISSING:
-                values[k] = v
+            ch = channels[k]
+            if (
+                isinstance(ch, DeltaChannel)
+                and (force_delta_snapshot or ch.is_snapshot_step(step))
+                and ch.is_available()
+            ):
+                # Eager snapshot: bump version if not already written this step
+                # so put() includes this channel in new_versions and stores blob.
+                if get_next_version is not None and (
+                    updated_channels is None or k not in updated_channels
+                ):
+                    channel_versions[k] = get_next_version(channel_versions[k], None)
+                values[k] = _DeltaSnapshot(ch.get())
+            else:
+                v = ch.checkpoint()
+                if v is not MISSING:
+                    values[k] = v
     return Checkpoint(
         v=LATEST_VERSION,
         ts=ts,
         id=id or str(uuid6(clock_seq=step)),
         channel_values=values,
-        channel_versions=checkpoint["channel_versions"],
+        channel_versions=channel_versions,
         versions_seen=checkpoint["versions_seen"],
         updated_channels=None if updated_channels is None else sorted(updated_channels),
     )
 
 
+def _needs_replay(spec: BaseChannel, stored: object) -> bool:
+    """True if `spec` is a `DeltaChannel` and the stored blob is a sentinel,
+    requiring an ancestor walk to reconstruct.
+
+    `_DeltaSnapshot` blobs and plain values (migration) resolve directly via
+    `from_checkpoint` — only `DELTA_SENTINEL` / `MISSING` trigger replay.
+    """
+    if not isinstance(spec, DeltaChannel):
+        return False
+    return stored is MISSING or stored is DELTA_SENTINEL
+
+
 def channels_from_checkpoint(
     specs: Mapping[str, BaseChannel | ManagedValueSpec],
     checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
 ) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Get channels from a checkpoint."""
+    """Hydrate channels from a checkpoint.
+
+    For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
+    is sufficient. `DeltaChannel` is the exception: sentinel blobs require an
+    ancestor walk via `saver._get_channel_writes_history`. The walk terminates
+    at the nearest `_DeltaSnapshot` blob (step-based) or a pre-migration plain
+    value, so read depth is bounded by `snapshot_frequency`.
+    """
     channel_specs: dict[str, BaseChannel] = {}
     managed_specs: dict[str, ManagedValueSpec] = {}
     for k, v in specs.items():
@@ -240,10 +125,51 @@ def channels_from_checkpoint(
             channel_specs[k] = v
         else:
             managed_specs[k] = v
+
     channels: dict[str, BaseChannel] = {}
-    for k, v in channel_specs.items():
-        ch = v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
-        ch.after_checkpoint(checkpoint["channel_versions"].get(k), checkpoint.get("id"))
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if _needs_replay(spec, stored) and saver is not None and config is not None:
+            delta_spec = cast(DeltaChannel, spec)
+            history = saver._get_channel_writes_history(config, k)
+            replay_ch = delta_spec.from_checkpoint(history.seed)
+            replay_ch.replay_writes(history.writes)
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs
+
+
+async def achannels_from_checkpoint(
+    specs: Mapping[str, BaseChannel | ManagedValueSpec],
+    checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
+) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
+    """Async version of `channels_from_checkpoint`. See docstring there."""
+    channel_specs: dict[str, BaseChannel] = {}
+    managed_specs: dict[str, ManagedValueSpec] = {}
+    for k, v in specs.items():
+        if isinstance(v, BaseChannel):
+            channel_specs[k] = v
+        else:
+            managed_specs[k] = v
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if _needs_replay(spec, stored) and saver is not None and config is not None:
+            delta_spec = cast(DeltaChannel, spec)
+            history = await saver._aget_channel_writes_history(config, k)
+            replay_ch = delta_spec.from_checkpoint(history.seed)
+            replay_ch.replay_writes(history.writes)
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(stored)
         channels[k] = ch
     return channels, managed_specs
 

libs/langgraph/langgraph/pregel/_loop.py

@@ -45,11 +45,13 @@ from langgraph._internal._constants import (
     CONFIG_KEY_REPLAY_STATE,
     CONFIG_KEY_RESUME_MAP,
     CONFIG_KEY_RESUMING,
+    CONFIG_KEY_RUNTIME,
     CONFIG_KEY_SCRATCHPAD,
     CONFIG_KEY_STREAM,
     CONFIG_KEY_TASK_ID,
     CONFIG_KEY_THREAD_ID,
     ERROR,
+    ERROR_SOURCE_NODE,
     INPUT,
     INTERRUPT,
     NS_END,
@@ -68,6 +70,7 @@ from langgraph.callbacks import (
     GraphResumeEvent,
 )
 from langgraph.channels.base import BaseChannel
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.constants import TAG_HIDDEN
 from langgraph.errors import (
@@ -86,14 +89,14 @@ from langgraph.pregel._algo import (
     checkpoint_null_version,
     increment,
     prepare_next_tasks,
+    prepare_node_error_handler_task,
     prepare_single_task,
     sanitize_untracked_values_in_send,
     should_interrupt,
     task_path_str,
 )
 from langgraph.pregel._checkpoint import (
-    _aassemble_delta_channels,
-    _assemble_delta_channels,
+    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -119,6 +122,7 @@ from langgraph.pregel.debug import (
     map_debug_tasks,
 )
 from langgraph.pregel.protocol import StreamChunk, StreamProtocol
+from langgraph.runtime import RunControl, Runtime
 from langgraph.types import (
     All,
     CachePolicy,
@@ -190,6 +194,8 @@ class PregelLoop:
     _migrate_checkpoint: Callable[[Checkpoint], None] | None
     submit: Submit
     channels: Mapping[str, BaseChannel]
+    # Only set on AsyncPregelLoop; sync loops keep this as None.
+    _delta_write_futs: list[Any] | None = None
     managed: ManagedValueMapping
     checkpoint: Checkpoint
     checkpoint_id_saved: str
@@ -204,10 +210,12 @@ class PregelLoop:
         "input",
         "pending",
         "done",
+        "draining",
         "interrupt_before",
         "interrupt_after",
         "out_of_steps",
     ]
+    control: RunControl | None
     tasks: dict[str, PregelExecutableTask]
     output: None | dict[str, Any] | Any = None
     updated_channels: set[str] | None = None
@@ -315,6 +323,8 @@ class PregelLoop:
             else ()
         )
         self.prev_checkpoint_config = None
+        runtime = self.config[CONF].get(CONFIG_KEY_RUNTIME)
+        self.control = runtime.control if isinstance(runtime, Runtime) else None
 
     def _push_graph_lifecycle_event(
         self,
@@ -322,11 +332,16 @@ class PregelLoop:
         *,
         interrupts: tuple[Interrupt, ...] = (),
     ) -> None:
+        # drain status never reaches lifecycle events: tick() returns False
+        # before pushing, and interrupts are raised through GraphInterrupt
+        if self.status == "draining":
+            raise RuntimeError("Draining status cannot emit lifecycle events")
+        status = self.status
         if kind == "resume":
             self._graph_lifecycle_events.append(
                 GraphResumeEvent(
                     run_id=None,
-                    status=self.status,
+                    status=status,
                     checkpoint_id=self.checkpoint["id"],
                     checkpoint_ns=self.checkpoint_ns,
                 )
@@ -335,7 +350,7 @@ class PregelLoop:
             self._graph_lifecycle_events.append(
                 GraphInterruptEvent(
                     run_id=None,
-                    status=self.status,
+                    status=status,
                     checkpoint_id=self.checkpoint["id"],
                     checkpoint_ns=self.checkpoint_ns,
                     interrupts=interrupts,
@@ -408,7 +423,7 @@ class PregelLoop:
                     task = self.tasks.get(task_id)
                 else:
                     task = None
-                self.submit(
+                fut = self.submit(
                     self.checkpointer_put_writes,
                     config,
                     writes_to_save,
@@ -416,12 +431,16 @@ class PregelLoop:
                     task_path_str(task.path) if task else "",
                 )
             else:
-                self.submit(
+                fut = self.submit(
                     self.checkpointer_put_writes,
                     config,
                     writes_to_save,
                     task_id,
                 )
+            if self._delta_write_futs is not None and any(
+                isinstance(self.specs.get(c), DeltaChannel) for c, _ in writes_to_save
+            ):
+                self._delta_write_futs.append(fut)
         # output writes
         if hasattr(self, "tasks"):
             self.output_writes(task_id, writes)
@@ -505,6 +524,16 @@ class PregelLoop:
             # return the new task, to be started if not run before
             return pushed
 
+    def schedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        raise NotImplementedError
+
+    async def aschedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        raise NotImplementedError
+
     def tick(self) -> bool:
         """Execute a single iteration of the Pregel loop.
 
@@ -563,6 +592,10 @@ class PregelLoop:
             self.status = "done"
             return False
 
+        if self.control is not None and self.control.drain_requested:
+            self.status = "draining"
+            return False
+
         # if there are pending writes from a previous loop, apply them
         if not self.is_replaying and self.checkpoint_pending_writes:
             self._match_writes(self.tasks)
@@ -629,7 +662,7 @@ class PregelLoop:
 
     def _match_writes(self, tasks: Mapping[str, PregelExecutableTask]) -> None:
         for tid, k, v in self.checkpoint_pending_writes:
-            if k in (ERROR, INTERRUPT, RESUME):
+            if k in (ERROR, ERROR_SOURCE_NODE, INTERRUPT, RESUME):
                 continue
             if task := tasks.get(tid):
                 task.writes.append((k, v))
@@ -833,8 +866,18 @@ class PregelLoop:
             # parent. For forks (source=update/fork), use the fork's parent
             # checkpoint ID since the fork was created after the subgraph's
             # checkpoints from the original execution.
+            #
+            # Only gate on is_time_traveling (not is_replaying). When the
+            # client resumes with an explicit checkpoint_id that happens to
+            # point at the current head (e.g. LangGraph Studio sending
+            # `checkpoint: {checkpoint_id}` alongside Command(resume=...)),
+            # is_replaying is True but is_time_traveling is False. In that
+            # case subgraphs should load their latest checkpoint normally,
+            # not go through ReplayState's before-bound lookup which would
+            # miss subgraph checkpoints created during processing of the
+            # current parent step.
             replay_state: ReplayState | None = None
-            if self.is_replaying:
+            if is_time_traveling:
                 replay_checkpoint_id = self.checkpoint["id"]
                 if (
                     self.checkpoint_metadata.get("source")
@@ -882,13 +925,11 @@ class PregelLoop:
             self.step,
             id=self.checkpoint["id"] if exiting else None,
             updated_channels=self.updated_channels,
+            get_next_version=self.checkpointer_get_next_version
+            if do_checkpoint
+            else None,
+            force_delta_snapshot=exiting and self.durability == "exit",
         )
-        if do_checkpoint and self.channels:
-            for k, ch in self.channels.items():
-                ch.after_checkpoint(
-                    self.checkpoint["channel_versions"].get(k),
-                    self.checkpoint.get("id"),
-                )
         # sanitize TASK channel in the checkpoint before saving (durability=="exit")
         if TASKS in self.checkpoint["channel_values"] and any(
             isinstance(channel, UntrackedValue) for channel in self.channels.values()
@@ -1198,6 +1239,45 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
                 self.output_writes(task.id, task.writes, cached=True)
         return pushed
 
+    def schedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        handler_node = self.nodes[failed_task.name].error_handler_node
+        if not handler_node:
+            return None
+        writes = list(failed_task.writes)
+        writes.append((ERROR_SOURCE_NODE, failed_task.name))
+        self.put_writes(
+            failed_task.id,
+            writes,
+        )
+        handler_task = prepare_node_error_handler_task(
+            failed_task,
+            handler_node_name=handler_node,
+            failed_error=error,
+            checkpoint=self.checkpoint,
+            pending_writes=self.checkpoint_pending_writes,
+            processes=self.nodes,
+            channels=self.channels,
+            managed=self.managed,
+            config=failed_task.config,
+            step=self.step,
+            stop=self.stop,
+            store=self.store,
+            checkpointer=self.checkpointer,
+            manager=self.manager,
+            retry_policy=self.retry_policy,
+            cache_policy=self.cache_policy,
+        )
+        if handler_task is None:
+            return None
+        self.tasks[handler_task.id] = handler_task
+        if not self.is_replaying:
+            self._match_writes({handler_task.id: handler_task})
+        for task in self.match_cached_writes():
+            self.output_writes(task.id, task.writes, cached=True)
+        return handler_task
+
     def put_writes(self, task_id: str, writes: WritesT) -> None:
         """Put writes for a task, to be read by the next tick."""
         super().put_writes(task_id, writes)
@@ -1270,21 +1350,11 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
             else []
         )
         self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
-        # Assemble any DeltaChannel chains before constructing channel objects.
-        if self.checkpointer is not None:
-            assembled = _assemble_delta_channels(
-                self.checkpoint, self.checkpoint_config, self.checkpointer
-            )
-            if assembled:
-                self.checkpoint = {
-                    **self.checkpoint,
-                    "channel_values": {
-                        **self.checkpoint["channel_values"],
-                        **assembled,
-                    },
-                }
         self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"
@@ -1379,6 +1449,11 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
         metadata: CheckpointMetadata,
         new_versions: ChannelVersions,
     ) -> RunnableConfig:
+        # Drain DeltaChannel write futures before committing the checkpoint so
+        # DELTA_SENTINEL blobs are never saved ahead of their backing writes.
+        if self._delta_write_futs:
+            futs, self._delta_write_futs = self._delta_write_futs, []
+            await asyncio.gather(*futs)
         try:
             if prev is not None:
                 await prev
@@ -1410,6 +1485,45 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
                 self.output_writes(task.id, task.writes, cached=True)
         return pushed
 
+    async def aschedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        handler_node = self.nodes[failed_task.name].error_handler_node
+        if not handler_node:
+            return None
+        writes = list(failed_task.writes)
+        writes.append((ERROR_SOURCE_NODE, failed_task.name))
+        self.put_writes(
+            failed_task.id,
+            writes,
+        )
+        handler_task = prepare_node_error_handler_task(
+            failed_task,
+            handler_node_name=handler_node,
+            failed_error=error,
+            checkpoint=self.checkpoint,
+            pending_writes=self.checkpoint_pending_writes,
+            processes=self.nodes,
+            channels=self.channels,
+            managed=self.managed,
+            config=failed_task.config,
+            step=self.step,
+            stop=self.stop,
+            store=self.store,
+            checkpointer=self.checkpointer,
+            manager=self.manager,
+            retry_policy=self.retry_policy,
+            cache_policy=self.cache_policy,
+        )
+        if handler_task is None:
+            return None
+        self.tasks[handler_task.id] = handler_task
+        if not self.is_replaying:
+            self._match_writes({handler_task.id: handler_task})
+        for task in await self.amatch_cached_writes():
+            self.output_writes(task.id, task.writes, cached=True)
+        return handler_task
+
     def put_writes(self, task_id: str, writes: WritesT) -> None:
         """Put writes for a task, to be read by the next tick."""
         super().put_writes(task_id, writes)
@@ -1484,23 +1598,15 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
             if saved.pending_writes is not None
             else []
         )
+        self._delta_write_futs = []
         self.submit = await self.stack.enter_async_context(
             AsyncBackgroundExecutor(self.config)
         )
-        if self.checkpointer is not None:
-            assembled = await _aassemble_delta_channels(
-                self.checkpoint, self.checkpoint_config, self.checkpointer
-            )
-            if assembled:
-                self.checkpoint = {
-                    **self.checkpoint,
-                    "channel_values": {
-                        **self.checkpoint["channel_values"],
-                        **assembled,
-                    },
-                }
-        self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+        self.channels, self.managed = await achannels_from_checkpoint(
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"

libs/langgraph/langgraph/pregel/_messages.py

@@ -14,7 +14,7 @@ from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
 from pydantic import BaseModel
 
-from langgraph._internal._constants import NS_END, NS_SEP
+from langgraph._internal._constants import NS_SEP
 from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
 from langgraph.pregel.protocol import StreamChunk
 from langgraph.types import Command
@@ -24,6 +24,11 @@ try:
 except ImportError:
     _StreamingCallbackHandler = object  # type: ignore
 
+try:
+    from langchain_core.tracers._streaming import _V2StreamingCallbackHandler
+except ImportError:
+    _V2StreamingCallbackHandler = object  # type: ignore
+
 T = TypeVar("T")
 Meta = tuple[tuple[str, ...], dict[str, Any]]
 
@@ -132,23 +137,15 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         **kwargs: Any,
     ) -> Any:
         if metadata and (not tags or (TAG_NOSTREAM not in tags)):
-            task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
-            checkpoint_ns = (
-                f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
-                if NS_END in task_checkpoint_ns
-                else task_checkpoint_ns
-            )
-            ns = tuple(task_checkpoint_ns.split(NS_SEP))[:-1]
+            ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
+                :-1
+            ]
             if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
                 return
-            stream_metadata = dict(metadata)
-            stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
-            # Preserve backwards-compatible streamed checkpoint metadata shape.
-            stream_metadata["checkpoint_ns"] = checkpoint_ns
             if tags:
                 if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
-                    stream_metadata["tags"] = filtered_tags
-            self.metadata[run_id] = (ns, stream_metadata)
+                    metadata["tags"] = filtered_tags
+            self.metadata[run_id] = (ns, metadata)
 
     def on_llm_new_token(
         self,
@@ -256,3 +253,126 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         **kwargs: Any,
     ) -> Any:
         self.metadata.pop(run_id, None)
+
+
+class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler):
+    """v2 variant of `StreamMessagesHandler`.
+
+    Declaring `_V2StreamingCallbackHandler` as a base flips
+    `BaseChatModel.invoke` to route through `_stream_chat_model_events`
+    (firing `on_stream_event`) instead of `_stream` (firing
+    `on_llm_new_token`). Inherits `on_stream_event` from the parent,
+    which forwards protocol events onto the messages stream channel.
+
+    Pregel attaches this class instead of the v1 handler only when
+    `StreamingHandler` opts in via the internal
+    `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
+    `graph.stream(stream_mode="messages")` callers keep the v1
+    AIMessageChunk shape.
+    """
+
+    def on_llm_new_token(
+        self,
+        token: str,
+        *,
+        chunk: ChatGenerationChunk | None = None,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Intentional no-op — v1 chunks are not used on v2-flagged runs.
+
+        The v2 marker already steers `invoke` to the event generator, so
+        `on_llm_new_token` should not fire under normal routing. This
+        override stays a pass-through (no call to `super()`) to make
+        the intent explicit and to guard against any caller (e.g. a
+        node that calls `model.stream()` directly, which still fires
+        the v1 callback) leaking AIMessageChunks onto a v2-flagged
+        messages stream.
+        """
+        # Intentionally empty: v2 handler does not forward v1 chunks.
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        super().__init__(stream, subgraphs, parent_ns=parent_ns)
+        self._streamed_run_ids: set[UUID] = set()
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        if meta := self.metadata.get(run_id):
+            if response.generations and response.generations[0]:
+                gen = response.generations[0][0]
+                if isinstance(gen, ChatGeneration):
+                    if run_id in self._streamed_run_ids:
+                        if gen.message.id is None:
+                            gen.message.id = str(uuid4())
+                        self.seen.add(gen.message.id)
+                    else:
+                        self._emit(meta, gen.message, dedupe=True)
+        self._streamed_run_ids.discard(run_id)
+        self.metadata.pop(run_id, None)
+
+    def on_llm_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._streamed_run_ids.discard(run_id)
+        super().on_llm_error(
+            error,
+            run_id=run_id,
+            parent_run_id=parent_run_id,
+            **kwargs,
+        )
+
+    def on_stream_event(
+        self,
+        event: dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Forward a protocol event from `stream_events(version="v3")` as a messages stream part.
+
+        Fires once per `MessagesData` event (`message-start`, per-block
+        `content-block-*`, `message-finish`). The transformer layer
+        correlates events back to a single `ChatModelStream` via
+        `metadata["run_id"]` — attached here so the v1
+        `stream_mode="messages"` output (which emits
+        `(AIMessageChunk, metadata)` via `on_llm_new_token`) keeps its
+        original metadata shape.
+
+        Lives on the v2 handler rather than the v1 base: content-block
+        events are a v2-only concept, and forwarding them only when the
+        v2 handler is attached keeps the message channel's shape
+        predictable for v1 callers.
+        """
+        if meta := self.metadata.get(run_id):
+            # Record message_id on message-start so on_chain_end's
+            # dedupe skips the finalized AIMessage the node returns
+            # (otherwise the messages projection double-counts: once
+            # from streaming, once from the chain output).
+            if event.get("event") == "message-start":
+                self._streamed_run_ids.add(run_id)
+                msg_id = event.get("message_id")
+                if msg_id:
+                    self.seen.add(msg_id)
+            v2_meta = {**meta[1], "run_id": str(run_id)}
+            self.stream((meta[0], "messages", (event, v2_meta)))

libs/langgraph/langgraph/pregel/_read.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
+from datetime import timedelta
 from functools import cached_property
 from typing import (
     Any,
@@ -11,10 +12,11 @@ from langchain_core.runnables import Runnable, RunnableConfig
 from langgraph._internal._config import merge_configs
 from langgraph._internal._constants import CONF, CONFIG_KEY_READ
 from langgraph._internal._runnable import RunnableCallable, RunnableSeq
+from langgraph._internal._timeout import coerce_timeout_policy
 from langgraph.pregel._utils import find_subgraph_pregel
 from langgraph.pregel._write import ChannelWrite
 from langgraph.pregel.protocol import PregelProtocol
-from langgraph.types import CachePolicy, RetryPolicy
+from langgraph.types import CachePolicy, RetryPolicy, TimeoutPolicy
 
 READ_TYPE = Callable[[str | Sequence[str], bool], Any | dict[str, Any]]
 INPUT_CACHE_KEY_TYPE = tuple[Callable[..., Any], tuple[str, ...]]
@@ -123,12 +125,25 @@ class PregelNode:
     cache_policy: CachePolicy | None
     """The cache policy to use when invoking the node."""
 
+    timeout: TimeoutPolicy | None
+    """Timeout policy for a single invocation.
+
+    If exceeded, `NodeTimeoutError` is raised and the retry policy (if any)
+    decides whether to retry. Supported only for async nodes.
+    """
+
     tags: Sequence[str] | None
     """Tags to attach to the node for tracing."""
 
     metadata: Mapping[str, Any] | None
     """Metadata to attach to the node for tracing."""
 
+    is_error_handler: bool
+    """Whether this node is registered as an error handler node."""
+
+    error_handler_node: str | None
+    """Optional handler node name for failures from this node."""
+
     subgraphs: Sequence[PregelProtocol]
     """Subgraphs used by the node."""
 
@@ -144,7 +159,10 @@ class PregelNode:
         bound: Runnable[Any, Any] | None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
+        is_error_handler: bool = False,
+        error_handler_node: str | None = None,
         subgraphs: Sequence[PregelProtocol] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
     ) -> None:
         self.channels = channels
         self.triggers = list(triggers)
@@ -156,8 +174,11 @@ class PregelNode:
             self.retry_policy = (retry_policy,)
         else:
             self.retry_policy = retry_policy
+        self.timeout = coerce_timeout_policy(timeout)
         self.tags = tags
         self.metadata = metadata
+        self.is_error_handler = is_error_handler
+        self.error_handler_node = error_handler_node
         if subgraphs is not None:
             self.subgraphs = subgraphs
         elif self.bound is not DEFAULT_BOUND:

libs/langgraph/langgraph/pregel/_retry.py

@@ -4,32 +4,487 @@ import asyncio
 import logging
 import random
 import sys
+import threading
 import time
+import weakref
 from collections.abc import Awaitable, Callable, Sequence
-from dataclasses import replace
-from typing import Any
+from contextlib import suppress
+from dataclasses import dataclass, replace
+from datetime import datetime, timedelta, timezone
+from typing import Any, Literal, NamedTuple
 
+from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.runnables import RunnableConfig
 
-from langgraph._internal._config import patch_configurable, recast_checkpoint_ns
+from langgraph._internal._config import (
+    merge_configs,
+    patch_configurable,
+    recast_checkpoint_ns,
+)
 from langgraph._internal._constants import (
     CONF,
+    CONFIG_KEY_CALL,
     CONFIG_KEY_CHECKPOINT_ID,
     CONFIG_KEY_CHECKPOINT_NS,
     CONFIG_KEY_RESUMING,
     CONFIG_KEY_RUNTIME,
+    CONFIG_KEY_SEND,
+    CONFIG_KEY_STREAM,
     CONFIG_KEY_TASK_ID,
     CONFIG_KEY_THREAD_ID,
+    CONFIG_KEY_TIMED_ATTEMPT_OBSERVER,
     NS_SEP,
 )
-from langgraph.errors import GraphBubbleUp, ParentCommand
+from langgraph._internal._runnable import create_task_in_config_context
+from langgraph._internal._timeout import sync_timeout_unsupported
+from langgraph.errors import GraphBubbleUp, NodeTimeoutError, ParentCommand
+from langgraph.pregel.protocol import StreamProtocol
 from langgraph.runtime import ExecutionInfo, Runtime
-from langgraph.types import Command, PregelExecutableTask, RetryPolicy
+from langgraph.types import Command, PregelExecutableTask, RetryPolicy, TimeoutPolicy
 
 logger = logging.getLogger(__name__)
 SUPPORTS_EXC_NOTES = sys.version_info >= (3, 11)
 
 
+def _timeout_secs(value: float | timedelta) -> float:
+    return value.total_seconds() if isinstance(value, timedelta) else value
+
+
+@dataclass(frozen=True, slots=True)
+class _ResolvedTimeout:
+    run_timeout_secs: float | None
+    idle_timeout_secs: float | None
+    refresh_on: Literal["auto", "heartbeat"] | None
+
+
+def _resolve_timeout(timeout: TimeoutPolicy) -> _ResolvedTimeout:
+    idle_timeout_secs = (
+        _timeout_secs(timeout.idle_timeout)
+        if timeout.idle_timeout is not None
+        else None
+    )
+    return _ResolvedTimeout(
+        run_timeout_secs=(
+            _timeout_secs(timeout.run_timeout)
+            if timeout.run_timeout is not None
+            else None
+        ),
+        idle_timeout_secs=idle_timeout_secs,
+        refresh_on=timeout.refresh_on if idle_timeout_secs is not None else None,
+    )
+
+
+class _AttemptContext(NamedTuple):
+    """Immutable per-attempt metadata shared across start/progress/finish events.
+
+    Built once at attempt start and referenced (not copied) by every emitted
+    `_AttemptEvent`, so per-event allocation is just the small event wrapper.
+
+    Intentionally underscore-prefixed: this and `_AttemptEvent` are part of an
+    internal observer contract consumed by langgraph-server. Do not move to
+    `langgraph.types` — server imports them by this path.
+    """
+
+    task_id: str
+    task_name: str
+    attempt: int
+    run_id: str | None
+    thread_id: str | None
+    checkpoint_ns: str | None
+    started_at: datetime
+    run_timeout_secs: float | None
+    idle_timeout_secs: float | None
+    refresh_on: Literal["auto", "heartbeat"] | None
+
+
+@dataclass(frozen=True, slots=True)
+class _AttemptEvent:
+    """One lifecycle event for a timed attempt.
+
+    Holds a reference to the shared `_AttemptContext` and the event-specific
+    fields. The observer must treat this and `context` as read-only — they
+    are reused across all events for the same attempt.
+    """
+
+    context: _AttemptContext
+    event: Literal["start", "progress", "finish"]
+    progress_at: datetime | None = None
+    finished_at: datetime | None = None
+    status: Literal["success", "error"] | None = None
+    error_type: str | None = None
+    error_message: str | None = None
+
+
+class _TimedAttemptScope:
+    """Guarded-config window for timed attempts.
+
+    The wrapped config marks writes, stream events, runtime stream writer calls,
+    child task scheduling, and any LangChain callback event emitted under the
+    node's run as observable progress when `refresh_on="auto"`.
+    `runtime.heartbeat()` exposes a manual progress signal for work that doesn't
+    otherwise emit any of these, and is the only progress signal when
+    `refresh_on="heartbeat"`.
+    Guarded writes are serialized with `close()` so cancelled background tasks
+    cannot persist writes past the timeout boundary. Stream/custom output is
+    best-effort: it is dropped after close is observed, but callbacks run outside
+    the lock because they may contain arbitrary user/runtime code.
+    """
+
+    __slots__ = (
+        "__weakref__",
+        "_active",
+        "_last_progress",
+        "_last_progress_emit",
+        "_lock",
+        "_on_progress",
+        "_progress_min_interval",
+        "_refresh_on",
+    )
+
+    def __init__(
+        self,
+        on_progress: Callable[[], None] | None = None,
+        progress_min_interval: float = 0.0,
+        refresh_on: Literal["auto", "heartbeat"] | None = None,
+    ) -> None:
+        self._active = True
+        self._last_progress = time.monotonic()
+        self._lock = threading.Lock()
+        self._on_progress = on_progress
+        self._progress_min_interval = progress_min_interval
+        self._refresh_on = refresh_on
+        # `-inf` so the first touch always passes the rate-limit gate.
+        self._last_progress_emit: float = float("-inf")
+
+    def wrap_config(self, config: RunnableConfig) -> RunnableConfig:
+        configurable = config.get(CONF, {})
+        patch: dict[str, Any] = {}
+        if (send := configurable.get(CONFIG_KEY_SEND)) is not None:
+            patch[CONFIG_KEY_SEND] = self._guard_send(send)
+        if (stream := configurable.get(CONFIG_KEY_STREAM)) is not None:
+            patch[CONFIG_KEY_STREAM] = self._guard_stream(stream)
+        if (call := configurable.get(CONFIG_KEY_CALL)) is not None:
+            patch[CONFIG_KEY_CALL] = self._guard_call(call)
+        if isinstance(runtime := configurable.get(CONFIG_KEY_RUNTIME), Runtime):
+            if self._refresh_on is not None:
+                patch[CONFIG_KEY_RUNTIME] = runtime.override(
+                    stream_writer=self._guard_stream_writer(runtime.stream_writer),
+                    heartbeat=self.touch,
+                )
+            else:
+                patch[CONFIG_KEY_RUNTIME] = runtime.override(
+                    stream_writer=self._guard_stream_writer(runtime.stream_writer)
+                )
+        new_config = patch_configurable(config, patch) if patch else config
+        if self._refresh_on == "auto":
+            return merge_configs(
+                new_config, {"callbacks": [_IdleProgressCallbackHandler(self)]}
+            )
+        return new_config
+
+    def touch(self) -> None:
+        # Avoid locking this hot progress path. We accept a small race window in
+        # timestamp ordering because idle_timeout is expected to be coarse compared
+        # with scheduler/thread timing.
+        now = time.monotonic()
+        self._last_progress = now
+        if self._on_progress is None:
+            return
+        # Best-effort rate limit: a benign race may emit a duplicate progress
+        # event under heavy concurrency, which observers must already tolerate
+        # (callbacks fire from arbitrary threads).
+        if now - self._last_progress_emit < self._progress_min_interval:
+            return
+        self._last_progress_emit = now
+        self._on_progress()
+
+    def close(self) -> None:
+        with self._lock:
+            self._active = False
+
+    async def wait_for_idle_timeout(self, idle_timeout_s: float) -> None:
+        while True:
+            with self._lock:
+                if not self._active:
+                    return
+                remaining = self._last_progress + idle_timeout_s - time.monotonic()
+            if remaining <= 0:
+                raise asyncio.TimeoutError
+            await asyncio.sleep(remaining)
+
+    def _guard_send(
+        self, send: Callable[[Sequence[tuple[str, Any]]], None]
+    ) -> Callable[[Sequence[tuple[str, Any]]], None]:
+        def guarded_send(writes: Sequence[tuple[str, Any]]) -> None:
+            with self._lock:
+                if self._active:
+                    if writes and self._refresh_on == "auto":
+                        self._last_progress = time.monotonic()
+                    send(writes)
+
+        return guarded_send
+
+    def _guard_stream(self, stream: StreamProtocol) -> StreamProtocol:
+        # No lock: stream callbacks fire from the event loop only, so the
+        # active-check + write happen atomically between awaits.
+        def guarded_stream(chunk: tuple[tuple[str, ...], str, Any]) -> None:
+            if not self._active:
+                return
+            if self._refresh_on == "auto":
+                self._last_progress = time.monotonic()
+            stream(chunk)
+
+        return StreamProtocol(guarded_stream, stream.modes)
+
+    def _guard_call(self, call: Callable[..., Any]) -> Callable[..., Any]:
+        # No lock: child-task scheduling happens from the event loop only.
+        def guarded_call(*args: Any, **kwargs: Any) -> Any:
+            if not self._active:
+                raise asyncio.CancelledError
+            if self._refresh_on == "auto":
+                self._last_progress = time.monotonic()
+            return call(*args, **kwargs)
+
+        return guarded_call
+
+    def _guard_stream_writer(
+        self, stream_writer: Callable[[Any], None]
+    ) -> Callable[[Any], None]:
+        def guarded_stream_writer(chunk: Any) -> None:
+            with self._lock:
+                if not self._active:
+                    return
+                if self._refresh_on == "auto":
+                    self._last_progress = time.monotonic()
+            stream_writer(chunk)
+
+        return guarded_stream_writer
+
+
+class _IdleProgressCallbackHandler(BaseCallbackHandler):
+    """Resets the idle timeout clock on any LangChain callback event.
+
+    Inherits via `config["callbacks"]`, so it sees only events emitted by
+    runs descended from the node's attempt — sibling nodes do not bleed
+    through. Holds the scope by weakref so a child manager that outlives
+    the attempt cannot keep the scope alive.
+    """
+
+    # Run inline so progress is recorded in callback emission order;
+    # thread-pool dispatch would introduce extra reordering.
+    run_inline = True
+
+    def __init__(self, scope: _TimedAttemptScope) -> None:
+        self._scope_ref = weakref.ref(scope)
+
+    def _touch(self, *args: Any, **kwargs: Any) -> None:
+        if (scope := self._scope_ref()) is not None:
+            scope.touch()
+
+    on_llm_start = _touch
+    on_chat_model_start = _touch
+    on_llm_new_token = _touch
+    on_llm_end = _touch
+    on_llm_error = _touch
+    on_chain_start = _touch
+    on_chain_end = _touch
+    on_chain_error = _touch
+    on_tool_start = _touch
+    on_tool_end = _touch
+    on_tool_error = _touch
+    on_retriever_start = _touch
+    on_retriever_end = _touch
+    on_retriever_error = _touch
+    on_agent_action = _touch
+    on_agent_finish = _touch
+    on_text = _touch
+    on_retry = _touch
+    on_custom_event = _touch
+
+
+def _drain_cancelled(task: asyncio.Task[Any]) -> None:
+    # Mark the abandoned task's exception as retrieved so asyncio doesn't log it.
+    with suppress(asyncio.CancelledError):
+        task.exception()
+
+
+def _start_timed_attempt(
+    task: PregelExecutableTask, config: RunnableConfig, timeout: _ResolvedTimeout
+) -> _AttemptContext | None:
+    configurable = config.get(CONF, {})
+    callback = configurable.get(CONFIG_KEY_TIMED_ATTEMPT_OBSERVER)
+    if callback is None:
+        return None
+    runtime = configurable.get(CONFIG_KEY_RUNTIME)
+    execution_info = runtime.execution_info if isinstance(runtime, Runtime) else None
+    context = _AttemptContext(
+        task_id=task.id,
+        task_name=task.name,
+        attempt=execution_info.node_attempt if execution_info is not None else 1,
+        run_id=execution_info.run_id if execution_info is not None else None,
+        thread_id=execution_info.thread_id if execution_info is not None else None,
+        checkpoint_ns=(
+            execution_info.checkpoint_ns if execution_info is not None else None
+        ),
+        started_at=datetime.now(timezone.utc),
+        run_timeout_secs=timeout.run_timeout_secs,
+        idle_timeout_secs=timeout.idle_timeout_secs,
+        refresh_on=timeout.refresh_on,
+    )
+    _dispatch_observer(callback, _AttemptEvent(context=context, event="start"))
+    return context
+
+
+def _finish_timed_attempt(
+    config: RunnableConfig,
+    context: _AttemptContext | None,
+    error: BaseException | None = None,
+) -> None:
+    if context is None:
+        return
+    callback = config.get(CONF, {}).get(CONFIG_KEY_TIMED_ATTEMPT_OBSERVER)
+    if callback is None:
+        return
+    _dispatch_observer(
+        callback,
+        _AttemptEvent(
+            context=context,
+            event="finish",
+            finished_at=datetime.now(timezone.utc),
+            status="error" if error is not None else "success",
+            error_type=type(error).__name__ if error is not None else None,
+            error_message=str(error) if error is not None else None,
+        ),
+    )
+
+
+def _emit_progress(
+    callback: Callable[[_AttemptEvent], None],
+    context: _AttemptContext,
+) -> None:
+    _dispatch_observer(
+        callback,
+        _AttemptEvent(
+            context=context,
+            event="progress",
+            progress_at=datetime.now(timezone.utc),
+        ),
+    )
+
+
+def _dispatch_observer(
+    callback: Callable[[_AttemptEvent], None],
+    event: _AttemptEvent,
+) -> None:
+    try:
+        callback(event)
+    except Exception:
+        logger.warning("Timed attempt observer failed", exc_info=True)
+
+
+async def _run_timeout_watchdog(run_timeout_s: float) -> None:
+    await asyncio.sleep(run_timeout_s)
+    raise asyncio.TimeoutError
+
+
+async def _arun_with_timeout(
+    task: PregelExecutableTask,
+    config: RunnableConfig,
+    timeout: _ResolvedTimeout,
+    attempt_ctx: _AttemptContext | None,
+    *,
+    stream: bool,
+) -> Any:
+    run_timeout_s = timeout.run_timeout_secs
+    idle_timeout_s = timeout.idle_timeout_secs
+    on_progress: Callable[[], None] | None = None
+    if attempt_ctx is not None:
+        callback = config.get(CONF, {}).get(CONFIG_KEY_TIMED_ATTEMPT_OBSERVER)
+        if callback is not None and idle_timeout_s is not None:
+            on_progress = lambda: _emit_progress(callback, attempt_ctx)  # noqa: E731
+    scope = _TimedAttemptScope(
+        on_progress=on_progress,
+        # Cap progress emission at ~4 events per idle window so token-rate
+        # callbacks don't flood the observer.
+        progress_min_interval=idle_timeout_s / 4 if idle_timeout_s is not None else 0.0,
+        refresh_on=timeout.refresh_on,
+    )
+    scoped_config = scope.wrap_config(config)
+    start = time.monotonic()
+    if stream:
+        # Yielded chunks count as progress only under `refresh_on="auto"`.
+        # `refresh_on="heartbeat"` is the strict mode where only explicit
+        # `runtime.heartbeat()` calls reset the idle clock.
+        async def run() -> Any:
+            async for _ in task.proc.astream(task.input, scoped_config):
+                if timeout.refresh_on == "auto":
+                    scope.touch()
+
+    else:
+
+        async def run() -> Any:
+            return await task.proc.ainvoke(task.input, scoped_config)
+
+    bg = create_task_in_config_context(run, scoped_config)
+    watchdogs: dict[asyncio.Task[None], Literal["idle", "run"]] = {}
+    if idle_timeout_s is not None:
+        watchdogs[asyncio.create_task(scope.wait_for_idle_timeout(idle_timeout_s))] = (
+            "idle"
+        )
+    if run_timeout_s is not None:
+        watchdogs[asyncio.create_task(_run_timeout_watchdog(run_timeout_s))] = "run"
+    try:
+        done, _ = await asyncio.wait(
+            {bg, *watchdogs}, return_when=asyncio.FIRST_COMPLETED
+        )
+        if bg in done:
+            # Task completed in time.
+            for watchdog in watchdogs:
+                watchdog.cancel()
+            # FIRST_COMPLETED can return both; a watchdog may have
+            # already raised TimeoutError before we cancelled it.
+            for watchdog in watchdogs:
+                with suppress(asyncio.CancelledError, asyncio.TimeoutError):
+                    await watchdog
+            return await bg
+        # bg was not in `done`, so every member of `done` is one of our
+        # watchdogs. Only a watchdog's TimeoutError converts to
+        # NodeTimeoutError; any TimeoutError raised by the proc itself
+        # propagates unchanged.
+        for watchdog in done:
+            kind = watchdogs[watchdog]
+            try:
+                await watchdog
+            except asyncio.TimeoutError as exc:
+                elapsed = time.monotonic() - start
+                scope.close()
+                task.writes.clear()
+                bg.cancel()
+                bg.add_done_callback(_drain_cancelled)
+                raise NodeTimeoutError(
+                    task.name,
+                    elapsed,
+                    kind=kind,
+                    idle_timeout=idle_timeout_s,
+                    run_timeout=run_timeout_s,
+                ) from exc
+            raise RuntimeError(
+                f"{kind} timeout watchdog completed without raising TimeoutError"
+            )
+        raise RuntimeError("timeout wait completed without task or watchdog")
+    except asyncio.CancelledError:
+        scope.close()
+        bg.cancel()
+        for watchdog in watchdogs:
+            watchdog.cancel()
+        bg.add_done_callback(_drain_cancelled)
+        raise
+    finally:
+        scope.close()
+        for watchdog in watchdogs:
+            watchdog.cancel()
+
+
 def _ensure_execution_info(
     runtime: Runtime, config: RunnableConfig, task: PregelExecutableTask
 ) -> Runtime:
@@ -90,6 +545,10 @@ def run_with_retry(
 ) -> None:
     """Run a task with retries."""
     retry_policy = task.retry_policy or retry_policy
+    if task.timeout is not None:
+        # `validate_timeout_supported` catches sync nodes at compile time;
+        # this is a runtime safety net for paths that may bypass that validation.
+        raise sync_timeout_unsupported(task.name)
     attempts = 0
     node_first_attempt_time = time.time()
     config = task.config
@@ -195,6 +654,9 @@ async def arun_with_retry(
 ) -> None:
     """Run a task asynchronously with retries."""
     retry_policy = task.retry_policy or retry_policy
+    resolved_timeout = (
+        _resolve_timeout(task.timeout) if task.timeout is not None else None
+    )
     attempts = 0
     node_first_attempt_time = time.time()
     config = task.config
@@ -229,35 +691,53 @@ async def arun_with_retry(
                     )
                 },
             )
+        attempt_ctx = (
+            _start_timed_attempt(task, config, resolved_timeout)
+            if resolved_timeout is not None
+            else None
+        )
         try:
-            # clear any writes from previous attempts
             task.writes.clear()
-            # run the task
+            if resolved_timeout is None:
+                if stream:
+                    async for _ in task.proc.astream(task.input, config):
+                        pass
+                    break
+                return await task.proc.ainvoke(task.input, config)
+            result = await _arun_with_timeout(
+                task, config, resolved_timeout, attempt_ctx, stream=stream
+            )
+            _finish_timed_attempt(config, attempt_ctx)
             if stream:
-                async for _ in task.proc.astream(task.input, config):
-                    pass
                 # if successful, end
                 break
-            else:
-                return await task.proc.ainvoke(task.input, config)
+            return result
         except ParentCommand as exc:
             ns: str = config[CONF][CONFIG_KEY_CHECKPOINT_NS]
             cmd = exc.args[0]
             # strip task_ids from namespace for comparison (ns format: "node1|node2:task_id")
             if cmd.graph in (ns, recast_checkpoint_ns(ns), task.name):
-                # this command is for the current graph, handle it
-                for w in task.writers:
-                    w.invoke(cmd, config)
+                try:
+                    # this command is for the current graph, handle it
+                    for w in task.writers:
+                        w.invoke(cmd, config)
+                except Exception as writer_exc:
+                    _finish_timed_attempt(config, attempt_ctx, writer_exc)
+                    raise
+                _finish_timed_attempt(config, attempt_ctx)
                 break
             elif cmd.graph == Command.PARENT:
                 # this command is for the parent graph, assign it to the parent.
                 exc.args = (replace(cmd, graph=_checkpoint_ns_for_parent_command(ns)),)
-            # bubble up
+            _finish_timed_attempt(config, attempt_ctx)
+            # bubble up the exception to the parent graph
             raise
         except GraphBubbleUp:
             # if interrupted, end
+            _finish_timed_attempt(config, attempt_ctx)
             raise
         except Exception as exc:
+            _finish_timed_attempt(config, attempt_ctx, exc)
             if SUPPORTS_EXC_NOTES:
                 exc.add_note(f"During task with name '{task.name}' and id '{task.id}'")
             if not retry_policy:

libs/langgraph/langgraph/pregel/_runner.py

@@ -10,8 +10,10 @@ from collections.abc import (
     AsyncIterator,
     Awaitable,
     Callable,
+    Collection,
     Iterable,
     Iterator,
+    Mapping,
     Sequence,
 )
 from functools import partial
@@ -46,6 +48,7 @@ from langgraph.types import (
     CachePolicy,
     PregelExecutableTask,
     RetryPolicy,
+    TimeoutPolicy,
 )
 
 F = TypeVar("F", concurrent.futures.Future, asyncio.Future)
@@ -71,6 +74,10 @@ SKIP_RERAISE_SET: weakref.WeakSet[concurrent.futures.Future | asyncio.Future] =
 class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
     event: E
     callback: weakref.ref[Callable[[PregelExecutableTask, BaseException | None], None]]
+    # Stop condition is injected by PregelRunner instead of hard-coded here.
+    # This lets the runner treat graph-error-handled exceptions as non-fatal
+    # so `on_done` does not trigger an early stop for those futures.
+    should_stop: Callable[[set[F]], bool]
     counter: int
     done: set[F]
     lock: threading.Lock
@@ -81,6 +88,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
         callback: weakref.ref[
             Callable[[PregelExecutableTask, BaseException | None], None]
         ],
+        should_stop: Callable[[set[F]], bool],
         future_type: type[F],
         # used for generic typing, newer py supports FutureDict[...](...)
     ) -> None:
@@ -88,6 +96,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
         self.lock = threading.Lock()
         self.event = event
         self.callback = callback
+        self.should_stop = should_stop
         self.counter = 0
         self.done: set[F] = set()
 
@@ -108,6 +117,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
         task: PregelExecutableTask,
         fut: F,
     ) -> None:
+        # Called automatically by future.add_done_callback registered in __setitem__.
         try:
             if cb := self.callback():
                 cb(task, _exception(fut))
@@ -115,7 +125,9 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
             with self.lock:
                 self.done.add(fut)
                 self.counter -= 1
-                if self.counter == 0 or _should_stop_others(self.done):
+                # Wake waiter when all tracked futures are done, or when runner-level
+                # stop condition is met (for example, a non-handled fatal exception).
+                if self.counter == 0 or self.should_stop(self.done):
                     self.event.set()
 
 
@@ -131,11 +143,34 @@ class PregelRunner:
         put_writes: weakref.ref[Callable[[str, Sequence[tuple[str, Any]]], None]],
         use_astream: bool = False,
         node_finished: Callable[[str], None] | None = None,
+        node_error_handler_map: Mapping[str, str] | None = None,
+        schedule_error_handler: Callable[
+            [PregelExecutableTask, BaseException], PregelExecutableTask | None
+        ]
+        | None = None,
+        aschedule_error_handler: Callable[
+            [PregelExecutableTask, BaseException],
+            Awaitable[PregelExecutableTask | None],
+        ]
+        | None = None,
     ) -> None:
         self.submit = submit
         self.put_writes = put_writes
         self.use_astream = use_astream
         self.node_finished = node_finished
+        self.node_error_handler_map = dict(node_error_handler_map or {})
+        self.error_handler_nodes = set(self.node_error_handler_map.values())
+        self.schedule_error_handler = schedule_error_handler
+        self.aschedule_error_handler = aschedule_error_handler
+        # Exception object ids that are already routed to graph-level error handler.
+        # These ids are consulted by stop/panic checks to avoid re-raising handled
+        # exceptions via the normal fatal path in the same run.
+        self._handled_exception_ids: set[int] = set()
+
+    def _should_route_to_error_handler(self, task: PregelExecutableTask) -> bool:
+        if task.name in self.error_handler_nodes:
+            return False
+        return task.name in self.node_error_handler_map
 
     def tick(
         self,
@@ -154,6 +189,9 @@ class PregelRunner:
         futures = FuturesDict(
             callback=weakref.WeakMethod(self.commit),
             event=threading.Event(),
+            should_stop=partial(
+                _should_stop_others, handled_exception_ids=self._handled_exception_ids
+            ),
             future_type=concurrent.futures.Future,
         )
         # give control back to the caller
@@ -163,6 +201,7 @@ class PregelRunner:
             return
         elif len(tasks) == 1 and timeout is None and get_waiter is None:
             t = tasks[0]
+            scheduled_error_handler = False
             try:
                 run_with_retry(
                     t,
@@ -181,12 +220,23 @@ class PregelRunner:
                 self.commit(t, None)
             except Exception as exc:
                 self.commit(t, exc)
+                if (
+                    not isinstance(exc, GraphBubbleUp)
+                    and self._should_route_to_error_handler(t)
+                    and self.schedule_error_handler is not None
+                ):
+                    self._handled_exception_ids.add(id(exc))
+                    if handler_task := self.schedule_error_handler(t, exc):
+                        tasks = (handler_task,)
+                        scheduled_error_handler = True
+                        # Continue to the regular scheduling path for handler execution.
                 if reraise and futures:
-                    # will be re-raised after futures are done
-                    fut: concurrent.futures.Future = concurrent.futures.Future()
-                    fut.set_exception(exc)
-                    futures.done.add(fut)
-                elif reraise:
+                    if id(exc) not in self._handled_exception_ids:
+                        # will be re-raised after futures are done
+                        fut: concurrent.futures.Future = concurrent.futures.Future()
+                        fut.set_exception(exc)
+                        futures.done.add(fut)
+                elif reraise and id(exc) not in self._handled_exception_ids:
                     if tb := exc.__traceback__:
                         while tb.tb_next is not None and any(
                             tb.tb_frame.f_code.co_filename.endswith(name)
@@ -195,10 +245,12 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures:  # maybe `t` scheduled another task
+            if not futures and not scheduled_error_handler:
+                # maybe `t` scheduled another task
                 return
             else:
-                tasks = ()  # don't reschedule this task
+                if not scheduled_error_handler:
+                    tasks = ()  # don't reschedule this task
         # add waiter task if requested
         if get_waiter is not None:
             futures[get_waiter()] = None
@@ -225,6 +277,7 @@ class PregelRunner:
         # each task is independent from all other concurrent tasks
         # yield updates/debug output as each task finishes
         end_time = timeout + time.monotonic() if timeout else None
+        handled_futures: set[concurrent.futures.Future[Any]] = set()
         while len(futures) > (1 if get_waiter is not None else 0):
             done, inflight = concurrent.futures.wait(
                 futures,
@@ -233,17 +286,49 @@ class PregelRunner:
             )
             if not done:
                 break  # timed out
+            done_for_stop: set[concurrent.futures.Future[Any]] = set()
             for fut in done:
                 task = futures.pop(fut)
                 if task is None:
                     # waiter task finished, schedule another
                     if inflight and get_waiter is not None:
                         futures[get_waiter()] = None
+                elif (
+                    (task_exc := _exception(fut))
+                    and self._should_route_to_error_handler(task)
+                    and not isinstance(task_exc, GraphBubbleUp)
+                ):
+                    self._handled_exception_ids.add(id(task_exc))
+                    SKIP_RERAISE_SET.add(fut)
+                    handled_futures.add(fut)
+                    if self.schedule_error_handler is not None:
+                        if handler_task := self.schedule_error_handler(task, task_exc):
+                            handler_fut = self.submit()(  # type: ignore[misc]
+                                run_with_retry,
+                                handler_task,
+                                retry_policy,
+                                configurable={
+                                    CONFIG_KEY_CALL: partial(
+                                        _call,
+                                        weakref.ref(handler_task),
+                                        retry_policy=retry_policy,
+                                        futures=weakref.ref(futures),
+                                        schedule_task=schedule_task,
+                                        submit=self.submit,
+                                    ),
+                                },
+                                __reraise_on_exit__=reraise,
+                            )
+                            futures[handler_fut] = handler_task
+                else:
+                    done_for_stop.add(fut)
             else:
                 # remove references to loop vars
                 del fut, task
             # maybe stop other tasks
-            if _should_stop_others(done):
+            if _should_stop_others(
+                done_for_stop, handled_exception_ids=self._handled_exception_ids
+            ):
                 break
             # give control back to the caller
             yield
@@ -258,6 +343,8 @@ class PregelRunner:
             _panic_or_proceed(
                 futures.done.union(f for f, t in futures.items() if t is not None),
                 panic=reraise,
+                handled_exception_ids=self._handled_exception_ids,
+                handled_futures=handled_futures,
             )
         except Exception as exc:
             if tb := exc.__traceback__:
@@ -291,6 +378,9 @@ class PregelRunner:
         futures = FuturesDict(
             callback=weakref.WeakMethod(self.commit),
             event=asyncio.Event(),
+            should_stop=partial(
+                _should_stop_others, handled_exception_ids=self._handled_exception_ids
+            ),
             future_type=asyncio.Future,
         )
         # give control back to the caller
@@ -300,6 +390,7 @@ class PregelRunner:
             return
         elif len(tasks) == 1 and get_waiter is None and timeout is None:
             t = tasks[0]
+            scheduled_error_handler = False
             try:
                 await arun_with_retry(
                     t,
@@ -321,12 +412,22 @@ class PregelRunner:
                 self.commit(t, None)
             except Exception as exc:
                 self.commit(t, exc)
+                if (
+                    not isinstance(exc, GraphBubbleUp)
+                    and self._should_route_to_error_handler(t)
+                    and self.aschedule_error_handler is not None
+                ):
+                    self._handled_exception_ids.add(id(exc))
+                    if handler_task := await self.aschedule_error_handler(t, exc):
+                        tasks = (handler_task,)
+                        scheduled_error_handler = True
                 if reraise and futures:
-                    # will be re-raised after futures are done
-                    fut: asyncio.Future = loop.create_future()
-                    fut.set_exception(exc)
-                    futures.done.add(fut)
-                elif reraise:
+                    if id(exc) not in self._handled_exception_ids:
+                        # will be re-raised after futures are done
+                        fut: asyncio.Future = loop.create_future()
+                        fut.set_exception(exc)
+                        futures.done.add(fut)
+                elif reraise and id(exc) not in self._handled_exception_ids:
                     if tb := exc.__traceback__:
                         while tb.tb_next is not None and any(
                             tb.tb_frame.f_code.co_filename.endswith(name)
@@ -335,10 +436,12 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures:  # maybe `t` scheduled another task
+            if not futures and not scheduled_error_handler:
+                # maybe `t` scheduled another task
                 return
             else:
-                tasks = ()  # don't reschedule this task
+                if not scheduled_error_handler:
+                    tasks = ()  # don't reschedule this task
         # add waiter task if requested
         if get_waiter is not None:
             futures[get_waiter()] = None
@@ -373,6 +476,7 @@ class PregelRunner:
         # each task is independent from all other concurrent tasks
         # yield updates/debug output as each task finishes
         end_time = timeout + loop.time() if timeout else None
+        handled_futures: set[asyncio.Future[Any]] = set()
         while len(futures) > (1 if get_waiter is not None else 0):
             done, inflight = await asyncio.wait(
                 futures,
@@ -381,17 +485,59 @@ class PregelRunner:
             )
             if not done:
                 break  # timed out
+            done_for_stop: set[asyncio.Future[Any]] = set()
             for fut in done:
                 task = futures.pop(fut)
                 if task is None:
                     # waiter task finished, schedule another
                     if inflight and get_waiter is not None:
                         futures[get_waiter()] = None
+                elif (
+                    (task_exc := _exception(fut))
+                    and self._should_route_to_error_handler(task)
+                    and not isinstance(task_exc, GraphBubbleUp)
+                ):
+                    self._handled_exception_ids.add(id(task_exc))
+                    SKIP_RERAISE_SET.add(fut)
+                    handled_futures.add(fut)
+                    if self.aschedule_error_handler is not None:
+                        if handler_task := await self.aschedule_error_handler(
+                            task, task_exc
+                        ):
+                            handler_fut = cast(
+                                asyncio.Future,
+                                self.submit()(  # type: ignore[misc]
+                                    arun_with_retry,
+                                    handler_task,
+                                    retry_policy,
+                                    stream=self.use_astream,
+                                    configurable={
+                                        CONFIG_KEY_CALL: partial(
+                                            _acall,
+                                            weakref.ref(handler_task),
+                                            retry_policy=retry_policy,
+                                            stream=self.use_astream,
+                                            futures=weakref.ref(futures),
+                                            schedule_task=schedule_task,
+                                            submit=self.submit,
+                                            loop=loop,
+                                        ),
+                                    },
+                                    __name__=handler_task.name,
+                                    __cancel_on_exit__=True,
+                                    __reraise_on_exit__=reraise,
+                                ),
+                            )
+                            futures[handler_fut] = handler_task
+                else:
+                    done_for_stop.add(fut)
             else:
                 # remove references to loop vars
                 del fut, task
             # maybe stop other tasks
-            if _should_stop_others(done):
+            if _should_stop_others(
+                done_for_stop, handled_exception_ids=self._handled_exception_ids
+            ):
                 break
             # give control back to the caller
             yield
@@ -411,6 +557,8 @@ class PregelRunner:
                 futures.done.union(f for f, t in futures.items() if t is not None),
                 timeout_exc_cls=asyncio.TimeoutError,
                 panic=reraise,
+                handled_exception_ids=self._handled_exception_ids,
+                handled_futures=handled_futures,
             )
         except Exception as exc:
             if tb := exc.__traceback__:
@@ -446,6 +594,11 @@ class PregelRunner:
             else:
                 # save error to checkpointer
                 task.writes.append((ERROR, exception))
+                if self._should_route_to_error_handler(task) and not isinstance(
+                    exception, GraphBubbleUp
+                ):
+                    # Mark early in commit path; loop-side routing may happen later.
+                    self._handled_exception_ids.add(id(exception))
                 self.put_writes()(task.id, task.writes)  # type: ignore[misc]
         else:
             if self.node_finished and (
@@ -461,6 +614,8 @@ class PregelRunner:
 
 def _should_stop_others(
     done: set[F],
+    *,
+    handled_exception_ids: set[int] | None = None,
 ) -> bool:
     """Check if any task failed, if so, cancel all other tasks.
     GraphInterrupts are not considered failures."""
@@ -468,7 +623,11 @@ def _should_stop_others(
         if fut.cancelled():
             continue
         elif exc := fut.exception():
-            if not isinstance(exc, GraphBubbleUp) and fut not in SKIP_RERAISE_SET:
+            if (
+                id(exc) not in (handled_exception_ids or set())
+                and not isinstance(exc, GraphBubbleUp)
+                and fut not in SKIP_RERAISE_SET
+            ):
                 return True
 
     return False
@@ -492,6 +651,9 @@ def _panic_or_proceed(
     *,
     timeout_exc_cls: type[Exception] = TimeoutError,
     panic: bool = True,
+    handled_exception_ids: set[int] | None = None,
+    handled_futures: Collection[concurrent.futures.Future[Any] | asyncio.Future[Any]]
+    | None = None,
 ) -> None:
     """Cancel remaining tasks if any failed, re-raise exception if panic is True."""
     done: set[concurrent.futures.Future[Any] | asyncio.Future[Any]] = set()
@@ -508,6 +670,10 @@ def _panic_or_proceed(
         # if any task failed
         fut = done.pop()
         if exc := _exception(fut):
+            if fut in (handled_futures or set()):
+                continue
+            if id(exc) in (handled_exception_ids or set()):
+                continue
             # cancel all pending tasks
             while inflight:
                 inflight.pop().cancel()
@@ -537,6 +703,7 @@ def _call(
     *,
     retry_policy: Sequence[RetryPolicy] | None = None,
     cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
     callbacks: Callbacks = None,
     futures: weakref.ref[FuturesDict],
     schedule_task: Callable[
@@ -560,6 +727,7 @@ def _call(
             retry_policy=retry_policy,
             cache_policy=cache_policy,
             callbacks=callbacks,
+            timeout=timeout,
         ),
     ):
         if fut := next(
@@ -624,6 +792,7 @@ def _acall(
     *,
     retry_policy: Sequence[RetryPolicy] | None = None,
     cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
     callbacks: Callbacks = None,
     # injected dependencies
     futures: weakref.ref[FuturesDict],
@@ -657,6 +826,7 @@ def _acall(
             input,
             retry_policy=retry_policy,
             cache_policy=cache_policy,
+            timeout=timeout,
             callbacks=callbacks,
             futures=futures,
             schedule_task=schedule_task,
@@ -678,6 +848,7 @@ async def _acall_impl(
     *,
     retry_policy: Sequence[RetryPolicy] | None = None,
     cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
     callbacks: Callbacks = None,
     # injected dependencies
     futures: weakref.ref[FuturesDict[asyncio.Future, asyncio.Event]],
@@ -703,6 +874,7 @@ async def _acall_impl(
                 retry_policy=retry_policy,
                 cache_policy=cache_policy,
                 callbacks=callbacks,
+                timeout=timeout,
             ),
         ):
             if fut := next(

libs/langgraph/langgraph/pregel/_tools.py

@@ -0,0 +1,268 @@
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Callable, Iterator
+from contextvars import ContextVar, Token
+from typing import Any, TypeVar, cast
+from uuid import UUID
+
+from langchain_core.callbacks import BaseCallbackHandler
+
+from langgraph._internal._constants import NS_SEP
+from langgraph.constants import TAG_NOSTREAM
+from langgraph.pregel.protocol import StreamChunk
+
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = object  # type: ignore[assignment,misc]
+
+
+T = TypeVar("T")
+
+ToolCallWriter = Callable[[Any], None]
+"""A closure bound to a single tool call that emits `tool-output-delta` events."""
+
+_tool_call_writer: ContextVar[ToolCallWriter | None] = ContextVar(
+    "langgraph_tool_call_writer", default=None
+)
+"""ContextVar holding the writer for the currently-executing tool call.
+
+Set by `StreamToolCallHandler.on_tool_start` and reset on end/error.
+Read by `ToolRuntime.emit_output_delta` (in `langgraph.prebuilt`).
+"""
+
+
+class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
+    """Callback handler that emits tool-call lifecycle events on the stream.
+
+    Fires on LangChain's `on_tool_*` callbacks and pushes to the `tools`
+    stream mode. Emits `tool-started` / `tool-output-delta` /
+    `tool-finished` / `tool-error` payloads keyed by `tool_call_id`.
+
+    While a tool is executing, this handler sets `_tool_call_writer` to a
+    closure bound to that call's namespace and `tool_call_id`.
+    `ToolRuntime.emit_output_delta` reads that ContextVar so tool bodies
+    can stream partial output without threading the writer through their
+    own signature.
+
+    Attached by `Pregel.stream` / `astream` when `"tools"` is in
+    `stream_modes`. `run_inline = True` keeps event ordering
+    deterministic.
+    """
+
+    run_inline = True
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        """Configure the handler to stream tool-call events.
+
+        Args:
+            stream: Callable that accepts a `StreamChunk` tuple
+                `(namespace, mode, payload)` and enqueues it.
+            subgraphs: Whether to emit events from tools called inside
+                nested subgraphs. When False, only tools at the
+                handler's own scope (`parent_ns`) emit.
+            parent_ns: Namespace where the handler was attached.
+                Mirrors the `StreamMessagesHandler` escape hatch:
+                tools whose containing namespace equals `parent_ns`
+                still emit even with `subgraphs=False`, so a node that
+                explicitly streams a subgraph with `stream_mode="tools"`
+                sees its own tools.
+        """
+        self.stream = stream
+        self.subgraphs = subgraphs
+        self.parent_ns = parent_ns
+        # run_id → (namespace, tool_call_id, ContextVar token)
+        # `on_tool_end` does not receive `tool_call_id` in kwargs, so
+        # we correlate by `run_id` which is present on every callback.
+        self._run_to_call: dict[
+            UUID, tuple[tuple[str, ...], str, Token[ToolCallWriter | None]]
+        ] = {}
+
+    def _ns_for_emit(
+        self,
+        metadata: dict[str, Any] | None,
+        tags: list[str] | None,
+    ) -> tuple[str, ...] | None:
+        """Resolve the namespace this tool call should emit at, or `None` to skip.
+
+        Mirrors `StreamMessagesHandler.on_chat_model_start`'s namespace
+        derivation: parses `langgraph_checkpoint_ns` (which ends with
+        the `node_name:task_id` of the calling node), drops that
+        trailing segment, and returns the containing subgraph's own
+        namespace. Returns `None` when the call should be silently
+        suppressed:
+
+        - `metadata` is missing — handler is attached to a context
+          without Pregel routing info.
+        - `TAG_NOSTREAM` is in `tags` — caller explicitly opted out.
+        - Tool runs in a subgraph (`len(ns) > 0`) and the handler was
+          attached with `subgraphs=False` and a different `parent_ns`
+          than the call's containing subgraph.
+        """
+        if not metadata:
+            return None
+        if tags and TAG_NOSTREAM in tags:
+            return None
+        nskey = metadata.get("langgraph_checkpoint_ns")
+        if not nskey:
+            ns: tuple[str, ...] = ()
+        else:
+            ns = tuple(cast(str, nskey).split(NS_SEP))[:-1]
+        if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
+            return None
+        return ns
+
+    def _start(
+        self,
+        serialized: dict[str, Any] | None,
+        input_str: str,
+        *,
+        run_id: UUID,
+        metadata: dict[str, Any] | None,
+        tags: list[str] | None,
+        inputs: dict[str, Any] | None,
+        kwargs: dict[str, Any],
+    ) -> None:
+        ns = self._ns_for_emit(metadata, tags)
+        if ns is None:
+            return
+        tool_call_id = cast("str | None", kwargs.get("tool_call_id")) or str(run_id)
+        tool_name = (
+            (serialized or {}).get("name")
+            or cast("str | None", kwargs.get("name"))
+            or ""
+        )
+
+        def writer(delta: Any) -> None:
+            self.stream(
+                (
+                    ns,
+                    "tools",
+                    {
+                        "event": "tool-output-delta",
+                        "tool_call_id": tool_call_id,
+                        "delta": delta,
+                    },
+                )
+            )
+
+        token = _tool_call_writer.set(writer)
+        self._run_to_call[run_id] = (ns, tool_call_id, token)
+
+        payload: dict[str, Any] = {
+            "event": "tool-started",
+            "tool_call_id": tool_call_id,
+            "tool_name": tool_name,
+        }
+        if inputs is not None:
+            payload["input"] = inputs
+        self.stream((ns, "tools", payload))
+
+    def _end(self, output: Any, *, run_id: UUID) -> None:
+        info = self._run_to_call.pop(run_id, None)
+        if info is None:
+            return
+        ns, tool_call_id, token = info
+        self._reset_writer(token)
+        self.stream(
+            (
+                ns,
+                "tools",
+                {
+                    "event": "tool-finished",
+                    "tool_call_id": tool_call_id,
+                    "output": output,
+                },
+            )
+        )
+
+    def _error(self, error: BaseException, *, run_id: UUID) -> None:
+        info = self._run_to_call.pop(run_id, None)
+        if info is None:
+            return
+        ns, tool_call_id, token = info
+        self._reset_writer(token)
+        self.stream(
+            (
+                ns,
+                "tools",
+                {
+                    "event": "tool-error",
+                    "tool_call_id": tool_call_id,
+                    "message": str(error),
+                },
+            )
+        )
+
+    def tap_output_aiter(
+        self, run_id: UUID, output: AsyncIterator[T]
+    ) -> AsyncIterator[T]:
+        """Pass-through — required by the `_StreamingCallbackHandler` protocol."""
+        return output
+
+    def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
+        """Pass-through — sync counterpart to `tap_output_aiter`."""
+        return output
+
+    @staticmethod
+    def _reset_writer(token: Token[ToolCallWriter | None]) -> None:
+        # Token is invalid if `on_tool_end` runs in a different context
+        # than `on_tool_start` (e.g. langchain may hand off to a thread
+        # worker without copying the context). Swallow that case; the
+        # ContextVar lifetime is bounded by the enclosing task anyway.
+        try:
+            _tool_call_writer.reset(token)
+        except ValueError:
+            pass
+
+    # ------------------------------------------------------------------
+    # Sync callbacks
+    # ------------------------------------------------------------------
+
+    def on_tool_start(
+        self,
+        serialized: dict[str, Any],
+        input_str: str,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        metadata: dict[str, Any] | None = None,
+        inputs: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._start(
+            serialized,
+            input_str,
+            run_id=run_id,
+            metadata=metadata,
+            tags=tags,
+            inputs=inputs,
+            kwargs=kwargs,
+        )
+
+    def on_tool_end(
+        self,
+        output: Any,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._end(output, run_id=run_id)
+
+    def on_tool_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._error(error, run_id=run_id)

libs/langgraph/langgraph/pregel/_utils.py

@@ -4,16 +4,27 @@ import ast
 import inspect
 import re
 import textwrap
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
+from functools import partial
 from typing import Any
 
-from langchain_core.runnables import Runnable, RunnableLambda, RunnableSequence
+from langchain_core.runnables import (
+    Runnable,
+    RunnableLambda,
+    RunnableParallel,
+    RunnableSequence,
+)
+from langchain_core.runnables.base import RunnableBindingBase
+from langchain_core.runnables.config import run_in_executor
 from langgraph.checkpoint.base import ChannelVersions
 from typing_extensions import override
 
 from langgraph._internal._runnable import RunnableCallable, RunnableSeq
+from langgraph._internal._timeout import sync_timeout_unsupported
 from langgraph.pregel.protocol import PregelProtocol
 
+_SEQUENCE_TYPES = (RunnableSeq, RunnableSequence)
+
 
 def get_new_channel_versions(
     previous_versions: ChannelVersions, current_versions: ChannelVersions
@@ -64,6 +75,68 @@ def find_subgraph_pregel(candidate: Runnable) -> PregelProtocol | None:
     return None
 
 
+def _sequence_steps(runnable: Runnable) -> Sequence[Runnable] | None:
+    if isinstance(runnable, _SEQUENCE_TYPES):
+        return runnable.steps
+    return None
+
+
+def _parallel_steps(runnable: Runnable) -> Sequence[Runnable] | None:
+    if isinstance(runnable, RunnableParallel):
+        return tuple(runnable.steps__.values())
+    return None
+
+
+def _has_method_override(runnable: Runnable, method_name: str) -> bool:
+    method = getattr(type(runnable), method_name, None)
+    return method is not None and method is not getattr(Runnable, method_name)
+
+
+def _is_executor_backed_afunc(afunc: Callable[..., Any] | None) -> bool:
+    return isinstance(afunc, partial) and afunc.func is run_in_executor
+
+
+def _has_native_async(runnable: Runnable) -> bool:
+    if isinstance(runnable, RunnableCallable):
+        return runnable.afunc is not None and not _is_executor_backed_afunc(
+            runnable.afunc
+        )
+    if isinstance(runnable, RunnableLambda):
+        return bool(getattr(runnable, "afunc", False))
+    return _has_method_override(runnable, "ainvoke")
+
+
+def _runnable_has_native_async(runnable: Runnable) -> bool:
+    """Return whether a runnable can be idle-timed without known sync code.
+
+    For custom runnable subclasses, an `ainvoke` override is treated as the
+    async contract. We do not introspect whether that implementation delegates
+    to blocking work internally — e.g. a subclass whose `ainvoke` calls
+    `asyncio.to_thread(self.invoke, ...)` will pass this check but the wrapped
+    sync work is still uncancellable. Idle-timeout enforcement on such a
+    runnable will fire `NodeTimeoutError` correctly, but the background thread
+    will keep running until its sync work returns.
+    """
+
+    while isinstance(runnable, RunnableBindingBase):
+        runnable = runnable.bound
+    steps = _sequence_steps(runnable)
+    if steps is None:
+        steps = _parallel_steps(runnable)
+    if steps is not None:
+        return all(_runnable_has_native_async(step) for step in steps)
+    # Raw callables and the common composition wrappers created by graph
+    # builders fall through here. We do not exhaustively unwrap every Runnable
+    # wrapper — wrappers that provide `ainvoke` are treated as owning the async
+    # contract.
+    return _has_native_async(runnable)
+
+
+def validate_timeout_supported(runnable: Runnable, *, name: str) -> None:
+    if not _runnable_has_native_async(runnable):
+        raise sync_timeout_unsupported(name)
+
+
 def get_function_nonlocals(func: Callable) -> list[Any]:
     """Get the nonlocal variables accessed by a function.
 

libs/langgraph/langgraph/runtime.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from collections.abc import Callable
 from dataclasses import dataclass, field, replace
 from typing import Any, Generic, cast
 
@@ -15,6 +16,7 @@ from langgraph.typing import ContextT
 __all__ = (
     "BaseUser",
     "ExecutionInfo",
+    "RunControl",
     "Runtime",
     "ServerInfo",
     "get_runtime",
@@ -74,16 +76,49 @@ class ServerInfo:
     """
 
 
+class RunControl:
+    """Run-scoped control surface for cooperative draining.
+
+    Intended for a single graph run. Create a fresh `RunControl` per run;
+    reusing a control after `request_drain()` leaves it drained.
+
+    Safe to call from any thread: the drain request is represented by a
+    single attribute write, so no lock is needed for this signal.
+    If more mutable state is added here, add synchronization.
+    """
+
+    __slots__ = ("_drain_reason",)
+
+    def __init__(self) -> None:
+        self._drain_reason: str | None = None
+
+    def request_drain(self, reason: str = "shutdown") -> None:
+        self._drain_reason = reason
+
+    @property
+    def drain_requested(self) -> bool:
+        return self._drain_reason is not None
+
+    @property
+    def drain_reason(self) -> str | None:
+        return self._drain_reason
+
+
 def _no_op_stream_writer(_: Any) -> None: ...
 
 
+def _no_op_heartbeat() -> None: ...
+
+
 class _RuntimeOverrides(TypedDict, Generic[ContextT], total=False):
     context: ContextT
     store: BaseStore | None
     stream_writer: StreamWriter
+    heartbeat: Callable[[], None]
     previous: Any
     execution_info: ExecutionInfo
     server_info: ServerInfo | None
+    control: RunControl | None
 
 
 @dataclass(**_DC_KWARGS)
@@ -162,7 +197,7 @@ class Runtime(Generic[ContextT]):
 
     context: ContextT = field(default=None)  # type: ignore[assignment]
     """Static context for the graph run, like `user_id`, `db_conn`, etc.
-    
+
     Can also be thought of as 'run dependencies'."""
 
     store: BaseStore | None = field(default=None)
@@ -171,9 +206,19 @@ class Runtime(Generic[ContextT]):
     stream_writer: StreamWriter = field(default=_no_op_stream_writer)
     """Function that writes to the custom stream."""
 
+    heartbeat: Callable[[], None] = field(default=_no_op_heartbeat)
+    """Record progress for the current node's `idle_timeout`.
+
+    Call this from inside long-running work that does not naturally emit
+    writes, stream chunks, child tasks, or LangChain callback events, to
+    prevent the node from being treated as idle. It is also the only
+    progress signal honored under `TimeoutPolicy(refresh_on="heartbeat")`.
+    Outside an idle-timed attempt this is a no-op.
+    """
+
     previous: Any = field(default=None)
     """The previous return value for the given thread.
-    
+
     Only available with the functional API when a checkpointer is provided.
     """
 
@@ -185,6 +230,13 @@ class Runtime(Generic[ContextT]):
     server_info: ServerInfo | None = field(default=None)
     """Metadata injected by LangGraph Server. None when running open-source LangGraph without LangSmith deployments."""
 
+    control: RunControl | None = field(default=None)
+    """Run-scoped control plane for cooperative draining.
+
+    Populated automatically during graph runs. None outside an active
+    graph runtime.
+    """
+
     def merge(self, other: Runtime[ContextT]) -> Runtime[ContextT]:
         """Merge two runtimes together.
 
@@ -196,9 +248,13 @@ class Runtime(Generic[ContextT]):
             stream_writer=other.stream_writer
             if other.stream_writer is not _no_op_stream_writer
             else self.stream_writer,
+            heartbeat=other.heartbeat
+            if other.heartbeat is not _no_op_heartbeat
+            else self.heartbeat,
             previous=self.previous if other.previous is None else other.previous,
             execution_info=other.execution_info or self.execution_info,
             server_info=other.server_info or self.server_info,
+            control=other.control or self.control,
         )
 
     def override(
@@ -217,13 +273,23 @@ class Runtime(Generic[ContextT]):
             execution_info=self.execution_info.patch(**overrides),
         )
 
+    @property
+    def drain_requested(self) -> bool:
+        return self.control.drain_requested if self.control is not None else False
+
+    @property
+    def drain_reason(self) -> str | None:
+        return self.control.drain_reason if self.control is not None else None
+
 
 DEFAULT_RUNTIME = Runtime(
     context=None,
     store=None,
     stream_writer=_no_op_stream_writer,
+    heartbeat=_no_op_heartbeat,
     previous=None,
     execution_info=None,
+    control=None,
 )
 
 

libs/langgraph/langgraph/stream/__init__.py

@@ -0,0 +1,45 @@
+"""Streaming infrastructure for LangGraph.
+
+Compile a graph with `transformers=[...]` and call `graph.stream_events(version="v3")` /
+`graph.astream_events(version="v3")` to drive a transformer pipeline that projects the
+graph's raw events into ergonomic per-channel streams.
+"""
+
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    AsyncSubgraphRunStream,
+    GraphRunStream,
+    SubgraphRunStream,
+)
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    CheckpointsTransformer,
+    CustomTransformer,
+    DebugTransformer,
+    LifecyclePayload,
+    LifecycleTransformer,
+    SubgraphStatus,
+    SubgraphTransformer,
+    TasksTransformer,
+    UpdatesTransformer,
+)
+
+__all__ = [
+    "AsyncGraphRunStream",
+    "AsyncSubgraphRunStream",
+    "CheckpointsTransformer",
+    "CustomTransformer",
+    "DebugTransformer",
+    "GraphRunStream",
+    "LifecyclePayload",
+    "LifecycleTransformer",
+    "ProtocolEvent",
+    "StreamChannel",
+    "StreamTransformer",
+    "SubgraphRunStream",
+    "SubgraphStatus",
+    "SubgraphTransformer",
+    "TasksTransformer",
+    "UpdatesTransformer",
+]

libs/langgraph/langgraph/stream/_convert.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+import time
+from typing import Any, cast
+
+from langgraph.stream._types import ProtocolEvent, _ProtocolEventParams
+from langgraph.types import StreamPart
+
+
+def convert_to_protocol_event(part: StreamPart) -> ProtocolEvent:
+    """Convert a v2 StreamPart to a ProtocolEvent.
+
+    Args:
+        part: A stream part with keys `type`, `ns`, `data`, and
+            optionally `interrupts` (present on values events).
+
+    Returns:
+        The equivalent ProtocolEvent.
+    """
+    part_dict = cast(dict[str, Any], part)
+    params: _ProtocolEventParams = {
+        "namespace": list(part_dict["ns"]),
+        "timestamp": int(time.time() * 1000),
+        "data": part_dict["data"],
+    }
+    if "interrupts" in part_dict:
+        params["interrupts"] = part_dict["interrupts"]
+    return {
+        "type": "event",
+        "method": part_dict["type"],
+        "params": params,
+    }

libs/langgraph/langgraph/stream/_mux.py

@@ -0,0 +1,498 @@
+from __future__ import annotations
+
+import asyncio
+import time
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langgraph.stream._types import (
+    ProtocolEvent,
+    StreamTransformer,
+    transformer_requires_async,
+)
+from langgraph.stream.stream_channel import StreamChannel
+
+TransformerFactory = Callable[["tuple[str, ...]"], StreamTransformer]
+"""Factory that builds a scoped transformer for a mux.
+
+Called once per `StreamMux` with the mux's scope (typically `()` for
+the root). Standard transformer classes accept a single positional
+scope argument, so the class itself is a valid factory. User
+transformers can close over their config:
+`lambda scope: MyTransformer(scope, foo=...)`.
+"""
+
+
+class StreamMux:
+    """Central event dispatcher for the streaming infrastructure.
+
+    Owns the main event log and routes events through a transformer
+    pipeline. StreamChannels with a name discovered in transformer
+    projections are auto-wired so that every `push()` also injects a
+    `ProtocolEvent` into the main log. StreamChannels without a name
+    are local-only.
+
+    Pass `is_async=True` when the mux will be consumed via async
+    iteration (`handler.astream()`). All StreamChannel instances
+    discovered during registration are automatically bound to the
+    matching mode.
+
+    Attributes:
+        extensions: Merged projection dict across all registered
+            transformers. Treat as read-only — mutations won't be
+            reflected back in individual transformers' state.
+        native_keys: Projection keys contributed by transformers with
+            `_native = True`.
+    """
+
+    def __init__(
+        self,
+        transformers: list[StreamTransformer] | None = None,
+        *,
+        is_async: bool = False,
+        factories: list[TransformerFactory] | None = None,
+        scope: tuple[str, ...] = (),
+        _assign_seq: bool = True,
+    ) -> None:
+        """Initialize the mux and register transformers in order.
+
+        Callers pass either `transformers` (pre-built instances) or
+        `factories` (callables producing fresh instances per mux). Each
+        transformer's `init()` is called, projections are merged into
+        `extensions`, `_native` keys are recorded in `native_keys`, and
+        any StreamChannel instances are bound and (if named) wired.
+
+        Args:
+            transformers: Already-built transformer instances. Registered
+                only on this mux — they are NOT cloned into child
+                mini-muxes built by `_make_child`. Use `factories` for
+                transformers that should propagate to nested scopes.
+            is_async: True for async dispatch (`apush` / `aclose` /
+                `afail`), False for the sync path.
+            factories: One-argument callables `(scope) -> StreamTransformer`.
+                Called once with this mux's `scope` here, and cloned
+                again per child scope by `_make_child` so each
+                sub-mux gets fresh instances.
+            scope: The namespace the mux operates within. The root mux
+                is `()`.
+            _assign_seq: Internal flag for child muxes. Root muxes assign
+                monotonic `seq` numbers when appending to their main event
+                log; child muxes share forwarded event objects and must not
+                mutate their envelopes.
+
+        Raises:
+            RuntimeError: If any transformer requires an async run but
+                the mux is in sync mode.
+            TypeError: If a transformer's `init()` doesn't return a dict.
+            ValueError: If transformers' projection keys collide.
+        """
+        self.is_async = is_async
+        self.scope: tuple[str, ...] = scope
+        self._assign_seq = _assign_seq
+        self._events: StreamChannel[ProtocolEvent] = StreamChannel()
+        self._events._bind(is_async=is_async)
+        self._events._bind_mux(self)
+        self._transformers: list[StreamTransformer] = []
+        self._channels: list[StreamChannel[Any]] = []
+        self._seq = 0
+        self._push_seq = 0
+
+        self.extensions: dict[str, Any] = {}
+        self.native_keys: set[str] = set()
+        self._projection_owners: dict[str, str] = {}
+        self._transformer_by_key: dict[str, StreamTransformer] = {}
+
+        # Stored only when constructed from factories — used by
+        # `_make_child` to clone the transformer pipeline at a deeper
+        # scope. Pre-built transformers can't be cloned, so a mux
+        # built with `transformers=` rejects child construction.
+        self._factories: list[TransformerFactory] | None = (
+            list(factories) if factories is not None else None
+        )
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+        # Factories run first (they propagate to child mini-muxes
+        # via `_make_child`), then any pre-built `transformers=`
+        # instances are registered as root-only — they aren't cloned
+        # for child scopes.
+        if factories is not None:
+            for factory in factories:
+                self._register(factory(scope))
+        for transformer in transformers or ():
+            self._register(transformer)
+
+    def transformer_by_key(self, key: str) -> StreamTransformer | None:
+        """Return the transformer that contributed `key` to the projection."""
+        return self._transformer_by_key.get(key)
+
+    def _next_push_seq(self) -> int:
+        self._push_seq += 1
+        return self._push_seq
+
+    # ------------------------------------------------------------------
+    # Pump wiring + mini-mux nesting
+    # ------------------------------------------------------------------
+
+    def bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback onto every projection in this mux.
+
+        Records the pump on the mux so child mini-muxes built by
+        `_make_child` can inherit it. Propagates to:
+        - the main event log (`self._events`)
+        - every projection StreamChannel in `extensions`
+        - any registered transformer that exposes `_bind_pump` (e.g.
+          `MessagesTransformer` so `ChatModelStream` instances drive the
+          shared pump from their cursors)
+        """
+        self._pump_fn = fn
+        self._events._request_more = fn
+        for ch in self._channels:
+            ch._request_more = fn
+        for transformer in self._transformers:
+            bind = getattr(transformer, "_bind_pump", None)
+            if bind is not None:
+                bind(fn)
+
+    def bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Async counterpart to `bind_pump`."""
+        self._apump_fn = fn
+        self._events._arequest_more = fn
+        for ch in self._channels:
+            ch._arequest_more = fn
+        for transformer in self._transformers:
+            abind = getattr(transformer, "_bind_apump", None)
+            if abind is not None:
+                abind(fn)
+
+    def _make_child(self, scope: tuple[str, ...]) -> StreamMux:
+        """Build a mini-mux with the same factories scoped to `scope`.
+
+        Used by `SubgraphTransformer` to attach a fresh transformer
+        pipeline to each discovered subgraph handle. The child mux
+        inherits the current pump bindings (so cursors on its
+        projection logs drive the root pump), carries the same factory
+        list forward to any grandchild subgraphs, and does not assign
+        `seq` numbers so forwarded events can be shared without
+        mutating their envelope.
+
+        Raises:
+            RuntimeError: If the mux was not constructed with
+                `factories=`. Mini-muxes require factories so each scope
+                gets its own fresh transformer instances.
+        """
+        if self._factories is None:
+            raise RuntimeError(
+                "StreamMux._make_child requires the mux to be constructed "
+                "with `factories=`; pre-built transformers can't be "
+                "cloned to a new scope."
+            )
+        child = StreamMux(
+            factories=self._factories,
+            is_async=self.is_async,
+            scope=scope,
+            _assign_seq=False,
+        )
+        if self._pump_fn is not None:
+            child.bind_pump(self._pump_fn)
+        if self._apump_fn is not None:
+            child.bind_apump(self._apump_fn)
+        return child
+
+    def _register(self, transformer: StreamTransformer) -> None:
+        """Register a single transformer.
+
+        Calls `transformer.init()`, stores the transformer for event
+        processing, binds any StreamChannel instances in the projection,
+        and merges the projection into `extensions`.
+        """
+        if transformer_requires_async(transformer) and not self.is_async:
+            raise RuntimeError(
+                f"{type(transformer).__name__} requires an async run — "
+                "it overrides aprocess/afinalize/afail or sets "
+                "requires_async=True. Use astream(), not stream()."
+            )
+        projection = transformer.init()
+        if not isinstance(projection, dict):
+            raise TypeError(
+                f"StreamTransformer.init() must return a dict, "
+                f"got {type(projection).__name__}"
+            )
+        conflicts = set(projection) & set(self.extensions)
+        if conflicts:
+            attributions = ", ".join(
+                f"{key!r} (owned by {self._projection_owners[key]})"
+                for key in sorted(conflicts)
+            )
+            raise ValueError(
+                f"Transformer {type(transformer).__name__} returned "
+                f"projection keys that conflict with already-registered "
+                f"keys: {attributions}"
+            )
+        is_native = bool(getattr(transformer, "_native", False))
+        self._transformers.append(transformer)
+        self._bind_and_wire(projection, native=is_native)
+        self.extensions.update(projection)
+        owner_name = type(transformer).__name__
+        for key in projection:
+            self._projection_owners[key] = owner_name
+            self._transformer_by_key[key] = transformer
+        if is_native:
+            self.native_keys.update(projection.keys())
+        transformer._on_register(self)
+
+    def push(self, event: ProtocolEvent) -> None:
+        """Route an event through all transformers, then append to the main log.
+
+        Each transformer's `process()` is called in registration order.
+        If any transformer returns False, the event is suppressed from
+        the main log, but transformers that already saw it keep their
+        side effects.
+
+        On the root mux, `seq` is assigned right before an event enters
+        the main log, not before the transformer pipeline runs. This
+        ensures that events auto-forwarded from StreamChannels during
+        `process()` get earlier seq numbers than the original event,
+        preserving monotonic ordering in the root log. Child muxes do
+        not assign `seq`, so subgraph forwarding can share event objects
+        without mutating their envelopes.
+
+        Args:
+            event: The protocol event to dispatch.
+        """
+        keep = True
+        for transformer in self._transformers:
+            if not transformer.process(event):
+                keep = False
+        if keep:
+            if self._assign_seq:
+                self._seq += 1
+                event["seq"] = self._seq
+            self._events.push(event)
+
+    def close(self) -> None:
+        """Finalize all transformers, close all projections and the main log.
+
+        StreamChannels discovered in transformer projections are
+        auto-closed after `finalize()` runs — transformers don't need
+        to close them manually. If any transformer's `finalize()` raises,
+        the remaining transformers, projections, and the main log are
+        still closed; the first error is re-raised after cleanup
+        completes.
+
+        Raises:
+            BaseException: The first error raised by a transformer's
+                `finalize()`, re-raised after cleanup finishes.
+        """
+        first_error: BaseException | None = None
+        for transformer in self._transformers:
+            try:
+                transformer.finalize()
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        for ch in self._channels:
+            if not ch._closed:
+                ch.close()
+        self._events.close()
+        if first_error is not None:
+            raise first_error
+
+    def fail(self, err: BaseException) -> None:
+        """Fail all transformers, projections, and the main log.
+
+        StreamChannels discovered in transformer projections are
+        auto-failed — transformers don't need to fail them manually.
+        If any transformer's `fail()` raises, the remaining
+        transformers, projections, and the main log are still failed.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        for transformer in self._transformers:
+            try:
+                transformer.fail(err)
+            except BaseException:
+                pass
+        for ch in self._channels:
+            if not ch._closed:
+                ch.fail(err)
+        self._events.fail(err)
+
+    # ------------------------------------------------------------------
+    # Async dispatch
+    # ------------------------------------------------------------------
+
+    async def apush(self, event: ProtocolEvent) -> None:
+        """Dispatch an event on the async lane.
+
+        Awaits each transformer's `aprocess` in registration order
+        before appending to the main log. A slow `aprocess` serializes
+        the pipeline by design — that's the guarantee that lets a later
+        transformer (or a synchronous consumer) see the result of the
+        async work. For decoupled work, use `schedule()` from inside
+        `process` / `aprocess` instead.
+
+        The main log append is a non-blocking `push` — matching v1's
+        `put_nowait` shape. The root mux assigns `seq`; child muxes do
+        not, so forwarded subgraph events can be shared without copying.
+        Memory is bounded by caller pace via the caller-driven pump; see
+        `StreamChannel` for the full tradeoff story.
+
+        Args:
+            event: The protocol event to dispatch.
+        """
+        keep = True
+        for transformer in self._transformers:
+            if not await transformer.aprocess(event):
+                keep = False
+        if keep:
+            if self._assign_seq:
+                self._seq += 1
+                event["seq"] = self._seq
+            self._events.push(event)
+
+    async def aclose(self) -> None:
+        """Finalize on the async lane.
+
+        Awaits every task started via `StreamTransformer.schedule()`
+        across all transformers, then calls `afinalize()` on each,
+        then auto-closes channels and the main event log.
+
+        If any scheduled task raised under `on_error="raise"`, or any
+        transformer's `afinalize` raises, the exception propagates.
+        The caller (the pump) handles it by routing into `afail`.
+
+        Raises:
+            BaseException: The first scheduled-task or `afinalize`
+                error, re-raised after cleanup.
+        """
+        pending = self._collect_scheduled_tasks()
+        if pending:
+            results = await asyncio.gather(*pending, return_exceptions=True)
+            first_err = next(
+                (
+                    r
+                    for r in results
+                    if isinstance(r, BaseException)
+                    and not isinstance(r, asyncio.CancelledError)
+                ),
+                None,
+            )
+            if first_err is not None:
+                raise first_err
+
+        first_error: BaseException | None = None
+        for transformer in self._transformers:
+            try:
+                await transformer.afinalize()
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        for ch in self._channels:
+            if not ch._closed:
+                ch.close()
+        self._events.close()
+        if first_error is not None:
+            raise first_error
+
+    async def afail(self, err: BaseException) -> None:
+        """Fail on the async lane.
+
+        Cancels every scheduled task across all transformers, awaits
+        them to completion, then runs each transformer's `afail` hook
+        and auto-fails channels and the main event log.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        pending = self._collect_scheduled_tasks()
+        for task in pending:
+            task.cancel()
+        if pending:
+            await asyncio.gather(*pending, return_exceptions=True)
+
+        for transformer in self._transformers:
+            try:
+                await transformer.afail(err)
+            except BaseException:
+                pass
+        for ch in self._channels:
+            if not ch._closed:
+                ch.fail(err)
+        if not self._events._closed:
+            self._events.fail(err)
+
+    def _collect_scheduled_tasks(self) -> list[asyncio.Task[Any]]:
+        """Return a snapshot of in-flight tasks scheduled via transformers."""
+        return [
+            task
+            for transformer in self._transformers
+            for task in getattr(transformer, "_stream_scheduled_tasks", ())
+            if not task.done()
+        ]
+
+    # ------------------------------------------------------------------
+    # Binding and StreamChannel auto-wiring
+    # ------------------------------------------------------------------
+
+    def _bind_and_wire(
+        self, projection: dict[str, Any], *, native: bool = False
+    ) -> None:
+        """Bind and optionally wire StreamChannel instances in a projection.
+
+        All StreamChannels are bound and tracked. Channels with a name
+        are additionally wired for protocol auto-forwarding.
+
+        Args:
+            projection: The projection dict returned by a transformer's
+                `init()`.
+            native: True when the owning transformer is `_native`.
+                Named channels owned by a native transformer use the
+                channel name directly as the protocol method;
+                user-defined channels are prefixed with `custom:`.
+        """
+        for value in projection.values():
+            if isinstance(value, StreamChannel):
+                value._bind(is_async=self.is_async)
+                value._bind_mux(self)
+                self._channels.append(value)
+                if value.name is not None:
+                    method = value.name if native else f"custom:{value.name}"
+
+                    def _make_forward(method_name: str) -> Callable[[Any], None]:
+                        def _forward(item: Any) -> None:
+                            self._forward(method_name, item)
+
+                        return _forward
+
+                    value._wire(_make_forward(method))
+
+    def _forward(self, method: str, item: Any) -> None:
+        """Inject a ProtocolEvent for a StreamChannel push.
+
+        Forwarded events bypass the transformer pipeline to avoid
+        infinite recursion (a transformer that pushes to a channel
+        during `process()` would re-trigger itself). These events are
+        visible in this mux's main event log but are not passed through
+        transformers' `process()` methods. Only the root mux assigns
+        `seq` to forwarded channel events.
+
+        Args:
+            method: The full protocol method (already with or without
+                the `custom:` prefix; resolved by `_bind_and_wire`).
+            item: The payload pushed onto the channel.
+        """
+        event: ProtocolEvent = {
+            "type": "event",
+            "method": method,
+            "params": {
+                "namespace": [],
+                "timestamp": int(time.time() * 1000),
+                "data": item,
+            },
+        }
+        if self._assign_seq:
+            self._seq += 1
+            event["seq"] = self._seq
+        self._events.push(event)

libs/langgraph/langgraph/stream/_types.py

@@ -0,0 +1,313 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Coroutine
+from typing import Any, ClassVar, Literal
+
+from typing_extensions import NotRequired, TypedDict
+
+_logger = logging.getLogger(__name__)
+
+
+class _ProtocolEventParams(TypedDict):
+    """Parameters for a protocol event.
+
+    `timestamp` is wall-clock milliseconds since the epoch and can go
+    backwards across NTP adjustments — use `ProtocolEvent.seq` for
+    ordering.
+    """
+
+    namespace: list[str]
+    timestamp: int
+    data: Any
+    interrupts: NotRequired[tuple[Any, ...]]
+
+
+class ProtocolEvent(TypedDict):
+    """A protocol event emitted by the streaming infrastructure.
+
+    Wraps a raw stream part (values, messages, custom, etc.) in a uniform
+    envelope with a monotonic sequence number assigned by the root StreamMux.
+    Consumers that need a total order across root events should use `seq`, not
+    `params.timestamp` (which is wall-clock and not monotonic).
+    """
+
+    type: Literal["event"]
+    eventId: NotRequired[str]
+    seq: NotRequired[int]
+    method: str  # StreamMode value: "values", "messages", "custom", etc.
+    params: _ProtocolEventParams
+
+
+class StreamTransformer(ABC):
+    """Extension point for custom stream projections.
+
+    Transformers observe protocol events flowing through the StreamMux and
+    build typed derived projections (StreamChannels, promises, etc.).
+
+    Set `_native = True` on a transformer to have its projection keys
+    exposed as direct attributes on the run stream (in addition to
+    appearing in `run.extensions`).
+
+    Subclasses must implement `init` and override at least one of
+    `process` / `aprocess`. The `finalize` / `afinalize` and `fail` /
+    `afail` hooks are optional — the default implementations are no-ops.
+    StreamChannel instances in the projection dict are auto-closed /
+    auto-failed by the mux, so most transformers don't need `finalize`
+    or `fail` at all.
+
+    Transformers that need async work pick the async lane by:
+
+    1. Overriding `aprocess` (and optionally `afinalize` / `afail`), or
+    2. Calling `self.schedule(coro)` from inside a sync `process`, or
+    3. Setting `requires_async = True` explicitly.
+
+    The mux detects these cases at registration and raises if they're
+    used under sync `stream()` — they only work under `astream()`.
+
+    Use `aprocess` when the pump must wait for async work before the
+    next transformer sees the event (e.g. PII redaction that mutates
+    `event` in place). Use `schedule()` for decoupled async work whose
+    result lands on an independent projection (e.g. async moderation
+    scoring, cost lookup, external tracing).
+
+    Attributes:
+        scope: Namespace the transformer operates within — `()` for the
+            root mux. Set at construction from the mux's scope (each
+            factory is called as `factory(scope)`).
+        requires_async: Explicit opt-in for transformers that need a
+            running event loop but don't override any async method (for
+            example, transformers that call `schedule()` from a sync
+            `process`). The mux also auto-detects the async lane when
+            `aprocess`, `afinalize`, or `afail` is overridden.
+        supports_sync: Set True only for transformers that override
+            async-lane hooks while still fully supporting the sync lane.
+            Such transformers may be registered under `stream()`.
+        required_stream_modes: Stream modes the graph must emit for
+            this transformer to have anything to process. Computed as
+            the union across all registered transformers to determine
+            which modes a `stream_events(version="v3")` run requests from the graph.
+            Empty tuple means the transformer consumes only synthetic
+            events (or is purely passive).
+    """
+
+    requires_async: ClassVar[bool] = False
+    supports_sync: ClassVar[bool] = False
+    required_stream_modes: ClassVar[tuple[str, ...]] = ()
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        """Initialize the transformer with its mux's scope.
+
+        Args:
+            scope: The namespace tuple the owning mux is scoped to.
+                `()` for the root. Factories receive this at
+                construction time (`factory(scope)` in `StreamMux`).
+        """
+        self.scope: tuple[str, ...] = scope
+
+    @abstractmethod
+    def init(self) -> dict[str, Any]:
+        """Return the projection dict.
+
+        Keys become entries in `run.extensions`. If the transformer has
+        `_native = True`, keys are also set as direct attributes on the
+        run stream.
+
+        StreamChannel instances in the return value are automatically
+        wired by the StreamMux for protocol event auto-forwarding.
+        """
+        ...
+
+    def _on_register(self, mux: Any) -> None:
+        """Called by `StreamMux._register` after this transformer is wired in.
+
+        Default is a no-op. Override to capture a reference to the
+        owning mux — needed for transformers that build mini-muxes
+        via `mux._make_child(...)` (e.g. `SubgraphTransformer`).
+        """
+
+    def process(self, event: ProtocolEvent) -> bool:
+        """Handle an event on the sync lane.
+
+        Called for every event before it is appended to the main event
+        log. Subclasses must override either `process` or `aprocess`.
+        The default raises so a missing override fails loudly rather
+        than silently passing every event through.
+
+        Args:
+            event: The protocol event to observe.
+
+        Returns:
+            True to keep the event in the main log, False to suppress it.
+        """
+        raise NotImplementedError(
+            f"{type(self).__name__} must override process() or aprocess()"
+        )
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        """Handle an event on the async lane.
+
+        The mux awaits this before dispatching to the next transformer,
+        so a slow `aprocess` serializes the pipeline. Use it only when
+        a later transformer — or a consumer reading the event
+        synchronously — must see the result of the async work (e.g.
+        PII redaction that mutates `event` in place).
+
+        The default delegates to `process`, so purely-sync transformers
+        run unchanged under `astream()`.
+
+        Args:
+            event: The protocol event to observe.
+
+        Returns:
+            True to keep the event in the main log, False to suppress it.
+        """
+        return self.process(event)
+
+    def finalize(self) -> None:
+        """Called when the run ends normally (sync lane).
+
+        Override to close StreamChannels, resolve promises, or perform
+        other teardown. StreamChannel instances in the projection dict
+        are auto-closed by the mux.
+        """
+
+    async def afinalize(self) -> None:
+        """Called when the run ends normally (async lane).
+
+        By the time this runs, the mux has already awaited every task
+        started via `schedule()`, so StreamChannels can be closed here
+        without a last-task-wins race.
+
+        The default delegates to `finalize`.
+        """
+        self.finalize()
+
+    def fail(self, err: BaseException) -> None:
+        """Called when the run ends with an error (sync lane).
+
+        Override to fail StreamChannels, reject promises, or perform
+        other teardown. StreamChannel instances in the projection dict
+        are auto-failed by the mux.
+
+        Args:
+            err: The exception that ended the run.
+        """
+
+    async def afail(self, err: BaseException) -> None:
+        """Called when the run ends with an error (async lane).
+
+        The mux cancels and awaits every task started via `schedule()`
+        before calling this, so cleanup doesn't race with in-flight work.
+
+        The default delegates to `fail`.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        self.fail(err)
+
+    # ------------------------------------------------------------------
+    # Scheduled async work
+    # ------------------------------------------------------------------
+
+    def schedule(
+        self,
+        coro: Coroutine[Any, Any, Any],
+        *,
+        on_error: Literal["log", "raise"] = "log",
+    ) -> asyncio.Task[Any]:
+        """Schedule a coroutine tied to this transformer's lifecycle.
+
+        The mux holds the task reference, awaits all scheduled tasks
+        during `aclose()` before calling `afinalize()`, and cancels
+        them on `afail()`. Authors don't need to track tasks or
+        implement the last-task-closes-the-log dance.
+
+        Requires a running event loop — call only under `astream()`.
+        Set `requires_async = True` on the class so registration under
+        sync `stream()` fails fast with a clear message.
+
+        Args:
+            coro: The coroutine to run. Its lifecycle is owned by the
+                mux from this point on.
+            on_error: `"log"` (default) catches and logs any exception
+                the coroutine raises, so a single failure doesn't tear
+                down the run. `"raise"` lets the exception propagate
+                when the mux joins pendings, converting the close path
+                into the fail path.
+
+        Returns:
+            The asyncio Task. Authors rarely need to await it directly
+            — consumers read results from whatever projection the
+            coroutine pushes into.
+
+        Raises:
+            RuntimeError: If called without a running event loop (i.e.
+                under sync `stream()` rather than `astream()`).
+        """
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            raise RuntimeError(
+                f"{type(self).__name__}.schedule() requires a running "
+                "event loop; this transformer must run under astream(), "
+                "not stream(). Set requires_async=True on the class so "
+                "this fails at registration rather than at first event."
+            ) from None
+
+        wrapped = self._wrap_scheduled(coro) if on_error == "log" else coro
+        task = asyncio.create_task(wrapped)
+        tasks = self._scheduled_task_set()
+        tasks.add(task)
+        task.add_done_callback(tasks.discard)
+        return task
+
+    @staticmethod
+    async def _wrap_scheduled(coro: Coroutine[Any, Any, Any]) -> Any:
+        try:
+            return await coro
+        except asyncio.CancelledError:
+            raise
+        except BaseException:
+            _logger.exception("Scheduled StreamTransformer task failed")
+
+    def _scheduled_task_set(self) -> set[asyncio.Task[Any]]:
+        """Return the lazily-allocated task set.
+
+        Avoids requiring subclasses to call `super().__init__()`.
+        """
+        tasks: set[asyncio.Task[Any]] | None = getattr(
+            self, "_stream_scheduled_tasks", None
+        )
+        if tasks is None:
+            tasks = set()
+            self._stream_scheduled_tasks = tasks
+        return tasks
+
+
+def transformer_requires_async(transformer: StreamTransformer) -> bool:
+    """Return True if the transformer needs a running event loop.
+
+    A transformer requires async if it explicitly opts in
+    (`requires_async = True`) or overrides any of the async-lane methods
+    (`aprocess`, `afinalize`, `afail`) without also declaring that it
+    supports the sync lane.
+
+    Args:
+        transformer: The transformer to inspect.
+
+    Returns:
+        True if the transformer cannot run under sync `stream()`.
+    """
+    if transformer.requires_async:
+        return True
+    if transformer.supports_sync:
+        return False
+    cls = type(transformer)
+    for name in ("aprocess", "afinalize", "afail"):
+        if getattr(cls, name) is not getattr(StreamTransformer, name):
+            return True
+    return False

libs/langgraph/langgraph/stream/run_stream.py

@@ -0,0 +1,608 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping
+from types import MappingProxyType, TracebackType
+from typing import TYPE_CHECKING, Any
+
+from langchain_core._api import beta
+
+from langgraph.stream._convert import convert_to_protocol_event
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+
+if TYPE_CHECKING:
+    from langgraph.stream.transformers import SubgraphStatus
+
+
+def _drive_until_done(pump: Callable[[], bool]) -> None:
+    """Call the sync pump until it returns False."""
+    while pump():
+        pass
+
+
+async def _adrive_until_done(pump: Callable[[], Awaitable[bool]]) -> None:
+    """Call the async pump until it returns False."""
+    while await pump():
+        pass
+
+
+@beta(message="The v3 streaming protocol on Pregel is experimental.")
+class GraphRunStream:
+    """Sync run stream with caller-driven pumping.
+
+    The caller's iteration on any projection (`values`, `messages`,
+    raw events, or `output`) drives the graph forward. No background
+    thread is used — the caller's `for` loop is the pump.
+
+    Projections are single-consumer — iterating `run.values` twice
+    raises. Use `projection.tee(n)` if you genuinely need fan-out.
+
+    All transformer projections live in `extensions`. Native transformer
+    projections (those with `_native = True`) are also set as direct
+    attributes on this instance (e.g. `run.values`, `run.messages`).
+
+    !!! warning
+
+        Returned by `Pregel.stream_events(version="v3")`, which is
+        experimental and may change.
+    """
+
+    def __init__(
+        self,
+        graph_iter: Iterator[Any] | None,
+        mux: StreamMux,
+        *,
+        wire_pump: bool = True,
+    ) -> None:
+        """Initialize the run stream.
+
+        Args:
+            graph_iter: Pull-based iterator over the graph's stream,
+                or `None` for nested run streams whose pump is driven
+                by an outer run (e.g. `SubgraphRunStream`).
+            mux: The StreamMux owning projections and the main log.
+            wire_pump: When True (default), bind `_pump_next` as the
+                mux's pump callable. Subclasses that inherit a parent
+                pump via `StreamMux._make_child` should pass False to
+                preserve the parent binding.
+        """
+        self._graph_iter = graph_iter
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        self._exhausted = False
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        self._scope_list: list[str] = list(mux.scope)
+        for key in mux.native_keys:
+            setattr(self, key, mux.extensions[key])
+        if wire_pump:
+            self._wire_request_more(mux)
+
+    def _wire_request_more(self, mux: StreamMux) -> None:
+        """Wire the sync pull callback through the mux.
+
+        Routing through `mux.bind_pump` (rather than walking
+        projections directly here) lets child mini-muxes built by
+        `mux._make_child(...)` inherit the same pump callable, so
+        cursors on a subgraph handle's projections drive the root
+        pump just like cursors on `run.values` do.
+        """
+        mux.bind_pump(self._pump_next)
+
+    def _observe_event(self, event: ProtocolEvent) -> None:
+        """Track values-event state for output/interrupted/interrupts."""
+        if event["method"] != "values":
+            return
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+
+    def _pump_next(self) -> bool:
+        """Pull one event from the graph and push it through the mux.
+
+        Returns:
+            True if an event was pulled, False if the graph is exhausted
+            or has raised. Always False when constructed with
+            `graph_iter=None` (the run is driven by an outer pump).
+        """
+        if self._exhausted or self._graph_iter is None:
+            return False
+        try:
+            part = next(self._graph_iter)
+            event = convert_to_protocol_event(part)
+            self._observe_event(event)
+            self._mux.push(event)
+            return True
+        except StopIteration:
+            self._mux.close()
+            self._exhausted = True
+            return False
+        except Exception as e:
+            self._mux.fail(e)
+            self._exhausted = True
+            return False
+
+    def abort(self) -> None:
+        """Stop the run early.
+
+        Closes the mux and marks the stream exhausted. The graph
+        iterator is dropped; any in-flight nodes see the closure on
+        their next yield point. Idempotent.
+        """
+        if self._exhausted:
+            return
+        self._exhausted = True
+        try:
+            self._mux.close()
+        except Exception:
+            pass
+
+    def __enter__(self) -> GraphRunStream:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        self.abort()
+
+    @property
+    def output(self) -> dict[str, Any] | None:
+        """Drive the run to completion and return the final state."""
+        _drive_until_done(self._pump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._latest
+
+    @property
+    def interrupted(self) -> bool:
+        """Drive the run to completion, then return whether it was
+        interrupted.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        _drive_until_done(self._pump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupted
+
+    @property
+    def interrupts(self) -> list[Any]:
+        """Drive the run to completion, then return interrupt payloads.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        _drive_until_done(self._pump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupts
+
+    def __iter__(self) -> Iterator[ProtocolEvent]:
+        """Subscribe to the main event log and iterate protocol events."""
+        return iter(self._mux._events)
+
+    def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
+        """Iterate multiple projections in arrival order, yielding ``(name, item)``.
+
+        Items are ordered by a monotonic push stamp assigned when each
+        transformer pushes into its `StreamChannel`. This gives strict
+        arrival ordering across projections, unlike round-robin.
+
+        Args:
+            *names: Projection keys to interleave. Must match keys in
+                ``extensions``.
+
+        Yields:
+            ``(name, item)`` tuples in arrival order across the named
+            projections.
+
+        Each named channel is locked for the duration of iteration and
+        released when the generator completes, is closed, or raises.
+        Channels cannot be subscribed concurrently — use `.tee(n)` if
+        you need fan-out.
+
+        Raises:
+            KeyError: If a name doesn't match a registered projection.
+
+        Example:
+            ```python
+            for name, item in run.interleave("messages", "values"):
+                if name == "messages":
+                    print("msg:", item)
+                else:
+                    print("val:", item)
+            ```
+        """
+        from langgraph.stream.stream_channel import StreamChannel
+
+        channels: dict[str, StreamChannel[Any]] = {}
+        try:
+            for name in names:
+                ch = self.extensions[name]
+                if not isinstance(ch, StreamChannel):
+                    raise TypeError(
+                        f"interleave() requires StreamChannel projections, "
+                        f"got {type(ch).__name__} for {name!r}"
+                    )
+                if ch._is_async is None:
+                    raise TypeError(
+                        f"StreamChannel {name!r} has not been bound yet. "
+                        "Register the transformer with a StreamMux first."
+                    )
+                if ch._is_async:
+                    raise TypeError(
+                        f"StreamChannel {name!r} is bound to async mode — "
+                        "sync interleave() cannot consume async channels."
+                    )
+                if ch._subscribed:
+                    raise RuntimeError(
+                        f"StreamChannel {name!r} already has a subscriber; "
+                        "use .tee(n) for fan-out."
+                    )
+                ch._subscribed = True
+                channels[name] = ch
+
+            done: set[str] = set()
+
+            while len(done) < len(channels):
+                best: tuple[int, str] | None = None
+                for name, ch in channels.items():
+                    if name in done:
+                        continue
+                    if ch._closed and not ch._items:
+                        if ch._error is not None:
+                            raise ch._error
+                        done.add(name)
+                        continue
+                    if ch._items:
+                        stamp = ch._items[0][0]
+                        if best is None or stamp < best[0]:
+                            best = (stamp, name)
+
+                if best is not None:
+                    _stamp, item = channels[best[1]]._items.popleft()
+                    yield (best[1], item)
+                else:
+                    pump = self._mux._pump_fn
+                    if pump is None or not pump():
+                        before = len(done)
+                        for name, ch in channels.items():
+                            if name not in done and not ch._items:
+                                if ch._closed:
+                                    if ch._error is not None:
+                                        raise ch._error
+                                    done.add(name)
+                        if len(done) == before:
+                            break
+        finally:
+            for ch in channels.values():
+                ch._subscribed = False
+
+
+@beta(message="The v3 streaming protocol on Pregel is experimental.")
+class AsyncGraphRunStream:
+    """Async run stream with caller-driven pumping.
+
+    Async iteration on any projection drives the graph forward — there
+    is no background task. Concurrent consumers share a single-flight
+    pump via an `asyncio.Lock`, so each awaiting cursor contributes one
+    event per acquisition. Backpressure comes from the logs: when a
+    subscribed log's buffer reaches `maxlen`, `apush` awaits the
+    subscriber to drain, which holds back the pump and paces the graph.
+
+    Projections are single-consumer — a second `aiter(run.values)`
+    raises. Use `projection.tee(n)` for fan-out.
+
+    Use as an async context manager to guarantee clean shutdown on
+    early exit:
+
+    ```python
+    async with await handler.astream(input) as run:
+        async for msg in run.messages:
+            ...
+    ```
+
+    !!! warning
+
+        Awaited from `Pregel.astream_events(version="v3")`, which is
+        experimental and may change.
+    """
+
+    def __init__(
+        self,
+        graph_aiter: AsyncIterator[Any] | None,
+        mux: StreamMux,
+        *,
+        wire_pump: bool = True,
+    ) -> None:
+        """Initialize the async run stream.
+
+        Args:
+            graph_aiter: Async iterator over the graph's stream, or
+                `None` for nested run streams whose pump is driven by
+                an outer run (e.g. `AsyncSubgraphRunStream`).
+            mux: The StreamMux owning projections and the main log.
+            wire_pump: When True (default), bind `_apump_next` as the
+                mux's async pump callable. Subclasses that inherit a
+                parent pump via `StreamMux._make_child` should pass
+                False to preserve the parent binding.
+        """
+        self._graph_aiter = graph_aiter
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        self._exhausted = False
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        self._scope_list: list[str] = list(mux.scope)
+        self._pump_cond = asyncio.Condition()
+        self._pumping = False
+        for key in mux.native_keys:
+            setattr(self, key, mux.extensions[key])
+        if wire_pump:
+            self._wire_arequest_more(mux)
+
+    def _observe_event(self, event: ProtocolEvent) -> None:
+        """Track values-event state for output/interrupted/interrupts."""
+        if event["method"] != "values":
+            return
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+
+    def _wire_arequest_more(self, mux: StreamMux) -> None:
+        """Wire the async pull callback through the mux.
+
+        Mirrors `_wire_request_more`: routing through
+        `mux.bind_apump` lets child mini-muxes inherit the pump
+        callable so cursors on subgraph handles drive the root
+        pump.
+        """
+        mux.bind_apump(self._apump_next)
+
+    async def _apump_next(self) -> bool:
+        """Drive one pump step, or wait for the active pumper to drive one.
+
+        "Take-a-number" semantics: at most one task at a time calls
+        `graph_aiter.__anext__()` (asyncio iterators can't be advanced
+        concurrently). Other callers wait on a Condition that the
+        active pumper notifies after each step. This lets a "passive"
+        consumer — one whose projection's buffer is being filled by the
+        active pumper's push — wake up as soon as its data lands,
+        instead of queueing on the pump and only observing its data one
+        graph event late.
+
+        `except Exception` is intentional — `CancelledError` and other
+        `BaseException` subclasses propagate, matching asyncio's
+        cancellation contract.
+
+        Returns:
+            True if a pump step completed (by this task or another),
+            False if the graph is exhausted.
+        """
+        async with self._pump_cond:
+            if self._exhausted or self._graph_aiter is None:
+                return False
+            if self._pumping:
+                # Another task is pumping; wait for its progress signal.
+                await self._pump_cond.wait()
+                return not self._exhausted
+            self._pumping = True
+
+        try:
+            try:
+                part = await self._graph_aiter.__anext__()
+                event = convert_to_protocol_event(part)
+                self._observe_event(event)
+                await self._mux.apush(event)
+                return True
+            except StopAsyncIteration:
+                self._exhausted = True
+                await self._mux.aclose()
+                return False
+            except Exception as e:
+                self._exhausted = True
+                await self._mux.afail(e)
+                return False
+        finally:
+            async with self._pump_cond:
+                self._pumping = False
+                self._pump_cond.notify_all()
+
+    async def abort(self) -> None:
+        """Stop the run early.
+
+        Marks the stream exhausted, wakes any pump-waiters, and closes
+        the mux. Any `apush` blocked on backpressure wakes and returns
+        without appending. Idempotent.
+        """
+        async with self._pump_cond:
+            if self._exhausted:
+                return
+            self._exhausted = True
+            self._pump_cond.notify_all()
+        try:
+            await self._mux.aclose()
+        except Exception:
+            pass
+
+    async def __aenter__(self) -> AsyncGraphRunStream:
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        await self.abort()
+
+    async def output(self) -> dict[str, Any] | None:
+        """Drive the run to completion and return the final state.
+
+        Methods (not properties) on the async lane so `run.output`
+        without `await` raises at type-check time instead of silently
+        yielding a coroutine object.
+
+        Example:
+            ```python
+            output = await run.output()
+            ```
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._latest
+
+    async def interrupted(self) -> bool:
+        """Drive the run to completion and return whether it was
+        interrupted.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupted
+
+    async def interrupts(self) -> list[Any]:
+        """Drive the run to completion and return interrupt payloads.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupts
+
+    def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
+        """Subscribe to the main event log and iterate protocol events."""
+        return self._mux._events.__aiter__()
+
+
+class _SubgraphRunStreamMixin:
+    """Subgraph metadata + parent-pump delegation shared by both lanes.
+
+    Inherits from `GraphRunStream` (or `AsyncGraphRunStream`) with
+    `graph_iter=None` + `wire_pump=False` — the mini-mux is driven
+    by the parent's pump (inherited via `StreamMux._make_child`), and
+    the handle never pulls upstream itself. Pump-driving methods
+    delegate to the parent pump so `handle.output` and friends drive
+    the root run.
+
+    Subclasses set the parent pump function captured at construction
+    (`_parent_pump_fn` / `_parent_apump_fn`) and override
+    `_pump_next` / `_apump_next` to delegate to it.
+
+    Status is updated in place by `SubgraphTransformer`. Iterate
+    `run.subgraphs` to receive handles as subgraphs spawn, then
+    drill into projections inside the loop body **before** the next
+    pump cycle — same lazy-subscribe constraint as root projections.
+    """
+
+    path: tuple[str, ...]
+    graph_name: str | None
+    trigger_call_id: str | None
+    status: SubgraphStatus
+    error: str | None
+    _seen_terminal: bool
+
+
+class SubgraphRunStream(GraphRunStream, _SubgraphRunStreamMixin):
+    """Sync handle for a discovered subgraph (extends `GraphRunStream`)."""
+
+    def __init__(
+        self,
+        mux: StreamMux,
+        *,
+        path: tuple[str, ...],
+        graph_name: str | None = None,
+        trigger_call_id: str | None = None,
+    ) -> None:
+        # Capture the parent-inherited pump before super().__init__
+        # touches anything; we delegate to it from `_pump_next`.
+        self._parent_pump_fn: Callable[[], bool] | None = mux._pump_fn
+        super().__init__(
+            graph_iter=None,
+            mux=mux,
+            wire_pump=False,
+        )
+        self.path = path
+        self.graph_name = graph_name
+        self.trigger_call_id = trigger_call_id
+        self.status = "started"
+        self.error = None
+        self._seen_terminal = False
+
+    def _pump_next(self) -> bool:
+        """Delegate to the parent's pump.
+
+        Cursors on this handle's projections call here when their
+        buffers empty. Driving the parent fans events into our
+        mini-mux, transparently advancing the whole run.
+        """
+        if (
+            self._exhausted
+            or self._seen_terminal
+            or self._mux._events._closed
+            or self._parent_pump_fn is None
+        ):
+            return False
+        return self._parent_pump_fn()
+
+
+class AsyncSubgraphRunStream(AsyncGraphRunStream, _SubgraphRunStreamMixin):
+    """Async handle for a discovered subgraph (extends `AsyncGraphRunStream`)."""
+
+    def __init__(
+        self,
+        mux: StreamMux,
+        *,
+        path: tuple[str, ...],
+        graph_name: str | None = None,
+        trigger_call_id: str | None = None,
+    ) -> None:
+        self._parent_apump_fn: Callable[[], Awaitable[bool]] | None = mux._apump_fn
+        super().__init__(
+            graph_aiter=None,
+            mux=mux,
+            wire_pump=False,
+        )
+        self.path = path
+        self.graph_name = graph_name
+        self.trigger_call_id = trigger_call_id
+        self.status = "started"
+        self.error = None
+        self._seen_terminal = False
+
+    async def _apump_next(self) -> bool:
+        """Delegate to the parent's async pump."""
+        if (
+            self._exhausted
+            or self._seen_terminal
+            or self._mux._events._closed
+            or self._parent_apump_fn is None
+        ):
+            return False
+        return await self._parent_apump_fn()

libs/langgraph/langgraph/stream/stream_channel.py

@@ -0,0 +1,341 @@
+from __future__ import annotations
+
+import asyncio
+from collections import deque
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
+from typing import TYPE_CHECKING, Generic, TypeVar
+
+if TYPE_CHECKING:
+    from langgraph.stream._mux import StreamMux
+
+T = TypeVar("T")
+
+
+class StreamChannel(Generic[T]):
+    """Single-consumer drainable queue for streaming events, with optional
+    protocol auto-forwarding.
+
+    When constructed with a `name`, the StreamMux auto-wires every
+    `push()` to also inject a `ProtocolEvent` into the main event stream
+    using the channel's name as the method. When constructed without a
+    name, the channel is local-only — items are only visible to
+    in-process consumers that iterate the channel directly.
+
+    Items are popped off the front as the consumer advances — there is
+    no retention beyond what's currently queued. A channel accepts
+    exactly one subscriber; a second `__iter__` / `__aiter__` call
+    raises. Use `tee(n)` / `atee(n)` for fan-out.
+
+    Starts unbound — neither `__iter__` nor `__aiter__` is available
+    until the StreamMux calls `_bind(is_async)`. After binding, only
+    the matching iteration protocol works; the other raises `TypeError`.
+
+    Pump wiring (set by the run stream, not by `_bind`):
+        - `_request_more`: sync pump callable, returns True if a new
+          event was produced.
+        - `_arequest_more`: async pump coroutine factory, same contract.
+
+    Memory is bounded by caller pace: both sync and async use caller-
+    driven pumps, so each cursor advance produces at most one event.
+
+    Lazy-subscribe: `push` appends to the local buffer only when a
+    subscriber has registered. Auto-forward via `_wire_fn` always fires
+    regardless of subscription state.
+
+    Lifecycle (`close` / `fail`) is managed by the mux — transformers
+    don't need to close their channels manually.
+    """
+
+    def __init__(self, name: str | None = None, *, maxlen: int | None = None) -> None:
+        """Initialize the channel.
+
+        Args:
+            name: Optional protocol channel name. When set, the
+                StreamMux wires every `push()` to also inject a
+                `ProtocolEvent` into the main event stream. Surfaced
+                on the wire as `custom:<name>` for user-defined
+                transformers, or as `<name>` for channels owned by a
+                native transformer (`_native = True`). When `None`,
+                the channel is local-only.
+            maxlen: Accepted for forward compatibility; currently
+                unused. The caller-driven pump bounds memory naturally
+                for single-consumer use.
+
+        Raises:
+            ValueError: If `maxlen` is not a positive integer or `None`.
+        """
+        if maxlen is not None and maxlen <= 0:
+            raise ValueError("StreamChannel maxlen must be a positive int or None")
+        self.name = name
+        self._items: deque[tuple[int, T]] = deque()
+        self._maxlen: int | None = maxlen
+        self._closed = False
+        self._error: BaseException | None = None
+
+        self._is_async: bool | None = None
+
+        self._subscribed = False
+
+        self._request_more: Callable[[], bool] | None = None
+        self._arequest_more: Callable[[], Awaitable[bool]] | None = None
+
+        self._wire_fn: Callable[[T], None] | None = None
+        self._mux: StreamMux | None = None
+
+    # ------------------------------------------------------------------
+    # Binding
+    # ------------------------------------------------------------------
+
+    def _bind_mux(self, mux: StreamMux) -> None:
+        self._mux = mux
+
+    def _bind(self, *, is_async: bool) -> None:
+        """Bind this channel to sync or async mode.
+
+        Called by the StreamMux after transformer registration. Must be
+        called exactly once before any iteration.
+
+        Args:
+            is_async: True to enable async iteration, False for sync.
+
+        Raises:
+            RuntimeError: If the channel has already been bound.
+        """
+        if self._is_async is not None:
+            raise RuntimeError("StreamChannel is already bound")
+        self._is_async = is_async
+
+    # ------------------------------------------------------------------
+    # Mux wiring (not called by transformers directly)
+    # ------------------------------------------------------------------
+
+    def _wire(self, fn: Callable[[T], None]) -> None:
+        """Install the auto-forward callback (called by StreamMux)."""
+        self._wire_fn = fn
+
+    # ------------------------------------------------------------------
+    # Producer API
+    # ------------------------------------------------------------------
+
+    def push(self, item: T) -> None:
+        """Append an item. Auto-forwards if wired.
+
+        The local buffer append is a no-op when no subscriber is
+        registered, but auto-forwarding always fires so wired events
+        reach the main event log regardless of subscription state.
+
+        Items are stored as `(stamp, item)` tuples where stamp is a
+        monotonic counter from the owning mux. Stamps are stripped by
+        the default cursors; raw stamped tuples are visible on `_items`.
+
+        Raises:
+            RuntimeError: If the channel is closed (and subscribed).
+        """
+        if self._subscribed:
+            if self._closed:
+                raise RuntimeError("Cannot push to a closed StreamChannel")
+            stamp = self._mux._next_push_seq() if self._mux is not None else 0
+            self._items.append((stamp, item))
+        if self._wire_fn is not None:
+            self._wire_fn(item)
+
+    def close(self) -> None:
+        """Mark the channel as complete."""
+        self._closed = True
+
+    def fail(self, err: BaseException) -> None:
+        """Mark the channel as errored.
+
+        Args:
+            err: The exception to surface to the subscriber.
+        """
+        self._error = err
+        self._closed = True
+
+    # ------------------------------------------------------------------
+    # Sync iteration (caller-driven pump)
+    # ------------------------------------------------------------------
+
+    def __iter__(self) -> Iterator[T]:
+        """Subscribe and return a sync cursor. Can be called only once.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to async mode.
+            RuntimeError: If the channel already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "StreamChannel has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if self._is_async:
+            raise TypeError(
+                "This StreamChannel is bound to async mode — use 'async for' instead."
+            )
+        if self._subscribed:
+            raise RuntimeError(
+                "StreamChannel already has a subscriber; use .tee(n) for fan-out."
+            )
+        self._subscribed = True
+        return self._sync_cursor()
+
+    def _sync_cursor(self) -> Iterator[T]:
+        while True:
+            if self._items:
+                _stamp, item = self._items.popleft()
+                yield item
+            elif self._closed:
+                if self._error is not None:
+                    raise self._error
+                return
+            elif self._request_more is not None:
+                if not self._request_more():
+                    if not self._items and not self._closed:
+                        return
+            else:
+                return
+
+    # ------------------------------------------------------------------
+    # Async iteration (caller-driven pump)
+    # ------------------------------------------------------------------
+
+    def __aiter__(self) -> AsyncIterator[T]:
+        """Subscribe and return an async cursor. Can be called only once.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to sync mode.
+            RuntimeError: If the channel already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "StreamChannel has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if not self._is_async:
+            raise TypeError(
+                "This StreamChannel is bound to sync mode — use 'for' instead."
+            )
+        if self._subscribed:
+            raise RuntimeError(
+                "StreamChannel already has a subscriber; use .atee(n) for fan-out."
+            )
+        self._subscribed = True
+        return self._async_cursor()
+
+    async def _async_cursor(self) -> AsyncIterator[T]:
+        while True:
+            if self._items:
+                _stamp, item = self._items.popleft()
+                yield item
+            elif self._closed:
+                if self._error is not None:
+                    raise self._error
+                return
+            elif self._arequest_more is not None:
+                if not await self._arequest_more():
+                    if not self._items and not self._closed:
+                        return
+            else:
+                return
+
+    # ------------------------------------------------------------------
+    # Fan-out via tee
+    # ------------------------------------------------------------------
+
+    def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
+        """Subscribe and return `n` independent sync iterators.
+
+        Each branch has its own buffer; items pulled from the
+        underlying cursor are copied into every branch. Branches are
+        naturally bounded by caller pace since the sync pump is
+        caller-driven.
+
+        Args:
+            n: Number of branches to create. Must be >= 1.
+
+        Returns:
+            A tuple of `n` iterators over the same underlying stream.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to async mode.
+            RuntimeError: If the channel already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("tee() requires n >= 1")
+        source = self.__iter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+
+        def branch(i: int) -> Iterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                elif exhausted[0]:
+                    return
+                else:
+                    try:
+                        item = next(source)
+                    except StopIteration:
+                        exhausted[0] = True
+                        return
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))
+
+    def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
+        """Subscribe and return `n` independent async iterators.
+
+        Caller-driven fan-out: each branch's `__anext__` either pops
+        from its own buffer or, under a shared `asyncio.Lock`, pulls
+        one item from the underlying cursor and distributes it to
+        every branch's buffer.
+
+        Args:
+            n: Number of branches to create. Must be >= 1.
+
+        Returns:
+            A tuple of `n` async iterators over the same underlying
+            stream.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to sync mode.
+            RuntimeError: If the channel already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("atee() requires n >= 1")
+        source = self.__aiter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+        error: list[BaseException | None] = [None]
+        lock = asyncio.Lock()
+
+        async def branch(i: int) -> AsyncIterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                    continue
+                if exhausted[0]:
+                    if error[0] is not None:
+                        raise error[0]
+                    return
+                async with lock:
+                    if buf or exhausted[0]:
+                        continue
+                    try:
+                        item = await source.__anext__()
+                    except StopAsyncIteration:
+                        exhausted[0] = True
+                        continue
+                    except Exception as e:
+                        error[0] = e
+                        exhausted[0] = True
+                        continue
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))

libs/langgraph/langgraph/stream/transformers.py

@@ -0,0 +1,928 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any, Literal, cast
+
+from langchain_core.language_models._compat_bridge import message_to_events
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessageChunk, BaseMessage
+from langchain_protocol.protocol import MessagesData
+from typing_extensions import NotRequired, TypedDict
+
+from langgraph.errors import GraphDrained, GraphInterrupt
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import AsyncSubgraphRunStream, SubgraphRunStream
+from langgraph.stream.stream_channel import StreamChannel
+
+if TYPE_CHECKING:
+    from collections.abc import Awaitable, Callable
+
+    from langgraph.stream._mux import StreamMux
+
+_logger = logging.getLogger(__name__)
+
+
+class ValuesTransformer(StreamTransformer):
+    """Capture values events as a drainable stream of state snapshots.
+
+    Provides the `run.values` projection. `run.output`,
+    `run.interrupted` and `run.interrupts` are tracked directly
+    by the run stream and do not depend on this transformer.
+
+    Native transformer — projection keys are exposed as direct
+    attributes on the run stream (e.g. `run.values`).
+
+    Only values events at the run's own level are captured; snapshots
+    from deeper subgraphs are left in the main event log but excluded
+    from the projection. "Own level" is defined by `scope`, which
+    `stream_events(version="v3")` / `astream_events(version="v3")` populate from the caller's
+    checkpoint namespace so that a nested `stream_events(version="v3")` call still
+    sees its own root snapshots.
+    """
+
+    _native = True
+    required_stream_modes = ("values",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        # Cached as a list once for cheap equality with the protocol
+        # event's `namespace` field, which is `list[str]`.
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"values": self._log}
+
+    @property
+    def error(self) -> BaseException | None:
+        """The error that ended the run, or `None` if it succeeded.
+
+        Set by the mux when it auto-fails the projection log.
+        """
+        return self._log._error
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "values":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+        self._log.push(params["data"])
+        return True
+
+
+class CustomTransformer(StreamTransformer):
+    """Capture custom events as a drainable stream of arbitrary payloads.
+
+    Nodes emit custom data via `get_stream_writer()`. This transformer
+    surfaces those events on `run.custom` as a `StreamChannel[Any]`,
+    preserving payloads in arrival order.
+
+    Only events at the run's own scope are captured; custom data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.custom` projection.
+
+    Native transformer — `run.custom` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("custom",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[Any] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"custom": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "custom":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class UpdatesTransformer(StreamTransformer):
+    """Capture updates events as a drainable stream of node outputs.
+
+    Surfaces `stream_mode="updates"` data on `run.updates` as a
+    `StreamChannel[dict[str, Any]]`. Each item is a dict mapping a node
+    (or task) name to the update it returned after a step.
+
+    Only events at the run's own scope are captured; updates from deeper
+    subgraphs are available on the respective subgraph handle's
+    `.updates` projection.
+
+    Native transformer — `run.updates` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("updates",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"updates": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "updates":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class MessagesTransformer(StreamTransformer):
+    """Capture messages events as ChatModelStream objects.
+
+    The messages projection yields one `ChatModelStream` (or
+    `AsyncChatModelStream`) per LLM call. Consumers iterate
+    `run.messages` to get stream handles, then use each handle's typed
+    projections (`.text`, `.reasoning`, `.tool_calls`, `.usage`,
+    `.output`) for per-message content.
+
+    Two input shapes are handled (via `params["data"] = (payload,
+    metadata)` from `StreamMessagesHandler`):
+
+    1. Protocol event (dict with `"event"` key) — emitted by
+       `stream_events(version="v3")` / `astream_events(version="v3")` via the `on_stream_event`
+       callback. Routed to an existing `ChatModelStream` by
+       `metadata["run_id"]`. A `message-start` event creates a new
+       stream; `message-finish` closes it.
+    2. Whole `AIMessage` — emitted from `on_chain_end` when a node
+       returns a finalized message. Replayed as a synthetic protocol
+       event lifecycle via `message_to_events`, then the
+       already-complete stream is pushed to the log.
+
+    V1 `AIMessageChunk` tuples (from `on_llm_new_token`) are not
+    streamed into this projection: chat models that want to populate
+    `run.messages` with content-block streaming must use
+    `stream_events(version="v3")` / `astream_events(version="v3")`. Models called via the legacy
+    `stream()` method still surface their final `AIMessage` via
+    `on_chain_end` when a node returns it as state.
+
+    Only events at the run's own level are projected; tokens from
+    deeper subgraphs are left in the main event log but excluded from
+    `.messages`. "Own level" is defined by `scope`, which
+    `stream_events(version="v3")` / `astream_events(version="v3")` populate from the caller's checkpoint
+    namespace so that a `stream_events(version="v3")` call inside a node still sees its
+    own root chat model streams on `.messages`. Consumers that need
+    subgraph tokens should iterate the raw event stream or register a
+    custom transformer.
+
+    Native transformer — the `messages` projection is exposed as a
+    direct attribute on the run stream.
+    """
+
+    _native = True
+    required_stream_modes = ("messages",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[ChatModelStream] = StreamChannel()
+        # Correlate protocol events back to a ChatModelStream by run_id
+        # (attached to the event's metadata by StreamMessagesHandler).
+        self._by_run: dict[str, ChatModelStream] = {}
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+        # Cached as a list once for cheap equality with the protocol
+        # event's `namespace` field, which is `list[str]`.
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"messages": self._log}
+
+    def _bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback. Called by GraphRunStream._wire_request_more."""
+        self._pump_fn = fn
+
+    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Wire the async pull callback.
+
+        Called by `AsyncGraphRunStream._wire_arequest_more` so each
+        `AsyncChatModelStream` this transformer creates can drive the
+        shared graph pump from its projection cursors.
+        """
+        self._apump_fn = fn
+
+    def _make_stream(
+        self,
+        *,
+        namespace: list[str],
+        node: str | None,
+        message_id: str | None,
+    ) -> ChatModelStream:
+        """Create a ChatModelStream (sync) or AsyncChatModelStream (async).
+
+        Wires whichever pump is bound. Prefers the async pump so nested
+        iteration under `AsyncGraphRunStream` drives the graph forward
+        without a background task. The unwired fallback (no pump bound)
+        is used by unit tests that dispatch events manually.
+        """
+        if self._apump_fn is not None:
+            astream = AsyncChatModelStream(
+                namespace=namespace,
+                node=node,
+                message_id=message_id,
+            )
+            astream.set_arequest_more(self._apump_fn)
+            return astream
+        if self._pump_fn is not None:
+            stream: ChatModelStream = ChatModelStream(
+                namespace=namespace,
+                node=node,
+                message_id=message_id,
+            )
+            stream.set_request_more(self._pump_fn)
+            return stream
+        return AsyncChatModelStream(
+            namespace=namespace,
+            node=node,
+            message_id=message_id,
+        )
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "messages":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+
+        payload, metadata = params["data"]
+        node: str | None = metadata.get("langgraph_node")
+        run_id = str(metadata.get("run_id", "")) if metadata else ""
+
+        if isinstance(payload, dict) and "event" in payload:
+            self._route_protocol_event(
+                cast("MessagesData", payload), run_id=run_id, node=node
+            )
+        elif isinstance(payload, BaseMessage) and not isinstance(
+            payload, AIMessageChunk
+        ):
+            self._route_whole_message(payload, node=node)
+        # Legacy AIMessageChunk tuples (from on_llm_new_token) are ignored;
+        # v1 streaming callers must switch to stream_events(version="v3") to populate this
+        # projection.
+
+        return True
+
+    def _route_protocol_event(
+        self,
+        event: MessagesData,
+        *,
+        run_id: str,
+        node: str | None,
+    ) -> None:
+        event_type = event.get("event")
+        if event_type == "message-start":
+            message_id = event.get("message_id")
+            stream = self._make_stream(
+                namespace=[],
+                node=node,
+                message_id=str(message_id) if message_id is not None else None,
+            )
+            self._by_run[run_id] = stream
+            self._log.push(stream)
+            stream.dispatch(event)
+        elif run_id in self._by_run:
+            stream = self._by_run[run_id]
+            stream.dispatch(event)
+            if event_type == "message-finish":
+                del self._by_run[run_id]
+
+    def _route_whole_message(self, message: BaseMessage, *, node: str | None) -> None:
+        stream = self._make_stream(namespace=[], node=node, message_id=message.id)
+        for evt in message_to_events(message, message_id=message.id):
+            stream.dispatch(evt)
+        self._log.push(stream)
+
+    def finalize(self) -> None:
+        """Clear any routing state — streams close themselves via `message-finish`."""
+        self._by_run.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Propagate run error to any streams still open when the graph fails."""
+        for stream in list(self._by_run.values()):
+            stream.fail(err)
+        self._by_run.clear()
+
+
+SubgraphStatus = Literal["started", "completed", "failed", "interrupted", "drained"]
+
+
+def _parse_ns_segment(segment: str) -> tuple[str, str | None]:
+    """Split a namespace segment into `(graph_name, trigger_call_id)`.
+
+    Segments are formatted `node_name:task_id` by `prepare_next_tasks`.
+    Returns `(segment, None)` if no `:` is present.
+    """
+    name, sep, task_id = segment.partition(":")
+    return name, task_id if sep else None
+
+
+class LifecyclePayload(TypedDict, total=False):
+    """Payload of a lifecycle event surfaced on the `lifecycle` channel.
+
+    Auto-forwarded as `lifecycle` protocol events (no `custom:` prefix
+    because `LifecycleTransformer` is a native transformer) so remote
+    SDK clients receive the same data in-process consumers see via
+    `run.lifecycle`.
+    """
+
+    event: SubgraphStatus
+    namespace: list[str]
+    graph_name: NotRequired[str]
+    trigger_call_id: NotRequired[str]
+    error: NotRequired[str]
+
+
+class _TasksLifecycleBase(StreamTransformer):
+    """Shared bookkeeping for `tasks`-event-driven lifecycle inference.
+
+    Both `LifecycleTransformer` (wire-serializable channel) and
+    `SubgraphTransformer` (in-process navigation handles) discover
+    subgraphs by watching the same `tasks` stream — `started` on the
+    first event at a tracked namespace, terminal status when the
+    parent's `TaskResultPayload` arrives. Centralizing the dispatch
+    + open-set bookkeeping here keeps the inference rules from
+    drifting between the two surfaces.
+
+    Subclasses provide three template-method hooks:
+
+    - `_should_track(ns)` — scope filter (e.g. multi-depth vs
+      direct-children-only).
+    - `_on_started(ns, graph_name, trigger_call_id)` — first sighting
+      action (push payload / build handle / etc.). Called once per
+      discovered namespace.
+    - `_on_terminal(ns, status, error)` — terminal action (push
+      terminal payload / mark handle status). Called once per
+      tracked namespace at result time, or via `finalize` / `fail`
+      sweeps if no parent result arrived.
+
+    Tasks events are suppressed from the main event log (`process`
+    returns False) — they're folded into whichever projection the
+    subclass populates; consumers iterating the raw protocol stream
+    see the higher-level view.
+    """
+
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._seen: set[tuple[str, ...]] = set()
+        # Maps tracked namespace -> task_id of the parent task whose
+        # `TaskResultPayload` will close it.
+        self._open: dict[tuple[str, ...], str] = {}
+
+    # --- Template-method hooks (subclass overrides) ---
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        """Scope filter — return True iff `ns` is in this transformer's region."""
+        raise NotImplementedError
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        """Fired once per discovered namespace (first observed task event)."""
+        raise NotImplementedError
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        """Fired once per tracked namespace when its parent's result arrives,
+        or via finalize/fail safety-net sweeps.
+        """
+        raise NotImplementedError
+
+    # --- Dispatch + bookkeeping (shared) ---
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "tasks":
+            return True
+        ns = tuple(event["params"]["namespace"])
+        data = event["params"]["data"]
+        if "result" in data:
+            self._handle_task_result(ns, data)
+        else:
+            self._handle_task_start(ns)
+        # Tasks events are folded into the synthesized projections;
+        # suppress from the main event log so iterators don't double-see
+        # the same information in two shapes.
+        return False
+
+    def _handle_task_start(self, ns: tuple[str, ...]) -> None:
+        if not self._should_track(ns) or ns in self._seen:
+            return
+        self._seen.add(ns)
+        graph_name, trigger_call_id = _parse_ns_segment(ns[-1])
+        self._on_started(ns, graph_name or None, trigger_call_id)
+        if trigger_call_id is not None:
+            self._open[ns] = trigger_call_id
+
+    def _pop_terminal_transitions(
+        self, ns: tuple[str, ...], data: dict[str, Any]
+    ) -> list[tuple[tuple[str, ...], SubgraphStatus, str | None]]:
+        """Return and remove tracked children closed by this task result."""
+        result_id = data.get("id")
+        if not result_id:
+            return []
+        transitions: list[tuple[tuple[str, ...], SubgraphStatus, str | None]] = []
+        for child_ns, parent_task_id in list(self._open.items()):
+            if child_ns[:-1] != ns or parent_task_id != result_id:
+                continue
+            status, error = _terminal_from_result(data)
+            transitions.append((child_ns, status, error))
+            del self._open[child_ns]
+        return transitions
+
+    def _handle_task_result(self, ns: tuple[str, ...], data: dict[str, Any]) -> None:
+        for child_ns, status, error in self._pop_terminal_transitions(ns, data):
+            self._on_terminal(child_ns, status, error)
+
+    def finalize(self) -> None:
+        """Emit `completed` for any tracked namespace still open at run end."""
+        for ns in list(self._open):
+            self._on_terminal(ns, "completed", None)
+        self._open.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Emit terminal status for any tracked namespace still open."""
+        status, error_str = _status_from_exception(err)
+        for ns in list(self._open):
+            self._on_terminal(ns, status, error_str)
+        self._open.clear()
+
+
+def _status_from_exception(err: BaseException) -> tuple[SubgraphStatus, str | None]:
+    """Map a run exception to a subgraph terminal status and error string."""
+    if isinstance(err, GraphDrained):
+        return "drained", None
+    if isinstance(err, GraphInterrupt):
+        return "interrupted", None
+    return "failed", str(err)
+
+
+def _terminal_from_result(
+    payload: dict[str, Any],
+) -> tuple[SubgraphStatus, str | None]:
+    """Map a `TaskResultPayload` to a `(status, error)` pair.
+
+    Order matters: a result with both `error` and `interrupts` prefers
+    the interrupt classification, since `GraphInterrupt` manifests as
+    a populated `interrupts` list, not as `error`.
+    """
+    if payload.get("interrupts"):
+        return "interrupted", None
+    error = payload.get("error")
+    if error:
+        return "failed", str(error)
+    return "completed", None
+
+
+class LifecycleTransformer(_TasksLifecycleBase):
+    """Surface subgraph lifecycle as `lifecycle` protocol events.
+
+    Pushes `LifecyclePayload` to a `StreamChannel` named `lifecycle`.
+    The channel is auto-forwarded by the mux so payloads land in the
+    main event log under `method = "lifecycle"` (native transformer —
+    no `custom:` prefix) — visible to remote SDK clients over the
+    wire and to in-process consumers via `run.lifecycle`.
+
+    Tracks subgraphs at every depth strictly below the transformer's
+    scope, so a graph → subgraph → subgraph chain produces lifecycle
+    events for both nested levels in a flat stream.
+
+    Native transformer — projection key `lifecycle` is exposed as
+    `run.lifecycle`.
+    """
+
+    _native = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._channel: StreamChannel[LifecyclePayload] = StreamChannel("lifecycle")
+
+    def init(self) -> dict[str, Any]:
+        return {"lifecycle": self._channel}
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        depth = len(self.scope)
+        return len(ns) > depth and ns[:depth] == self.scope
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        if trigger_call_id is None:
+            # Without a task id we can't correlate a parent-result
+            # event back to this namespace — skip the started payload
+            # and rely on finalize/fail to close.
+            return
+        payload: LifecyclePayload = {"event": "started", "namespace": list(ns)}
+        if graph_name:
+            payload["graph_name"] = graph_name
+        payload["trigger_call_id"] = trigger_call_id
+        self._channel.push(payload)
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        payload: LifecyclePayload = {"event": status, "namespace": list(ns)}
+        if error is not None:
+            payload["error"] = error
+        self._channel.push(payload)
+
+
+class SubgraphTransformer(_TasksLifecycleBase):
+    """Discover subgraph invocations as in-process navigation handles.
+
+    Per discovered direct-child subgraph, builds a `SubgraphRunStream`
+    (or `AsyncSubgraphRunStream`) wrapping a child mini-mux scoped to
+    the subgraph's namespace. Consumers iterate `run.subgraphs` to
+    receive handles, then drill into `handle.values` / `handle.messages`
+    / `handle.subgraphs` (recursive grandchildren) / `handle.lifecycle`.
+
+    Each mini-mux owns its own scope and uses its own
+    `SubgraphTransformer` to discover its direct children, so
+    grandchildren live on the child handle — never on the root's
+    `subgraphs` log. Forwarding events into the matching child mini-mux
+    is what keeps the child's projections populated.
+
+    Native transformer — `subgraphs` is exposed as `run.subgraphs`.
+    """
+
+    _native = True
+    supports_sync = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[SubgraphRunStream | AsyncSubgraphRunStream] = (
+            StreamChannel()
+        )
+        self._handles: dict[
+            tuple[str, ...], SubgraphRunStream | AsyncSubgraphRunStream
+        ] = {}
+        self._mux: StreamMux | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"subgraphs": self._log}
+
+    def _on_register(self, mux: Any) -> None:
+        self._mux = mux
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        # Direct children only — grandchildren are picked up by the
+        # child mini-mux's own SubgraphTransformer.
+        depth = len(self.scope)
+        return len(ns) == depth + 1 and ns[:depth] == self.scope
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        if self._mux is None:
+            return
+        try:
+            child_mux = self._mux._make_child(ns)
+        except RuntimeError:
+            return
+        handle_cls = AsyncSubgraphRunStream if child_mux.is_async else SubgraphRunStream
+        handle = handle_cls(
+            mux=child_mux,
+            path=ns,
+            graph_name=graph_name,
+            trigger_call_id=trigger_call_id,
+        )
+        self._handles[ns] = handle
+        self._log.push(handle)
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        handle = self._handles.get(ns)
+        if handle is None or not self._mark_terminal(handle, status, error):
+            return
+        self._close_or_fail_handle(handle, status, error)
+
+    async def _aon_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        handle = self._handles.get(ns)
+        if handle is None or not self._mark_terminal(handle, status, error):
+            return
+        await self._aclose_or_fail_handle(handle, status, error)
+
+    def _mark_terminal(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> bool:
+        """Mark a handle terminal once. Returns True on first transition."""
+        if handle._seen_terminal:
+            return False
+        handle.status = status
+        if error is not None and handle.error is None:
+            handle.error = error
+        handle._seen_terminal = True
+        return True
+
+    def _close_or_fail_handle(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        if handle._mux is None or handle._mux._events._closed:
+            return
+        if status == "failed":
+            handle._mux.fail(RuntimeError(error or "Subgraph failed"))
+        else:
+            handle._mux.close()
+
+    async def _aclose_or_fail_handle(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        if handle._mux is None or handle._mux._events._closed:
+            return
+        if status == "failed":
+            await handle._mux.afail(RuntimeError(error or "Subgraph failed"))
+        else:
+            await handle._mux.aclose()
+
+    def _handle_for_event(
+        self, event: ProtocolEvent
+    ) -> SubgraphRunStream | AsyncSubgraphRunStream | None:
+        ns = tuple(event["params"]["namespace"])
+        depth = len(self.scope)
+        if len(ns) < depth + 1:
+            return None
+        handle = self._handles.get(ns[: depth + 1])
+        if handle is None or handle._mux is None or handle._mux._events._closed:
+            return None
+        return handle
+
+    def process(self, event: ProtocolEvent) -> bool:
+        # Run tasks bookkeeping first so a `started` handle exists
+        # by the time we forward the event to the child mini-mux.
+        keep = super().process(event)
+        handle = self._handle_for_event(event)
+        if handle is not None:
+            handle._observe_event(event)
+            handle._mux.push(event)
+        return keep
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        # Async counterpart: repeats the tasks bookkeeping here so
+        # child mini-muxes receive events through their async lane.
+        if event["method"] == "tasks":
+            ns = tuple(event["params"]["namespace"])
+            data = event["params"]["data"]
+            if "result" in data:
+                for child_ns, status, error in self._pop_terminal_transitions(ns, data):
+                    await self._aon_terminal(child_ns, status, error)
+            else:
+                self._handle_task_start(ns)
+            keep = False
+        else:
+            keep = True
+        handle = self._handle_for_event(event)
+        if handle is not None:
+            handle._observe_event(event)
+            await handle._mux.apush(event)
+        return keep
+
+    def _complete_open_handles(self) -> BaseException | None:
+        first_error: BaseException | None = None
+        for ns in list(self._open):
+            try:
+                self._on_terminal(ns, "completed", None)
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        self._open.clear()
+        for handle in self._handles.values():
+            if self._mark_terminal(handle, "completed", None):
+                try:
+                    self._close_or_fail_handle(handle, "completed", None)
+                except BaseException as e:
+                    if first_error is None:
+                        first_error = e
+        return first_error
+
+    async def _acomplete_open_handles(self) -> BaseException | None:
+        first_error: BaseException | None = None
+        for ns in list(self._open):
+            try:
+                await self._aon_terminal(ns, "completed", None)
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        self._open.clear()
+        for handle in self._handles.values():
+            if self._mark_terminal(handle, "completed", None):
+                try:
+                    await self._aclose_or_fail_handle(handle, "completed", None)
+                except BaseException as e:
+                    if first_error is None:
+                        first_error = e
+        return first_error
+
+    def finalize(self) -> None:
+        first_error = self._complete_open_handles()
+        if first_error is not None:
+            raise first_error
+
+    async def afinalize(self) -> None:
+        first_error = await self._acomplete_open_handles()
+        if first_error is not None:
+            raise first_error
+
+    def fail(self, err: BaseException) -> None:
+        status, error_str = _status_from_exception(err)
+        self._open.clear()
+        for handle in self._handles.values():
+            self._mark_terminal(handle, status, error_str)
+            if handle._mux is not None and not handle._mux._events._closed:
+                try:
+                    handle._mux.fail(err)
+                except Exception:
+                    _logger.warning(
+                        "Error failing subgraph mini-mux at %s; "
+                        "subscribers may not see the terminal error.",
+                        handle.path,
+                        exc_info=True,
+                    )
+
+    async def afail(self, err: BaseException) -> None:
+        status, error_str = _status_from_exception(err)
+        self._open.clear()
+        for handle in self._handles.values():
+            self._mark_terminal(handle, status, error_str)
+            if handle._mux is not None and not handle._mux._events._closed:
+                try:
+                    await handle._mux.afail(err)
+                except Exception:
+                    _logger.warning(
+                        "Error failing subgraph mini-mux at %s; "
+                        "subscribers may not see the terminal error.",
+                        handle.path,
+                        exc_info=True,
+                    )
+
+
+class CheckpointsTransformer(StreamTransformer):
+    """Capture checkpoint events as a drainable stream.
+
+    Surfaces `stream_mode="checkpoints"` data on `run.checkpoints` as
+    a `StreamChannel[dict[str, Any]]`. Each item is in the same format
+    as returned by `get_state()`.
+
+    Checkpoint events are only emitted when a checkpointer is configured
+    on the graph. When no checkpointer is present, the projection exists
+    but receives no events.
+
+    Only events at the run's own scope are captured; checkpoint data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.checkpoints` projection.
+
+    Native transformer — `run.checkpoints` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("checkpoints",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"checkpoints": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "checkpoints":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class DebugTransformer(StreamTransformer):
+    """Capture debug events as a drainable stream.
+
+    Surfaces `stream_mode="debug"` data on `run.debug` as a
+    `StreamChannel[dict[str, Any]]`. Each item is a debug event with
+    step-level detail (checkpoint snapshots, task payloads, and
+    task results wrapped with step number and timestamp).
+
+    Only events at the run's own scope are captured; debug data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.debug` projection.
+
+    Native transformer — `run.debug` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("debug",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"debug": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "debug":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class TasksTransformer(StreamTransformer):
+    """Capture raw task events as a drainable stream.
+
+    Surfaces `stream_mode="tasks"` data on `run.tasks` as a
+    `StreamChannel[dict[str, Any]]`. Each item is a task payload
+    (start or result).
+
+    `LifecycleTransformer` and `SubgraphTransformer` also consume
+    `tasks` events for subgraph discovery and lifecycle tracking.
+    This transformer captures the raw payloads independently for
+    consumers who need task-level detail.
+
+    Only events at the run's own scope are captured; task data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.tasks` projection.
+
+    Native transformer — `run.tasks` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"tasks": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "tasks":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True

libs/langgraph/langgraph/types.py

@@ -4,6 +4,7 @@ import sys
 from collections import deque
 from collections.abc import Callable, Hashable, Sequence
 from dataclasses import asdict, dataclass
+from datetime import timedelta
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -67,6 +68,7 @@ __all__ = (
     "CheckpointPayload",
     "DebugPayload",
     "RetryPolicy",
+    "TimeoutPolicy",
     "CachePolicy",
     "Interrupt",
     "StateUpdate",
@@ -423,6 +425,83 @@ class RetryPolicy(NamedTuple):
     """List of exception classes that should trigger a retry, or a callable that returns `True` for exceptions that should trigger a retry."""
 
 
+def _coerce_timeout_seconds(
+    value: float | timedelta | None, *, field: str
+) -> float | None:
+    if value is None:
+        return None
+    seconds = value.total_seconds() if isinstance(value, timedelta) else float(value)
+    if seconds <= 0:
+        raise ValueError(f"{field} must be greater than 0")
+    return seconds
+
+
+@dataclass(**_DC_KWARGS)
+class TimeoutPolicy:
+    """Configuration for timing out node attempts.
+
+    !!! note "Cooperative cancellation"
+
+        Timeouts rely on asyncio cancellation. If your node uses synchronous
+        time.sleep() or other CPU-bound work that blocks the GIL, the timeout will not
+        be fired until after the event loop has been released.
+
+    !!! note "Inline callback dispatch"
+
+        Under `refresh_on="auto"`, an internal handler refreshes the timeout on any
+        callback event that occurs in the execution of the node or its nested descendants.
+    """
+
+    run_timeout: float | timedelta | None = None
+    """Hard wall-clock cap (in seconds) for a single node attempt.
+
+    This timeout is never refreshed by progress signals or `runtime.heartbeat()`.
+    """
+
+    idle_timeout: float | timedelta | None = None
+    """Maximum time (in seconds) a single node attempt may go without observable progress."""
+
+    refresh_on: Literal["auto", "heartbeat"] = "auto"
+    """Which signals refresh `idle_timeout`.
+
+    `"auto"` refreshes on standard graph progress signals and explicit heartbeats.
+    `"heartbeat"` refreshes only on explicit `runtime.heartbeat()` calls.
+    """
+
+    @classmethod
+    def coerce(
+        cls, value: float | timedelta | TimeoutPolicy | None
+    ) -> TimeoutPolicy | None:
+        """Normalize a timeout value to positive-second policy fields."""
+        if value is None:
+            return None
+        if isinstance(value, TimeoutPolicy):
+            # Fast path: a policy already produced by coerce() has float
+            # timeouts and a validated refresh_on, so we can return it as-is.
+            # `frozen=True` makes this safe to share.
+            rt, it = value.run_timeout, value.idle_timeout
+            if (
+                value.refresh_on in ("auto", "heartbeat")
+                and (rt is None or (type(rt) is float and rt > 0))
+                and (it is None or (type(it) is float and it > 0))
+                and (rt is not None or it is not None)
+            ):
+                return value
+        else:
+            value = cls(run_timeout=value)
+        if value.refresh_on not in ("auto", "heartbeat"):
+            raise ValueError("refresh_on must be 'auto' or 'heartbeat'")
+        run_timeout = _coerce_timeout_seconds(value.run_timeout, field="run_timeout")
+        idle_timeout = _coerce_timeout_seconds(value.idle_timeout, field="idle_timeout")
+        if run_timeout is None and idle_timeout is None:
+            return None
+        return cls(
+            run_timeout=run_timeout,
+            idle_timeout=idle_timeout,
+            refresh_on=value.refresh_on,
+        )
+
+
 KeyFuncT = TypeVar("KeyFuncT", bound=Callable[..., str | bytes])
 
 
@@ -548,6 +627,7 @@ class PregelExecutableTask:
     path: tuple[str | int | tuple, ...]
     writers: Sequence[Runnable] = ()
     subgraphs: Sequence[PregelProtocol] = ()
+    timeout: TimeoutPolicy | None = None
 
 
 class StateSnapshot(NamedTuple):
@@ -587,6 +667,8 @@ class Send:
     Attributes:
         node (str): The name of the target node to send the message to.
         arg (Any): The state or message to send to the target node.
+        timeout (TimeoutPolicy | None): Optional timeout policy for this specific
+            pushed task. If omitted, the target node's timeout policy is used.
 
     !!! example
 
@@ -616,33 +698,47 @@ class Send:
         ```
     """
 
-    __slots__ = ("node", "arg")
+    __slots__ = ("node", "arg", "timeout")
 
     node: str
     arg: Any
+    timeout: TimeoutPolicy | None
 
-    def __init__(self, /, node: str, arg: Any) -> None:
+    def __init__(
+        self,
+        /,
+        node: str,
+        arg: Any,
+        *,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
+    ) -> None:
         """
         Initialize a new instance of the `Send` class.
 
         Args:
             node: The name of the target node to send the message to.
             arg: The state or message to send to the target node.
+            timeout: Optional timeout policy for this specific pushed task. A
+                number or `timedelta` is treated as a hard `run_timeout`.
         """
         self.node = node
         self.arg = arg
+        self.timeout = TimeoutPolicy.coerce(timeout)
 
     def __hash__(self) -> int:
-        return hash((self.node, self.arg))
+        return hash((self.node, self.arg, self.timeout))
 
     def __repr__(self) -> str:
-        return f"Send(node={self.node!r}, arg={self.arg!r})"
+        if self.timeout is None:
+            return f"Send(node={self.node!r}, arg={self.arg!r})"
+        return f"Send(node={self.node!r}, arg={self.arg!r}, timeout={self.timeout!r})"
 
     def __eq__(self, value: object) -> bool:
         return (
             isinstance(value, Send)
             and self.node == value.node
             and self.arg == value.arg
+            and self.timeout == value.timeout
         )
 
 

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.2.0a4"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -24,10 +24,10 @@ classifiers = [
     'Programming Language :: Python :: 3.13',
 ]
 dependencies = [
-    "langchain-core==1.3.0a2",
-    "langgraph-checkpoint>=2.1.0,<5.0.0",
+    "langchain-core>=1.4.0a2,<2",
+    "langgraph-checkpoint>=4.1.0a3,<5.0.0",
     "langgraph-sdk>=0.3.0,<0.4.0",
-    "langgraph-prebuilt>=1.0.9,<1.1.0",
+    "langgraph-prebuilt>=1.1.0a1,<1.2.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",
 ]
@@ -38,7 +38,7 @@ Homepage = "https://docs.langchain.com/oss/python/langgraph/overview"
 Documentation = "https://reference.langchain.com/python/langgraph/"
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/langgraph"
 Changelog = "https://github.com/langchain-ai/langgraph/releases"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/langgraph/tests/test_channels.py

@@ -1,18 +1,34 @@
 import operator
 from collections.abc import Sequence
+from typing import Annotated
 
 import pytest
+from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+from langgraph.checkpoint.base import DELTA_SENTINEL
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
+from typing_extensions import NotRequired, TypedDict
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import _messages_delta_reducer
+from langgraph.graph.state import _get_channel
+from langgraph.types import Overwrite
 
 pytestmark = pytest.mark.anyio
 
 
+# ---------------------------------------------------------------------------
+# Core channel primitives
+# ---------------------------------------------------------------------------
+
+
 def test_last_value() -> None:
     channel = LastValue(int).from_checkpoint(MISSING)
     assert channel.ValueType is int
@@ -95,95 +111,57 @@ def test_untracked_value() -> None:
     assert channel.ValueType is dict
     assert channel.UpdateType is dict
 
-    # UntrackedValue should start empty
     with pytest.raises(EmptyChannelError):
         channel.get()
 
-    # Should be able to update with a value
     test_data = {"session": "test", "temp": "dir"}
     channel.update([test_data])
     assert channel.get() == test_data
 
-    # Update with new value
     new_data = {"session": "updated", "temp": "newdir"}
     channel.update([new_data])
     assert channel.get() == new_data
 
-    # On checkpoint, UntrackedValue should return MISSING
     checkpoint = channel.checkpoint()
     assert checkpoint is MISSING
 
-    # Creating from checkpoint with MISSING should start empty
     new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
     with pytest.raises(EmptyChannelError):
         new_channel.get()
 
 
+# ---------------------------------------------------------------------------
+# DeltaChannel — message reducer
+# ---------------------------------------------------------------------------
+
+
 def test_delta_channel_basic_two_steps() -> None:
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.base import DeltaValue
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
 
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
-    ch.after_checkpoint(None)
-
-    # Step 1: one message added
     ch.update([HumanMessage(content="hi", id="h1")])
     d1 = ch.checkpoint()
-    assert isinstance(d1, DeltaValue)
-    assert len(d1.delta) == 1
-    assert d1.prev_checkpoint_id is None  # first ever step
-    ch.after_checkpoint("v1", checkpoint_id="cid1")
+    assert d1 is DELTA_SENTINEL
 
-    # Step 2: another message
     ch.update([AIMessage(content="hello", id="a1")])
     d2 = ch.checkpoint()
-    assert d2.prev_checkpoint_id == "cid1"
-    assert len(d2.delta) == 1
-    ch.after_checkpoint("v2")
+    assert d2 is DELTA_SENTINEL
 
-    # Full accumulated value is preserved in memory
     assert len(ch.get()) == 2
     assert ch.get()[0].content == "hi"
     assert ch.get()[1].content == "hello"
 
 
-def test_delta_channel_after_checkpoint_no_op_when_unchanged() -> None:
-    from langchain_core.messages import HumanMessage
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
-    ch.after_checkpoint(None)
-    ch.update([HumanMessage(content="hi", id="h1")])
-    ch.after_checkpoint("v1")
-
-    # Same version: no-op
-    ch.after_checkpoint("v1")
-    assert ch._base_version == "v1"
-    assert ch._pending == []
-
-
-def test_delta_channel_from_checkpoint_chain() -> None:
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.base import DeltaChainValue
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    spec = DeltaChannel(add_messages)
-    chain = DeltaChainValue(
-        base=None,
-        deltas=[
-            [HumanMessage(content="hi", id="h1")],
-            [AIMessage(content="hello", id="a1")],
-            [HumanMessage(content="bye", id="h2")],
-        ],
+def test_delta_channel_from_checkpoint_writes_list() -> None:
+    """replay_writes on a fresh channel replays through the operator."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", HumanMessage(content="bye", id="h2")),
+        ]
     )
-    ch = spec.from_checkpoint(chain)
     msgs = ch.get()
     assert len(msgs) == 3
     assert msgs[0].content == "hi"
@@ -192,333 +170,483 @@ def test_delta_channel_from_checkpoint_chain() -> None:
 
 
 def test_delta_channel_from_checkpoint_backwards_compat() -> None:
-    from langchain_core.messages import HumanMessage
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    # Old BinaryOperatorAggregate checkpoint: plain list
-    spec = DeltaChannel(add_messages)
+    spec = DeltaChannel(_messages_delta_reducer, list)
     old_value = [HumanMessage(content="old", id="h1")]
     ch = spec.from_checkpoint(old_value)
     assert ch.get() == old_value
 
 
-def test_delta_channel_overwrite_resets_chain() -> None:
-    from langchain_core.messages import HumanMessage
-    from langgraph.checkpoint.base import DeltaValue
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-    from langgraph.types import Overwrite
-
-    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
-    ch.after_checkpoint(None)
+def test_delta_channel_overwrite() -> None:
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
     ch.update([HumanMessage(content="old", id="h1")])
-    ch.after_checkpoint("v1")
 
-    # Overwrite should create a root blob (prev_checkpoint_id=None)
     ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
     d = ch.checkpoint()
-    assert isinstance(d, DeltaValue)
-    assert d.prev_checkpoint_id is None  # chain root
-    assert len(d.delta) == 1
-    assert d.delta[0].content == "new"
+    assert d is DELTA_SENTINEL
+    assert len(ch.get()) == 1
+    assert ch.get()[0].content == "new"
 
 
-def test_delta_channel_assembly_fallback_via_get_tuple() -> None:
-    """Assembly falls back to get_tuple for savers without get_channel_blob."""
-    from unittest.mock import MagicMock
-
-    from langgraph.checkpoint.base import (
-        CheckpointTuple,
-        DeltaChainValue,
-        DeltaValue,
-        empty_checkpoint,
-    )
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-    from langgraph.pregel._checkpoint import _assemble_delta_channels
-
-    msg1 = {"type": "human", "content": "hello"}
-    msg2 = {"type": "ai", "content": "world"}
-
-    cp1 = empty_checkpoint()
-    cp1["id"] = "cp1"
-    cp1["channel_values"]["messages"] = [msg1]
-
-    cp2 = empty_checkpoint()
-    cp2["id"] = "cp2"
-    cp2["channel_values"]["messages"] = DeltaValue(
-        delta=[msg2], prev_checkpoint_id="cp1"
-    )
-
-    saver = MagicMock()
-    saver.get_channel_blob.return_value = NotImplemented
-    saver.get_tuple.return_value = CheckpointTuple(
-        config={
-            "configurable": {
-                "thread_id": "t1",
-                "checkpoint_ns": "",
-                "checkpoint_id": "cp1",
-            }
-        },
-        checkpoint=cp1,
-        metadata={},
-        parent_config=None,
-        pending_writes=[],
-    )
-
-    config = {"configurable": {"thread_id": "t1", "checkpoint_ns": ""}}
-    assembled = _assemble_delta_channels(cp2, config, saver)
-
-    assert "messages" in assembled
-    chain = assembled["messages"]
-    assert isinstance(chain, DeltaChainValue)
-    assert chain.base == [msg1]
-    assert chain.deltas == [[msg2]]
-
-    from langchain_core.messages import AIMessage, HumanMessage
-
-    spec = DeltaChannel(add_messages)
-    ch = spec.from_checkpoint(chain)
-    result = ch.get()
-    assert len(result) == 2
-    assert isinstance(result[0], HumanMessage) and result[0].content == "hello"
-    assert isinstance(result[1], AIMessage) and result[1].content == "world"
-
-
-def test_delta_channel_remove_message_delta_and_replay() -> None:
-    """RemoveMessage stored in a delta must round-trip correctly through the chain."""
-    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
-    from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    spec = DeltaChannel(add_messages)
+def test_delta_channel_remove_message_and_replay() -> None:
+    """RemoveMessage must round-trip correctly when writes are replayed."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
     ch = spec.from_checkpoint(MISSING)
-    ch.after_checkpoint(None)
 
-    # Step 1: add two messages
     ch.update([HumanMessage(content="hi", id="h1")])
     ch.update([AIMessage(content="hello", id="a1")])
-    d1 = ch.checkpoint()
-    assert isinstance(d1, DeltaValue)
-    ch.after_checkpoint("v1", checkpoint_id="cid1")
     assert ch.get() == [
         HumanMessage(content="hi", id="h1"),
         AIMessage(content="hello", id="a1"),
     ]
 
-    # Step 2: remove the AI message
     ch.update([RemoveMessage(id="a1")])
-    d2 = ch.checkpoint()
-    assert isinstance(d2, DeltaValue)
-    assert d2.prev_checkpoint_id == "cid1"
-    assert any(isinstance(w, RemoveMessage) for w in d2.delta)
-    ch.after_checkpoint("v2", checkpoint_id="cid2")
     assert ch.get() == [HumanMessage(content="hi", id="h1")]
 
-    # Replay the full chain from scratch — must reproduce the post-remove state
-    chain = DeltaChainValue(base=None, deltas=[d1.delta, d2.delta])
-    ch2 = spec.from_checkpoint(chain)
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", RemoveMessage(id="a1")),
+        ]
+    )
     assert ch2.get() == [HumanMessage(content="hi", id="h1")]
 
 
-def test_delta_channel_update_by_id_delta_and_replay() -> None:
-    """Updating a message by ID stored in a delta must round-trip correctly."""
-    from langchain_core.messages import HumanMessage
-    from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    spec = DeltaChannel(add_messages)
+def test_delta_channel_update_by_id_and_replay() -> None:
+    """Updating a message by ID must round-trip correctly through writes replay."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
     ch = spec.from_checkpoint(MISSING)
-    ch.after_checkpoint(None)
 
-    # Step 1: add a message
     ch.update([HumanMessage(content="original", id="h1")])
-    d1 = ch.checkpoint()
-    assert isinstance(d1, DeltaValue)
-    ch.after_checkpoint("v1", checkpoint_id="cid1")
-
-    # Step 2: update the same message by ID
     ch.update([HumanMessage(content="updated", id="h1")])
-    d2 = ch.checkpoint()
-    assert isinstance(d2, DeltaValue)
-    assert d2.prev_checkpoint_id == "cid1"
-    ch.after_checkpoint("v2", checkpoint_id="cid2")
     assert ch.get() == [HumanMessage(content="updated", id="h1")]
 
-    # Replay the full chain — must produce the updated message, not the original
-    chain = DeltaChainValue(base=None, deltas=[d1.delta, d2.delta])
-    ch2 = spec.from_checkpoint(chain)
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="original", id="h1")),
+            ("t1", "messages", HumanMessage(content="updated", id="h1")),
+        ]
+    )
     assert len(ch2.get()) == 1
     assert ch2.get()[0].content == "updated"
 
 
-def test_delta_channel_snapshot_every_emits_plain_list() -> None:
-    """snapshot_every=N causes a plain-list snapshot after N steps; next deltas chain to it."""
-    from langchain_core.messages import HumanMessage
-    from langgraph.checkpoint.base import DeltaValue
+def test_delta_channel_checkpoint_returns_sentinel() -> None:
+    """checkpoint() always returns DELTA_SENTINEL regardless of state."""
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
+    assert ch.checkpoint() is DELTA_SENTINEL
 
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    SNAP = 3
-    spec = DeltaChannel(add_messages, snapshot_every=SNAP)
-    ch = spec.from_checkpoint(MISSING)
-    # First after_checkpoint anchors _base_version without counting a step.
-    ch.after_checkpoint("v0", checkpoint_id="cid0")
-
-    # Steps 1..SNAP: each should stay as DeltaValue; counter increments each step.
-    for i in range(1, SNAP + 1):
-        ch.update([HumanMessage(content=f"m{i}", id=f"h{i}")])
-        ckpt = ch.checkpoint()
-        assert isinstance(ckpt, DeltaValue), f"expected DeltaValue at step {i}"
-        ch.after_checkpoint(f"v{i}", checkpoint_id=f"cid{i}")
-
-    # Step SNAP+1: _steps_since_snapshot == SNAP → snapshot fires
-    ch.update([HumanMessage(content="snap", id="hsnap")])
-    snap = ch.checkpoint()
-    assert isinstance(snap, list), "expected plain-list snapshot at snapshot_every step"
-    assert len(snap) == SNAP + 1
-
-    # After snapshot, counter resets — next step is DeltaValue again
-    ch.after_checkpoint("vsnap", checkpoint_id="cidsnap")
-    ch.update([HumanMessage(content="post", id="hpost")])
-    post = ch.checkpoint()
-    assert isinstance(post, DeltaValue)
-    assert post.prev_checkpoint_id == "cidsnap"
+    ch.update([HumanMessage(content="hi", id="h1")])
+    assert ch.checkpoint() is DELTA_SENTINEL
 
 
-def test_delta_channel_snapshot_every_end_to_end() -> None:
-    """Graph with snapshot_every: get_state returns correct accumulated value after snapshot."""
-    from typing import Annotated
+# ---------------------------------------------------------------------------
+# DeltaChannel — snapshot frequency
+# ---------------------------------------------------------------------------
 
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-    from typing_extensions import TypedDict
 
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
+def test_delta_channel_snapshot_step_based() -> None:
+    """Snapshots fire on every Nth step regardless of whether the channel was written.
+
+    With snapshot_frequency=N, every Nth pregel step produces a _DeltaSnapshot
+    blob — even if the channel had no write that step (eager snapshot). This
+    bounds the ancestor walk to at most N steps on any read.
+    """
 
     class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages, snapshot_every=2)]
+        messages: Annotated[
+            list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=5)
+        ]
+        other: str
 
-    counter = {"n": 0}
+    def node_a(state: State) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
+
+    def node_b(state: State) -> dict:
+        return {"other": "y"}
+
+    g = StateGraph(State)
+    g.add_node("a", node_a)
+    g.add_node("b", node_b)
+    g.add_edge(START, "a")
+    g.add_edge("a", "b")
+    saver = InMemorySaver()
+    graph = g.compile(checkpointer=saver)
+
+    config = {"configurable": {"thread_id": "t1"}}
+    for i in range(6):
+        graph.invoke(
+            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "other": ""},
+            config,
+        )
+
+    msg_blob_values = [
+        saver.serde.loads_typed((type_tag, blob))
+        for k, (type_tag, blob) in saver.blobs.items()
+        if k[2] == "messages" and type_tag == "msgpack" and blob
+    ]
+    snapshots = [v for v in msg_blob_values if isinstance(v, _DeltaSnapshot)]
+    assert snapshots, "expected at least one _DeltaSnapshot blob for messages"
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 12  # 6 human + 6 AI
+
+
+def test_delta_channel_snapshot_fires_even_when_not_written() -> None:
+    """Eager snapshot: _DeltaSnapshot stored at snapshot step even when the
+    channel had no write that step (node_b doesn't touch messages).
+    """
+
+    class State(TypedDict):
+        messages: Annotated[
+            list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=3)
+        ]
+        tick: int
+
+    def writer(state: State) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
+
+    def ticker(state: State) -> dict:
+        return {"tick": state["tick"] + 1}
+
+    g = StateGraph(State)
+    g.add_node("writer", writer)
+    g.add_node("ticker", ticker)
+    g.add_edge(START, "writer")
+    g.add_edge("writer", "ticker")
+    saver = InMemorySaver()
+    graph = g.compile(checkpointer=saver)
+
+    config = {"configurable": {"thread_id": "t1"}}
+    for i in range(5):
+        graph.invoke(
+            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "tick": 0},
+            config,
+        )
+
+    msg_blobs = {
+        k: saver.serde.loads_typed((t, b))
+        for k, (t, b) in saver.blobs.items()
+        if k[2] == "messages" and t == "msgpack" and b
+    }
+    snapshots = {k: v for k, v in msg_blobs.items() if isinstance(v, _DeltaSnapshot)}
+    assert snapshots, (
+        "eager snapshots must fire even on steps where messages wasn't written"
+    )
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 10  # 5 human + 5 AI
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — end-to-end (InMemorySaver)
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_inmemory_saver_assembles_writes() -> None:
+    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer, list)]
+
+    n = {"v": 0}
 
     def respond(state: State) -> dict:
-        counter["n"] += 1
-        return {
-            "messages": [
-                AIMessage(content=f"ai-{counter['n']}", id=f"ai-{counter['n']}")
-            ]
-        }
+        n["v"] += 1
+        return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
 
     builder = StateGraph(State)
     builder.add_node("respond", respond)
     builder.add_edge(START, "respond")
-    graph = builder.compile(checkpointer=InMemorySaver())
-    config = {"configurable": {"thread_id": "snap-test"}}
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "t1"}}
 
-    # Run 5 turns — snapshot fires after 2 steps, then again after 2 more
-    for i in range(5):
-        graph.invoke({"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config)
+    graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
+    graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
+
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "messages" not in saved.checkpoint["channel_values"]
 
     state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # 5 human + 5 AI = 10 total
-    assert len(msgs) == 10, f"expected 10 messages, got {len(msgs)}: {msgs}"
+    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
 
 
-def test_delta_channel_assembly_fast_path_returns_delta_value() -> None:
-    """get_channel_blob returning a DeltaValue continues chain traversal (fast-path)."""
-    from unittest.mock import MagicMock
+# ---------------------------------------------------------------------------
+# DeltaChannel — dict reducer
+# ---------------------------------------------------------------------------
 
-    from langgraph.checkpoint.base import (
-        DeltaChainValue,
-        DeltaValue,
-        empty_checkpoint,
+
+def _delta_channel_with_type(op, typ):
+    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
+    return _get_channel("_test", Annotated[typ, DeltaChannel(op)])
+
+
+def test_delta_channel_dict_reducer_fresh_channel() -> None:
+    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    assert ch.is_available()
+    assert ch.get() == {}
+
+
+def test_delta_channel_dict_reducer_basic_updates() -> None:
+    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+
+    ch.update([{"a": 1}])
+    d1 = ch.checkpoint()
+    assert d1 is DELTA_SENTINEL
+
+    ch.update([{"b": 2}])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
+    """replay_writes on a fresh channel replays through a dict merge reducer."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", {"b": 2}),
+            ("t2", "files", {"c": 3}),
+        ]
     )
+    assert ch.get() == {"a": 1, "b": 2, "c": 3}
 
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-    from langgraph.pregel._checkpoint import _assemble_delta_channels
 
-    msg1 = {"type": "human", "content": "one"}
-    msg2 = {"type": "ai", "content": "two"}
-    msg3 = {"type": "human", "content": "three"}
+def test_delta_channel_dict_reducer_with_deletions() -> None:
+    """Dict reducer that treats None values as deletions works end-to-end."""
 
-    # cp3 → cp2 (DeltaValue) → cp1 (base list)
-    dv_cp2 = DeltaValue(delta=[msg2], prev_checkpoint_id="cp1")
-    cp3 = empty_checkpoint()
-    cp3["id"] = "cp3"
-    cp3["channel_values"]["messages"] = DeltaValue(
-        delta=[msg3], prev_checkpoint_id="cp2"
+    def merge_files(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            for k, v in w.items():
+                if v is None:
+                    result.pop(k, None)
+                else:
+                    result[k] = v
+        return result
+
+    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
+    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
+    ch.update([{"file1.py": None, "file3.py": "content3"}])
+    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+    spec = _delta_channel_with_type(merge_files, dict)
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
+            ("t1", "files", {"file1.py": None, "file3.py": "content3"}),
+        ]
     )
-
-    saver = MagicMock()
-
-    def _get_blob(thread_id, ns, checkpoint_id, channel):
-        if checkpoint_id == "cp2":
-            return dv_cp2  # DeltaValue — chain continues
-        if checkpoint_id == "cp1":
-            return [msg1]  # plain list — chain root
-        return NotImplemented
-
-    saver.get_channel_blob.side_effect = _get_blob
-
-    config = {"configurable": {"thread_id": "t1", "checkpoint_ns": ""}}
-    assembled = _assemble_delta_channels(cp3, config, saver)
-
-    chain = assembled["messages"]
-    assert isinstance(chain, DeltaChainValue)
-    assert chain.base == [msg1]
-    assert chain.deltas == [[msg2], [msg3]]
-
-    spec = DeltaChannel(add_messages)
-    ch = spec.from_checkpoint(chain)
-    # add_messages converts dicts to message objects; check by type and content
-
-    result = ch.get()
-    assert len(result) == 3
-    assert result[0].content == "one"
-    assert result[1].content == "two"
-    assert result[2].content == "three"
+    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
 
 
-def test_delta_channel_assembly_broken_chain_logs_warning() -> None:
-    """If a prev_checkpoint_id points to a missing checkpoint, log a warning and use partial chain."""
-    from unittest.mock import MagicMock
+def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
+    """Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
 
-    from langgraph.checkpoint.base import DeltaValue, empty_checkpoint
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
 
-    from langgraph.pregel._checkpoint import _assemble_delta_channels
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    ch.update([{"a": 1}])
+    ch.update([Overwrite({"b": 2, "c": 3})])
+    assert ch.get() == {"b": 2, "c": 3}
 
-    cp = empty_checkpoint()
-    cp["id"] = "cp2"
-    cp["channel_values"]["messages"] = DeltaValue(
-        delta=["msg2"], prev_checkpoint_id="cp-missing"
+
+def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
+    """Overwrite(dict) embedded in replayed writes must reconstruct as dict."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", Overwrite({"x": 10, "y": 20})),
+            ("t2", "files", {"z": 30}),
+        ]
     )
+    assert ch.get() == {"x": 10, "y": 20, "z": 30}
 
-    saver = MagicMock()
-    saver.get_channel_blob.return_value = NotImplemented
-    saver.get_tuple.return_value = None  # checkpoint not found
 
-    config = {"configurable": {"thread_id": "t1", "checkpoint_ns": ""}}
+def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
+    """DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`."""
 
-    assembled = _assemble_delta_channels(cp, config, saver)
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
 
-    # Should still assemble — with partial chain (just the current delta, base=None)
-    assert "messages" in assembled
-    from langgraph.checkpoint.base import DeltaChainValue
+    annotation = Annotated[NotRequired[dict[str, int]], DeltaChannel(merge_dicts)]
+    ch = _get_channel("files", annotation).from_checkpoint(MISSING)
+    assert ch.get() == {}
+    ch.update([{"a": 1}])
+    ch.update([{"b": 2}])
+    assert ch.get() == {"a": 1, "b": 2}
 
-    chain = assembled["messages"]
-    assert isinstance(chain, DeltaChainValue)
-    assert chain.base is None
-    assert chain.deltas == [["msg2"]]
+
+def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
+    """End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel."""
+
+    def merge_files(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            for k, v in w.items():
+                if v is None:
+                    result.pop(k, None)
+                else:
+                    result[k] = v
+        return result
+
+    class State(TypedDict):
+        files: Annotated[dict[str, str], DeltaChannel(merge_files)]
+
+    turn = {"v": 0}
+
+    def write_file(state: State) -> dict:
+        turn["v"] += 1
+        n = turn["v"]
+        return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
+
+    builder = StateGraph(State)
+    builder.add_node("write_file", write_file)
+    builder.add_edge(START, "write_file")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "fs"}}
+
+    for _ in range(3):
+        graph.invoke({"files": {}}, config)
+
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "files" not in saved.checkpoint["channel_values"]
+    state = graph.get_state(config)
+    assert state.values["files"] == {
+        "/doc_1.txt": "content for turn 1",
+        "/doc_2.txt": "content for turn 2",
+        "/doc_3.txt": "content for turn 3",
+    }
+
+    def delete_file(state: State) -> dict:
+        return {"files": {"/doc_1.txt": None}}
+
+    builder2 = StateGraph(State)
+    builder2.add_node("write_file", write_file)
+    builder2.add_node("delete_file", delete_file)
+    builder2.add_edge(START, "write_file")
+    builder2.add_edge("write_file", "delete_file")
+    turn["v"] = 0
+    saver2 = InMemorySaver()
+    graph2 = builder2.compile(checkpointer=saver2)
+    config2 = {"configurable": {"thread_id": "fs2"}}
+    graph2.invoke({"files": {}}, config2)
+    state2 = graph2.get_state(config2)
+    assert state2.values["files"] == {}
+
+
+def test_delta_channel_dict_reducer_backwards_compat() -> None:
+    """A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    old_value = {"a": 1, "b": 2}
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — seed / pre-delta migration
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_from_checkpoint_honors_seed() -> None:
+    """A non-sentinel value to from_checkpoint is used as the pre-delta seed.
+
+    Guards the pre-delta migration path: when the saver's ancestor walk hits
+    a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
+    the post-migration state correctly rather than replaying from empty.
+    """
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    seed = [HumanMessage(content="pre-delta", id="p1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes(
+        [
+            ("t0", "messages", AIMessage(content="delta-1", id="d1")),
+            ("t1", "messages", HumanMessage(content="delta-2", id="d2")),
+        ]
+    )
+    msgs = ch.get()
+    assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
+
+
+def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
+    """Reconstruction at a pre-delta ancestor with no newer deltas returns
+    just the seed — the saver's terminator fired immediately."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    seed = [HumanMessage(content="only-snap", id="s1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes([])
+    assert ch.get() == seed
+
+
+def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
+    """`seed=None` must start replay from None, not from an empty channel.
+
+    The DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
+    explicitly should feed None to the reducer as the left operand.
+    """
+
+    def replace(state, writes):
+        return writes[-1] if writes else state
+
+    spec = DeltaChannel(replace, list)
+    ch = spec.from_checkpoint(None)
+    ch.replay_writes([("t0", "x", "after")])
+    assert ch.get() == "after"

libs/langgraph/tests/test_delta_channel_benchmark.py

@@ -1,37 +1,46 @@
-"""Benchmark: DeltaChannel vs BinaryOperatorAggregate storage and time.
+"""Benchmark: DeltaChannel snapshot_frequency — storage vs. read-depth tradeoff.
 
 Run directly:  python tests/test_delta_channel_benchmark.py
 Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
 
-Simulates realistic multi-turn conversations with paragraph-length messages
-(~100 tokens each) scaling up to 1M-token-equivalent histories.
+Part 1 — baseline (original): DeltaChannel(inf) vs add_messages (BinOp).
+Part 2 — snapshot_frequency sweep: shows the storage/read-latency tradeoff
+  across frequencies [1, 5, 10, 50, inf] at scale.
 
-Token estimates: 1 token ≈ 4 chars; each turn ≈ 200 tokens (human + AI).
-A 1M-token conversation ≈ 5,000 turns of realistic messages.
+Key insight:
+  snapshot_frequency=inf  → O(N) storage, O(N) read depth (pure delta)
+  snapshot_frequency=N    → O(N²/N) storage, O(N) read depth bounded by freq
+  snapshot_frequency=1    → O(N²) storage, O(1) read depth (full snapshot)
 """
 
 from __future__ import annotations
 
+import contextlib
+import math
+import os
 import sys
 import time
 from typing import Annotated, Any
 
+import pytest
 from langchain_core.messages import AIMessage, HumanMessage
 from langgraph.checkpoint.memory import MemorySaver
 from typing_extensions import TypedDict
 
 from langgraph.channels.delta import DeltaChannel
 from langgraph.graph import END, StateGraph
-from langgraph.graph.message import add_messages
+from langgraph.graph.message import _messages_delta_reducer, add_messages
 
 try:
-    from langgraph.checkpoint.sqlite import SqliteSaver
+    from langgraph.checkpoint.postgres import PostgresSaver
 
-    _SQLITE_AVAILABLE = True
+    _POSTGRES_AVAILABLE = True
+    _POSTGRES_URI = os.environ.get(
+        "LANGGRAPH_BENCH_POSTGRES_URI",
+        "postgres://postgres@localhost:5432/postgres?sslmode=disable",
+    )
 except ImportError:
-    _SQLITE_AVAILABLE = False
-
-SNAPSHOT_EVERY = 50
+    _POSTGRES_AVAILABLE = False
 
 # ---------------------------------------------------------------------------
 # Realistic message payload (~100 tokens / ~400 chars each)
@@ -40,7 +49,9 @@ SNAPSHOT_EVERY = 50
 _HUMAN_TEMPLATE = (
     "I need help understanding the implications of {topic} on our system architecture. "
     "Specifically, I'm concerned about how this interacts with our existing {concern} "
-    "and whether we need to refactor the {component} layer before proceeding."
+    "and whether we need to refactor the {component} layer before proceeding. "
+    "We've had prior incidents in this area and want to be deliberate. "
+    "What should we prioritize first, and are there known failure modes we should design around from the start?"
 )
 
 _AI_TEMPLATE = (
@@ -107,11 +118,21 @@ class BinaryState(TypedDict):
 
 
 class DeltaState(TypedDict):
-    messages: Annotated[list, DeltaChannel(add_messages)]
+    messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
 
 
-class DeltaSnapshotState(TypedDict):
-    messages: Annotated[list, DeltaChannel(add_messages, snapshot_every=SNAPSHOT_EVERY)]
+def _make_delta_state(snapshot_frequency: int | float) -> type:
+    """Create a TypedDict with DeltaChannel at the given snapshot_frequency."""
+    channel = DeltaChannel(
+        _messages_delta_reducer, snapshot_frequency=snapshot_frequency
+    )
+    # Use the functional TypedDict form so the Annotated type is stored as an
+    # already-evaluated object rather than a forward-reference string (which
+    # would fail when get_type_hints tries to resolve 'snapshot_frequency').
+    return TypedDict(  # type: ignore[return-value]
+        f"DeltaState_freq{snapshot_frequency}",
+        {"messages": Annotated[list, channel]},
+    )
 
 
 # ---------------------------------------------------------------------------
@@ -157,9 +178,8 @@ def _run_turns(
     """Run n_turns conversation turns.
 
     Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
-    blob_bytes is -1 for savers without in-memory blob stores (e.g. SQLite).
-    Read latency is measured as the time to invoke the graph with no new
-    messages after the full history is built — this forces state rehydration.
+    Read latency is the average of 5 get_state calls after the full history
+    is built — forces state rehydration including ancestor replay if needed.
     """
     graph = _make_graph(state_cls, checkpointer)
     config = {"configurable": {"thread_id": "bench"}}
@@ -172,16 +192,16 @@ def _run_turns(
         )
     write_elapsed = time.perf_counter() - t0
 
-    # Measure read/rehydration: get_state forces the channel to rebuild
     t1 = time.perf_counter()
     for _ in range(5):
         graph.get_state(config)
     read_elapsed = (time.perf_counter() - t1) / 5
 
-    if isinstance(graph.checkpointer, MemorySaver):
-        blob_bytes = _total_blob_bytes(graph.checkpointer)
-    else:
-        blob_bytes = -1
+    blob_bytes = (
+        _total_blob_bytes(graph.checkpointer)
+        if isinstance(graph.checkpointer, MemorySaver)
+        else -1
+    )
     return write_elapsed, read_elapsed, blob_bytes
 
 
@@ -194,7 +214,6 @@ def _fmt_bytes(n: int) -> str:
 
 
 def _approx_tokens(n_turns: int) -> str:
-    # ~100 tokens human + ~100 tokens AI per turn
     tokens = n_turns * 200
     if tokens >= 1_000_000:
         return f"~{tokens / 1_000_000:.1f}M tok"
@@ -204,146 +223,248 @@ def _approx_tokens(n_turns: int) -> str:
 
 
 # ---------------------------------------------------------------------------
-# Benchmark matrix
+# Checkpointer factories
 # ---------------------------------------------------------------------------
 
-# Turn counts chosen to span from a short session to a long-running agent conversation.
-# Storage and time complexity differences are clearly visible by 500 turns.
-# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
-TURN_COUNTS = [50, 100, 200, 500]
+
+@contextlib.contextmanager
+def _pg_saver(thread_id: str = "bench"):
+    """Context manager that yields a fresh PostgresSaver and cleans up after."""
+    with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
+        saver.setup()
+        with saver._cursor() as cur:
+            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
+                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
+        yield saver
+        with saver._cursor() as cur:
+            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
+                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
 
 
-def _checkpointer_factories() -> list[tuple[str, Any]]:
-    """Return (label, context_manager_or_none) pairs for available checkpointers."""
-    factories: list[tuple[str, Any]] = [("InMemory", None)]
-    if _SQLITE_AVAILABLE:
-        import tempfile
+def _checkpointers() -> list[tuple[str, Any]]:
+    """Return (label, saver_or_None) pairs for available checkpointers."""
+    result: list[tuple[str, Any]] = [("InMemory", None)]
+    if _POSTGRES_AVAILABLE:
+        try:
+            import psycopg
 
-        factories.append(("SQLite", tempfile.NamedTemporaryFile(suffix=".db")))
-    return factories
+            psycopg.connect(_POSTGRES_URI).close()
+            result.append(("Postgres", "postgres"))
+        except Exception:
+            pass
+    return result
 
 
-def run_benchmark() -> None:
-    print()
-    print(
-        "DeltaChannel vs add_messages (BinaryOperatorAggregate) — checkpoint storage & latency"
-    )
-    print("Simulating realistic multi-turn conversations up to ~1M-token histories")
-    print("(5,000 turns × ~200 tokens/turn ≈ 1M tokens — Claude's full context window)")
-    print()
+# ---------------------------------------------------------------------------
+# Part 1: baseline DeltaChannel(inf) vs add_messages
+# ---------------------------------------------------------------------------
 
-    checkpointers: list[tuple[str, Any]] = [("InMemory (fast-path)", None)]
-    if _SQLITE_AVAILABLE:
-        checkpointers.append(("SQLite (get_tuple fallback)", "sqlite"))
-
-    for cp_label, cp_hint in checkpointers:
-        print(f"--- Checkpointer: {cp_label} ---")
-        _run_benchmark_for_checkpointer(cp_hint)
+BASELINE_TURN_COUNTS = [10, 25, 50, 100, 500]
+DELTA_ONLY_TURN_COUNTS = [1000]
 
 
-def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
-    import contextlib
-    import tempfile
+def _run_baseline_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
+    W = 72
 
-    @contextlib.contextmanager
     def _make_saver():
         if cp_hint is None:
-            yield None
-        else:
-            with tempfile.NamedTemporaryFile(suffix=".db") as f:
-                with SqliteSaver.from_conn_string(f.name) as saver:
-                    yield saver
+            return contextlib.nullcontext(None)
+        return _pg_saver()
 
-    W = 120
-    print("=" * W)
-    header = (
-        f"{'turns':>6}  {'ctx size':>10}  "
-        f"{'add_msgs (bytes)':>18}  {'delta (bytes)':>15}  {'delta+snap (bytes)':>18}  "
-        f"{'storage saved':>14}  "
-        f"{'read: add_msgs':>14}  {'read: delta+snap':>16}"
-    )
-    print(header)
-    print("-" * W)
-
-    results = []
-    for turns in TURN_COUNTS:
+    rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
+    for turns in BASELINE_TURN_COUNTS:
         with _make_saver() as saver:
             b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
         with _make_saver() as saver:
             d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt))
+    for turns in DELTA_ONLY_TURN_COUNTS:
         with _make_saver() as saver:
-            s_wt, s_rt, s_bytes = _run_turns(turns, DeltaSnapshotState, saver)
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, None, d_bytes, None, d_rt, None, d_wt))
 
-        # For non-InMemory savers, blob_bytes are unavailable (-1); use read times only
-        if b_bytes < 0 or s_bytes < 0:
-            b_bytes_str = "n/a"
-            d_bytes_str = "n/a"
-            s_bytes_str = "n/a"
-            storage_ratio_str = "n/a"
+    def _bytes_or_na(v: Any) -> str:
+        if v is None or v < 0:
+            return "n/a"
+        return _fmt_bytes(v)
+
+    def _ms_or_na(v: Any) -> str:
+        return "n/a" if v is None else f"{v * 1000:.1f}ms"
+
+    print(f"\n  [{cp_label}] Storage (blob bytes)")
+    print(
+        f"  {'turns':>6}  {'ctx':>10}  {'add_msgs':>12}  {'delta(inf)':>12}  {'savings':>8}"
+    )
+    print("  " + "-" * (W - 2))
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        if b_bytes is None or b_bytes < 0 or d_bytes is None or d_bytes < 0:
+            ratio_str = "n/a"
         else:
-            storage_ratio = b_bytes / s_bytes if s_bytes else float("inf")
-            b_bytes_str = _fmt_bytes(b_bytes)
-            d_bytes_str = _fmt_bytes(d_bytes)
-            s_bytes_str = _fmt_bytes(s_bytes)
-            storage_ratio_str = f"{storage_ratio:.1f}x"
-            results.append((turns, b_bytes, s_bytes, b_rt, s_rt, storage_ratio))
-
+            ratio = b_bytes / d_bytes if d_bytes else float("inf")
+            ratio_str = f"{ratio:.0f}x"
         print(
-            f"{turns:>6}  {_approx_tokens(turns):>10}  "
-            f"{b_bytes_str:>18}  {d_bytes_str:>15}  {s_bytes_str:>18}  "
-            f"{storage_ratio_str:>14}  "
-            f"{b_rt * 1000:>12.1f}ms  {s_rt * 1000:>14.1f}ms"
+            f"  {turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_bytes_or_na(b_bytes):>12}  {_bytes_or_na(d_bytes):>12}  {ratio_str:>8}"
         )
 
-    print("=" * W)
+    print(f"\n  [{cp_label}] Read latency (avg of 5 get_state calls)")
+    print(f"  {'turns':>6}  {'ctx':>10}  {'add_msgs':>12}  {'delta(inf)':>12}")
+    print("  " + "-" * (W - 2))
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        print(
+            f"  {turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_ms_or_na(b_rt):>12}  {_ms_or_na(d_rt):>12}"
+        )
+
+
+def run_baseline_benchmark() -> None:
+    print()
+    print("Part 1 — DeltaChannel(inf) vs add_messages: storage & latency")
+    print("=" * 72)
+    for cp_label, cp_hint in _checkpointers():
+        _run_baseline_for_checkpointer(cp_label, cp_hint)
     print()
 
-    if results:
-        best = results[-1]
-        turns, b_bytes, s_bytes, b_rt, s_rt, ratio = best
-        print(f"Key findings at max scale ({turns} turns):")
-        print(
-            f"  Storage:  {_fmt_bytes(b_bytes)} (add_messages)  →  {_fmt_bytes(s_bytes)} (DeltaChannel+snapshot) — {ratio:.0f}x reduction"
-        )
-        print(
-            f"  Read latency: {b_rt * 1000:.1f}ms (add_messages)  vs  {s_rt * 1000:.1f}ms (DeltaChannel+snapshot)"
-        )
-        print()
+
+# ---------------------------------------------------------------------------
+# Part 2: snapshot_frequency sweep
+# ---------------------------------------------------------------------------
+
+# Frequencies to test. 1 = always snapshot (like BinOp), inf = pure delta.
+SNAPSHOT_FREQUENCIES: list[int | float] = [1, 5, 10, 50, math.inf]
+
+# Turn counts for the sweep — high enough to show storage divergence.
+SWEEP_TURN_COUNTS = [50, 100, 500]
+
+
+def _freq_label(freq: int | float) -> str:
+    if freq == math.inf:
+        return "inf"
+    return str(int(freq))
+
+
+def _run_sweep_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
+    def _make_saver():
+        if cp_hint is None:
+            return contextlib.nullcontext(None)
+        return _pg_saver()
+
+    # Collect results: {turns: {freq_label: (write_s, read_s, bytes)}}
+    results: dict[int, dict[str, tuple[float, float, int]]] = {}
+    for turns in SWEEP_TURN_COUNTS:
+        results[turns] = {}
+        for freq in SNAPSHOT_FREQUENCIES:
+            state_cls = _make_delta_state(freq)
+            with _make_saver() as saver:
+                wt, rt, bb = _run_turns(turns, state_cls, saver)
+            results[turns][_freq_label(freq)] = (wt, rt, bb)
+
+    freq_labels = [_freq_label(f) for f in SNAPSHOT_FREQUENCIES]
+    col_w = 12
+
+    header = f"  {'turns':>6}  {'ctx':>10}" + "".join(
+        f"  {f'freq={freq_label}':>{col_w}}" for freq_label in freq_labels
+    )
+
+    print(f"\n  [{cp_label}] Storage (blob bytes) — lower is better")
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for turns in SWEEP_TURN_COUNTS:
+        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
+        for label in freq_labels:
+            _, _, bb = results[turns][label]
+            row += f"  {_fmt_bytes(bb) if bb >= 0 else 'n/a':>{col_w}}"
+        print(row)
+
+    print(f"\n  [{cp_label}] Read latency (avg of 5 get_state) — lower is better")
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for turns in SWEEP_TURN_COUNTS:
+        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
+        for label in freq_labels:
+            _, rt, _ = results[turns][label]
+            row += f"  {f'{rt * 1000:.1f}ms':>{col_w}}"
+        print(row)
+
+    print(
+        f"\n  [{cp_label}] Per-invoke write latency (total / turns) — lower is better"
+    )
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for turns in SWEEP_TURN_COUNTS:
+        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
+        for label in freq_labels:
+            wt, _, _ = results[turns][label]
+            row += f"  {f'{(wt / turns) * 1000:.1f}ms':>{col_w}}"
+        print(row)
+
+
+def run_snapshot_freq_benchmark() -> None:
+    print()
+    print("Part 2 — DeltaChannel snapshot_frequency sweep")
+    print("Lower freq → fewer snapshots → less storage but deeper read replay")
+    print("=" * 80)
+    for cp_label, cp_hint in _checkpointers():
+        _run_sweep_for_checkpointer(cp_label, cp_hint)
+    print()
     print("Legend:")
     print(
-        "  add_msgs      = Annotated[list, add_messages]  — current default, O(N²) storage"
-    )
-    print(
-        "  delta         = DeltaChannel(add_messages)     — O(N) storage, unbounded chain at read"
-    )
-    print(
-        f"  delta+snap    = DeltaChannel(add_messages, snapshot_every={SNAPSHOT_EVERY})  — O(N) storage, O(1) read depth"
+        "  freq=1    snapshot every write (full blob always — same as add_messages / BinOp)"
     )
+    print("  freq=N    snapshot every N writes; read walks at most N ancestor writes")
+    print("  freq=inf  pure delta; read walks entire ancestor chain")
     print()
 
 
 # ---------------------------------------------------------------------------
-# Pytest entry point
+# Pytest entry points
 # ---------------------------------------------------------------------------
 
 
-def test_delta_channel_benchmark(capsys: Any) -> None:
-    """Storage grows O(N²) for add_messages, O(N) for DeltaChannel."""
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+def test_delta_channel_baseline_benchmark(capsys: Any) -> None:
+    """DeltaChannel(inf) uses less storage than add_messages at scale."""
     with capsys.disabled():
-        run_benchmark()
+        run_baseline_benchmark()
 
-    # Correctness assertion: DeltaChannel must use less storage at scale.
-    for turns in [100, 200]:
+    for turns in [25, 50]:
         _, _, b_bytes = _run_turns(turns, BinaryState)
         _, _, d_bytes = _run_turns(turns, DeltaState)
-        _, _, s_bytes = _run_turns(turns, DeltaSnapshotState)
         assert d_bytes < b_bytes, (
             f"DeltaChannel should use less storage at {turns} turns, "
             f"got delta={d_bytes} binary={b_bytes}"
         )
-        assert s_bytes < b_bytes, (
-            f"DeltaChannel+snapshot should use less storage at {turns} turns, "
-            f"got snapshot={s_bytes} binary={b_bytes}"
+
+
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+def test_snapshot_freq_benchmark(capsys: Any) -> None:
+    """snapshot_frequency trades storage for bounded read depth."""
+    with capsys.disabled():
+        run_snapshot_freq_benchmark()
+
+    # Correctness: results at all frequencies should agree on final state.
+    n_turns = 20
+    states: dict[str, list] = {}
+    for freq in SNAPSHOT_FREQUENCIES:
+        state_cls = _make_delta_state(freq)
+        graph = _make_graph(state_cls)
+        config = {"configurable": {"thread_id": "correctness"}}
+        for i in range(n_turns):
+            graph.invoke(
+                {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
+                config,
+            )
+        state = graph.get_state(config)
+        states[_freq_label(freq)] = [m.id for m in state.values["messages"]]
+
+    ref = states["inf"]
+    for label, msg_ids in states.items():
+        assert msg_ids == ref, (
+            f"freq={label} produced different message IDs than freq=inf"
         )
 
 
@@ -352,5 +473,6 @@ def test_delta_channel_benchmark(capsys: Any) -> None:
 # ---------------------------------------------------------------------------
 
 if __name__ == "__main__":
-    run_benchmark()
+    run_baseline_benchmark()
+    run_snapshot_freq_benchmark()
     sys.exit(0)

libs/langgraph/tests/test_delta_channel_migration.py

@@ -0,0 +1,613 @@
+"""Tests for the BinaryOperatorAggregate -> DeltaChannel migration path.
+
+A thread written under `BinaryOperatorAggregate(...)` must keep working
+after its annotation is swapped to `DeltaChannel(...)` on the same
+checkpointer — pre-migration state visible at each *settled* ancestor
+checkpoint is preserved, and post-migration writes fold on top through
+the reducer.
+
+Mechanism under test: the saver's `_get_channel_writes_history(config,
+channel)` walks the parent chain; when it encounters an ancestor whose
+`channel_values[channel]` is a real value (not `DELTA_SENTINEL`), it
+returns that as the `seed`. `DeltaChannel.from_checkpoint(seed)` uses
+it as the base value, and `replay_writes(writes)` folds on-path deltas.
+
+Scenarios covered:
+
+1. **Basic migration (sync + async)**: build pre-migration state with
+   `BinaryOperatorAggregate`, swap the annotation to `DeltaChannel` on
+   the same checkpointer, and verify that every settled pre-migration
+   super-step boundary (`next=('__start__',)`) round-trips exactly
+   under the delta-channel view.
+2. **Time travel into a pre-migration checkpoint** after migration —
+   `graph.get_state(pre_migration_config)` at a settled ancestor
+   returns the same state as under the binop channel.
+3. **Continuing a migrated thread**: driving one more super-step after
+   migration produces a state that includes the pre-migration settled
+   prefix plus the new delta write — proving `from_checkpoint(seed)` +
+   `replay_writes` correctly fold post-migration deltas onto the
+   pre-migration seed.
+4. **Base-saver fallback path**: a third-party-style subclass that
+   removes the optimized `InMemorySaver` override and falls back to
+   `BaseCheckpointSaver._get_channel_writes_history` must produce the
+   same result as the optimized path.
+5. **Channel-type isolation across threads**: two threads on the same
+   checkpointer under the delta-channel graph — one freshly-started,
+   one migrated from pre-migration state — don't cross-contaminate.
+   The parent-chain walk is scoped to the thread.
+
+TODO: add postgres variants in the existing `libs/checkpoint-postgres`
+test files (different fixture setup; not this file).
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import _messages_delta_reducer, add_messages
+
+pytestmark = pytest.mark.anyio
+
+
+# ---------------------------------------------------------------------------
+# Graph factories
+#
+# A minimal reducer (`operator.add` on lists of str) with a noop node keeps
+# state change localized to the HumanMessage-like payload passed through
+# `invoke`. That isolates the pre/post-migration parity assertions to
+# channel-hydration semantics.
+# ---------------------------------------------------------------------------
+
+
+def _noop(_state: Any) -> dict:
+    return {}
+
+
+def _list_concat(state: list, writes: list) -> list:
+    result = list(state)
+    for w in writes:
+        result.extend(w if isinstance(w, list) else [w])
+    return result
+
+
+def _binop_graph(checkpointer: Any) -> Any:
+    class BinopState(TypedDict):
+        items: Annotated[list, BinaryOperatorAggregate(list, operator.add)]
+
+    return (
+        StateGraph(BinopState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_graph(checkpointer: Any) -> Any:
+    class DeltaState(TypedDict):
+        items: Annotated[list, DeltaChannel(_list_concat)]
+
+    return (
+        StateGraph(DeltaState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _drive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        graph.invoke({"items": [f"{tag}{i}"]}, config)
+
+
+async def _adrive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        await graph.ainvoke({"items": [f"{tag}{i}"]}, config)
+
+
+def _settled_boundaries(history: list) -> list[tuple[dict, list]]:
+    """Return `[(config, items), ...]` for every checkpoint in `history`
+    whose `next == ('__start__',)` — the stable boundaries between invokes.
+    """
+    return [
+        (s.config, list(s.values.get("items", [])))
+        for s in history
+        if s.next == ("__start__",)
+    ]
+
+
+# ---------------------------------------------------------------------------
+# 1. Basic migration (sync + async)
+# ---------------------------------------------------------------------------
+
+
+def test_basic_migration_preserves_pre_migration_state() -> None:
+    """Build state under `BinaryOperatorAggregate`, migrate to
+    `DeltaChannel` on the same checkpointer, and verify that every
+    settled pre-migration super-step boundary round-trips exactly.
+
+    Settled boundaries (`next=('__start__',)`) are the stable hydration
+    targets for the migration path: writes that produced the NEXT
+    super-step are kept as `pending_writes` on the ancestor, so walking
+    from a descendant finds the ancestor's blob as the seed and
+    reconstructs the correct state.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-sync"}}
+
+    # Pre-migration: accumulate items across 3 invokes.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert len(pre_boundaries) >= 2, "expected multiple settled boundaries"
+
+    # Migrate: swap the annotation on the same checkpointer.
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = delta.get_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"snapshot mismatch at {cfg['configurable']['checkpoint_id']}: "
+            f"expected {items}, got {snap.values.get('items', [])}"
+        )
+
+
+async def test_basic_migration_preserves_pre_migration_state_async() -> None:
+    """Async variant of the basic migration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    pre_history = [s async for s in binop.aget_state_history(config)]
+    pre_boundaries = _settled_boundaries(pre_history)
+    assert len(pre_boundaries) >= 2
+
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = await delta.aget_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"async snapshot mismatch at {cfg['configurable']['checkpoint_id']}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 2. Time travel into a pre-migration checkpoint after migration
+# ---------------------------------------------------------------------------
+
+
+def test_time_travel_into_pre_migration_checkpoint() -> None:
+    """After migration, `graph.get_state(pre_migration_config)` at a
+    settled ancestor returns the state as stored at that point."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "time-travel"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert pre_boundaries, "no settled ancestors to time-travel to"
+
+    delta = _delta_graph(checkpointer)
+
+    # Pick the oldest non-empty boundary — a long distance to walk back.
+    non_empty = [(cfg, items) for cfg, items in pre_boundaries if items]
+    assert non_empty, "expected at least one non-empty boundary"
+    target_cfg, expected_items = non_empty[-1]
+
+    snap = delta.get_state(target_cfg)
+    assert list(snap.values.get("items", [])) == expected_items
+
+
+# ---------------------------------------------------------------------------
+# 3. Continuing a migrated thread: deltas fold onto pre-migration seed
+# ---------------------------------------------------------------------------
+
+
+def test_continuing_migrated_thread_folds_deltas_on_seed() -> None:
+    """Resume a pre-migration settled ancestor via `invoke(None, cfg)`
+    under the delta-channel graph. Since the pre-migration checkpoint
+    has an existing `pending_writes` entry (the input for the NEXT
+    super-step), re-running from that ancestor reproduces the same
+    post-ancestor state as the original binop run.
+
+    This proves the seed-terminator + write-replay pipeline works
+    end-to-end across the migration boundary.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "continue"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Pick the oldest settled boundary with non-empty state.
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    target_cfg, seed_items = next(
+        (cfg, items) for cfg, items in reversed(pre_boundaries) if items
+    )
+    assert seed_items, "need a non-empty seed boundary"
+
+    # Migrate and resume from the pre-migration ancestor. `invoke(None,
+    # cfg)` replays the pending writes staged at `cfg` under the new
+    # channel; the reducer folds those deltas onto the seed.
+    delta = _delta_graph(checkpointer)
+    result = delta.invoke(None, target_cfg)
+
+    # The resumed state must include the pre-migration seed items in order.
+    result_items = list(result.get("items", []))
+    for idx, prefix_item in enumerate(seed_items):
+        assert result_items[idx] == prefix_item, (
+            f"pre-migration seed item at {idx} not preserved: "
+            f"got {result_items[: idx + 1]}, expected {seed_items}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 4. Base-saver fallback path
+# ---------------------------------------------------------------------------
+
+
+class _ThirdPartyStyleSaver(InMemorySaver):
+    """Simulates a third-party saver that inherits the reference
+    `_get_channel_writes_history` implementation from
+    `BaseCheckpointSaver` rather than overriding it.
+
+    We rebind the two methods to the base-class versions (via MRO) so
+    the fallback path is exercised even though the storage layer is
+    still the in-memory one.
+    """
+
+    # MRO: [_ThirdPartyStyleSaver, InMemorySaver, BaseCheckpointSaver, ...]
+    _get_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+    )
+    _aget_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+    )
+
+
+def test_base_saver_fallback_matches_optimized_override() -> None:
+    """The reference `BaseCheckpointSaver` implementation must produce
+    the same migration behavior as the optimized `InMemorySaver`
+    override. We drive the same migration scenario through both savers
+    and assert per-snapshot parity in the delta-channel view."""
+
+    # Fast path: optimized InMemorySaver override.
+    fast_saver = InMemorySaver()
+    fast_config = {"configurable": {"thread_id": "fast"}}
+    fast_binop = _binop_graph(fast_saver)
+    _drive(fast_binop, fast_config, "u", 3)
+    fast_delta = _delta_graph(fast_saver)
+    fast_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in fast_delta.get_state_history(fast_config)
+    ]
+
+    # Slow path: base-class fallback.
+    slow_saver = _ThirdPartyStyleSaver()
+    slow_config = {"configurable": {"thread_id": "slow"}}
+    slow_binop = _binop_graph(slow_saver)
+    _drive(slow_binop, slow_config, "u", 3)
+    slow_delta = _delta_graph(slow_saver)
+    slow_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in slow_delta.get_state_history(slow_config)
+    ]
+
+    assert slow_history == fast_history, (
+        "base-saver fallback should match optimized-override behavior; "
+        f"fast={fast_history}, slow={slow_history}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 5. Thread isolation under mixed-generation storage
+# ---------------------------------------------------------------------------
+
+
+def test_delta_and_migrated_threads_do_not_cross_contaminate() -> None:
+    """Two threads sharing a checkpointer — one migrated from
+    pre-migration state, one freshly-started under DeltaChannel — must
+    maintain independent state. The parent-chain walk in
+    `_get_channel_writes_history` must be scoped to the target thread.
+    """
+
+    checkpointer = InMemorySaver()
+    migrated_cfg = {"configurable": {"thread_id": "migrated"}}
+    fresh_cfg = {"configurable": {"thread_id": "fresh"}}
+
+    # Thread A: pre-migration build-up.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, migrated_cfg, "m", 2)
+
+    # Thread B: fresh delta-channel run.
+    delta = _delta_graph(checkpointer)
+    _drive(delta, fresh_cfg, "f", 2)
+
+    # Thread A: migrate and confirm its state is anchored in its own
+    # thread's pre-migration history (tag 'm'), never mixing in tag 'f'.
+    migrated_boundaries = _settled_boundaries(
+        list(delta.get_state_history(migrated_cfg))
+    )
+    assert migrated_boundaries, "migrated thread has no settled boundaries"
+    for _, items in migrated_boundaries:
+        for it in items:
+            assert it.startswith("m"), (
+                f"migrated thread leaked item from other thread: {it}"
+            )
+
+    # Thread B: settled boundaries must only contain 'f' tags.
+    fresh_boundaries = _settled_boundaries(list(delta.get_state_history(fresh_cfg)))
+    assert fresh_boundaries, "fresh thread has no settled boundaries"
+    for _, items in fresh_boundaries:
+        for it in items:
+            assert it.startswith("f"), (
+                f"fresh thread leaked item from migrated thread: {it}"
+            )
+
+
+# ---------------------------------------------------------------------------
+# 6. Tip-of-pre-migration hydration: the latest checkpoint from a binop-run
+# thread has a real accumulated value in its own `channel_values["items"]`.
+# When hydrated under the delta-channel graph via `get_state(config)` with no
+# `checkpoint_id`, the short-circuit must use that value directly instead of
+# walking ancestors (which would skip the tip's own blob).
+# ---------------------------------------------------------------------------
+
+
+def test_tip_of_pre_migration_hydrates_directly() -> None:
+    """`graph.get_state(config)` at the latest (pre-migration) checkpoint
+    returns the full accumulated list stored in that checkpoint's own
+    `channel_values`. The hydration must not walk ancestors past it."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-sync"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    binop_tip = binop.get_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"], (
+        f"sanity: pre-migration tip should accumulate all 3 items, got {expected_items}"
+    )
+
+    delta = _delta_graph(checkpointer)
+
+    snap = delta.get_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+async def test_tip_of_pre_migration_hydrates_directly_async() -> None:
+    """Async variant of the tip-of-pre-migration hydration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    binop_tip = await binop.aget_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"]
+
+    delta = _delta_graph(checkpointer)
+
+    snap = await delta.aget_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"async tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 7. `update_state` after migration writes a real value to the new
+# checkpoint's `channel_values` (not a sentinel). Hydration must use it
+# directly — the ancestor walk would skip this blob and return stale state.
+# ---------------------------------------------------------------------------
+
+
+def test_update_state_after_migration_uses_written_value() -> None:
+    """After migrating and running at least one post-migration super-step
+    (so the thread's tip has a `DELTA_SENTINEL`), `update_state` writes a
+    concrete value to a new checkpoint's `channel_values`. `get_state`
+    must reflect that concrete value."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "update-state"}}
+
+    # Pre-migration: accumulate a little state.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Migrate and run one more super-step so the tip is a post-migration
+    # checkpoint with `DELTA_SENTINEL` in its own `channel_values`.
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # `update_state` writes a concrete value into a new checkpoint's blob
+    # via the reducer against the hydrated prior state.
+    delta.update_state(config, {"items": ["x", "y"]})
+
+    snap = delta.get_state(config)
+    updated_items = list(snap.values.get("items", []))
+    # Must include the "x","y" update; without the hydration fix, the
+    # update_state-written blob would be skipped in favor of an ancestor
+    # walk, and the update values would disappear.
+    assert "x" in updated_items and "y" in updated_items, (
+        f"update_state values missing from snapshot: {updated_items}"
+    )
+    # The "x","y" items should be folded onto the prior accumulated state,
+    # not stand alone. This verifies the update-written blob is used
+    # directly by `get_state` (no ancestor walk past it).
+    assert len(updated_items) >= 4, (
+        f"update_state snapshot should preserve pre-update state, got {updated_items}"
+    )
+    assert updated_items[-2:] == ["x", "y"], (
+        f"update_state deltas should be at the tail, got {updated_items}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 8. Fork from an `update_state` checkpoint: a new run branched off the
+# update_state-produced checkpoint must see that checkpoint's concrete
+# `channel_values` as its base, with new deltas folded on top.
+# ---------------------------------------------------------------------------
+
+
+def test_fork_from_update_state_checkpoint() -> None:
+    """Branching a new run from the checkpoint produced by `update_state`
+    must use that checkpoint's concrete blob as the base. Additional
+    deltas from the forked run fold onto it through the reducer."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "fork"}}
+
+    # Pre-migration build-up, then migrate and add one post-migration step.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # Apply `update_state` and capture the returned config (references
+    # the new checkpoint produced by the update).
+    update_cfg = delta.update_state(config, {"items": ["x", "y"]})
+
+    update_snap = delta.get_state(update_cfg)
+    base_items = list(update_snap.values.get("items", []))
+    assert "x" in base_items and "y" in base_items, (
+        f"update_state values missing from snapshot: {base_items}"
+    )
+    assert base_items[-2:] == ["x", "y"], (
+        f"sanity: update_state deltas should be at the tail, got {base_items}"
+    )
+
+    # Fork: invoke from the update_state checkpoint with a new delta.
+    forked = delta.invoke({"items": ["fork0"]}, update_cfg)
+    forked_items = list(forked.get("items", []))
+    # The fork must see the update_state-written blob as its base (not
+    # walk past it), and the new delta must fold on top of it.
+    assert forked_items[: len(base_items)] == base_items, (
+        f"fork lost update_state base: base={base_items}, forked={forked_items}"
+    )
+    assert forked_items[-1] == "fork0", f"fork delta not appended: {forked_items}"
+
+
+# ---------------------------------------------------------------------------
+# 9. Migration from `add_messages` → `DeltaChannel(_messages_delta_reducer)`
+#
+# `add_messages` is the primary real-world use case: it creates a
+# BinaryOperatorAggregate with dedup-by-ID and RemoveMessage semantics.
+# After swapping the annotation to DeltaChannel, pre-migration blobs
+# (plain lists of Message objects) must be used directly as the seed.
+# ---------------------------------------------------------------------------
+
+
+def _add_messages_graph(checkpointer: Any) -> Any:
+    class MessagesState(TypedDict):
+        messages: Annotated[list, add_messages]
+
+    return (
+        StateGraph(MessagesState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_messages_graph(checkpointer: Any) -> Any:
+    class DeltaMessagesState(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    return (
+        StateGraph(DeltaMessagesState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def test_add_messages_to_delta_migration_preserves_message_history() -> None:
+    """Migration from `add_messages` to `DeltaChannel(_messages_delta_reducer)`
+    preserves message ordering and IDs at both the tip and settled ancestor
+    boundaries.
+
+    The pre-migration blob is a plain list of Message objects; DeltaChannel
+    must use it directly as the seed without walking ancestors past it.
+    """
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "add-messages-migration"}}
+
+    pre_graph = _add_messages_graph(checkpointer)
+    pre_graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
+    pre_graph.invoke({"messages": [AIMessage(content="hi", id="a1")]}, config)
+    pre_graph.invoke({"messages": [HumanMessage(content="thanks", id="h2")]}, config)
+
+    pre_tip = pre_graph.get_state(config)
+    assert [m.id for m in pre_tip.values["messages"]] == ["h1", "a1", "h2"]
+
+    delta_graph = _delta_messages_graph(checkpointer)
+
+    # Tip: latest checkpoint has a full list blob — must use it directly.
+    snap = delta_graph.get_state(config)
+    assert [m.id for m in snap.values["messages"]] == ["h1", "a1", "h2"], (
+        f"tip hydration mismatch: got {[m.id for m in snap.values['messages']]}"
+    )
+
+    # Settled ancestor boundaries must also match.
+    pre_settled = [
+        [m.id for m in s.values.get("messages", [])]
+        for s in pre_graph.get_state_history(config)
+        if s.next == ("__start__",)
+    ]
+    delta_settled = [
+        [m.id for m in s.values.get("messages", [])]
+        for s in delta_graph.get_state_history(config)
+        if s.next == ("__start__",)
+    ]
+    assert delta_settled == pre_settled, (
+        f"settled boundary mismatch after migration: "
+        f"pre={pre_settled}, delta={delta_settled}"
+    )
+
+
+async def test_add_messages_to_delta_migration_preserves_message_history_async() -> (
+    None
+):
+    """Async variant of the add_messages migration test."""
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "add-messages-migration-async"}}
+
+    pre_graph = _add_messages_graph(checkpointer)
+    await pre_graph.ainvoke(
+        {"messages": [HumanMessage(content="hello", id="h1")]}, config
+    )
+    await pre_graph.ainvoke({"messages": [AIMessage(content="hi", id="a1")]}, config)
+
+    delta_graph = _delta_messages_graph(checkpointer)
+    snap = await delta_graph.aget_state(config)
+    assert [m.id for m in snap.values["messages"]] == ["h1", "a1"], (
+        f"async tip hydration mismatch: got {[m.id for m in snap.values['messages']]}"
+    )

libs/langgraph/tests/test_graph_callbacks.py

@@ -275,3 +275,70 @@ def test_graph_callbacks_accept_base_callback_manager() -> None:
 
     assert "__interrupt__" in first
     assert len(graph_handler.interrupt_events) == 1
+
+
+def test_non_graph_handler_via_add_handler_does_not_crash() -> None:
+    """Non-GraphCallbackHandler added via add_handler should not raise.
+
+    Libraries like opentelemetry-instrumentation-langchain monkey-patch
+    BaseCallbackManager.__init__ and inject handlers via add_handler().
+    These handlers inherit from BaseCallbackHandler, not
+    GraphCallbackHandler. They must be silently accepted — graph lifecycle
+    events will simply not be dispatched to them.
+    """
+    from langgraph.callbacks import _GraphCallbackManager
+
+    manager = _GraphCallbackManager()
+    plain_handler = _LangChainCustomEventHandler()
+
+    manager.add_handler(plain_handler, inherit=True)
+    assert plain_handler in manager.handlers
+
+
+def test_non_graph_handler_does_not_receive_lifecycle_events() -> None:
+    """Non-GraphCallbackHandler added alongside a GraphCallbackHandler
+    should not interfere with lifecycle event dispatch."""
+    graph = _build_interrupt_graph()
+    graph_handler = _GraphEventHandler()
+    plain_handler = _LangChainCustomEventHandler()
+
+    config = {
+        "configurable": {"thread_id": "graph-callback-mixed-handlers"},
+        "callbacks": [plain_handler, graph_handler],
+    }
+
+    first = graph.invoke({"answer": None}, config)
+    assert "__interrupt__" in first
+
+    assert len(graph_handler.interrupt_events) == 1
+    assert plain_handler.events == []
+
+    resumed = graph.invoke(Command(resume="done"), config)
+    assert resumed == {"answer": "done"}
+    assert len(graph_handler.resume_events) == 1
+    assert plain_handler.events == []
+
+
+@pytest.mark.anyio
+@NEEDS_CONTEXTVARS
+async def test_non_graph_handler_does_not_receive_lifecycle_events_async() -> None:
+    """Async variant: non-GraphCallbackHandler should not interfere."""
+    graph = _build_interrupt_graph()
+    graph_handler = _GraphEventHandler()
+    plain_handler = _LangChainCustomEventHandler()
+
+    config = {
+        "configurable": {"thread_id": "graph-callback-mixed-handlers-async"},
+        "callbacks": [plain_handler, graph_handler],
+    }
+
+    first = await graph.ainvoke({"answer": None}, config)
+    assert "__interrupt__" in first
+
+    assert len(graph_handler.interrupt_events) == 1
+    assert plain_handler.events == []
+
+    resumed = await graph.ainvoke(Command(resume="done"), config)
+    assert resumed == {"answer": "done"}
+    assert len(graph_handler.resume_events) == 1
+    assert plain_handler.events == []

libs/langgraph/tests/test_interleave_arrival_order.py

@@ -0,0 +1,359 @@
+"""Tests for arrival-ordered interleave and push stamps."""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.stream import StreamChannel, StreamTransformer
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+from langgraph.stream.run_stream import GraphRunStream
+from langgraph.stream.transformers import ValuesTransformer
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+class _TwoChannelTransformer(StreamTransformer):
+    """Transformer that exposes two named channels for testing interleave."""
+
+    _native = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._alpha: StreamChannel[str] = StreamChannel("alpha")
+        self._beta: StreamChannel[str] = StreamChannel("beta")
+
+    def init(self) -> dict[str, Any]:
+        return {"alpha": self._alpha, "beta": self._beta}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class SimpleState(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _build_simple_graph():
+    def node_a(state: SimpleState) -> dict:
+        return {"value": state["value"] + "A", "items": ["a"]}
+
+    def node_b(state: SimpleState) -> dict:
+        return {"value": state["value"] + "B", "items": ["b"]}
+
+    builder = StateGraph(SimpleState)
+    builder.add_node("node_a", node_a)
+    builder.add_node("node_b", node_b)
+    builder.add_edge(START, "node_a")
+    builder.add_edge("node_a", "node_b")
+    builder.add_edge("node_b", END)
+    return builder.compile()
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: push stamps on StreamChannel
+# ---------------------------------------------------------------------------
+
+
+class TestPushStamps:
+    def test_stamps_are_monotonic_across_channels(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        beta = mux.extensions["beta"]
+
+        alpha._subscribed = True
+        beta._subscribed = True
+
+        alpha.push("a1")
+        beta.push("b1")
+        alpha.push("a2")
+        beta.push("b2")
+
+        all_stamped = list(alpha._items) + list(beta._items)
+        stamps = [s for s, _ in all_stamped]
+        assert len(set(stamps)) == 4
+        items_by_arrival = [item for _, item in sorted(all_stamped)]
+        assert items_by_arrival == ["a1", "b1", "a2", "b2"]
+
+    def test_regular_iter_strips_stamps(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        it = iter(alpha)
+        alpha.push("a1")
+        alpha.push("a2")
+        alpha.close()
+        items = list(it)
+        assert items == ["a1", "a2"]
+        assert all(isinstance(item, str) for item in items)
+
+    def test_events_channel_gets_real_stamps(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+
+        alpha._subscribed = True
+        alpha.push("a1")
+
+        mux._events._subscribed = True
+        mux._events.push({"method": "test", "data": "x"})
+
+        alpha.push("a2")
+
+        all_stamps = [s for s, _ in alpha._items] + [s for s, _ in mux._events._items]
+        assert len(set(all_stamps)) == len(all_stamps), "all stamps should be unique"
+        assert all(s > 0 for s in all_stamps), "no stamp should be zero"
+
+    def test_channel_without_mux_gets_zero_stamp(self) -> None:
+        ch: StreamChannel[str] = StreamChannel()
+        ch._bind(is_async=False)
+        ch._subscribed = True
+        ch.push("x")
+        assert list(ch._items) == [(0, "x")]
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: interleave arrival order
+# ---------------------------------------------------------------------------
+
+
+class TestInterleaveArrivalOrder:
+    def test_arrival_order_not_round_robin(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        beta = mux.extensions["beta"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        # interleave() subscribes channels directly and reads _items
+        # for stamp-ordered iteration. We simulate the pump by wiring
+        # a custom callback that pushes items in a known order.
+        push_script = [
+            ("alpha", "a1"),
+            ("alpha", "a2"),
+            ("beta", "b1"),
+            ("alpha", "a3"),
+            ("beta", "b2"),
+        ]
+        push_iter = iter(push_script)
+        channels = {"alpha": alpha, "beta": beta}
+
+        def fake_pump() -> bool:
+            try:
+                name, item = next(push_iter)
+                channels[name].push(item)
+                return True
+            except StopIteration:
+                mux.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha", "beta"))
+        names = [name for name, _ in result]
+        items = [item for _, item in result]
+
+        assert items == ["a1", "a2", "b1", "a3", "b2"]
+        assert names == ["alpha", "alpha", "beta", "alpha", "beta"]
+
+    def test_single_projection(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        push_script = [("alpha", "a1"), ("alpha", "a2")]
+        push_iter = iter(push_script)
+
+        def fake_pump() -> bool:
+            try:
+                _, item = next(push_iter)
+                alpha.push(item)
+                return True
+            except StopIteration:
+                mux.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha"))
+        assert result == [("alpha", "a1"), ("alpha", "a2")]
+
+    def test_empty_projection(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        push_script = [("alpha", "a1"), ("alpha", "a2")]
+        push_iter = iter(push_script)
+        channels = {"alpha": alpha}
+
+        def fake_pump() -> bool:
+            try:
+                name, item = next(push_iter)
+                channels[name].push(item)
+                return True
+            except StopIteration:
+                mux.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha", "beta"))
+        assert result == [("alpha", "a1"), ("alpha", "a2")]
+
+    def test_unknown_projection_raises(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        run = GraphRunStream(None, mux, wire_pump=False)
+        mux.close()
+        with pytest.raises((KeyError, AttributeError)):
+            list(run.interleave("alpha", "does_not_exist"))
+
+    def test_all_empty(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        def fake_pump() -> bool:
+            mux.close()
+            return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha", "beta"))
+        assert result == []
+
+    def test_error_propagation(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        beta = mux.extensions["beta"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        err = RuntimeError("boom")
+
+        push_script = [
+            ("alpha", "a1"),
+            ("beta", "b1"),
+        ]
+        push_iter = iter(push_script)
+        channels = {"alpha": alpha, "beta": beta}
+
+        def fake_pump() -> bool:
+            try:
+                name, item = next(push_iter)
+                channels[name].push(item)
+                return True
+            except StopIteration:
+                alpha.fail(err)
+                beta.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        collected = []
+        with pytest.raises(RuntimeError, match="boom"):
+            for pair in run.interleave("alpha", "beta"):
+                collected.append(pair)
+
+        assert ("alpha", "a1") in collected
+        assert ("beta", "b1") in collected
+
+
+# ---------------------------------------------------------------------------
+# Integration test: interleave with stream_events(version="v3")
+# ---------------------------------------------------------------------------
+
+
+class TestInterleaveIntegration:
+    def test_interleave_values_and_messages(self) -> None:
+        run = _build_simple_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        tagged = list(run.interleave("values", "messages"))
+        names = [name for name, _ in tagged]
+        assert set(names).issubset({"values", "messages"})
+        assert names.count("values") >= 1
+
+    def test_interleave_rejects_already_subscribed(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        # Subscribe alpha via iter first
+        _ = iter(alpha)
+        mux.close()
+
+        with pytest.raises(RuntimeError, match="already has a subscriber"):
+            list(run.interleave("alpha"))
+
+    def test_interleave_releases_projections_on_completion(self) -> None:
+        run = _build_simple_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        list(run.interleave("values", "messages"))
+        # Subscriptions should be released after the generator completes,
+        # so the channels can be re-iterated (they'll be empty / closed).
+        assert run.extensions["values"]._subscribed is False
+        assert run.extensions["messages"]._subscribed is False
+
+    def test_interleave_releases_projections_on_early_break(self) -> None:
+        run = _build_simple_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        gen = run.interleave("values", "messages")
+        next(gen)
+        gen.close()
+        assert run.extensions["values"]._subscribed is False
+        assert run.extensions["messages"]._subscribed is False
+
+    def test_interleave_releases_projections_on_validation_failure(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        # Pre-subscribe alpha so that interleave will fail validation when
+        # it gets to the second name. The first (already-validated) channel
+        # should still be released.
+        run = GraphRunStream(None, mux, wire_pump=False)
+        mux.close()
+        alpha._subscribed = True
+
+        with pytest.raises(RuntimeError, match="already has a subscriber"):
+            list(run.interleave("values", "alpha"))
+
+        assert mux.extensions["values"]._subscribed is False

libs/langgraph/tests/test_pregel.py

@@ -16,7 +16,7 @@ from typing import Annotated, Any, Literal, get_type_hints
 
 import pytest
 from langchain_core.language_models import GenericFakeChatModel
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage
 from langchain_core.runnables import (
     RunnableConfig,
     RunnableLambda,
@@ -25,6 +25,7 @@ from langchain_core.runnables import (
 from langchain_core.runnables.graph import Edge
 from langgraph.cache.base import BaseCache
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     BaseCheckpointSaver,
     Checkpoint,
     CheckpointMetadata,
@@ -41,6 +42,7 @@ from typing_extensions import NotRequired, TypedDict
 
 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
@@ -49,7 +51,7 @@ from langgraph.config import get_stream_writer
 from langgraph.errors import GraphRecursionError, InvalidUpdateError, ParentCommand
 from langgraph.func import entrypoint, task
 from langgraph.graph import END, START, StateGraph
-from langgraph.graph.message import MessagesState, add_messages
+from langgraph.graph.message import MessagesState, _messages_delta_reducer, add_messages
 from langgraph.pregel import (
     NodeBuilder,
     Pregel,
@@ -120,6 +122,29 @@ def test_graph_validation() -> None:
         graph.invoke({"hello": "there"})
 
 
+def test_request_drain_allows_inflight_call_scheduling(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    from langgraph.runtime import RunControl
+
+    @task
+    def child(x: int) -> int:
+        return x + 1
+
+    control = RunControl()
+
+    @entrypoint(checkpointer=sync_checkpointer)
+    def graph(x: int) -> int:
+        control.request_drain()
+        fut = child(x)
+        return fut.result()
+
+    config = {"configurable": {"thread_id": "drain-call-sync"}}
+
+    assert graph.invoke(1, config=config, control=control) == 2
+    assert control.drain_requested
+
+
 def test_invalid_checkpointer_type() -> None:
     class State(TypedDict):
         foo: str
@@ -9404,15 +9429,9 @@ def test_fork_does_not_apply_pending_writes(
 
 async def test_delta_channel_end_to_end_inmemory() -> None:
     """Full graph run: DeltaChannel accumulates correctly across multiple turns."""
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
 
     class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
 
     def respond(state: State) -> dict:
         n = len(state["messages"])
@@ -9446,15 +9465,9 @@ async def test_delta_channel_end_to_end_inmemory() -> None:
 
 async def test_delta_channel_time_travel() -> None:
     """Time-travel back to turn-1 checkpoint and resume; continuation must not include turn-2 deltas."""
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
 
     class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
 
     counter = {"n": 0}
 
@@ -9504,15 +9517,9 @@ async def test_delta_channel_time_travel() -> None:
 
 async def test_delta_channel_remove_message_end_to_end() -> None:
     """RemoveMessage inside a DeltaChannel graph must persist and reload correctly."""
-    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
 
     class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
 
     def respond(state: State) -> dict:
         return {"messages": [AIMessage(content="reply", id="ai-1")]}
@@ -9551,15 +9558,9 @@ async def test_delta_channel_remove_message_end_to_end() -> None:
 
 async def test_delta_channel_update_by_id_end_to_end() -> None:
     """Updating a message by ID via DeltaChannel must persist and reload correctly."""
-    from langchain_core.messages import HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
 
     class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
 
     def update_msg(state: State) -> dict:
         # re-send h1 with updated content
@@ -9587,3 +9588,91 @@ async def test_delta_channel_update_by_id_end_to_end() -> None:
     assert "h1" in ids  # h1 persists (updated, not duplicated)
     assert "h2" in ids
     assert ids.count("h1") == 1, "h1 must not be duplicated"
+
+
+async def test_delta_channel_durability_exit_stores_snapshot() -> None:
+    """DeltaChannel must reload from a durability='exit' checkpoint."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    def respond(state: State) -> dict:
+        return {"messages": [AIMessage(content="reply", id="ai1")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    graph = builder.compile(checkpointer=InMemorySaver())
+    config = {"configurable": {"thread_id": "delta-exit-test"}}
+
+    result = graph.invoke(
+        {"messages": [HumanMessage(content="hello", id="h1")]},
+        config,
+        durability="exit",
+    )
+    assert [m.content for m in result["messages"]] == ["hello", "reply"]
+
+    state = graph.get_state(config)
+    assert [m.content for m in state.values["messages"]] == ["hello", "reply"]
+
+
+async def test_delta_channel_async_write_ordering() -> None:
+    """In async mode, DeltaChannel write futures are awaited before the checkpoint
+    is committed, so aput_writes always precedes aput for sentinel checkpoints."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    def respond(state: State) -> dict:
+        i = len(state["messages"])
+        return {"messages": [AIMessage(content=f"r{i}", id=f"ai{i}")]}
+
+    order: list[str] = []
+    original_aput_writes = InMemorySaver.aput_writes
+    original_aput = InMemorySaver.aput
+
+    async def tracked_aput_writes(self, config, writes, task_id, task_path=""):
+        result = await original_aput_writes(self, config, writes, task_id, task_path)
+        order.append("aput_writes")
+        return result
+
+    async def tracked_aput(self, config, checkpoint, metadata, new_versions):
+        has_sentinel = any(
+            v is DELTA_SENTINEL for v in checkpoint.get("channel_values", {}).values()
+        )
+        order.append("aput_sentinel" if has_sentinel else "aput_other")
+        return await original_aput(self, config, checkpoint, metadata, new_versions)
+
+    InMemorySaver.aput_writes = tracked_aput_writes
+    InMemorySaver.aput = tracked_aput
+    try:
+        builder = StateGraph(State)
+        builder.add_node("respond", respond)
+        builder.add_edge(START, "respond")
+        graph = builder.compile(checkpointer=InMemorySaver())
+        config = {"configurable": {"thread_id": "async-ordering-test"}}
+
+        for i in range(3):
+            await graph.ainvoke(
+                {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config
+            )
+
+        # Every aput_sentinel must be preceded by at least one aput_writes
+        for i, event in enumerate(order):
+            if event == "aput_sentinel":
+                preceding = order[:i]
+                assert "aput_writes" in preceding, (
+                    f"aput_sentinel at {i} had no preceding aput_writes: {order}"
+                )
+                last_write_idx = max(
+                    j for j, e in enumerate(order[:i]) if e == "aput_writes"
+                )
+                assert last_write_idx < i, (
+                    f"aput_writes at {last_write_idx} should precede aput_sentinel at {i}: {order}"
+                )
+    finally:
+        InMemorySaver.aput_writes = original_aput_writes
+        InMemorySaver.aput = original_aput
+
+    state = await graph.aget_state(config)
+    assert len(state.values["messages"]) == 6  # 3 human + 3 AI

libs/langgraph/tests/test_pregel_async.py

@@ -16,6 +16,7 @@ from typing import (
     Literal,
     Optional,
 )
+from unittest.mock import patch
 from uuid import UUID
 
 import pytest
@@ -48,6 +49,7 @@ from langgraph.channels.topic import Topic
 from langgraph.errors import (
     GraphRecursionError,
     InvalidUpdateError,
+    NodeError,
     ParentCommand,
 )
 from langgraph.func import entrypoint, task
@@ -215,6 +217,30 @@ async def test_checkpoint_errors() -> None:
             pass
 
 
+@NEEDS_CONTEXTVARS
+async def test_request_drain_allows_inflight_acall_scheduling(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    from langgraph.runtime import RunControl
+
+    @task
+    async def child(x: int) -> int:
+        return x + 1
+
+    control = RunControl()
+
+    @entrypoint(checkpointer=async_checkpointer)
+    async def graph(x: int) -> int:
+        control.request_drain()
+        fut = child(x)
+        return await fut
+
+    config = {"configurable": {"thread_id": "drain-call-async"}}
+
+    assert await graph.ainvoke(1, config=config, control=control) == 2
+    assert control.drain_requested
+
+
 async def test_py_async_with_cancel_behavior() -> None:
     """This test confirms that in all versions of Python we support, __aexit__
     is not cancelled when the coroutine containing the async with block is cancelled."""
@@ -6101,6 +6127,36 @@ async def test_parent_command(
     )
 
 
+async def test_delta_channel_durability_exit_stores_snapshot_async() -> None:
+    """DeltaChannel must reload from an async durability='exit' checkpoint."""
+    from langchain_core.messages import AIMessage
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import _messages_delta_reducer
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    async def respond(state: State) -> dict:
+        return {"messages": [AIMessage(content="reply", id="ai1")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    graph = builder.compile(checkpointer=InMemorySaver())
+    config = {"configurable": {"thread_id": "delta-exit-async-test"}}
+
+    result = await graph.ainvoke(
+        {"messages": [HumanMessage(content="hello", id="h1")]},
+        config,
+        durability="exit",
+    )
+    assert [m.content for m in result["messages"]] == ["hello", "reply"]
+
+    state = await graph.aget_state(config)
+    assert [m.content for m in state.values["messages"]] == ["hello", "reply"]
+
+
 @NEEDS_CONTEXTVARS
 async def test_interrupt_subgraph(async_checkpointer: BaseCheckpointSaver) -> None:
     class State(TypedDict):
@@ -9709,3 +9765,126 @@ async def test_fork_does_not_apply_pending_writes(
 
     # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
     assert result == {"value": 121}
+
+
+async def test_graph_error_handler_async_runtime_info() -> None:
+    class State(TypedDict):
+        foo: str
+
+    attempts = 0
+    captured: dict[str, object] = {}
+
+    async def always_failing_node(state: State) -> State:
+        nonlocal attempts
+        attempts += 1
+        raise ValueError("Always fails async")
+
+    async def err_handler_node(state: State, error: NodeError) -> State:
+        captured["from_node_name"] = error.node
+        captured["from_node_error"] = error.error
+        return {"foo": "handled_async"}
+
+    graph = (
+        StateGraph(State)
+        .add_node(
+            "always_failing",
+            always_failing_node,
+            retry_policy=RetryPolicy(
+                max_attempts=2,
+                initial_interval=0.01,
+                jitter=False,
+                retry_on=ValueError,
+            ),
+            error_handler=err_handler_node,
+        )
+        .add_edge(START, "always_failing")
+        .compile()
+    )
+
+    with patch("asyncio.sleep"):
+        result = await graph.ainvoke({"foo": ""})
+
+    assert attempts == 2
+    assert result["foo"] == "handled_async"
+    assert captured["from_node_name"] == "always_failing"
+    assert isinstance(captured["from_node_error"], BaseException)
+
+
+@NEEDS_CONTEXTVARS
+async def test_graph_error_handler_does_not_swallow_interrupt_concurrent() -> None:
+    """When a graph error handler is configured and a node calls interrupt()
+    concurrently with other nodes, the interrupt must still be raised — not
+    silently swallowed."""
+
+    class State(TypedDict):
+        foo: str
+
+    async def node_a(state: State) -> State:
+        val = interrupt("need human input")
+        return {"foo": f"a_{val}"}
+
+    async def node_b(state: State) -> State:
+        return {}
+
+    async def err_handler(state: State) -> State:
+        return {"foo": "handled"}
+
+    checkpointer = InMemorySaver()
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a, error_handler=err_handler)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge(START, "node_b")
+        .compile(checkpointer=checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "test-interrupt-concurrent-async"}}
+
+    await graph.ainvoke({"foo": ""}, config)
+
+    state = await graph.aget_state(config)
+    assert len(state.tasks) > 0
+
+    interrupts = [t for t in state.tasks if hasattr(t, "interrupts") and t.interrupts]
+    assert len(interrupts) > 0, (
+        "GraphInterrupt was swallowed — interrupt() in node_a "
+        "should have paused execution"
+    )
+
+
+async def test_node_error_handler_handles_subgraph_internal_failure_async() -> None:
+    class SubState(TypedDict):
+        foo: str
+
+    class ParentState(TypedDict):
+        foo: str
+
+    captured: dict[str, object] = {}
+
+    async def sub_fail_node(state: SubState) -> SubState:
+        raise ValueError("async subgraph boom")
+
+    async def parent_handler(state: ParentState, error: NodeError) -> ParentState:
+        captured["from_node_name"] = error.node
+        captured["from_node_error"] = error.error
+        return {"foo": "handled_async_subgraph"}
+
+    subgraph = (
+        StateGraph(SubState)
+        .add_node("sub_fail_node", sub_fail_node)
+        .add_edge(START, "sub_fail_node")
+        .compile()
+    )
+
+    parent_graph = (
+        StateGraph(ParentState)
+        .add_node("subgraph_node", subgraph, error_handler=parent_handler)
+        .add_edge(START, "subgraph_node")
+        .compile()
+    )
+
+    result = await parent_graph.ainvoke({"foo": ""})
+    assert result["foo"] == "handled_async_subgraph"
+    assert captured["from_node_name"] == "subgraph_node"
+    assert isinstance(captured["from_node_error"], BaseException)

libs/langgraph/tests/test_rehydrate_sweep.py

@@ -1,185 +0,0 @@
-"""Sweep snapshot_every values to find the storage vs. time-travel tradeoff.
-
-Run directly:  python tests/test_rehydrate_sweep.py
-Run via pytest: pytest tests/test_rehydrate_sweep.py -s
-"""
-
-from __future__ import annotations
-
-import sys
-import time
-from typing import Annotated, Any
-
-from langchain_core.messages import AIMessage, HumanMessage
-from langgraph.checkpoint.memory import MemorySaver
-from typing_extensions import TypedDict
-
-from langgraph.channels.delta import DeltaChannel
-from langgraph.graph import END, StateGraph
-from langgraph.graph.message import add_messages
-
-# ---------------------------------------------------------------------------
-# Config
-# ---------------------------------------------------------------------------
-
-REHYDRATE_SWEEP = [5, 10, 25, 50, 100, None]  # None = no rehydration (pure diff)
-TURN_COUNTS = [50, 100, 250, 500]
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-
-def _make_state(snapshot_every: int | None) -> type:
-    channel = DeltaChannel(add_messages, snapshot_every=snapshot_every)
-    return TypedDict("S", {"messages": Annotated[list, channel]})
-
-
-def _make_graph(state_cls: type) -> Any:
-    def human_node(state: Any) -> dict:
-        return {}
-
-    def ai_node(state: Any) -> dict:
-        last = state["messages"][-1]
-        return {"messages": [AIMessage(content=f"reply-to-{last.id}")]}
-
-    g = StateGraph(state_cls)
-    g.add_node("human", human_node)
-    g.add_node("ai", ai_node)
-    g.add_edge("human", "ai")
-    g.add_edge("ai", END)
-    g.set_entry_point("human")
-    return g.compile(checkpointer=MemorySaver())
-
-
-def _total_blob_bytes(saver: MemorySaver) -> int:
-    total = 0
-    for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
-        if blob is not None:
-            total += len(blob)
-    return total
-
-
-def _measure_time_travel_ms(graph: Any, config: dict) -> float:
-    """Time how long it takes to get state at the very first checkpoint (worst case)."""
-    history = list(graph.get_state_history(config))
-    if not history:
-        return 0.0
-    oldest = history[-1]
-    t0 = time.perf_counter()
-    graph.get_state(oldest.config)
-    return (time.perf_counter() - t0) * 1000
-
-
-def _run(n_turns: int, snapshot_every: int | None) -> tuple[float, int, float]:
-    """Returns (write_ms, blob_bytes, time_travel_ms)."""
-    state_cls = _make_state(snapshot_every)
-    graph = _make_graph(state_cls)
-    saver: MemorySaver = graph.checkpointer  # type: ignore[assignment]
-    config = {"configurable": {"thread_id": "sweep"}}
-
-    t0 = time.perf_counter()
-    for i in range(n_turns):
-        graph.invoke(
-            {"messages": [HumanMessage(content=f"msg-{i}", id=f"h{i}")]}, config
-        )
-    write_ms = (time.perf_counter() - t0) * 1000
-
-    blob_bytes = _total_blob_bytes(saver)
-    tt_ms = _measure_time_travel_ms(graph, config)
-    return write_ms, blob_bytes, tt_ms
-
-
-# ---------------------------------------------------------------------------
-# ASCII sparkline
-# ---------------------------------------------------------------------------
-
-
-def _sparkline(values: list[float], width: int = 20) -> str:
-    bars = " ▁▂▃▄▅▆▇█"
-    lo, hi = min(values), max(values)
-    span = hi - lo or 1
-    chars = [bars[round((v - lo) / span * (len(bars) - 1))] for v in values]
-    return "".join(chars).ljust(width)
-
-
-# ---------------------------------------------------------------------------
-# Main
-# ---------------------------------------------------------------------------
-
-
-def run_sweep() -> None:
-    label = {v: (str(v) if v is not None else "None(∞)") for v in REHYDRATE_SWEEP}
-
-    print()
-    print("snapshot_every sweep — storage vs time-travel cost")
-    print("=" * 90)
-
-    for turns in TURN_COUNTS:
-        print(f"\n--- {turns} turns ---")
-        col_w = 12
-        header = (
-            f"{'snapshot_every':>18}  "
-            f"{'blob_bytes':>{col_w}}  "
-            f"{'write_ms':>{col_w}}  "
-            f"{'time_travel_ms':>{col_w}}"
-        )
-        print(header)
-        print("-" * 60)
-
-        tt_vals: list[float] = []
-        byte_vals: list[int] = []
-        write_vals: list[float] = []
-        rows: list[tuple] = []
-
-        for rv in REHYDRATE_SWEEP:
-            write_ms, blob_bytes, tt_ms = _run(turns, rv)
-            rows.append((rv, blob_bytes, write_ms, tt_ms))
-            byte_vals.append(blob_bytes)
-            write_vals.append(write_ms)
-            tt_vals.append(tt_ms)
-
-        for rv, blob_bytes, write_ms, tt_ms in rows:
-            print(
-                f"{label[rv]:>18}  "
-                f"{blob_bytes:>{col_w},}  "
-                f"{write_ms:>{col_w}.1f}  "
-                f"{tt_ms:>{col_w}.2f}"
-            )
-
-        print()
-        print(
-            f"  bytes       spark: [{_sparkline(byte_vals)}]  "
-            f"lo={min(byte_vals):,}  hi={max(byte_vals):,}"
-        )
-        print(
-            f"  time-travel spark: [{_sparkline(tt_vals)}]  "
-            f"lo={min(tt_vals):.2f}ms  hi={max(tt_vals):.2f}ms"
-        )
-        print(
-            f"  write       spark: [{_sparkline(write_vals)}]  "
-            f"lo={min(write_vals):.1f}ms  hi={max(write_vals):.1f}ms"
-        )
-
-    print()
-    print("=" * 90)
-    print(
-        "snapshot_every=None means pure diff (no snapshots) — "
-        "lowest storage, highest time-travel cost."
-    )
-    print(
-        "Lower snapshot_every = more frequent full snapshots = "
-        "faster time-travel, more storage."
-    )
-    print()
-
-
-def test_rehydrate_sweep(capsys: Any) -> None:
-    with capsys.disabled():
-        run_sweep()
-
-
-if __name__ == "__main__":
-    run_sweep()
-    sys.exit(0)

libs/langgraph/tests/test_runtime.py

@@ -1,3 +1,6 @@
+import asyncio
+import threading
+import time
 from dataclasses import dataclass
 from typing import Any
 
@@ -6,8 +9,15 @@ from langgraph.checkpoint.memory import MemorySaver
 from pydantic import BaseModel, ValidationError
 from typing_extensions import TypedDict
 
+from langgraph.errors import GraphDrained
 from langgraph.graph import END, START, StateGraph
-from langgraph.runtime import ExecutionInfo, Runtime, ServerInfo, get_runtime
+from langgraph.runtime import (
+    ExecutionInfo,
+    RunControl,
+    Runtime,
+    ServerInfo,
+    get_runtime,
+)
 
 
 def test_injected_runtime() -> None:
@@ -79,6 +89,183 @@ def test_merge_runtime() -> None:
     assert runtime1.merge(runtime3).context.api_key == "abc"  # type: ignore
 
 
+def test_merge_runtime_preserves_run_control() -> None:
+    control = RunControl()
+    runtime1 = Runtime(control=control)
+    runtime2 = Runtime(context=None)
+
+    assert runtime1.merge(runtime2).control is control
+
+
+def test_run_control_request_drain_stops_future_steps() -> None:
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    control = RunControl()
+
+    def first_node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"first": "done"}
+
+    def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    with pytest.raises(GraphDrained, match="shutdown"):
+        graph.compile().invoke({}, control=control)
+
+
+@pytest.mark.anyio
+async def test_run_control_request_drain_stops_future_steps_async() -> None:
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    control = RunControl()
+
+    async def first_node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"first": "done"}
+
+    async def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    with pytest.raises(GraphDrained, match="shutdown"):
+        await graph.compile().ainvoke({}, control=control)
+
+
+def test_drain_requested_in_terminal_step_finishes_normally() -> None:
+    class State(TypedDict, total=False):
+        value: str
+
+    control = RunControl()
+
+    def node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"value": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.add_edge(START, "node")
+    graph.add_edge("node", END)
+
+    assert graph.compile().invoke({}, control=control) == {"value": "done"}
+    assert control.drain_requested
+
+
+def test_drain_with_exit_durability_persists_resume_checkpoint() -> None:
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    control = RunControl()
+
+    def first_node(state: State) -> dict[str, str]:
+        control.request_drain("sigterm")
+        return {"first": "done"}
+
+    def second_node(state: State) -> dict[str, str]:
+        return {"second": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    compiled = graph.compile(checkpointer=MemorySaver())
+    config = {"configurable": {"thread_id": "drain-exit"}}
+
+    with pytest.raises(GraphDrained, match="sigterm"):
+        compiled.invoke({}, config, durability="exit", control=control)
+
+    assert compiled.invoke(None, config, durability="exit") == {
+        "first": "done",
+        "second": "done",
+    }
+
+
+def test_drain_from_subgraph_can_resume_parent() -> None:
+    class State(TypedDict, total=False):
+        child_first: str
+        child_second: str
+        parent_second: str
+
+    control = RunControl()
+
+    def child_first(state: State) -> dict[str, str]:
+        control.request_drain("sigterm")
+        return {"child_first": "done"}
+
+    def child_second(state: State) -> dict[str, str]:
+        return {"child_second": "done"}
+
+    child_builder = StateGraph(State)
+    child_builder.add_node("child_first", child_first)
+    child_builder.add_node("child_second", child_second)
+    child_builder.add_edge(START, "child_first")
+    child_builder.add_edge("child_first", "child_second")
+    child_builder.add_edge("child_second", END)
+    child_graph = child_builder.compile(checkpointer=True)
+
+    def parent_second(state: State) -> dict[str, str]:
+        return {"parent_second": "done"}
+
+    parent_builder = StateGraph(State)
+    parent_builder.add_node("child", child_graph)
+    parent_builder.add_node("parent_second", parent_second)
+    parent_builder.add_edge(START, "child")
+    parent_builder.add_edge("child", "parent_second")
+    parent_builder.add_edge("parent_second", END)
+
+    compiled = parent_builder.compile(checkpointer=MemorySaver())
+    config = {"configurable": {"thread_id": "drain-subgraph"}}
+
+    with pytest.raises(GraphDrained, match="sigterm"):
+        compiled.invoke({}, config, control=control)
+
+    assert compiled.invoke(None, config) == {
+        "child_first": "done",
+        "child_second": "done",
+        "parent_second": "done",
+    }
+
+
+@pytest.mark.anyio
+async def test_drain_requested_in_terminal_step_finishes_normally_async() -> None:
+    class State(TypedDict, total=False):
+        value: str
+
+    control = RunControl()
+
+    async def node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"value": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.add_edge(START, "node")
+    graph.add_edge("node", END)
+
+    assert await graph.compile().ainvoke({}, control=control) == {"value": "done"}
+    assert control.drain_requested
+
+
 def test_runtime_propogated_to_subgraph() -> None:
     @dataclass
     class Context:
@@ -392,6 +579,334 @@ def test_context_coercion_pydantic_validation_errors() -> None:
         )
 
 
+def test_external_drain_concurrent_sync() -> None:
+    """External thread calls request_drain() while graph is mid-execution."""
+
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    started = threading.Event()
+
+    def first_node(state: State) -> dict[str, str]:
+        started.set()
+        time.sleep(0.05)
+        return {"first": "done"}
+
+    def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    control = RunControl()
+    compiled = graph.compile()
+
+    exc_holder: list[BaseException | None] = [None]
+
+    def run_graph() -> None:
+        try:
+            compiled.invoke({}, control=control)
+        except GraphDrained as e:
+            exc_holder[0] = e
+
+    t = threading.Thread(target=run_graph)
+    t.start()
+
+    started.wait(timeout=5)
+    control.request_drain("sigterm")
+
+    t.join(timeout=10)
+
+    exc = exc_holder[0]
+    assert isinstance(exc, GraphDrained)
+    assert exc.reason == "sigterm"
+
+
+@pytest.mark.anyio
+async def test_external_drain_concurrent_async() -> None:
+    """External task calls request_drain() while graph is mid-execution."""
+
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    started = asyncio.Event()
+
+    async def first_node(state: State) -> dict[str, str]:
+        started.set()
+        await asyncio.sleep(0.05)
+        return {"first": "done"}
+
+    async def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    control = RunControl()
+    compiled = graph.compile()
+
+    async def drain_after_start() -> None:
+        await started.wait()
+        control.request_drain("sigterm")
+
+    drain_task = asyncio.create_task(drain_after_start())
+
+    with pytest.raises(GraphDrained, match="sigterm"):
+        await compiled.ainvoke({}, control=control)
+
+    await drain_task
+
+
+@pytest.mark.anyio
+async def test_drain_then_cancel_after_graceful_timeout() -> None:
+    """Simulate: drain requested -> node still running -> graceful timeout -> cancel.
+
+    This shows what happens when a long-running node doesn't finish within
+    the graceful period after drain is requested.
+    """
+
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    node_started = asyncio.Event()
+    node_cancelled = asyncio.Event()
+    node_finished = asyncio.Event()
+
+    async def slow_node(state: State) -> dict[str, str]:
+        node_started.set()
+        try:
+            await asyncio.sleep(30)  # very long operation
+        except asyncio.CancelledError:
+            node_cancelled.set()
+            raise
+        node_finished.set()
+        return {"first": "done"}
+
+    async def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", slow_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    control = RunControl()
+    compiled = graph.compile()
+
+    # Phase 1: start graph
+    graph_task = asyncio.create_task(compiled.ainvoke({}, control=control))
+
+    # Phase 2: wait for node to start, then request drain
+    await node_started.wait()
+    control.request_drain("sigterm")
+
+    # Phase 3: graceful timeout — node is still running, cancel after 1s
+    graceful_timeout = 1.0
+    await asyncio.sleep(graceful_timeout)
+
+    assert not node_finished.is_set(), "node should still be running"
+    assert not node_cancelled.is_set(), "node should not be cancelled yet"
+
+    # Phase 4: force cancel
+    graph_task.cancel()
+
+    with pytest.raises(asyncio.CancelledError):
+        await graph_task
+
+    # The node received CancelledError at the await point
+    assert node_cancelled.is_set(), "node should have received CancelledError"
+    assert not node_finished.is_set(), "node should NOT have finished normally"
+
+
+@pytest.mark.anyio
+async def test_cancel_ainvoke_with_async_node() -> None:
+    """Cancel ainvoke running an async node: CancelledError is delivered
+    at the await point and the node stops immediately."""
+
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    timeline: list[str] = []
+    node_started = asyncio.Event()
+
+    async def slow_async_node(state: State) -> dict[str, str]:
+        timeline.append(f"async_node:start thread={threading.current_thread().name}")
+        node_started.set()
+        try:
+            await asyncio.sleep(30)
+        except asyncio.CancelledError:
+            timeline.append("async_node:cancelled")
+            raise
+        timeline.append("async_node:finished")
+        return {"first": "done"}
+
+    async def second_node(state: State) -> dict[str, str]:
+        timeline.append("second_node:run")
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", slow_async_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    compiled = graph.compile()
+    graph_task = asyncio.create_task(compiled.ainvoke({}))
+
+    await node_started.wait()
+    timeline.append("test:cancel")
+    graph_task.cancel()
+
+    with pytest.raises(asyncio.CancelledError):
+        await graph_task
+    timeline.append("test:done")
+
+    # async node runs on the event loop thread (MainThread)
+    assert any("MainThread" in e for e in timeline if "async_node:start" in e)
+    # CancelledError was delivered at the await point — node stopped
+    assert "async_node:cancelled" in timeline
+    # Node did NOT run to completion
+    assert "async_node:finished" not in timeline
+    # Second node never ran
+    assert "second_node:run" not in timeline
+
+
+@pytest.mark.anyio
+async def test_cancel_ainvoke_with_sync_node() -> None:
+    """Cancel ainvoke running a sync node.
+
+    Sync nodes in ainvoke run on a separate thread (via run_in_executor),
+    NOT on the event loop thread. Cancelling the asyncio task disconnects
+    from the thread future, but the thread keeps running as an orphan and
+    completes on its own.
+
+    Key difference from async nodes:
+    - async node: CancelledError stops the coroutine at an await point
+    - sync node: cancel only disconnects asyncio; the thread runs to completion
+
+    In shutdown case, we will ignore this because the instance will be destroyed soon.
+    """
+
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    timeline: list[str] = []
+    node_started = threading.Event()
+    node_finished = threading.Event()
+
+    def slow_sync_node(state: State) -> dict[str, str]:
+        timeline.append(f"sync_node:start thread={threading.current_thread().name}")
+        node_started.set()
+        time.sleep(1)
+        timeline.append("sync_node:after_sleep")
+        node_finished.set()
+        return {"first": "done"}
+
+    def second_node(state: State) -> dict[str, str]:
+        timeline.append("second_node:run")
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", slow_sync_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    control = RunControl()
+    compiled = graph.compile()
+
+    timeline.append(f"test:main thread={threading.current_thread().name}")
+    graph_task = asyncio.create_task(compiled.ainvoke({}, control=control))
+
+    loop = asyncio.get_event_loop()
+    await loop.run_in_executor(None, node_started.wait, 5)
+
+    timeline.append("test:cancel+drain")
+    graph_task.cancel()
+    control.request_drain("sigterm")
+
+    with pytest.raises(asyncio.CancelledError):
+        await graph_task
+    timeline.append("test:exc=CancelledError")
+
+    # Sync node runs on a background thread (asyncio_*), NOT MainThread
+    sync_start = next(e for e in timeline if "sync_node:start" in e)
+    assert "MainThread" not in sync_start, (
+        "sync node should run on a background thread, not the event loop thread"
+    )
+
+    # At this point, the asyncio task is done but the thread is orphaned.
+    # The sync node has NOT finished yet — cancel only disconnected asyncio.
+    assert not node_finished.is_set(), (
+        "sync node should still be running in its background thread"
+    )
+
+    # Wait for the orphaned thread to complete on its own.
+    await loop.run_in_executor(None, node_finished.wait, 5)
+    assert node_finished.is_set()
+
+    # After the orphaned thread finishes, the full timeline looks like:
+    #   test:main thread=MainThread
+    #   sync_node:start thread=asyncio_N    <- background thread
+    #   test:cancel+drain                   <- cancel + drain fired
+    #   test:exc=CancelledError             <- asyncio disconnected
+    #   sync_node:after_sleep               <- thread ran to completion anyway
+    assert "sync_node:after_sleep" in timeline
+    # Second node never ran
+    assert "second_node:run" not in timeline
+
+    # Verify timeline ordering: cancel happened before node finished
+    cancel_idx = timeline.index("test:cancel+drain")
+    sleep_idx = timeline.index("sync_node:after_sleep")
+    assert cancel_idx < sleep_idx, (
+        "cancel was issued while the sync node was still sleeping"
+    )
+
+
+def test_drain_with_control_parameter_sync() -> None:
+    """Control parameter is wired through invoke -> stream."""
+
+    class State(TypedDict, total=False):
+        value: str
+
+    ran = False
+
+    def node(state: State) -> dict[str, str]:
+        nonlocal ran
+        ran = True
+        return {"value": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.add_edge(START, "node")
+    graph.add_edge("node", END)
+
+    # Pre-drained control stops before executing the first pending task.
+    control = RunControl()
+    control.request_drain("pre-drained")
+
+    with pytest.raises(GraphDrained, match="pre-drained"):
+        graph.compile().invoke({}, control=control)
+    assert not ran
+
+
 # --- ExecutionInfo unit tests ---
 
 

libs/langgraph/tests/test_stream_data_transformers.py

@@ -0,0 +1,707 @@
+"""Tests for CustomTransformer, UpdatesTransformer, CheckpointsTransformer, DebugTransformer, TasksTransformer.
+
+These transformers capture raw protocol events for their respective stream
+modes and expose them as native projections on the run stream (run.custom,
+run.updates, run.checkpoints, run.debug, run.tasks). Tests dispatch synthetic
+protocol events through a StreamMux to isolate transformer logic; the final
+group exercises real graphs through stream_events(version="v3").
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from typing import Annotated, Any
+
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    CheckpointsTransformer,
+    CustomTransformer,
+    DebugTransformer,
+    LifecycleTransformer,
+    TasksTransformer,
+    UpdatesTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+def _custom_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "custom",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _checkpoints_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "checkpoints",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _debug_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "debug",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _tasks_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _updates_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "updates",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _arm(mux: StreamMux, transformer: Any) -> None:
+    """Force projection logs to accept pushes (skip lazy-subscribe gate)."""
+    mux._events._subscribed = True
+    transformer._log._subscribed = True
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _drain(transformer: Any) -> list[Any]:
+    return _unstamped(transformer._log._items)
+
+
+# ---------------------------------------------------------------------------
+# CustomTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_custom_captures_root_scope_events() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], {"status": "processing"}))
+    mux.push(_custom_event([], {"status": "done"}))
+
+    items = _drain(t)
+    assert items == [{"status": "processing"}, {"status": "done"}]
+
+
+def test_custom_ignores_subgraph_scope_events() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event(["subgraph:abc"], {"from": "child"}))
+
+    assert _drain(t) == []
+
+
+def test_custom_scoped_transformer_captures_own_scope() -> None:
+    t = CustomTransformer(scope=("agent:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], {"from": "root"}))
+    mux.push(_custom_event(["agent:abc"], {"from": "self"}))
+    mux.push(_custom_event(["agent:abc", "deep:def"], {"from": "child"}))
+
+    items = _drain(t)
+    assert items == [{"from": "self"}]
+
+
+def test_custom_preserves_any_payload_type() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], "string_payload"))
+    mux.push(_custom_event([], 42))
+    mux.push(_custom_event([], [1, 2, 3]))
+
+    assert _drain(t) == ["string_payload", 42, [1, 2, 3]]
+
+
+def test_custom_does_not_suppress_from_main_log() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], "data"))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "custom" in methods
+
+
+def test_custom_ignores_other_methods() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(
+        {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": [], "timestamp": TS, "data": {}},
+        }
+    )
+    assert _drain(t) == []
+
+
+def test_custom_required_stream_modes() -> None:
+    assert CustomTransformer.required_stream_modes == ("custom",)
+
+
+def test_custom_is_native() -> None:
+    assert getattr(CustomTransformer, "_native", False) is True
+
+
+def test_custom_init_returns_correct_key() -> None:
+    t = CustomTransformer()
+    projection = t.init()
+    assert "custom" in projection
+    assert isinstance(projection["custom"], StreamChannel)
+
+
+# ---------------------------------------------------------------------------
+# CheckpointsTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_checkpoints_captures_root_scope_events() -> None:
+    t = CheckpointsTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    checkpoint_data = {"values": {"x": 1}, "next": ["node_b"]}
+    mux.push(_checkpoints_event([], checkpoint_data))
+
+    items = _drain(t)
+    assert items == [checkpoint_data]
+
+
+def test_checkpoints_ignores_subgraph_events() -> None:
+    t = CheckpointsTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_checkpoints_event(["child:abc"], {"values": {"x": 1}}))
+
+    assert _drain(t) == []
+
+
+def test_checkpoints_scoped_transformer() -> None:
+    t = CheckpointsTransformer(scope=("sub:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_checkpoints_event([], {"from": "root"}))
+    mux.push(_checkpoints_event(["sub:abc"], {"from": "self"}))
+
+    assert _drain(t) == [{"from": "self"}]
+
+
+def test_checkpoints_does_not_suppress_from_main_log() -> None:
+    t = CheckpointsTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_checkpoints_event([], {"values": {}}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "checkpoints" in methods
+
+
+def test_checkpoints_required_stream_modes() -> None:
+    assert CheckpointsTransformer.required_stream_modes == ("checkpoints",)
+
+
+def test_checkpoints_is_native() -> None:
+    assert getattr(CheckpointsTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# DebugTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_debug_captures_root_scope_events() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    debug_data = {
+        "step": 0,
+        "type": "checkpoint",
+        "timestamp": "2026-01-01T00:00:00Z",
+        "payload": {"values": {"x": 1}},
+    }
+    mux.push(_debug_event([], debug_data))
+
+    items = _drain(t)
+    assert items == [debug_data]
+
+
+def test_debug_ignores_subgraph_events() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_debug_event(["child:abc"], {"step": 0, "type": "task"}))
+
+    assert _drain(t) == []
+
+
+def test_debug_captures_multiple_event_types() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_debug_event([], {"step": 0, "type": "checkpoint", "payload": {}}))
+    mux.push(_debug_event([], {"step": 1, "type": "task", "payload": {}}))
+    mux.push(_debug_event([], {"step": 1, "type": "task_result", "payload": {}}))
+
+    items = _drain(t)
+    assert len(items) == 3
+    assert [d["type"] for d in items] == ["checkpoint", "task", "task_result"]
+
+
+def test_debug_does_not_suppress_from_main_log() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_debug_event([], {"step": 0}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "debug" in methods
+
+
+def test_debug_required_stream_modes() -> None:
+    assert DebugTransformer.required_stream_modes == ("debug",)
+
+
+def test_debug_is_native() -> None:
+    assert getattr(DebugTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# TasksTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_tasks_captures_root_scope_events() -> None:
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    task_start = {"id": "t1", "name": "my_node", "input": None, "triggers": []}
+    mux.push(_tasks_event([], task_start))
+
+    items = _drain(t)
+    assert items == [task_start]
+
+
+def test_tasks_captures_start_and_result() -> None:
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    start = {"id": "t1", "name": "a", "input": None, "triggers": []}
+    result = {"id": "t1", "name": "a", "result": {"output": 42}, "error": None}
+    mux.push(_tasks_event([], start))
+    mux.push(_tasks_event([], result))
+
+    items = _drain(t)
+    assert items == [start, result]
+
+
+def test_tasks_ignores_subgraph_events() -> None:
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_tasks_event(["child:abc"], {"id": "t1", "name": "x"}))
+
+    assert _drain(t) == []
+
+
+def test_tasks_scoped_transformer() -> None:
+    t = TasksTransformer(scope=("agent:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_tasks_event([], {"id": "t1"}))
+    mux.push(_tasks_event(["agent:abc"], {"id": "t2"}))
+    mux.push(_tasks_event(["agent:abc", "deep:def"], {"id": "t3"}))
+
+    assert _drain(t) == [{"id": "t2"}]
+
+
+def test_tasks_does_not_suppress_from_main_log() -> None:
+    """TasksTransformer returns True — it doesn't suppress tasks events.
+
+    (LifecycleTransformer suppresses them, but that's independent.)
+    """
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_tasks_event([], {"id": "t1"}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "tasks" in methods
+
+
+def test_tasks_required_stream_modes() -> None:
+    assert TasksTransformer.required_stream_modes == ("tasks",)
+
+
+def test_tasks_is_native() -> None:
+    assert getattr(TasksTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# UpdatesTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_updates_captures_root_scope_events() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    update = {"my_node": {"value": "hello!"}}
+    mux.push(_updates_event([], update))
+
+    items = _drain(t)
+    assert items == [update]
+
+
+def test_updates_captures_multiple_steps() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event([], {"node_a": {"x": 1}}))
+    mux.push(_updates_event([], {"node_b": {"x": 2}}))
+
+    items = _drain(t)
+    assert items == [{"node_a": {"x": 1}}, {"node_b": {"x": 2}}]
+
+
+def test_updates_ignores_subgraph_events() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event(["child:abc"], {"inner_node": {"v": 1}}))
+
+    assert _drain(t) == []
+
+
+def test_updates_scoped_transformer() -> None:
+    t = UpdatesTransformer(scope=("agent:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event([], {"from": "root"}))
+    mux.push(_updates_event(["agent:abc"], {"from": "self"}))
+
+    assert _drain(t) == [{"from": "self"}]
+
+
+def test_updates_does_not_suppress_from_main_log() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event([], {"n": {}}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "updates" in methods
+
+
+def test_updates_required_stream_modes() -> None:
+    assert UpdatesTransformer.required_stream_modes == ("updates",)
+
+
+def test_updates_is_native() -> None:
+    assert getattr(UpdatesTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# Cross-transformer: unrelated events pass through
+# ---------------------------------------------------------------------------
+
+
+def test_unrelated_events_ignored_by_all() -> None:
+    """Non-matching method events don't land in any transformer's log."""
+    transformers = [
+        CustomTransformer(),
+        UpdatesTransformer(),
+        CheckpointsTransformer(),
+        DebugTransformer(),
+        TasksTransformer(),
+    ]
+    mux = StreamMux(transformers, is_async=False)
+    mux._events._subscribed = True
+    for t in transformers:
+        t._log._subscribed = True
+
+    mux.push(
+        {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
+        }
+    )
+
+    for t in transformers:
+        assert _unstamped(t._log._items) == []
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: real graphs through stream_events(version="v3")
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _my_node(state: _State) -> dict[str, Any]:
+    from langgraph.config import get_stream_writer
+
+    writer = get_stream_writer()
+    writer({"status": "working", "node": "my_node"})
+    return {"value": state["value"] + "!", "items": ["done"]}
+
+
+def _make_simple_graph() -> Any:
+    builder = StateGraph(_State, input_schema=_State)
+    builder.add_node("my_node", _my_node)
+    builder.add_edge(START, "my_node")
+    builder.add_edge("my_node", END)
+    return builder.compile()
+
+
+def test_stream_events_v3_custom_projection_opt_in() -> None:
+    """run.custom surfaces get_stream_writer() payloads when opted in."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "hello", "items": []}, version="v3", transformers=[CustomTransformer]
+    )
+
+    custom_events = list(run.custom)
+    assert len(custom_events) >= 1
+    assert any(e.get("status") == "working" for e in custom_events)
+
+
+def test_stream_events_v3_custom_and_values_coexist() -> None:
+    """Both run.custom and run.values work in the same run."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "hello", "items": []}, version="v3", transformers=[CustomTransformer]
+    )
+
+    custom_events = list(run.custom)
+    assert run.output is not None
+    assert run.output["value"] == "hello!"
+    assert len(custom_events) >= 1
+
+
+def test_stream_events_v3_tasks_projection_opt_in() -> None:
+    """run.tasks surfaces raw task events when opted in via transformers=."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []}, transformers=[TasksTransformer], version="v3"
+    )
+
+    tasks_events = list(run.tasks)
+    assert len(tasks_events) >= 1
+    names = [t.get("name") for t in tasks_events if "name" in t]
+    assert "my_node" in names
+
+
+def test_stream_events_v3_debug_projection_opt_in() -> None:
+    """run.debug surfaces debug events when opted in via transformers=."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []}, transformers=[DebugTransformer], version="v3"
+    )
+
+    debug_events = list(run.debug)
+    assert len(debug_events) >= 1
+    types = {d.get("type") for d in debug_events}
+    assert types & {"checkpoint", "task", "task_result"}
+
+
+def test_stream_events_v3_updates_projection_opt_in() -> None:
+    """run.updates surfaces node output dicts when opted in via transformers=."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []}, version="v3", transformers=[UpdatesTransformer]
+    )
+
+    updates = list(run.updates)
+    assert len(updates) >= 1
+    node_names = {k for u in updates for k in u if k != "__interrupt__"}
+    assert "my_node" in node_names
+
+
+def test_stream_events_v3_all_transformers_interleaved() -> None:
+    """All five transformers registered together, consumed via interleave."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        transformers=[
+            CustomTransformer,
+            UpdatesTransformer,
+            CheckpointsTransformer,
+            DebugTransformer,
+            TasksTransformer,
+        ],
+    )
+
+    collected: dict[str, list[Any]] = {
+        "custom": [],
+        "updates": [],
+        "debug": [],
+        "tasks": [],
+    }
+    for name, item in run.interleave("custom", "updates", "debug", "tasks"):
+        collected[name].append(item)
+
+    assert len(collected["custom"]) >= 1
+    assert len(collected["updates"]) >= 1
+    assert len(collected["tasks"]) >= 1
+    assert len(collected["debug"]) >= 1
+    types = {d.get("type") for d in collected["debug"]}
+    assert types & {"checkpoint", "task", "task_result"}
+    node_names = {k for u in collected["updates"] for k in u if k != "__interrupt__"}
+    assert "my_node" in node_names
+
+    assert run.output is not None
+    assert run.output["value"] == "x!"
+
+
+def test_stream_events_v3_all_transformers_with_checkpointer() -> None:
+    """All transformers with a checkpointer — run.checkpoints populated."""
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    builder = StateGraph(_State, input_schema=_State)
+    builder.add_node("my_node", _my_node)
+    builder.add_edge(START, "my_node")
+    builder.add_edge("my_node", END)
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        config={"configurable": {"thread_id": "test-all"}},
+        transformers=[
+            CustomTransformer,
+            UpdatesTransformer,
+            CheckpointsTransformer,
+            DebugTransformer,
+            TasksTransformer,
+        ],
+    )
+
+    collected: dict[str, list[Any]] = {
+        "custom": [],
+        "updates": [],
+        "checkpoints": [],
+        "debug": [],
+        "tasks": [],
+    }
+    for name, item in run.interleave(
+        "custom", "updates", "checkpoints", "debug", "tasks"
+    ):
+        collected[name].append(item)
+
+    assert len(collected["checkpoints"]) >= 1
+    assert len(collected["custom"]) >= 1
+
+
+def test_stream_events_v3_checkpoints_projection_opt_in() -> None:
+    """run.checkpoints surfaces checkpoint data when opted in with a checkpointer."""
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    builder = StateGraph(_State, input_schema=_State)
+    builder.add_node("my_node", _my_node)
+    builder.add_edge(START, "my_node")
+    builder.add_edge("my_node", END)
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        config={"configurable": {"thread_id": "test-ckpt-standalone"}},
+        transformers=[CheckpointsTransformer],
+    )
+
+    checkpoints = list(run.checkpoints)
+    assert len(checkpoints) >= 1
+
+
+# ---------------------------------------------------------------------------
+# TasksTransformer + LifecycleTransformer co-registration
+# ---------------------------------------------------------------------------
+
+
+def test_tasks_and_lifecycle_coregistration() -> None:
+    """When both are in the same StreamMux, LifecycleTransformer suppresses
+    tasks events from the main log (returns False) while TasksTransformer
+    still captures them into its own log.
+    """
+    lifecycle = LifecycleTransformer()
+    tasks = TasksTransformer()
+    mux = StreamMux([lifecycle, tasks], is_async=False)
+    mux._events._subscribed = True
+    tasks._log._subscribed = True
+    lifecycle._channel._subscribed = True
+
+    task_data = {"id": "t1", "name": "my_node", "input": None, "triggers": []}
+    mux.push(_tasks_event([], task_data))
+
+    assert _drain(tasks) == [task_data]
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "tasks" not in methods
+
+
+def test_tasks_and_lifecycle_coregistration_e2e() -> None:
+    """E2e: TasksTransformer captures task events even when LifecycleTransformer
+    is present and suppressing them from the main log.
+    """
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        transformers=[TasksTransformer],
+    )
+
+    tasks_events = list(run.tasks)
+    assert len(tasks_events) >= 1
+    names = [t.get("name") for t in tasks_events if "name" in t]
+    assert "my_node" in names

libs/langgraph/tests/test_stream_events_v3.py

@@ -19,9 +19,11 @@ from typing_extensions import TypedDict, assert_type
 
 from langgraph._internal._constants import INTERRUPT
 from langgraph.constants import END, START
+from langgraph.errors import GraphDrained
 from langgraph.func import entrypoint
 from langgraph.graph import StateGraph
 from langgraph.graph.message import MessagesState
+from langgraph.runtime import RunControl
 from langgraph.types import (
     CheckpointPayload,
     CheckpointStreamPart,
@@ -229,6 +231,32 @@ class TestV2Stream:
         for c in chunks:
             _assert_stream_part_shape(c)
 
+    def test_stream_events_v3_accepts_control_for_drain(self) -> None:
+        class DrainState(TypedDict, total=False):
+            value: str
+            skipped: str
+
+        control = RunControl()
+
+        def first_node(state: DrainState) -> dict[str, str]:
+            control.request_drain("sigterm")
+            return {"value": "done"}
+
+        def second_node(state: DrainState) -> dict[str, str]:
+            return {"skipped": "nope"}
+
+        builder = StateGraph(DrainState)
+        builder.add_node("first", first_node)
+        builder.add_node("second", second_node)
+        builder.add_edge(START, "first")
+        builder.add_edge("first", "second")
+        builder.add_edge("second", END)
+        graph = builder.compile()
+
+        run = graph.stream_events({}, control=control, version="v3")
+        with pytest.raises(GraphDrained, match="sigterm"):
+            list(run.values)
+
     def test_subgraphs_ns(self) -> None:
         outer = _make_subgraph()
         chunks = list(
@@ -1096,7 +1124,7 @@ class TestV2ValidationErrors:
 
     _INVALID_INPUT: dict[str, Any] = {"value": [1, 2, 3], "items": []}
 
-    def test_stream_v2_pydantic_validation_error(self) -> None:
+    def test_stream_events_v3_pydantic_validation_error(self) -> None:
         """Invalid input to stream with v2 + pydantic state raises ValidationError."""
         graph = _make_pydantic_graph()
         with pytest.raises(ValidationError):

libs/langgraph/tests/test_stream_events_v3_e2e.py

@@ -0,0 +1,808 @@
+"""End-to-end tests exercising all stream_events(version="v3") projections together.
+
+Each test builds a realistic graph (subgraphs, LLM calls, custom writers,
+interrupts) and verifies that every projection — values, messages, lifecycle,
+subgraphs, raw events, output, interleave — produces correct, consistent
+results through a single stream_events(version="v3") / astream_events(version="v3") run.
+"""
+
+from __future__ import annotations
+
+import operator
+import sys
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessage
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import MessagesState, StateGraph
+from langgraph.stream import StreamChannel, StreamTransformer
+from langgraph.stream._types import ProtocolEvent
+from langgraph.types import StreamWriter, interrupt
+
+NEEDS_CONTEXTVARS = pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="Python 3.11+ is required for async contextvars support",
+)
+
+
+# ---------------------------------------------------------------------------
+# State and graph builders
+# ---------------------------------------------------------------------------
+
+
+class AgentState(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _make_nested_graph():
+    """Build a two-level graph with pure state transforms.
+
+    Structure:
+        outer:
+            router_node  (state transform)
+            inner_graph  (compiled subgraph)
+
+        inner_graph:
+            process_node  (state transform)
+    """
+
+    def process_node(state: AgentState) -> dict[str, Any]:
+        return {"value": state["value"] + "_processed", "items": ["processed"]}
+
+    inner_builder: StateGraph = StateGraph(AgentState, input_schema=AgentState)
+    inner_builder.add_node("process_node", process_node)
+    inner_builder.add_edge(START, "process_node")
+    inner_builder.add_edge("process_node", END)
+    inner_graph = inner_builder.compile()
+
+    def router_node(state: AgentState) -> dict[str, Any]:
+        return {"value": state["value"] + "_routed", "items": ["routed"]}
+
+    outer_builder: StateGraph = StateGraph(AgentState, input_schema=AgentState)
+    outer_builder.add_node("router", router_node)
+    outer_builder.add_node("inner", inner_graph)
+    outer_builder.add_edge(START, "router")
+    outer_builder.add_edge("router", "inner")
+    outer_builder.add_edge("inner", END)
+    return outer_builder.compile()
+
+
+def _make_messages_graph():
+    """Flat graph with an LLM call for messages projection testing."""
+    model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+    def call_model(state: MessagesState) -> dict[str, Any]:
+        return {"messages": model.invoke(state["messages"])}
+
+    return (
+        StateGraph(MessagesState)
+        .add_node("call_model", call_model)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", END)
+        .compile()
+    )
+
+
+def _make_messages_subgraph():
+    """Outer graph with a MessagesState subgraph that returns an AIMessage.
+
+    Uses the whole-message fallback path (node returns AIMessage directly)
+    to exercise messages through a subgraph boundary.
+    """
+
+    def return_message(state: MessagesState) -> dict[str, Any]:
+        return {"messages": AIMessage(content="from subgraph", id="sub-msg-1")}
+
+    inner = (
+        StateGraph(MessagesState)
+        .add_node("return_message", return_message)
+        .add_edge(START, "return_message")
+        .add_edge("return_message", END)
+        .compile()
+    )
+
+    class OuterState(TypedDict):
+        messages: Annotated[list[Any], operator.add]
+        done: bool
+
+    def pre_node(state: OuterState) -> dict[str, Any]:
+        return {"done": False}
+
+    return (
+        StateGraph(OuterState)
+        .add_node("pre", pre_node)
+        .add_node("inner", inner)
+        .add_edge(START, "pre")
+        .add_edge("pre", "inner")
+        .add_edge("inner", END)
+        .compile()
+    )
+
+
+def _make_custom_writer_graph():
+    """Graph where a node emits custom stream events via StreamWriter."""
+
+    def writer_node(state: AgentState, *, writer: StreamWriter) -> dict[str, Any]:
+        writer({"step": "start", "detail": "beginning work"})
+        writer({"step": "middle", "detail": "processing"})
+        writer({"step": "end", "detail": "done"})
+        return {"value": state["value"] + "_custom", "items": ["custom"]}
+
+    builder = StateGraph(AgentState)
+    builder.add_node("writer_node", writer_node)
+    builder.add_edge(START, "writer_node")
+    builder.add_edge("writer_node", END)
+    return builder.compile()
+
+
+def _make_interrupt_graph():
+    """Graph that interrupts after the first node."""
+
+    def step_one(state: AgentState) -> dict[str, Any]:
+        return {"value": state["value"] + "_step1", "items": ["step1"]}
+
+    def step_two(state: AgentState) -> dict[str, Any]:
+        answer = interrupt("need approval")
+        return {"value": state["value"] + f"_{answer}", "items": ["step2"]}
+
+    builder = StateGraph(AgentState)
+    builder.add_node("step_one", step_one)
+    builder.add_node("step_two", step_two)
+    builder.add_edge(START, "step_one")
+    builder.add_edge("step_one", "step_two")
+    builder.add_edge("step_two", END)
+    return builder.compile(checkpointer=InMemorySaver())
+
+
+def _make_error_subgraph():
+    """Graph with a subgraph that raises."""
+
+    def failing_node(state: AgentState) -> dict[str, Any]:
+        raise ValueError("subgraph explosion")
+
+    inner_builder = StateGraph(AgentState)
+    inner_builder.add_node("fail", failing_node)
+    inner_builder.add_edge(START, "fail")
+    inner_builder.add_edge("fail", END)
+    inner = inner_builder.compile()
+
+    outer_builder = StateGraph(AgentState)
+    outer_builder.add_node("inner", inner)
+    outer_builder.add_edge(START, "inner")
+    outer_builder.add_edge("inner", END)
+    return outer_builder.compile()
+
+
+class _CustomPassthroughTransformer(StreamTransformer):
+    required_stream_modes = ("custom",)
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _CounterTransformer(StreamTransformer):
+    """Custom transformer that counts values events via a StreamChannel."""
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._channel: StreamChannel[int] = StreamChannel("counter")
+        self._count = 0
+
+    def init(self) -> dict[str, Any]:
+        return {"counter": self._channel}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] == "values":
+            self._count += 1
+            self._channel.push(self._count)
+        return True
+
+
+# ---------------------------------------------------------------------------
+# Sync end-to-end: all projections on nested graph
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2ESync:
+    def test_all_projections_nested_graph(self) -> None:
+        """Run a nested graph through stream_events(version="v3") and verify values + lifecycle."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        values_snapshots: list[dict[str, Any]] = []
+        lifecycle_events: list[dict[str, Any]] = []
+        for name, item in run.interleave("values", "lifecycle"):
+            if name == "values":
+                values_snapshots.append(item)
+            elif name == "lifecycle":
+                lifecycle_events.append(item)
+
+        assert len(values_snapshots) >= 1
+        final = values_snapshots[-1]
+        assert "routed" in final["items"]
+        assert "processed" in final["items"]
+        assert "_routed" in final["value"]
+        assert "_processed" in final["value"]
+
+        assert len(lifecycle_events) >= 2
+        started = [e for e in lifecycle_events if e["event"] == "started"]
+        completed = [e for e in lifecycle_events if e["event"] == "completed"]
+        assert len(started) >= 1
+        assert len(completed) >= 1
+
+    def test_subgraph_handles_with_drill_down(self) -> None:
+        """Subgraph handles yield and support values drill-down."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        handles = []
+        for handle in run.subgraphs:
+            child_values = list(handle.values)
+            handles.append(
+                {
+                    "path": handle.path,
+                    "graph_name": handle.graph_name,
+                    "values_count": len(child_values),
+                }
+            )
+
+        assert len(handles) >= 1
+        assert handles[0]["values_count"] >= 1
+
+        output = run.output
+        assert output is not None
+        assert "_routed" in output["value"]
+        assert "_processed" in output["value"]
+
+    def test_raw_events_have_monotonic_seq(self) -> None:
+        """Raw protocol events have monotonically increasing seq numbers."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+        events = list(run)
+        assert len(events) > 0
+
+        seqs = [e["seq"] for e in events]
+        for i in range(1, len(seqs)):
+            assert seqs[i] > seqs[i - 1], f"seq not monotonic at {i}: {seqs}"
+
+        for event in events:
+            assert event["type"] == "event"
+            assert "method" in event
+            assert isinstance(event["params"]["timestamp"], int)
+
+    def test_output_matches_final_values_snapshot(self) -> None:
+        """output property returns the same state as the last values snapshot."""
+        run1 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        snapshots = list(run1.values)
+        final_via_values = snapshots[-1]
+
+        run2 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        final_via_output = run2.output
+
+        assert final_via_values == final_via_output
+
+    def test_context_manager_and_abort(self) -> None:
+        """Context manager calls abort, marking the stream exhausted."""
+        graph = _make_nested_graph()
+        with graph.stream_events({"value": "x", "items": []}, version="v3") as run:
+            first_val = next(iter(run.values))
+            assert isinstance(first_val, dict)
+        assert run._exhausted is True
+
+    def test_extensions_has_all_native_keys(self) -> None:
+        """Extensions dict exposes all native projection keys."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+        _ = run.output
+
+        assert "values" in run.extensions
+        assert "messages" in run.extensions
+        assert "lifecycle" in run.extensions
+        assert "subgraphs" in run.extensions
+        assert run.values is run.extensions["values"]
+        assert run.messages is run.extensions["messages"]
+        assert run.lifecycle is run.extensions["lifecycle"]
+        assert run.subgraphs is run.extensions["subgraphs"]
+
+
+# ---------------------------------------------------------------------------
+# Sync: messages projection
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2EMessages:
+    def test_messages_projection_from_invoke(self) -> None:
+        """Messages projection captures LLM calls via model.invoke() auto-routing."""
+        graph = _make_messages_graph()
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        streams = list(run.messages)
+
+        assert len(streams) >= 1
+        for stream in streams:
+            assert isinstance(stream, ChatModelStream)
+        assert streams[0].output.text == "hello world"
+
+    def test_messages_text_deltas(self) -> None:
+        """Text deltas from the messages projection concatenate correctly."""
+        model = GenericFakeChatModel(messages=iter(["streamed answer"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "go"}, version="v3")
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "streamed answer"
+
+    def test_messages_from_whole_ai_message(self) -> None:
+        """Node returning AIMessage directly produces a complete stream."""
+
+        def return_msg(state: MessagesState) -> dict[str, Any]:
+            return {"messages": AIMessage(content="hardcoded", id="msg-1")}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("return_msg", return_msg)
+            .add_edge(START, "return_msg")
+            .add_edge("return_msg", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert stream.output.text == "hardcoded"
+        assert stream.message_id == "msg-1"
+
+    def test_root_messages_only_shows_root_scope(self) -> None:
+        """Root messages projection doesn't surface subgraph-scoped messages."""
+        graph = _make_messages_subgraph()
+        run = graph.stream_events({"messages": ["hi"], "done": False}, version="v3")
+        root_streams = list(run.messages)
+        # The message is emitted inside the subgraph, so the root
+        # messages projection (scoped to root namespace) doesn't see it.
+        assert root_streams == []
+
+    def test_subgraph_handle_messages_drill_down(self) -> None:
+        """Drilling into subgraph handle's messages surfaces subgraph messages."""
+        graph = _make_messages_subgraph()
+        run = graph.stream_events({"messages": ["hi"], "done": False}, version="v3")
+
+        found_messages = False
+        for handle in run.subgraphs:
+            child_messages = list(handle.messages)
+            if child_messages:
+                found_messages = True
+                assert isinstance(child_messages[0], ChatModelStream)
+                assert child_messages[0].output.text == "from subgraph"
+        assert found_messages
+
+
+# ---------------------------------------------------------------------------
+# Sync: custom stream writer + custom transformer
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2ECustom:
+    def test_custom_events_with_passthrough_transformer(self) -> None:
+        """Custom StreamWriter events appear on the main log when a
+        transformer declares the custom mode."""
+        graph = _make_custom_writer_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CustomPassthroughTransformer],
+        )
+        events = list(run)
+        custom = [e for e in events if e["method"] == "custom"]
+        assert len(custom) == 3
+        steps = [e["params"]["data"]["step"] for e in custom]
+        assert steps == ["start", "middle", "end"]
+
+    def test_custom_events_suppressed_without_transformer(self) -> None:
+        """Without a custom-mode transformer, custom events don't flow."""
+        graph = _make_custom_writer_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+        events = list(run)
+        custom = [e for e in events if e["method"] == "custom"]
+        assert custom == []
+
+    def test_custom_transformer_with_stream_channel(self) -> None:
+        """A custom transformer with a StreamChannel produces extension data."""
+        graph = _make_nested_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+
+        assert "counter" in run.extensions
+        counter_iter = iter(run.extensions["counter"])
+        _ = run.output
+        counts = list(counter_iter)
+        assert len(counts) >= 1
+        assert all(isinstance(c, int) for c in counts)
+        assert counts == sorted(counts)
+
+    def test_custom_channel_events_on_main_log(self) -> None:
+        """StreamChannel auto-forward injects custom:<name> events into the main log."""
+        graph = _make_nested_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+        events = list(run)
+        counter_events = [e for e in events if e["method"] == "custom:counter"]
+        assert len(counter_events) >= 1
+        assert all(isinstance(e["params"]["data"], int) for e in counter_events)
+
+
+# ---------------------------------------------------------------------------
+# Sync: interrupt handling
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2EInterrupt:
+    def test_interrupt_sets_flags_and_surfaces_interrupts(self) -> None:
+        """Interrupted run has correct flags and interrupt payloads."""
+        graph = _make_interrupt_graph()
+        config: dict[str, Any] = {"configurable": {"thread_id": "int-1"}}
+        run = graph.stream_events({"value": "x", "items": []}, config, version="v3")
+
+        output = run.output
+        assert output is not None
+        assert run.interrupted is True
+        assert len(run.interrupts) > 0
+        assert output["items"] == ["step1"]
+        assert "_step1" in output["value"]
+
+    def test_interrupt_values_snapshot_has_partial_state(self) -> None:
+        """Values snapshots captured before the interrupt reflect partial state."""
+        graph = _make_interrupt_graph()
+        config: dict[str, Any] = {"configurable": {"thread_id": "int-2"}}
+        run = graph.stream_events({"value": "x", "items": []}, config, version="v3")
+
+        snapshots = list(run.values)
+        assert len(snapshots) >= 1
+        last = snapshots[-1]
+        assert "step1" in last["items"]
+
+
+# ---------------------------------------------------------------------------
+# Sync: error propagation
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2EErrors:
+    def test_subgraph_error_propagates_through_output(self) -> None:
+        """Error in a subgraph propagates through output."""
+        graph = _make_error_subgraph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        with pytest.raises(ValueError, match="subgraph explosion"):
+            _ = run.output
+
+    def test_subgraph_error_propagates_through_raw_events(self) -> None:
+        graph = _make_error_subgraph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        with pytest.raises(ValueError, match="subgraph explosion"):
+            list(run)
+
+    def test_error_subgraph_handle_status(self) -> None:
+        """Subgraph handle surfaces the error status."""
+        graph = _make_error_subgraph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        handle = next(iter(run.subgraphs))
+        with pytest.raises(RuntimeError, match="subgraph explosion"):
+            _ = handle.output
+        assert handle.status == "failed"
+        assert handle.error == "subgraph explosion"
+
+
+# ---------------------------------------------------------------------------
+# Async end-to-end
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+@NEEDS_CONTEXTVARS
+class TestStreamV2E2EAsync:
+    async def test_all_projections_async(self) -> None:
+        """Async run exercises values projection."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+
+        values_snapshots = [s async for s in run.values]
+        assert len(values_snapshots) >= 1
+        final = values_snapshots[-1]
+        assert "_routed" in final["value"]
+        assert "_processed" in final["value"]
+
+    async def test_async_output(self) -> None:
+        """Async output returns the final state."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        output = await run.output()
+        assert output is not None
+        assert output["value"] == "x_routed_processed"
+        assert "routed" in output["items"]
+        assert "processed" in output["items"]
+
+    async def test_async_raw_events(self) -> None:
+        """Async raw event iteration yields well-formed ProtocolEvents."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        events = [e async for e in run]
+        assert len(events) > 0
+        seqs = [e["seq"] for e in events]
+        for i in range(1, len(seqs)):
+            assert seqs[i] > seqs[i - 1]
+
+    async def test_async_messages_projection(self) -> None:
+        """Async messages projection captures LLM streams."""
+        model = GenericFakeChatModel(messages=iter(["async answer"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": await model.ainvoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+        streams = [s async for s in run.messages]
+        assert len(streams) >= 1
+        for s in streams:
+            assert isinstance(s, AsyncChatModelStream)
+        assert (await streams[0].output).text == "async answer"
+
+    async def test_async_interrupt(self) -> None:
+        """Async interrupted run has correct flags."""
+        graph = _make_interrupt_graph()
+        config: dict[str, Any] = {"configurable": {"thread_id": "async-int-1"}}
+        run = await graph.astream_events(
+            {"value": "x", "items": []}, config, version="v3"
+        )
+
+        output = await run.output()
+        assert output is not None
+        assert await run.interrupted() is True
+        assert len(await run.interrupts()) > 0
+
+    async def test_async_error_propagation(self) -> None:
+        """Async error from subgraph propagates through output."""
+        graph = _make_error_subgraph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        with pytest.raises(ValueError, match="subgraph explosion"):
+            await run.output()
+
+    async def test_async_context_manager(self) -> None:
+        """Async context manager calls abort on exit."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        async with run:
+            _ = await anext(aiter(run.values))
+        assert run._exhausted is True
+
+    async def test_async_extensions_present(self) -> None:
+        """Async run has all native extensions."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        _ = await run.output()
+        assert "values" in run.extensions
+        assert "messages" in run.extensions
+        assert "lifecycle" in run.extensions
+        assert "subgraphs" in run.extensions
+
+    async def test_async_custom_transformer(self) -> None:
+        """Async custom transformer with StreamChannel works."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+        assert "counter" in run.extensions
+        counter_cursor = aiter(run.extensions["counter"])
+        _ = await run.output()
+        counts = [c async for c in counter_cursor]
+        assert len(counts) >= 1
+        assert counts == sorted(counts)
+
+
+# ---------------------------------------------------------------------------
+# Sync: combined projections stress test
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2ECombined:
+    def test_interleave_all_native_projections(self) -> None:
+        """Interleave values + messages + lifecycle without deadlock."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        seen_names: set[str] = set()
+        for name, _item in run.interleave("values", "messages", "lifecycle"):
+            seen_names.add(name)
+
+        assert "values" in seen_names
+        assert "lifecycle" in seen_names
+
+    def test_multiple_custom_transformers(self) -> None:
+        """Multiple custom transformers can coexist."""
+
+        class TagTransformer(StreamTransformer):
+            def __init__(self, scope: tuple[str, ...] = ()) -> None:
+                super().__init__(scope)
+                self._channel: StreamChannel[str] = StreamChannel("tags")
+
+            def init(self) -> dict[str, Any]:
+                return {"tags": self._channel}
+
+            def process(self, event: ProtocolEvent) -> bool:
+                if event["method"] == "values":
+                    self._channel.push(
+                        f"tag:{event['params']['data'].get('value', '')}"
+                    )
+                return True
+
+        graph = _make_nested_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer, TagTransformer],
+        )
+
+        assert "counter" in run.extensions
+        assert "tags" in run.extensions
+
+        counter_iter = iter(run.extensions["counter"])
+        tags_iter = iter(run.extensions["tags"])
+        _ = run.output
+        counts = list(counter_iter)
+        tags = list(tags_iter)
+
+        assert len(counts) >= 1
+        assert len(tags) >= 1
+        assert all(t.startswith("tag:") for t in tags)
+
+    def test_two_sibling_subgraphs_both_discoverable(self) -> None:
+        """Two sequential subgraph invocations produce two handles."""
+
+        class _S(TypedDict):
+            items: Annotated[list[str], operator.add]
+
+        def _item(name: str):
+            def node(state: _S) -> dict[str, Any]:
+                return {"items": [name]}
+
+            return node
+
+        inner_a = (
+            StateGraph(_S)
+            .add_node("add_a", _item("a"))
+            .add_edge(START, "add_a")
+            .add_edge("add_a", END)
+            .compile()
+        )
+        inner_b = (
+            StateGraph(_S)
+            .add_node("add_b", _item("b"))
+            .add_edge(START, "add_b")
+            .add_edge("add_b", END)
+            .compile()
+        )
+
+        outer = (
+            StateGraph(_S)
+            .add_node("sub_a", inner_a)
+            .add_node("sub_b", inner_b)
+            .add_edge(START, "sub_a")
+            .add_edge("sub_a", "sub_b")
+            .add_edge("sub_b", END)
+            .compile()
+        )
+
+        run = outer.stream_events({"items": []}, version="v3")
+        handles = []
+        for handle in run.subgraphs:
+            list(handle.values)
+            handles.append(handle)
+
+        assert len(handles) == 2
+        names = [h.graph_name for h in handles]
+        assert "sub_a" in names
+        assert "sub_b" in names
+        assert all(h.status == "completed" for h in handles)
+
+        output = run.output
+        assert output is not None
+        assert set(output["items"]) == {"a", "b"}
+
+    def test_lifecycle_matches_subgraph_handles(self) -> None:
+        """Lifecycle events and subgraph handles agree on discovered subgraphs."""
+        run1 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        handle_paths: list[tuple[str, ...]] = []
+        for handle in run1.subgraphs:
+            list(handle.values)
+            handle_paths.append(handle.path)
+
+        run2 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        lifecycle = list(run2.lifecycle)
+
+        started_ns = [
+            tuple(e["namespace"]) for e in lifecycle if e["event"] == "started"
+        ]
+        # Handle paths use format "graph_name:call_id", lifecycle namespaces
+        # use the same format. Both should have the same graph_name prefix.
+        handle_prefixes = {p[0].split(":")[0] for p in handle_paths}
+        lifecycle_prefixes = {ns[0].split(":")[0] for ns in started_ns}
+        assert handle_prefixes == lifecycle_prefixes
+
+    def test_values_plus_messages_plus_custom(self) -> None:
+        """Values, messages, and a custom transformer all produce data in one run."""
+        model = GenericFakeChatModel(messages=iter(["combined test"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events(
+            {"messages": "hi"},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+
+        counter_iter = iter(run.extensions["counter"])
+        values_iter = iter(run.values)
+        messages_iter = iter(run.messages)
+
+        values = list(values_iter)
+        messages = list(messages_iter)
+        counts = list(counter_iter)
+
+        assert len(values) >= 1
+        assert len(messages) >= 1
+        assert len(counts) >= 1
+        assert messages[0].output.text == "combined test"

libs/langgraph/tests/test_stream_lifecycle_transformer.py

@@ -0,0 +1,406 @@
+"""Tests for LifecycleTransformer.
+
+Consumes the `tasks` stream mode and emits subgraph lifecycle payloads
+on the `lifecycle` channel for both in-process iteration via
+`run.lifecycle` and wire delivery via `custom:lifecycle` protocol
+events. Most tests dispatch synthetic protocol events through a
+`StreamMux` to keep the inference logic isolated; the end-of-file
+group exercises the path through real graphs (multi-depth
+discovery, nested `stream_events(version="v3")` calls with non-empty `parent_ns`).
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from typing import Annotated, Any
+
+from typing_extensions import TypedDict
+
+from langgraph._internal._constants import CONF, CONFIG_KEY_CHECKPOINT_NS
+from langgraph.constants import END, START
+from langgraph.errors import GraphInterrupt
+from langgraph.graph import StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.transformers import (
+    LifecyclePayload,
+    LifecycleTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+def _tasks_start(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+) -> dict[str, Any]:
+    """Build a `tasks` ProtocolEvent carrying a TaskPayload (start)."""
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "input": None,
+                "triggers": [],
+            },
+        },
+    }
+
+
+def _tasks_result(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+    error: str | None = None,
+    interrupts: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    """Build a `tasks` ProtocolEvent carrying a TaskResultPayload (finish)."""
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "error": error,
+                "interrupts": interrupts or [],
+                "result": {},
+            },
+        },
+    }
+
+
+def _arm(mux: StreamMux) -> None:
+    """Force projection channels to accept pushes (skip lazy-subscribe gate).
+
+    `StreamChannel.push` only appends to the local buffer when a
+    subscriber is attached. Tests that inspect `_items` directly need
+    the gate flipped before any event is dispatched.
+    """
+    mux._events._subscribed = True
+    for transformer in mux._transformers:
+        if isinstance(transformer, LifecycleTransformer):
+            transformer._channel._subscribed = True
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _drain_lifecycle(mux: StreamMux) -> list[LifecyclePayload]:
+    """Snapshot the lifecycle channel's buffer."""
+    transformer = mux.transformer_by_key("lifecycle")
+    assert isinstance(transformer, LifecycleTransformer)
+    return _unstamped(transformer._channel._items)
+
+
+def _build_lifecycle_mux(*, scope: tuple[str, ...] = ()) -> StreamMux:
+    mux = StreamMux([LifecycleTransformer(scope=scope)], is_async=False)
+    _arm(mux)
+    return mux
+
+
+# ---------------------------------------------------------------------------
+# LifecycleTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_started_emitted_on_first_direct_child_task() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc123"], task_id="t1", name="tool"))
+
+    [payload] = _drain_lifecycle(mux)
+    assert payload["event"] == "started"
+    assert payload["namespace"] == ["agent:abc123"]
+    assert payload["graph_name"] == "agent"
+    assert payload["trigger_call_id"] == "abc123"
+
+
+def test_started_dedup_on_repeat_namespace() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="a"))
+    mux.push(_tasks_start(["agent:abc"], task_id="t2", name="b"))
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started"]
+
+
+def test_grandchild_namespace_discovered() -> None:
+    """Subgraphs at any depth below scope are tracked, not just direct children."""
+    mux = _build_lifecycle_mux()
+    # First-seen task at length-2 ns means a 2nd-level subgraph started.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t1", name="x"))
+
+    [payload] = _drain_lifecycle(mux)
+    assert payload["event"] == "started"
+    assert payload["namespace"] == ["agent:abc", "tool:def"]
+
+
+def test_nested_chain_emits_started_at_each_depth() -> None:
+    """A graph → subgraph → subgraph chain produces a started event per level."""
+    mux = _build_lifecycle_mux()
+    # Subgraph1 starts emitting tasks (events tagged with its own ns).
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    # Subgraph1 invokes subgraph2; subgraph2's first task event arrives.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["namespace"] for p in payloads] == [
+        ["agent:abc"],
+        ["agent:abc", "tool:def"],
+    ]
+    assert all(p["event"] == "started" for p in payloads)
+
+
+def test_nested_chain_emits_completed_at_each_depth() -> None:
+    """Each subgraph in a nested chain closes when its parent task result arrives."""
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    # Subgraph2's owning task (id=def, inside subgraph1) finishes.
+    mux.push(_tasks_result(["agent:abc"], task_id="def", name="tool"))
+    # Subgraph1's owning task (id=abc, at root) finishes.
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    payloads = _drain_lifecycle(mux)
+    events = [(p["event"], p["namespace"]) for p in payloads]
+    assert events == [
+        ("started", ["agent:abc"]),
+        ("started", ["agent:abc", "tool:def"]),
+        ("completed", ["agent:abc", "tool:def"]),
+        ("completed", ["agent:abc"]),
+    ]
+
+
+def test_completed_on_parent_task_result() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    events = [p["event"] for p in _drain_lifecycle(mux)]
+    assert events == ["started", "completed"]
+
+
+def test_failed_on_parent_task_result_with_error() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "failed"]
+    assert payloads[1]["error"] == "boom"
+
+
+def test_interrupted_on_parent_task_result_with_interrupts() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "interrupted"]
+
+
+def test_interrupt_takes_precedence_over_error() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            error="should-be-suppressed",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    last = _drain_lifecycle(mux)[-1]
+    assert last["event"] == "interrupted"
+    assert "error" not in last
+
+
+def test_finalize_completes_open_subgraphs() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.close()
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "completed"]
+
+
+def test_fail_emits_interrupted_for_graph_interrupt() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(GraphInterrupt())
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "interrupted"]
+    assert "error" not in payloads[1]
+
+
+def test_fail_emits_failed_for_other_exceptions() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(RuntimeError("boom"))
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "failed"]
+    assert payloads[1]["error"] == "boom"
+
+
+def test_unrelated_methods_pass_through() -> None:
+    """Non-`tasks` events are not consumed and don't emit lifecycle."""
+    mux = _build_lifecycle_mux()
+    mux.push(
+        {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": ["agent:abc"], "timestamp": TS, "data": {}},
+        }
+    )
+    assert _drain_lifecycle(mux) == []
+
+
+def test_scoped_transformer_filters_outside_scope_but_tracks_all_depths() -> None:
+    """Scope filters the prefix; subgraphs at any depth below scope are tracked."""
+    mux = _build_lifecycle_mux(scope=("agent:abc",))
+    # Root-level task — out of scope (no shared prefix).
+    mux.push(_tasks_start(["other:1"], task_id="t1", name="other"))
+    # Direct child of agent:abc — in scope.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="tool"))
+    # Grandchild of agent:abc — also in scope, tracked at its own depth.
+    mux.push(
+        _tasks_start(["agent:abc", "tool:def", "deep:ghi"], task_id="t3", name="deep")
+    )
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["namespace"] for p in payloads] == [
+        ["agent:abc", "tool:def"],
+        ["agent:abc", "tool:def", "deep:ghi"],
+    ]
+
+
+def test_required_stream_modes_declared() -> None:
+    assert LifecycleTransformer.required_stream_modes == ("tasks",)
+
+
+def test_protocol_event_method_is_native() -> None:
+    """Native transformer — auto-forwarded events use `lifecycle`, not `custom:lifecycle`."""
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    methods = {evt["method"] for evt in _unstamped(mux._events._items)}
+    assert "lifecycle" in methods
+    assert "custom:lifecycle" not in methods
+
+
+def test_tasks_events_suppressed_from_main_log() -> None:
+    """Tasks events are folded into lifecycle and don't appear on the main log."""
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "tasks" not in methods
+    # Lifecycle events did make it through, though.
+    assert "lifecycle" in methods
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: real graphs through stream_events(version="v3")
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _passthrough(state: _State) -> dict[str, Any]:
+    return {"value": state["value"] + "!", "items": ["x"]}
+
+
+def _make_two_level_nested() -> Any:
+    """Build outer → middle → inner. Three Pregel instances, two nesting levels."""
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("inner_node", _passthrough)
+    inner_b.add_edge(START, "inner_node")
+    inner_b.add_edge("inner_node", END)
+    inner = inner_b.compile()
+
+    middle_b: StateGraph = StateGraph(_State, input_schema=_State)
+    middle_b.add_node("inner", inner)
+    middle_b.add_edge(START, "inner")
+    middle_b.add_edge("inner", END)
+    middle = middle_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("middle", middle)
+    outer_b.add_edge(START, "middle")
+    outer_b.add_edge("middle", END)
+    return outer_b.compile()
+
+
+def test_stream_events_v3_real_graph_emits_lifecycle_at_each_depth() -> None:
+    """Outer graph with two nested subgraphs surfaces lifecycle for both."""
+    graph = _make_two_level_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    # Iterating the projection drives the pump and drains synthesized
+    # lifecycle events at the same time.
+    payloads = list(run.lifecycle)
+    # Each subgraph instance produces a started + a terminal event. Two
+    # nested instances, so four payloads total in some interleaving.
+    by_event = {p["event"] for p in payloads}
+    assert "started" in by_event
+    assert "completed" in by_event
+    # Two distinct namespaces — direct child of root, and grandchild.
+    namespaces = {tuple(p["namespace"]) for p in payloads}
+    direct_children = {ns for ns in namespaces if len(ns) == 1}
+    grandchildren = {ns for ns in namespaces if len(ns) == 2}
+    assert direct_children, f"expected a level-1 lifecycle namespace, got {namespaces}"
+    assert grandchildren, f"expected a level-2 lifecycle namespace, got {namespaces}"
+    # Every direct-child namespace has a matching grandchild whose path extends it.
+    for parent in direct_children:
+        assert any(gc[: len(parent)] == parent for gc in grandchildren), (
+            f"grandchild does not extend parent {parent}: {grandchildren}"
+        )
+
+
+def test_stream_events_v3_with_nested_parent_ns_scopes_lifecycle() -> None:
+    """When `stream_events(version="v3")` is called with a non-empty checkpoint_ns in config,
+    `_resolve_parent_ns` returns that namespace and the registered
+    `LifecycleTransformer` is constructed with `scope=parent_ns`. This
+    exercises the path that exists today purely for nested-stream_events(version="v3")
+    callers; the test simulates such a caller by injecting a
+    checkpoint_ns into the config.
+    """
+    graph = _make_two_level_nested()
+    config = {CONF: {CONFIG_KEY_CHECKPOINT_NS: "outer:abc"}}
+    run = graph.stream_events({"value": "x", "items": []}, config=config, version="v3")
+
+    payloads = list(run.lifecycle)
+    # Every emitted lifecycle namespace must extend the caller's scope —
+    # nothing at root-level, nothing under a sibling prefix.
+    for p in payloads:
+        ns = tuple(p["namespace"])
+        assert ns[:1] == ("outer:abc",), (
+            f"namespace {ns} not within scoped prefix ('outer:abc',)"
+        )

libs/langgraph/tests/test_stream_messages_transformer.py

@@ -0,0 +1,883 @@
+"""Tests for MessagesTransformer: protocol event routing, whole-message fallback,
+legacy v1 chunk filtering, and end-to-end via stream_events(version="v3") / astream_events(version="v3")."""
+
+from __future__ import annotations
+
+import time
+from typing import Any
+
+import pytest
+from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.runnables import RunnableConfig
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import MessagesState, StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.run_stream import GraphRunStream
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import MessagesTransformer, ValuesTransformer
+
+TS = int(time.time() * 1000)
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _proto_event(
+    event: dict[str, Any],
+    *,
+    run_id: str = "run-1",
+    node: str = "llm",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a protocol event dict (v2 path)."""
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (event, {"langgraph_node": node, "run_id": run_id}),
+        },
+    }
+
+
+def _v1_chunk(
+    text: str,
+    msg_id: str = "msg-1",
+    *,
+    finish: bool = False,
+    node: str = "llm",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a v1 AIMessageChunk tuple."""
+    rm: dict[str, Any] = {"finish_reason": "stop"} if finish else {}
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (
+                AIMessageChunk(content=text, id=msg_id, response_metadata=rm),
+                {"langgraph_node": node},
+            ),
+        },
+    }
+
+
+def _whole_msg(
+    text: str,
+    msg_id: str = "msg-10",
+    *,
+    node: str = "node",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a completed AIMessage."""
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (AIMessage(content=text, id=msg_id), {"langgraph_node": node}),
+        },
+    }
+
+
+def _make_sync_transformer() -> tuple[
+    MessagesTransformer, StreamChannel[ChatModelStream]
+]:
+    t = MessagesTransformer()
+    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    log._bind(is_async=False)
+    # Subscribe up front so pushes during process() are retained.
+    log._subscribed = True
+    t._bind_pump(lambda: False)
+    return t, log
+
+
+def _make_async_transformer() -> tuple[
+    MessagesTransformer, StreamChannel[ChatModelStream]
+]:
+    t = MessagesTransformer()
+    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    log._bind(is_async=True)
+    log._subscribed = True
+    return t, log
+
+
+def _lifecycle(
+    *, text: str = "hello world", message_id: str = "run-1"
+) -> list[dict[str, Any]]:
+    """Produce a valid protocol event lifecycle: start, delta, finish."""
+    half = len(text) // 2
+    first, second = text[:half], text[half:]
+    return [
+        {"event": "message-start", "role": "ai", "message_id": message_id},
+        {
+            "event": "content-block-start",
+            "index": 0,
+            "content_block": {"type": "text", "text": ""},
+        },
+        {
+            "event": "content-block-delta",
+            "index": 0,
+            "content_block": {"type": "text", "text": first},
+        },
+        {
+            "event": "content-block-delta",
+            "index": 0,
+            "content_block": {"type": "text", "text": second},
+        },
+        {
+            "event": "content-block-finish",
+            "index": 0,
+            "content_block": {"type": "text", "text": text},
+        },
+        {"event": "message-finish", "reason": "stop"},
+    ]
+
+
+def _simple_graph():
+    def call_model(state: MessagesState) -> dict[str, Any]:
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+        stream = model.stream_events(state["messages"], version="v3")
+        return {"messages": stream.output}
+
+    return (
+        StateGraph(MessagesState)
+        .add_node("call_model", call_model)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", END)
+        .compile()
+    )
+
+
+# ---------------------------------------------------------------------------
+# Protocol event routing
+# ---------------------------------------------------------------------------
+
+
+class TestProtocolEventRouting:
+    def test_message_start_creates_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+            )
+        )
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.message_id == "run-1"
+
+    def test_full_lifecycle_yields_done_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle(text="hello world"):
+            t.process(_proto_event(evt, run_id="run-1"))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.done
+        assert stream.output.text == "hello world"
+
+    def test_message_finish_cleans_up_routing(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle():
+            t.process(_proto_event(evt, run_id="run-1"))
+        assert t._by_run == {}
+
+    def test_events_without_prior_start_are_ignored(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {
+                    "event": "content-block-delta",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": "orphan"},
+                },
+                run_id="unknown",
+            )
+        )
+        log.close()
+        assert _unstamped(log._items) == []
+
+    def test_concurrent_streams_routed_by_run_id(self) -> None:
+        t, log = _make_sync_transformer()
+        life_a = _lifecycle(text="aaaa", message_id="run-a")
+        life_b = _lifecycle(text="bbbb", message_id="run-b")
+        for a, b in zip(life_a, life_b):
+            t.process(_proto_event(a, run_id="run-a"))
+            t.process(_proto_event(b, run_id="run-b"))
+        log.close()
+        streams = _unstamped(log._items)
+        assert len(streams) == 2
+        by_id = {s.message_id: s for s in streams}
+        assert by_id["run-a"].output.text == "aaaa"
+        assert by_id["run-b"].output.text == "bbbb"
+
+    def test_text_deltas_accumulated_on_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle(text="abcdef"):
+            t.process(_proto_event(evt))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert "".join(stream._text_proj._deltas) == "abcdef"
+
+    def test_stream_pushed_on_message_start_not_finish(self) -> None:
+        # Consumer can see the stream before message-finish arrives.
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+            )
+        )
+        assert len(log._items) == 1
+
+    def test_node_metadata_set_on_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+                node="my_llm",
+            )
+        )
+        (stream,) = _unstamped(log._items)
+        assert stream.node == "my_llm"
+
+
+# ---------------------------------------------------------------------------
+# Whole-message fallback
+# ---------------------------------------------------------------------------
+
+
+class TestWholeMessageFallback:
+    def test_whole_ai_message_produces_complete_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_whole_msg("the full answer"))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.done
+        assert stream.output.text == "the full answer"
+
+    def test_whole_message_has_full_lifecycle(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_whole_msg("full"))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert [e["event"] for e in stream._events] == [
+            "message-start",
+            "content-block-start",
+            "content-block-delta",
+            "content-block-finish",
+            "message-finish",
+        ]
+
+
+# ---------------------------------------------------------------------------
+# Filtering
+# ---------------------------------------------------------------------------
+
+
+class TestFiltering:
+    def test_non_messages_events_pass_through(self) -> None:
+        t, _ = _make_sync_transformer()
+        assert (
+            t.process(
+                {
+                    "type": "event",
+                    "method": "values",
+                    "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
+                }
+            )
+            is True
+        )
+
+    def test_subgraph_namespace_dropped(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            {
+                "type": "event",
+                "method": "messages",
+                "params": {
+                    "namespace": ["subgraph"],
+                    "timestamp": TS,
+                    "data": (
+                        {"event": "message-start", "message_id": "run-x"},
+                        {"run_id": "run-x"},
+                    ),
+                },
+            }
+        )
+        log.close()
+        assert _unstamped(log._items) == []
+
+    def test_legacy_v1_chunks_ignored(self) -> None:
+        # v1 AIMessageChunk tuples (from on_llm_new_token) are not streamed
+        # into this projection; callers must migrate to stream_events(version="v3").
+        t, log = _make_sync_transformer()
+        t.process(_v1_chunk("hello"))
+        t.process(_v1_chunk(" world", finish=True))
+        log.close()
+        assert _unstamped(log._items) == []
+
+
+# ---------------------------------------------------------------------------
+# Lifecycle: fail / finalize
+# ---------------------------------------------------------------------------
+
+
+class TestLifecycle:
+    def test_fail_propagates_to_open_streams(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+            )
+        )
+        streams = _unstamped(log._items)
+        err = RuntimeError("graph died")
+        t.fail(err)
+        assert t._by_run == {}
+        assert streams[0]._error is err
+
+    def test_finalize_clears_routing_state(self) -> None:
+        t, _ = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+            )
+        )
+        assert "run-1" in t._by_run
+        t.finalize()
+        assert t._by_run == {}
+
+
+# ---------------------------------------------------------------------------
+# Async mode
+# ---------------------------------------------------------------------------
+
+
+class TestAsyncMode:
+    def test_async_mode_creates_async_stream(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="async stream"):
+            t.process(_proto_event(evt))
+        assert isinstance(_unstamped(log._items)[0], AsyncChatModelStream)
+
+    @pytest.mark.anyio
+    async def test_text_projection_yields_deltas(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="hello world"):
+            t.process(_proto_event(evt))
+        (stream,) = _unstamped(log._items)
+        assert isinstance(stream, AsyncChatModelStream)
+        assert "".join([d async for d in stream.text]) == "hello world"
+
+    @pytest.mark.anyio
+    async def test_output_awaitable(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="async"):
+            t.process(_proto_event(evt))
+        (stream,) = _unstamped(log._items)
+        assert (await stream.output).text == "async"
+
+
+# ---------------------------------------------------------------------------
+# GraphRunStream integration
+# ---------------------------------------------------------------------------
+
+
+class TestWireRequestMore:
+    def test_bind_pump_called_on_wire(self) -> None:
+        values_t = ValuesTransformer()
+        messages_t = MessagesTransformer()
+        mux = StreamMux([values_t, messages_t], is_async=False)
+
+        assert messages_t._pump_fn is None
+        run = GraphRunStream(iter([]), mux)
+        assert messages_t._pump_fn is not None
+        assert messages_t._pump_fn() is False
+        assert run._exhausted
+
+    def test_created_streams_have_request_more(self) -> None:
+        values_t = ValuesTransformer()
+        messages_t = MessagesTransformer()
+        mux = StreamMux([values_t, messages_t], is_async=False)
+        GraphRunStream(iter([]), mux)
+
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+        for evt in _lifecycle():
+            messages_t.process(_proto_event(evt))
+
+        (stream,) = _unstamped(log._items)
+        assert stream._request_more is messages_t._pump_fn
+
+
+# ---------------------------------------------------------------------------
+# End-to-end via StreamMux
+# ---------------------------------------------------------------------------
+
+
+class TestViaMux:
+    def _make_mux(
+        self,
+    ) -> tuple[MessagesTransformer, StreamMux, StreamChannel[ChatModelStream]]:
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=False)
+        t._bind_pump(lambda: False)
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+        return t, mux, log
+
+    def test_streaming_via_mux(self) -> None:
+        t, mux, log = self._make_mux()
+        for evt in _lifecycle(text="mux stream"):
+            mux.push(_proto_event(evt))
+        mux.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.output.text == "mux stream"
+
+    def test_whole_message_via_mux(self) -> None:
+        t, mux, log = self._make_mux()
+        mux.push(_whole_msg("result"))
+        mux.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.output.text == "result"
+
+    @pytest.mark.anyio
+    async def test_async_streaming_via_mux(self) -> None:
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=True)
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+
+        for evt in _lifecycle(text="async mux"):
+            await mux.apush(_proto_event(evt))
+
+        (stream,) = _unstamped(log._items)
+        assert (await stream.output).text == "async mux"
+        await mux.aclose()
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: graph → stream_events(version="v3") → run.messages (node calls stream_events)
+# ---------------------------------------------------------------------------
+
+
+class TestEndToEnd:
+    """stream_events(version="v3") path: node calls model.stream_events() explicitly."""
+
+    def test_node_calling_stream_v2_populates_messages(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = model.stream_events(state["messages"], version="v3")
+            return {"messages": stream.output}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.text == "hello world"
+
+    def test_node_stream_v2_text_deltas_iterate(self) -> None:
+        """Consumer can iterate `.text` on the streamed message in real time."""
+        model = GenericFakeChatModel(messages=iter(["streamed answer"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = model.stream_events(state["messages"], version="v3")
+            return {"messages": stream.output}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "go"}, version="v3")
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "streamed answer"
+
+    def test_non_llm_message_returned_from_node(self) -> None:
+        """Whole-message fallback: node returns a finalized AIMessage directly."""
+
+        def return_message(state: MessagesState) -> dict[str, Any]:
+            return {"messages": AIMessage(content="hardcoded", id="msg-abc")}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("return_message", return_message)
+            .add_edge(START, "return_message")
+            .add_edge("return_message", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert stream.output.text == "hardcoded"
+
+    @pytest.mark.anyio
+    async def test_async_node_calling_astream_v2(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["async answer"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = await model.astream_events(state["messages"], version="v3")
+            return {"messages": await stream}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+        streams = [s async for s in run.messages]
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
+        assert (await streams[0].output).text == "async answer"
+
+    @pytest.mark.anyio
+    async def test_nested_async_iteration_yields_text_deltas(self) -> None:
+        """Inner stream.text drives the shared graph pump via the async pump binding."""
+        import asyncio
+
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = await model.astream_events(state["messages"], version="v3")
+            return {"messages": await stream}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+
+        async def consume() -> list[str]:
+            collected: list[str] = []
+            async for stream in run.messages:
+                async for delta in stream.text:
+                    collected.append(delta)
+            return collected
+
+        assert "".join(await asyncio.wait_for(consume(), timeout=2.0)) == "hello world"
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: graph → stream_events(version="v3") → run.messages (node calls invoke)
+# ---------------------------------------------------------------------------
+
+
+class TestEndToEndV2Invoke:
+    """Auto-routing path: stream_events(version="v3") injects CONFIG_KEY_STREAM_MESSAGES_V2,
+    causing BaseChatModel to drive the v2 protocol event generator even for
+    model.invoke()."""
+
+    def _graph(self, model):
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        return (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+    def test_invoke_populates_messages(self) -> None:
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["hello world"]))
+        ).stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.text == "hello world"
+
+    def test_invoke_emits_protocol_events(self) -> None:
+        """Iterating the stream yields the full v2 lifecycle, not v1 chunks."""
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["streamed answer"]))
+        ).stream_events({"messages": "go"}, version="v3")
+        (stream,) = list(run.messages)
+
+        events = list(stream)
+        event_types = [e.get("event") for e in events]
+        assert "message-start" in event_types
+        assert "content-block-start" in event_types
+        assert "content-block-delta" in event_types
+        assert "content-block-finish" in event_types
+        assert "message-finish" in event_types
+        # Sanity: every event is a dict carrying an "event" key — not an
+        # AIMessageChunk tuple from the v1 path.
+        for event in events:
+            assert isinstance(event, dict)
+            assert "event" in event
+        # Typed projection still assembles the final text.
+        assert stream.output.text == "streamed answer"
+
+    def test_invoke_text_deltas_iterate(self) -> None:
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["delta streaming works"]))
+        ).stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "delta streaming works"
+
+    def test_invoke_two_nodes_two_streams(self) -> None:
+        model_a = GenericFakeChatModel(messages=iter(["alpha"]))
+        model_b = GenericFakeChatModel(messages=iter(["beta"]))
+
+        def node_a(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model_a.invoke(state["messages"])}
+
+        def node_b(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model_b.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("node_a", node_a)
+            .add_node("node_b", node_b)
+            .add_edge(START, "node_a")
+            .add_edge("node_a", "node_b")
+            .add_edge("node_b", END)
+            .compile()
+        )
+
+        streams = list(graph.stream_events({"messages": "hi"}, version="v3").messages)
+        assert len(streams) == 2
+        assert {s.output.text for s in streams} == {"alpha", "beta"}
+
+    def test_invoke_plus_constructed_message_two_streams(self) -> None:
+        """Live-streamed node + constructed-message node → two ChatModelStreams."""
+        model = GenericFakeChatModel(messages=iter(["live stream"]))
+
+        def streaming_node(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        def constructed_node(state: MessagesState) -> dict[str, Any]:
+            return {"messages": [AIMessage(content="hardcoded", id="constructed-1")]}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("streaming_node", streaming_node)
+            .add_node("constructed_node", constructed_node)
+            .add_edge(START, "streaming_node")
+            .add_edge("streaming_node", "constructed_node")
+            .add_edge("constructed_node", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        streams = list(run.messages)
+        assert len(streams) == 2
+        assert streams[0].node == "streaming_node"
+        assert streams[0].output.text == "live stream"
+        assert streams[1].node == "constructed_node"
+        assert streams[1].output.text == "hardcoded"
+        assert streams[1].message_id == "constructed-1"
+
+    @pytest.mark.anyio
+    async def test_ainvoke_populates_messages(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["async invoke"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": await model.ainvoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+        streams = [s async for s in run.messages]
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
+        assert (await streams[0].output).text == "async invoke"
+
+
+# ---------------------------------------------------------------------------
+# Regression: direct stream_mode="messages" must stay v1
+# ---------------------------------------------------------------------------
+
+
+class TestDirectMessagesModeStaysV1:
+    def test_direct_graph_stream_messages_yields_ai_message_chunks(self) -> None:
+        """graph.stream(stream_mode="messages") must not leak v2 event dicts —
+        the v2 flag is only injected by stream_events(version="v3") / astream_events(version="v3")."""
+        model = GenericFakeChatModel(messages=iter(["legacy path"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        parts = list(graph.stream({"messages": "hi"}, stream_mode="messages"))
+        assert parts, "expected stream_mode='messages' to emit tuples"
+        for payload, _metadata in parts:
+            assert isinstance(payload, AIMessageChunk)
+        assert (
+            "".join(p[0].content for p in parts if isinstance(p[0].content, str))
+            == "legacy path"
+        )
+
+    def test_nested_graph_stream_messages_stays_v1_under_outer_stream_events_v3(
+        self,
+    ) -> None:
+        """An outer `stream_events(version="v3")` run must not flip an inner direct
+        `stream_mode="messages"` call onto the v2 event protocol."""
+        model = GenericFakeChatModel(messages=iter(["nested legacy path"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        inner = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        class OuterState(TypedDict, total=False):
+            saw_only_chunks: bool
+            first_payload_type: str
+            text: str
+
+        def call_subgraph(state: OuterState, config: RunnableConfig) -> dict[str, Any]:
+            parts = list(
+                inner.stream(
+                    {"messages": "hi"},
+                    config,
+                    stream_mode="messages",
+                )
+            )
+            assert parts
+            payloads = [payload for payload, _metadata in parts]
+            return {
+                "saw_only_chunks": all(
+                    isinstance(payload, AIMessageChunk) for payload in payloads
+                ),
+                "first_payload_type": type(payloads[0]).__name__,
+                "text": "".join(
+                    payload.content
+                    for payload in payloads
+                    if isinstance(payload, AIMessageChunk)
+                    and isinstance(payload.content, str)
+                ),
+            }
+
+        outer = (
+            StateGraph(OuterState)
+            .add_node("call_subgraph", call_subgraph)
+            .add_edge(START, "call_subgraph")
+            .add_edge("call_subgraph", END)
+            .compile()
+        )
+
+        result = outer.stream_events({}, version="v3").output
+
+        assert result is not None
+        assert result["saw_only_chunks"] is True
+        assert result["first_payload_type"] == "AIMessageChunk"
+        assert result["text"] == "nested legacy path"
+
+
+# ---------------------------------------------------------------------------
+# StreamMessagesHandlerV2 unit
+# ---------------------------------------------------------------------------
+
+
+class TestStreamMessagesHandlerV2Unit:
+    def test_on_llm_new_token_is_noop(self) -> None:
+        """v2 handler must not emit v1 chunks even when on_llm_new_token fires."""
+        from uuid import uuid4
+
+        from langchain_core.outputs import ChatGenerationChunk
+
+        from langgraph.pregel._messages import StreamMessagesHandlerV2
+
+        emitted: list[Any] = []
+        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
+        run_id = uuid4()
+        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
+
+        handler.on_llm_new_token(
+            "hello",
+            chunk=ChatGenerationChunk(message=AIMessageChunk(content="hello")),
+            run_id=run_id,
+        )
+
+        assert emitted == []
+
+    def test_on_llm_end_dedupes_when_final_message_id_differs(self) -> None:
+        """A streamed v2 message should not be emitted again from the final
+        AIMessage fallback when its final id does not match `message-start`."""
+        from uuid import uuid4
+
+        from langchain_core.outputs import ChatGeneration, LLMResult
+
+        from langgraph.pregel._messages import StreamMessagesHandlerV2
+
+        emitted: list[Any] = []
+        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
+        run_id = uuid4()
+        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
+
+        handler.on_stream_event(
+            {"event": "message-start", "message_id": "stream-msg-1"},
+            run_id=run_id,
+        )
+        handler.on_llm_end(
+            LLMResult(
+                generations=[
+                    [
+                        ChatGeneration(
+                            message=AIMessage(content="hello", id="final-msg-1")
+                        )
+                    ]
+                ]
+            ),
+            run_id=run_id,
+        )
+
+        assert len(emitted) == 1

libs/langgraph/tests/test_stream_subgraph_transformer.py

@@ -0,0 +1,856 @@
+"""Tests for SubgraphTransformer.
+
+Subscribes to `tasks` events and produces in-process `SubgraphRunStream`
+handles backed by mini-muxes (built via `StreamMux._make_child`). The
+synthetic-event tests isolate the inference / mini-mux wiring; the
+real-graph tests exercise the end-to-end navigation path through
+`stream_events(version="v3")`.
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from collections.abc import AsyncIterator
+from functools import partial
+from typing import Annotated, Any
+
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.errors import GraphInterrupt
+from langgraph.graph import StateGraph
+from langgraph.pregel.main import _normalize_stream_transformer_factories
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    AsyncSubgraphRunStream,
+    GraphRunStream,
+    SubgraphRunStream,
+)
+from langgraph.stream.transformers import (
+    LifecycleTransformer,
+    MessagesTransformer,
+    SubgraphTransformer,
+    ValuesTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _tasks_start(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "input": None,
+                "triggers": [],
+            },
+        },
+    }
+
+
+def _tasks_result(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+    error: str | None = None,
+    interrupts: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "error": error,
+                "interrupts": interrupts or [],
+                "result": {},
+            },
+        },
+    }
+
+
+def _native_factories() -> list[Any]:
+    """Mirror the factory list `Pregel.stream_events(version="v3")` registers."""
+    return [
+        ValuesTransformer,
+        MessagesTransformer,
+        LifecycleTransformer,
+        SubgraphTransformer,
+    ]
+
+
+def _stream_part(
+    method: str,
+    namespace: tuple[str, ...],
+    data: Any,
+) -> dict[str, Any]:
+    return {"type": method, "ns": namespace, "data": data}
+
+
+async def _astream_parts(*parts: dict[str, Any]) -> AsyncIterator[dict[str, Any]]:
+    for part in parts:
+        yield part
+
+
+def _arm(mux: StreamMux) -> None:
+    """Pre-subscribe every projection in the mux so synthetic pushes accumulate.
+
+    Real consumer code subscribes by iterating the projection; tests
+    inspect `_items` directly, so the lazy-subscribe gate has to be
+    flipped manually before any synthetic events are pushed.
+    """
+    mux._events._subscribed = True
+    for value in mux.extensions.values():
+        if hasattr(value, "_subscribed"):
+            value._subscribed = True
+
+
+def _arm_recursive(mux: StreamMux) -> None:
+    """Arm `mux` and every mini-mux currently held by SubgraphTransformer handles.
+
+    Mini-muxes are created during `mux.push(...)` when a new direct
+    child is discovered. Tests must call this after each push that
+    might have created a new mini-mux so subsequent pushes' projection
+    side effects accumulate (rather than dropping silently against an
+    unsubscribed log).
+    """
+    _arm(mux)
+    for handle in _subgraph_transformer(mux)._handles.values():
+        if handle._mux is not None:
+            _arm_recursive(handle._mux)
+
+
+def _build_root_mux(*, scope: tuple[str, ...] = ()) -> StreamMux:
+    mux = StreamMux(
+        factories=_native_factories(),
+        scope=scope,
+        is_async=False,
+    )
+    _arm(mux)
+    return mux
+
+
+def _subgraph_transformer(mux: StreamMux) -> SubgraphTransformer:
+    transformer = mux.transformer_by_key("subgraphs")
+    assert isinstance(transformer, SubgraphTransformer)
+    return transformer
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _drain_subgraphs(mux: StreamMux) -> list[SubgraphRunStream]:
+    return _unstamped(_subgraph_transformer(mux)._log._items)
+
+
+def _child_mux(handle: SubgraphRunStream | AsyncSubgraphRunStream) -> StreamMux:
+    assert handle._mux is not None
+    return handle._mux
+
+
+def _event_items(mux: StreamMux) -> list[ProtocolEvent]:
+    return _unstamped(mux._events._items)
+
+
+def _lifecycle_payloads(mux: StreamMux) -> list[dict[str, Any]]:
+    lifecycle_t = mux.transformer_by_key("lifecycle")
+    assert isinstance(lifecycle_t, LifecycleTransformer)
+    return _unstamped(lifecycle_t._channel._items)
+
+
+# ---------------------------------------------------------------------------
+# Synthetic-event tests
+# ---------------------------------------------------------------------------
+
+
+def test_handle_created_on_first_direct_child_task() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.path == ("agent:abc",)
+    assert handle.graph_name == "agent"
+    assert handle.trigger_call_id == "abc"
+    assert handle.status == "started"
+    _child_mux(handle)  # mini-mux backed
+
+
+def test_handle_status_completes_on_parent_result() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "completed"
+    assert handle.error is None
+
+
+def test_handle_status_failed_with_error() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "failed"
+    assert handle.error == "boom"
+
+
+def test_handle_status_interrupted() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "interrupted"
+
+
+def test_grandchild_discovered_via_child_mini_mux() -> None:
+    """Each mini-mux owns its own scope; grandchildren live on the child handle."""
+    mux = _build_root_mux()
+    # Direct child started — creates the mini-mux.
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    # Pre-subscribe the freshly-created mini-mux so subsequent
+    # forwarded events land on its projections (consumer would
+    # subscribe naturally by iterating handle.subgraphs, but the
+    # test inspects `_items` directly).
+    _arm_recursive(mux)
+    # Grandchild's first task event flows down into the child mini-mux.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    [child_handle] = _drain_subgraphs(mux)
+    assert child_handle.path == ("agent:abc",)
+    # The grandchild appears on the CHILD'S subgraphs projection.
+    grandchildren = _unstamped(child_handle.subgraphs._items)
+    assert len(grandchildren) == 1
+    assert grandchildren[0].path == ("agent:abc", "tool:def")
+
+
+def test_finalize_completes_open_handles() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.close()
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "completed"
+
+
+def test_fail_marks_open_handles_interrupted_for_graph_interrupt() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(GraphInterrupt())
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "interrupted"
+
+
+def test_fail_marks_open_handles_failed_for_other_errors() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(RuntimeError("boom"))
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "failed"
+    assert handle.error == "boom"
+
+
+def test_child_mux_requires_factories() -> None:
+    """A mux constructed only from `transformers=` can't clone factories."""
+    transformer = SubgraphTransformer()
+    mux = StreamMux(transformers=[transformer], is_async=False)
+    with pytest.raises(RuntimeError, match="factories"):
+        mux._make_child(("anything",))
+
+
+def test_subgraph_and_lifecycle_agree_on_terminal_status() -> None:
+    """Both transformers consume the same tasks signal — no drift."""
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    [handle] = _drain_subgraphs(mux)
+    payloads = _lifecycle_payloads(mux)
+    assert handle.status == "failed"
+    assert payloads[-1]["event"] == "failed"
+    assert handle.error == payloads[-1]["error"]
+
+
+def test_required_stream_modes_declared() -> None:
+    assert SubgraphTransformer.required_stream_modes == ("tasks",)
+
+
+def test_tasks_events_suppressed_from_main_log() -> None:
+    """Tasks events are folded into discovery and don't appear on the main log."""
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    methods = [evt["method"] for evt in _event_items(mux)]
+    assert "tasks" not in methods
+
+
+class _ChildEventObserver(StreamTransformer):
+    """Records child-scope event identity without mutating it."""
+
+    records: list[tuple[tuple[str, ...], int, int, bool]] = []
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if self.scope and event["method"] == "values":
+            self.records.append(
+                (
+                    self.scope,
+                    id(event),
+                    id(event["params"]["data"]),
+                    "seq" in event,
+                )
+            )
+        return True
+
+
+def test_child_forwarding_reuses_event_without_assigning_seq() -> None:
+    _ChildEventObserver.records = []
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildEventObserver,
+        ],
+        is_async=False,
+    )
+    _arm(mux)
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    data = {"x": 1}
+    event: ProtocolEvent = {
+        "type": "event",
+        "method": "values",
+        "params": {
+            "namespace": ["agent:abc"],
+            "timestamp": TS,
+            "data": data,
+        },
+    }
+    mux.push(event)
+
+    assert _ChildEventObserver.records == [(("agent:abc",), id(event), id(data), False)]
+    [root_event] = [evt for evt in _event_items(mux) if evt["method"] == "values"]
+    assert root_event is event
+    assert "seq" in root_event
+
+
+class _AsyncProbeTransformer(StreamTransformer):
+    """Async-only transformer used to verify mini-mux async dispatch."""
+
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self.seen: list[tuple[str, ...]] = []
+        self.finalized = False
+        self.failed: BaseException | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"async_probe": self}
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        self.seen.append(tuple(event["params"]["namespace"]))
+        return True
+
+    async def afinalize(self) -> None:
+        self.finalized = True
+
+    async def afail(self, err: BaseException) -> None:
+        self.failed = err
+
+
+@pytest.mark.anyio
+async def test_async_child_mini_mux_uses_async_lane() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _AsyncProbeTransformer,
+        ],
+        is_async=True,
+    )
+    await mux.apush(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    handle = _subgraph_transformer(mux)._handles[("agent:abc",)]
+    assert isinstance(handle, AsyncSubgraphRunStream)
+    probe = _child_mux(handle).transformer_by_key("async_probe")
+    assert isinstance(probe, _AsyncProbeTransformer)
+    assert probe.seen == [("agent:abc",)]
+
+    await mux.apush(_tasks_result([], task_id="abc", name="agent"))
+    assert probe.finalized is True
+
+
+@pytest.mark.anyio
+async def test_async_child_mini_mux_fail_uses_async_lane() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _AsyncProbeTransformer,
+        ],
+        is_async=True,
+    )
+    await mux.apush(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    handle = _subgraph_transformer(mux)._handles[("agent:abc",)]
+    probe = _child_mux(handle).transformer_by_key("async_probe")
+    assert isinstance(probe, _AsyncProbeTransformer)
+
+    err = RuntimeError("boom")
+    await mux.afail(err)
+    assert probe.failed is err
+
+
+class _StandardCtorTransformer(StreamTransformer):
+    """Transformer class that inherits the standard scoped constructor."""
+
+    def init(self) -> dict[str, Any]:
+        return {"standard_ctor": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ScopedTransformer(StreamTransformer):
+    """Transformer class that uses the inherited scoped construction."""
+
+    def init(self) -> dict[str, Any]:
+        return {"scoped": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ConfigurableFactoryTransformer(StreamTransformer):
+    """Transformer built by a configured per-scope factory."""
+
+    def __init__(self, scope: tuple[str, ...] = (), *, label: str) -> None:
+        super().__init__(scope)
+        self.label = label
+
+    def init(self) -> dict[str, Any]:
+        return {"configurable": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ChildExploder(StreamTransformer):
+    """Raise from child mini-muxes to verify errors propagate upstream."""
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if self.scope and event["method"] == "values":
+            raise RuntimeError("child boom")
+        return True
+
+
+class _ChildFinalizeExploder(StreamTransformer):
+    """Raise from child mini-mux finalization."""
+
+    supports_sync = True
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+    def finalize(self) -> None:
+        if self.scope:
+            raise RuntimeError("child finalize boom")
+
+    async def afinalize(self) -> None:
+        if self.scope:
+            raise RuntimeError("child afinalize boom")
+
+
+def test_normalize_transformer_factories_supports_scoped_classes() -> None:
+    factories = _normalize_stream_transformer_factories(
+        [_StandardCtorTransformer, _ScopedTransformer]
+    )
+
+    standard_ctor = factories[0](("child",))
+    scoped = factories[1](("child",))
+    assert isinstance(standard_ctor, _StandardCtorTransformer)
+    assert standard_ctor.scope == ("child",)
+    assert isinstance(scoped, _ScopedTransformer)
+    assert scoped.scope == ("child",)
+
+
+def test_normalize_transformer_factories_supports_configured_factories() -> None:
+    factories = _normalize_stream_transformer_factories(
+        [partial(_ConfigurableFactoryTransformer, label="configured")]
+    )
+
+    built = factories[0](("child",))
+    assert isinstance(built, _ConfigurableFactoryTransformer)
+    assert built.label == "configured"
+    assert built.scope == ("child",)
+
+
+def test_normalize_transformer_factories_rejects_instances() -> None:
+    with pytest.raises(TypeError, match="pre-built instance"):
+        _normalize_stream_transformer_factories([_StandardCtorTransformer()])
+
+
+def test_child_forwarding_errors_fail_sync_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildExploder,
+        ],
+        is_async=False,
+    )
+    run = GraphRunStream(
+        iter(
+            [
+                _stream_part(
+                    "tasks",
+                    ("agent:abc",),
+                    {
+                        "id": "t1",
+                        "name": "tool",
+                        "input": None,
+                        "triggers": [],
+                    },
+                ),
+                _stream_part("values", ("agent:abc",), {"x": 1}),
+            ]
+        ),
+        mux,
+    )
+
+    handle = next(iter(run.subgraphs))
+    assert handle.path == ("agent:abc",)
+    with pytest.raises(RuntimeError, match="child boom"):
+        _ = run.output
+    assert run._mux._events._error is not None
+
+
+@pytest.mark.anyio
+async def test_child_forwarding_errors_fail_async_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildExploder,
+        ],
+        is_async=True,
+    )
+    run = AsyncGraphRunStream(
+        _astream_parts(
+            _stream_part(
+                "tasks",
+                ("agent:abc",),
+                {
+                    "id": "t1",
+                    "name": "tool",
+                    "input": None,
+                    "triggers": [],
+                },
+            ),
+            _stream_part("values", ("agent:abc",), {"x": 1}),
+        ),
+        mux,
+    )
+
+    handle = await run.subgraphs.__aiter__().__anext__()
+    assert handle.path == ("agent:abc",)
+    with pytest.raises(RuntimeError, match="child boom"):
+        await run.output()
+    assert run._mux._events._error is not None
+
+
+def test_child_finalize_errors_propagate_to_sync_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildFinalizeExploder,
+        ],
+        is_async=False,
+    )
+    run = GraphRunStream(
+        iter(
+            [
+                _stream_part(
+                    "tasks",
+                    ("agent:abc",),
+                    {
+                        "id": "t1",
+                        "name": "tool",
+                        "input": None,
+                        "triggers": [],
+                    },
+                )
+            ]
+        ),
+        mux,
+    )
+
+    with pytest.raises(RuntimeError, match="child finalize boom"):
+        _ = run.output
+
+
+@pytest.mark.anyio
+async def test_child_finalize_errors_propagate_to_async_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildFinalizeExploder,
+        ],
+        is_async=True,
+    )
+    run = AsyncGraphRunStream(
+        _astream_parts(
+            _stream_part(
+                "tasks",
+                ("agent:abc",),
+                {
+                    "id": "t1",
+                    "name": "tool",
+                    "input": None,
+                    "triggers": [],
+                },
+            )
+        ),
+        mux,
+    )
+
+    with pytest.raises(RuntimeError, match="child afinalize boom"):
+        await run.output()
+
+
+# ---------------------------------------------------------------------------
+# End-to-end real-graph tests
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _passthrough(state: _State) -> dict[str, Any]:
+    return {"value": state["value"] + "!", "items": ["x"]}
+
+
+def _make_two_level_nested() -> Any:
+    """outer → middle → inner. Three Pregel instances, two nesting levels."""
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("inner_node", _passthrough)
+    inner_b.add_edge(START, "inner_node")
+    inner_b.add_edge("inner_node", END)
+    inner = inner_b.compile()
+
+    middle_b: StateGraph = StateGraph(_State, input_schema=_State)
+    middle_b.add_node("inner", inner)
+    middle_b.add_edge(START, "inner")
+    middle_b.add_edge("inner", END)
+    middle = middle_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("middle", middle)
+    outer_b.add_edge(START, "middle")
+    outer_b.add_edge("middle", END)
+    return outer_b.compile()
+
+
+def _item_node(item: str):
+    def node(state: _State) -> dict[str, Any]:
+        return {"items": [item]}
+
+    return node
+
+
+def _make_two_sibling_subgraphs() -> Any:
+    """outer → one → two, where both nodes are compiled subgraphs."""
+    one_b: StateGraph = StateGraph(_State, input_schema=_State)
+    one_b.add_node("add_one", _item_node("one"))
+    one_b.add_edge(START, "add_one")
+    one_b.add_edge("add_one", END)
+    one = one_b.compile()
+
+    two_b: StateGraph = StateGraph(_State, input_schema=_State)
+    two_b.add_node("add_two", _item_node("two"))
+    two_b.add_edge(START, "add_two")
+    two_b.add_edge("add_two", END)
+    two = two_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("one", one)
+    outer_b.add_node("two", two)
+    outer_b.add_edge(START, "one")
+    outer_b.add_edge("one", "two")
+    outer_b.add_edge("two", END)
+    return outer_b.compile()
+
+
+def _failing_node(state: _State) -> dict[str, Any]:
+    raise ValueError("child boom")
+
+
+def _make_failing_nested() -> Any:
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("fail", _failing_node)
+    inner_b.add_edge(START, "fail")
+    inner_b.add_edge("fail", END)
+    inner = inner_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("inner", inner)
+    outer_b.add_edge(START, "inner")
+    outer_b.add_edge("inner", END)
+    return outer_b.compile()
+
+
+def test_stream_events_v3_real_graph_yields_subgraph_handles() -> None:
+    """Iterating `run.subgraphs` yields handles for direct-child subgraphs."""
+    graph = _make_two_level_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    handle_paths: list[tuple[str, ...]] = []
+    final_status: dict[tuple[str, ...], str] = {}
+    for handle in run.subgraphs:
+        # Drill into the handle's projections inside the loop body so
+        # the mini-mux is subscribed before the next pump cycle.
+        list(handle.values)
+        handle_paths.append(handle.path)
+        final_status[handle.path] = handle.status
+
+    assert len(handle_paths) == 1
+    assert handle_paths[0][0].startswith("middle:")
+    assert final_status[handle_paths[0]] == "completed"
+
+
+def test_stream_events_v3_grandchild_visible_on_child_handle() -> None:
+    """Drilling into `handle.subgraphs` surfaces nested grandchildren."""
+    graph = _make_two_level_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    grandchild_paths: list[tuple[str, ...]] = []
+    middle_path: tuple[str, ...] | None = None
+    for middle_handle in run.subgraphs:
+        # Subscribe to grandchildren before the next pump cycle.
+        for inner_handle in middle_handle.subgraphs:
+            # Subscribe to inner.values so its mini-mux drains.
+            list(inner_handle.values)
+            grandchild_paths.append(inner_handle.path)
+        middle_path = middle_handle.path
+
+    assert middle_path is not None
+    assert len(grandchild_paths) == 1
+    inner_path = grandchild_paths[0]
+    assert inner_path[1].startswith("inner:")
+    assert inner_path[: len(middle_path)] == middle_path
+
+
+def test_subgraph_output_stops_at_own_terminal_without_draining_siblings() -> None:
+    """A handle's `output` must not pump past its terminal event.
+
+    If it over-pumps the root run, the second sibling handle is yielded
+    only after it has already completed, so subscribing to `values`
+    inside the loop body misses its events.
+    """
+    graph = _make_two_sibling_subgraphs()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    paths: list[tuple[str, ...]] = []
+    second_values: list[dict[str, Any]] = []
+    for handle in run.subgraphs:
+        paths.append(handle.path)
+        if handle.graph_name == "one":
+            assert handle.output is not None
+            assert handle.status == "completed"
+        elif handle.graph_name == "two":
+            second_values = list(handle.values)
+
+    assert [path[0].split(":", 1)[0] for path in paths] == ["one", "two"]
+    assert second_values
+    assert second_values[-1]["items"] == ["one", "two"]
+
+
+def test_aborted_subgraph_handle_does_not_fail_parent_forwarding() -> None:
+    graph = _make_two_sibling_subgraphs()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    seen: list[str | None] = []
+    for handle in run.subgraphs:
+        seen.append(handle.graph_name)
+        if handle.graph_name == "one":
+            # Subscribe before aborting to ensure forwarding into the
+            # closed mini-mux would have raised without the closed check.
+            iter(handle.values)
+            handle.abort()
+        elif handle.graph_name == "two":
+            assert list(handle.values)
+
+    assert seen == ["one", "two"]
+
+
+def test_failed_subgraph_output_raises_terminal_error() -> None:
+    graph = _make_failing_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    handle = next(iter(run.subgraphs))
+    with pytest.raises(RuntimeError, match="child boom"):
+        _ = handle.output
+    assert handle.status == "failed"
+    assert handle.error == "child boom"

libs/langgraph/tests/test_time_travel.py

@@ -1113,6 +1113,70 @@ def test_subgraph_interrupt_replay_from_parent_then_resume(
     ]
 
 
+def test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Resume with Command(resume=...) plus the current head checkpoint_id
+    in config. The subgraph must continue from the interrupted node, not
+    restart from scratch. Explicit checkpoint_id triggers is_replaying but
+    this is a resume, not a time-travel, so ReplayState should not apply."""
+
+    called: list[str] = []
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["sub_a"]}
+
+    def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("Provide input:")
+        return {"value": [f"human:{answer}"]}
+
+    def step_b(state: State) -> State:
+        called.append("step_b")
+        return {"value": ["sub_b"]}
+
+    subgraph = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_human", ask_human)
+        .add_node("step_b", step_b)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_human")
+        .add_edge("ask_human", "step_b")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("subgraph_node", subgraph)
+        .add_edge(START, "subgraph_node")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt fires in subgraph
+    graph.invoke({"value": []}, config)
+    assert called == ["step_a", "ask_human"]
+
+    # Resume with explicit head checkpoint_id in config
+    head_checkpoint_id = graph.get_state(config).config["configurable"]["checkpoint_id"]
+    called.clear()
+    resume_config = {
+        "configurable": {
+            "thread_id": "1",
+            "checkpoint_id": head_checkpoint_id,
+            "checkpoint_ns": "",
+        }
+    }
+    result = graph.invoke(Command(resume="answer"), resume_config)
+
+    assert called == ["ask_human", "step_b"]
+    assert "__interrupt__" not in result
+    assert result["value"] == ["sub_a", "human:answer", "sub_b"]
+
+
 def test_subgraph_replay_loads_accumulated_state_then_resume(
     sync_checkpointer: BaseCheckpointSaver,
 ) -> None:

libs/langgraph/tests/test_tool_stream_handler.py

@@ -0,0 +1,290 @@
+"""Tests for StreamToolCallHandler and ToolRuntime.emit_output_delta.
+
+These tests exercise the langgraph-core piece in isolation — the prebuilt
+`ToolCallTransformer` has its own test file. Here we feed real graphs
+through `Pregel.stream(stream_mode=["tools", ...])` and inspect the raw
+`(ns, mode, payload)` tuples on the `tools` channel.
+"""
+
+from __future__ import annotations
+
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode, ToolRuntime
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.pregel._tools import _tool_call_writer
+
+
+class _State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def _caller_sync(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
+    def caller(state: _State) -> dict:
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
+                )
+            ]
+        }
+
+    return caller
+
+
+def _caller_async(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
+    async def caller(state: _State) -> dict:
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
+                )
+            ]
+        }
+
+    return caller
+
+
+def _build_graph(caller, tools) -> Any:
+    sg = StateGraph(_State)
+    sg.add_node("caller", caller)
+    sg.add_node("tools", ToolNode(tools))
+    sg.add_edge(START, "caller")
+    sg.add_edge("caller", "tools")
+    sg.add_edge("tools", END)
+    return sg.compile()
+
+
+def _tool_events(stream) -> list[tuple[tuple[str, ...], dict]]:
+    """Collect `(ns, payload)` for every `tools`-mode chunk."""
+    out: list[tuple[tuple[str, ...], dict]] = []
+    for ns, mode, payload in stream:
+        if mode == "tools":
+            out.append((tuple(ns), payload))
+    return out
+
+
+class TestSyncGraphSyncTool:
+    def test_started_finished_cycle(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return f"echoed:{text}"
+
+        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        assert [p["event"] for _, p in events] == [
+            "tool-started",
+            "tool-finished",
+        ]
+        assert events[0][1]["tool_call_id"] == "tc1"
+        assert events[0][1]["tool_name"] == "echo"
+        assert events[0][1]["input"] == {"text": "hi"}
+        # ToolNode wraps the return in a ToolMessage.
+        assert events[1][1]["tool_call_id"] == "tc1"
+
+    def test_emit_output_delta_produces_delta_events(self) -> None:
+        @tool
+        def streaming_echo(text: str, runtime: ToolRuntime) -> str:
+            """stream chunks."""
+            for chunk in ("a", "b", "c"):
+                runtime.emit_output_delta(chunk)
+            return text
+
+        graph = _build_graph(
+            _caller_sync("streaming_echo", {"text": "x"}), [streaming_echo]
+        )
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        deltas = [p["delta"] for _, p in events if p["event"] == "tool-output-delta"]
+        assert deltas == ["a", "b", "c"]
+        # The deltas must be bracketed by started and finished.
+        ordered = [p["event"] for _, p in events]
+        assert ordered[0] == "tool-started"
+        assert ordered[-1] == "tool-finished"
+
+    def test_tool_error_event(self) -> None:
+        @tool
+        def boom() -> str:
+            """raises."""
+            raise ValueError("nope")
+
+        graph = _build_graph(_caller_sync("boom", {}), [boom])
+        events: list[tuple[tuple[str, ...], dict]] = []
+        with pytest.raises(ValueError, match="nope"):
+            for ns, mode, payload in graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            ):
+                if mode == "tools":
+                    events.append((tuple(ns), payload))
+
+        kinds = [p["event"] for _, p in events]
+        assert kinds == ["tool-started", "tool-error"]
+        assert events[1][1]["message"] == "nope"
+
+    def test_writer_unset_outside_tool(self) -> None:
+        # Outside any tool body the ContextVar that ToolRuntime reads
+        # is unset — emitting from there would be a no-op.
+        assert _tool_call_writer.get() is None
+
+    def test_no_events_without_tools_mode(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return text
+
+        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
+        # No "tools" in stream_mode — handler is not attached and zero
+        # `tools`-method events fire.
+        chunks = list(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["values"],
+                subgraphs=True,
+            )
+        )
+        assert all(
+            not (isinstance(c, tuple) and len(c) == 3 and c[1] == "tools")
+            for c in chunks
+        )
+
+
+class TestAsyncGraphAsyncTool:
+    @pytest.mark.anyio
+    async def test_async_tool_produces_events(self) -> None:
+        @tool
+        async def aecho(text: str, runtime: ToolRuntime) -> str:
+            """async echo."""
+            runtime.emit_output_delta(text)
+            return f"got:{text}"
+
+        graph = _build_graph(_caller_async("aecho", {"text": "hi"}), [aecho])
+        events: list[tuple[tuple[str, ...], dict]] = []
+        async for ns, mode, payload in graph.astream(
+            {"messages": []},
+            stream_mode=["tools"],
+            subgraphs=True,
+        ):
+            if mode == "tools":
+                events.append((tuple(ns), payload))
+
+        kinds = [p["event"] for _, p in events]
+        assert kinds == ["tool-started", "tool-output-delta", "tool-finished"]
+        assert events[1][1]["delta"] == "hi"
+
+
+class TestConcurrentToolCalls:
+    def test_parallel_tool_calls_do_not_bleed(self) -> None:
+        @tool
+        def streamer(marker: str, runtime: ToolRuntime) -> str:
+            """emits marker twice."""
+            runtime.emit_output_delta(f"{marker}-1")
+            runtime.emit_output_delta(f"{marker}-2")
+            return marker
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "streamer", "args": {"marker": "A"}, "id": "a"},
+                            {"name": "streamer", "args": {"marker": "B"}, "id": "b"},
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [streamer])
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        # Group deltas by tool_call_id.
+        by_id: dict[str, list[str]] = {}
+        for _, p in events:
+            if p["event"] == "tool-output-delta":
+                by_id.setdefault(p["tool_call_id"], []).append(p["delta"])
+        assert by_id["a"] == ["A-1", "A-2"]
+        assert by_id["b"] == ["B-1", "B-2"]
+
+
+class TestSubgraphNamespacePropagation:
+    def test_tool_inside_subgraph_emits_with_subgraph_ns(self) -> None:
+        @tool
+        def inner_tool(text: str) -> str:
+            """inner tool."""
+            return text
+
+        def sub_caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {
+                                "name": "inner_tool",
+                                "args": {"text": "x"},
+                                "id": "tc1",
+                            }
+                        ],
+                    )
+                ]
+            }
+
+        inner = StateGraph(_State)
+        inner.add_node("sub_caller", sub_caller)
+        inner.add_node("sub_tools", ToolNode([inner_tool]))
+        inner.add_edge(START, "sub_caller")
+        inner.add_edge("sub_caller", "sub_tools")
+        inner.add_edge("sub_tools", END)
+        inner_graph = inner.compile()
+
+        outer = StateGraph(_State)
+        outer.add_node("sub", inner_graph)
+        outer.add_edge(START, "sub")
+        outer.add_edge("sub", END)
+        graph = outer.compile()
+
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        # All `tools` events should carry a non-empty namespace rooted
+        # at the `sub` node.
+        assert events, "expected at least one tools event"
+        for ns, _ in events:
+            assert ns  # non-empty
+            assert ns[0].startswith("sub:")

libs/langgraph/uv.lock

@@ -1348,10 +1348,11 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.4.0a2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
+    { name = "langchain-protocol" },
     { name = "langsmith" },
     { name = "packaging" },
     { name = "pydantic" },
@@ -1360,14 +1361,26 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3c/93/68bafa047f8e1770d0cf0f61d6c70889f1dec42ef6bd263540d916c421b9/langchain_core-1.4.0a2.tar.gz", hash = "sha256:b723c7961b615c7f2180ce2bcf352fdad8247bc51a60adecd3d97088235c120d", size = 916486, upload-time = "2026-05-01T15:02:19.029Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/8e/933e0ba7ba0430ce264e36b178d581b255239ad45093872483142d93478c/langchain_core-1.4.0a2-py3-none-any.whl", hash = "sha256:a5c689f8404357df797120c012da7704144a953b2ae18f258df263301e7badd5", size = 546297, upload-time = "2026-05-01T15:02:17.731Z" },
+]
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/bf/efb5e2ed832e4d6d45590e25a9e5191986b291b543bc6a807b48bee070b0/langchain_protocol-0.0.14.tar.gz", hash = "sha256:bc1e8553122e6ede310280462d5813023a172ff2785ccbbdec54d43f3a15e5f2", size = 5862, upload-time = "2026-04-29T16:40:18.657Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/e9/06c47ecb2aff08f83dfa30058da3bf86be64862c19569043ed5331bbeecd/langchain_protocol-0.0.14-py3-none-any.whl", hash = "sha256:ffc35089779bd8ca217015180cef5e660fc3b074efdaa0f2e95df73583f1a047", size = 6984, upload-time = "2026-04-29T16:40:17.841Z" },
 ]
 
 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.2.0a4"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1439,7 +1452,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.4.0a2,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "../prebuilt" },
     { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1548,7 +1561,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -1596,7 +1609,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.5"
+version = "3.1.0a3"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -1706,7 +1719,7 @@ inmem = [
 requires-dist = [
     { name = "click", specifier = ">=8.1.7" },
     { name = "httpx", specifier = ">=0.24.0" },
-    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
+    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.9.0" },
     { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
     { name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
     { name = "pathspec", specifier = ">=0.11.0" },
@@ -1742,7 +1755,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.1.0a1"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -1751,7 +1764,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", specifier = ">=1.3.1" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
 ]
 
@@ -1826,20 +1839,20 @@ requires-dist = [
 dev = [
     { name = "codespell" },
     { name = "langgraph", editable = "." },
-    { name = "mypy", specifier = "==1.19.1" },
+    { name = "mypy", specifier = "==1.20.2" },
     { name = "pydantic", specifier = ">=2.12.4" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watch" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "ruff", specifier = "==0.15.12" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
 lint = [
     { name = "codespell" },
-    { name = "mypy", specifier = "==1.19.1" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "mypy", specifier = "==1.20.2" },
+    { name = "ruff", specifier = "==0.15.12" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
@@ -2140,7 +2153,7 @@ wheels = [
 
 [[package]]
 name = "nbconvert"
-version = "7.17.0"
+version = "7.17.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -2158,9 +2171,9 @@ dependencies = [
     { name = "pygments" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/38/47/81f886b699450d0569f7bc551df2b1673d18df7ff25cc0c21ca36ed8a5ff/nbconvert-7.17.0.tar.gz", hash = "sha256:1b2696f1b5be12309f6c7d707c24af604b87dfaf6d950794c7b07acab96dda78", size = 862855, upload-time = "2026-01-29T16:37:48.478Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/b1/708e53fe2e429c103c6e6e159106bcf0357ac41aa4c28772bd8402339051/nbconvert-7.17.1.tar.gz", hash = "sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2", size = 865311, upload-time = "2026-04-08T00:44:14.914Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0d/4b/8d5f796a792f8a25f6925a96032f098789f448571eb92011df1ae59e8ea8/nbconvert-7.17.0-py3-none-any.whl", hash = "sha256:4f99a63b337b9a23504347afdab24a11faa7d86b405e5c8f9881cd313336d518", size = 261510, upload-time = "2026-01-29T16:37:46.322Z" },
+    { url = "https://files.pythonhosted.org/packages/67/f8/bb0a9d5f46819c821dc1f004aa2cc29b1d91453297dbf5ff20470f00f193/nbconvert-7.17.1-py3-none-any.whl", hash = "sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8", size = 261927, upload-time = "2026-04-08T00:44:12.845Z" },
 ]
 
 [[package]]
@@ -3018,11 +3031,11 @@ wheels = [
 
 [[package]]
 name = "python-dotenv"
-version = "1.2.1"
+version = "1.2.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f0/26/19cadc79a718c5edbec86fd4919a6b6d3f681039a2f6d66d14be94e75fb9/python_dotenv-1.2.1.tar.gz", hash = "sha256:42667e897e16ab0d66954af0e60a9caa94f0fd4ecf3aaf6d2d260eec1aa36ad6", size = 44221, upload-time = "2025-10-26T15:12:10.434Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135, upload-time = "2026-03-01T16:00:26.196Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/14/1b/a298b06749107c305e1fe0f814c6c74aea7b2f1e10989cb30f544a1b3253/python_dotenv-1.2.1-py3-none-any.whl", hash = "sha256:b81ee9561e9ca4004139c6cbba3a238c32b03e4894671e181b671e8cb8425d61", size = 21230, upload-time = "2025-10-26T15:12:09.109Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101, upload-time = "2026-03-01T16:00:25.09Z" },
 ]
 
 [[package]]

libs/prebuilt/langgraph/prebuilt/__init__.py

@@ -1,5 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
 
+from langgraph.prebuilt._tool_call_transformer import ToolCallTransformer
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import (
     InjectedState,
@@ -13,6 +14,7 @@ from langgraph.prebuilt.tool_validator import ValidationNode
 __all__ = [
     "create_react_agent",
     "ToolNode",
+    "ToolCallTransformer",
     "tools_condition",
     "ValidationNode",
     "InjectedState",

libs/prebuilt/langgraph/prebuilt/_tool_call_stream.py

@@ -0,0 +1,117 @@
+"""In-process handle for a single tool call's streaming execution.
+
+Mirrors the shape of `ChatModelStream` from langchain-core but simpler —
+a tool has one output channel, no content-block multiplexing. Populated
+by `ToolCallTransformer` as `tool-started` / `tool-output-delta` /
+`tool-finished` / `tool-error` events flow in on the `tools` channel.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Iterator
+from typing import Any
+
+from langgraph.stream.stream_channel import StreamChannel
+
+
+class ToolCallStream:
+    """Scoped view of a single tool call's lifecycle.
+
+    Yielded on `run.tool_calls` once per `tool-started` event. Fields
+    are populated as events arrive:
+
+    - `tool_call_id`, `tool_name`, `input`: stable from the start event.
+    - `output_deltas`: a `StreamChannel` of delta chunks. Iterate (sync or
+      async) to consume partial output in arrival order.
+    - `output`: terminal payload from `tool-finished`, or `None` if the
+      call failed or is still in flight.
+    - `error`: terminal error string from `tool-error`, or `None` if the
+      call succeeded or is still in flight.
+    - `completed`: True once a terminal event (`tool-finished` or
+      `tool-error`) has been observed.
+
+    `ToolCallStream` is not meant to be constructed by end users — it's
+    produced by `ToolCallTransformer` as events flow through the mux.
+    """
+
+    def __init__(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        input: dict[str, Any] | None = None,
+    ) -> None:
+        """Initialize a fresh handle for a tool call.
+
+        Args:
+            tool_call_id: The `tool_call_id` from the AIMessage.
+            tool_name: The tool's name.
+            input: The tool's input arguments (as reported by
+                `on_tool_start`), or `None` if none were captured.
+        """
+        self.tool_call_id = tool_call_id
+        self.tool_name = tool_name
+        self.input = input
+        self._output_deltas: StreamChannel[Any] = StreamChannel()
+        self.output: Any = None
+        self.error: str | None = None
+        self.completed = False
+
+    @property
+    def output_deltas(self) -> StreamChannel[Any]:
+        """The channel of streamed `tool-output-delta` payloads.
+
+        Iterate (sync or async depending on how the run was started)
+        to consume partial output in arrival order. The log closes when
+        the tool finishes or errors.
+        """
+        return self._output_deltas
+
+    def _bind(self, *, is_async: bool) -> None:
+        """Bind the deltas log to sync or async iteration.
+
+        Called by `ToolCallTransformer` when constructing this handle so
+        the log matches the enclosing mux's mode.
+        """
+        self._output_deltas._bind(is_async=is_async)
+
+    def _push_delta(self, delta: Any) -> None:
+        self._output_deltas.push(delta)
+
+    def _finish(self, output: Any) -> None:
+        self.output = output
+        self.completed = True
+        self._output_deltas.close()
+
+    def _fail(self, message: str) -> None:
+        self.error = message
+        self.completed = True
+        self._output_deltas.close()
+
+    def __iter__(self) -> Iterator[Any]:
+        """Iterate delta chunks synchronously.
+
+        Equivalent to `iter(self.output_deltas)`. Raises `TypeError` if
+        the underlying log is bound to async mode.
+        """
+        return iter(self._output_deltas)
+
+    def __aiter__(self) -> AsyncIterator[Any]:
+        """Iterate delta chunks asynchronously.
+
+        Equivalent to `aiter(self.output_deltas)`. Raises `TypeError`
+        if the underlying log is bound to sync mode.
+        """
+        return self._output_deltas.__aiter__()
+
+    def __repr__(self) -> str:
+        status = (
+            "completed"
+            if self.completed and self.error is None
+            else "failed"
+            if self.completed
+            else "running"
+        )
+        return (
+            f"ToolCallStream(tool_call_id={self.tool_call_id!r}, "
+            f"tool_name={self.tool_name!r}, status={status})"
+        )

libs/prebuilt/langgraph/prebuilt/_tool_call_transformer.py

@@ -0,0 +1,129 @@
+"""Transformer that projects `tools` channel events into `ToolCallStream`s."""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.stream_channel import StreamChannel
+
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
+
+
+class ToolCallTransformer(StreamTransformer):
+    """Project `tools` channel events into `ToolCallStream` handles.
+
+    Each `tool-started` event spawns a `ToolCallStream`, pushed onto
+    `run.tool_calls`. Subsequent `tool-output-delta` events append to
+    that stream's deltas log; `tool-finished` and `tool-error` close it.
+
+    Native transformer — the `tool_calls` projection is exposed as a
+    direct attribute on the run stream.
+
+    A nameless `StreamChannel[ToolCallStream]` is used (no protocol
+    auto-forwarding) because the live handles are not serializable and
+    should not be injected into the main event log. Wire consumers
+    subscribe to the `tools` channel instead, where the raw protocol
+    events flow through untouched by this transformer (`process`
+    returns `True`).
+
+    Registered explicitly by users at compile time via
+    `builder.compile(transformers=[ToolCallTransformer])` — not a
+    default built-in, so the `tools` channel is user-opt-in.
+    """
+
+    _native = True
+    required_stream_modes = ("tools",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[ToolCallStream] = StreamChannel()
+        self._active: dict[str, ToolCallStream] = {}
+        self._is_async = False
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"tool_calls": self._log}
+
+    def _bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback onto this transformer.
+
+        Called by `StreamMux.bind_pump`. Stored so each new
+        `ToolCallStream` created by `process` can wire its deltas log
+        for pump-driven iteration.
+        """
+        self._pump_fn = fn
+        self._is_async = False
+
+    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Async counterpart to `_bind_pump`."""
+        self._apump_fn = fn
+        self._is_async = True
+
+    def _new_stream(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        tool_input: dict[str, Any] | None,
+    ) -> ToolCallStream:
+        stream = ToolCallStream(tool_call_id, tool_name, tool_input)
+        stream._bind(is_async=self._is_async)
+        if self._apump_fn is not None:
+            stream._output_deltas._arequest_more = self._apump_fn
+        if self._pump_fn is not None:
+            stream._output_deltas._request_more = self._pump_fn
+        return stream
+
+    def process(self, event: ProtocolEvent) -> bool:
+        # Namespace filtering is handled by the mux via `scope_exact`.
+        if event["method"] != "tools":
+            return True
+
+        data = event["params"]["data"]
+        tool_call_id = data.get("tool_call_id")
+        if tool_call_id is None:
+            return True
+        event_type = data.get("event")
+
+        stream: ToolCallStream | None
+        if event_type == "tool-started":
+            stream = self._new_stream(
+                tool_call_id,
+                data.get("tool_name", ""),
+                data.get("input"),
+            )
+            self._active[tool_call_id] = stream
+            self._log.push(stream)
+        elif event_type == "tool-output-delta":
+            stream = self._active.get(tool_call_id)
+            if stream is not None:
+                stream._push_delta(data.get("delta"))
+        elif event_type == "tool-finished":
+            stream = self._active.pop(tool_call_id, None)
+            if stream is not None:
+                stream._finish(data.get("output"))
+        elif event_type == "tool-error":
+            stream = self._active.pop(tool_call_id, None)
+            if stream is not None:
+                stream._fail(data.get("message", ""))
+
+        # Pass-through — wire consumers subscribe to the `tools` channel
+        # directly and reconstruct handles client-side.
+        return True
+
+    def finalize(self) -> None:
+        """Close any still-active tool streams left open at run end."""
+        for stream in self._active.values():
+            if not stream.completed:
+                stream._finish(None)
+        self._active.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Fail any still-active tool streams when the run errors."""
+        message = str(err)
+        for stream in self._active.values():
+            if not stream.completed:
+                stream._fail(message)
+        self._active.clear()

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -44,7 +44,7 @@ import inspect
 import json
 from collections.abc import Awaitable, Callable
 from copy import copy, deepcopy
-from dataclasses import dataclass, replace
+from dataclasses import dataclass, field, replace
 from types import UnionType
 from typing import (
     TYPE_CHECKING,
@@ -82,9 +82,11 @@ from langchain_core.tools.base import (
     _is_injected_arg_type,
     get_all_basemodel_annotations,
 )
+from langgraph._internal._constants import CONF, CONFIG_KEY_READ
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.pregel._tools import _tool_call_writer
 from langgraph.runtime import ExecutionInfo, ServerInfo  # noqa: TC002
 from langgraph.store.base import BaseStore  # noqa: TC002
 from langgraph.types import Command, Send, StreamWriter
@@ -614,6 +616,7 @@ class _InjectedArgs:
     store: str | None
     runtime: str | None
     all_injected_keys: set[str]
+    _optional_state_args: set[str]
 
 
 class ToolNode(RunnableCallable):
@@ -799,7 +802,7 @@ class ToolNode(RunnableCallable):
         # Construct ToolRuntime instances at the top level for each tool call
         tool_runtimes = []
         for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input)
+            state = self._extract_state(input, cfg)
             tool_runtime = ToolRuntime(
                 state=state,
                 tool_call_id=call["id"],
@@ -807,6 +810,7 @@ class ToolNode(RunnableCallable):
                 context=runtime.context,
                 store=runtime.store,
                 stream_writer=runtime.stream_writer,
+                tools=list(self.tools_by_name.values()),
                 execution_info=runtime.execution_info,
                 server_info=runtime.server_info,
             )
@@ -833,7 +837,7 @@ class ToolNode(RunnableCallable):
         # Construct ToolRuntime instances at the top level for each tool call
         tool_runtimes = []
         for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input)
+            state = self._extract_state(input, cfg)
             tool_runtime = ToolRuntime(
                 state=state,
                 tool_call_id=call["id"],
@@ -841,6 +845,7 @@ class ToolNode(RunnableCallable):
                 context=runtime.context,
                 store=runtime.store,
                 stream_writer=runtime.stream_writer,
+                tools=list(self.tools_by_name.values()),
                 execution_info=runtime.execution_info,
                 server_info=runtime.server_info,
             )
@@ -856,14 +861,30 @@ class ToolNode(RunnableCallable):
 
     def _combine_tool_outputs(
         self,
-        outputs: list[ToolMessage | Command],
+        outputs: list[ToolMessage | Command | list[ToolMessage | Command]],
         input_type: Literal["list", "dict", "tool_calls"],
     ) -> list[Command | list[ToolMessage] | dict[str, list[ToolMessage]]]:
+        # Flatten list entries from tools that returned multiple items
+        flat_outputs: list[ToolMessage | Command]
+        if any(isinstance(output, list) for output in outputs):
+            flat_outputs = []
+            for output in outputs:
+                if isinstance(output, list):
+                    flat_outputs.extend(output)
+                else:
+                    flat_outputs.append(output)
+        else:
+            flat_outputs = cast("list[ToolMessage | Command]", outputs)
+
         # preserve existing behavior for non-command tool outputs for backwards
         # compatibility
-        if not any(isinstance(output, Command) for output in outputs):
+        if not any(isinstance(output, Command) for output in flat_outputs):
             # TypedDict, pydantic, dataclass, etc. should all be able to load from dict
-            return outputs if input_type == "list" else {self._messages_key: outputs}
+            return (
+                flat_outputs
+                if input_type == "list"
+                else {self._messages_key: flat_outputs}
+            )
 
         # LangGraph will automatically handle list of Command and non-command node
         # updates
@@ -873,7 +894,7 @@ class ToolNode(RunnableCallable):
 
         # combine all parent commands with goto into a single parent command
         parent_command: Command | None = None
-        for output in outputs:
+        for output in flat_outputs:
             if isinstance(output, Command):
                 if (
                     output.graph is Command.PARENT
@@ -903,7 +924,7 @@ class ToolNode(RunnableCallable):
         request: ToolCallRequest,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
         """Execute tool call with configured error handling.
 
         Args:
@@ -912,7 +933,7 @@ class ToolNode(RunnableCallable):
             config: Runnable configuration.
 
         Returns:
-            ToolMessage or Command.
+            ToolMessage, Command, or list of Command/ToolMessage.
 
         Raises:
             Exception: If tool fails and handle_tool_errors is False.
@@ -944,6 +965,11 @@ class ToolNode(RunnableCallable):
                     call["name"], exc, call["args"], filtered_errors
                 ) from exc
 
+            # Inside try so validation errors route through _handle_tool_errors
+            return self._normalize_tool_response(
+                response, request.tool_call, input_type
+            )
+
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios,
         # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation
@@ -985,23 +1011,12 @@ class ToolNode(RunnableCallable):
                 status="error",
             )
 
-        # Process successful response
-        if isinstance(response, Command):
-            # Validate Command before returning to handler
-            return self._validate_tool_command(response, request.tool_call, input_type)
-        if isinstance(response, ToolMessage):
-            response.content = cast("str | list", msg_content_output(response.content))
-            return response
-
-        msg = f"Tool {call['name']} returned unexpected type: {type(response)}"
-        raise TypeError(msg)
-
     def _run_one(
         self,
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         tool_runtime: ToolRuntime,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
         """Execute single tool call with wrap_tool_call wrapper if configured.
 
         Args:
@@ -1056,7 +1071,7 @@ class ToolNode(RunnableCallable):
         request: ToolCallRequest,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
         """Execute tool call asynchronously with configured error handling.
 
         Args:
@@ -1065,7 +1080,7 @@ class ToolNode(RunnableCallable):
             config: Runnable configuration.
 
         Returns:
-            ToolMessage or Command.
+            ToolMessage, Command, or list of Command/ToolMessage.
 
         Raises:
             Exception: If tool fails and handle_tool_errors is False.
@@ -1097,6 +1112,11 @@ class ToolNode(RunnableCallable):
                     call["name"], exc, call["args"], filtered_errors
                 ) from exc
 
+            # Inside try so validation errors route through _handle_tool_errors
+            return self._normalize_tool_response(
+                response, request.tool_call, input_type
+            )
+
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios,
         # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation
@@ -1138,23 +1158,12 @@ class ToolNode(RunnableCallable):
                 status="error",
             )
 
-        # Process successful response
-        if isinstance(response, Command):
-            # Validate Command before returning to handler
-            return self._validate_tool_command(response, request.tool_call, input_type)
-        if isinstance(response, ToolMessage):
-            response.content = cast("str | list", msg_content_output(response.content))
-            return response
-
-        msg = f"Tool {call['name']} returned unexpected type: {type(response)}"
-        raise TypeError(msg)
-
     async def _arun_one(
         self,
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         tool_runtime: ToolRuntime,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
         """Execute single tool call asynchronously with awrap_tool_call wrapper if configured.
 
         Args:
@@ -1270,18 +1279,37 @@ class ToolNode(RunnableCallable):
         return None
 
     def _extract_state(
-        self, input: list[AnyMessage] | dict[str, Any] | BaseModel
+        self,
+        input: list[AnyMessage] | dict[str, Any] | BaseModel,
+        config: RunnableConfig,
     ) -> list[AnyMessage] | dict[str, Any] | BaseModel:
-        """Extract state from input, handling ToolCallWithContext if present.
+        """Extract state from input.
 
-        Args:
-            input: The input which may be raw state or ToolCallWithContext.
+        Three input shapes:
 
-        Returns:
-            The actual state to pass to wrap_tool_call wrappers.
+        - `ToolCallWithContext` dict — legacy Send payload carrying an inlined
+          state snapshot; return `input["state"]`.
+        - list of `ToolCall` dicts — new Send payload with no inlined state;
+          hydrate state from channels via `CONFIG_KEY_READ`.
+        - regular graph state (dict/list/BaseModel) — return `input` as-is.
         """
         if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
             return input["state"]
+        if (
+            isinstance(input, list)
+            and input
+            and isinstance(input[-1], dict)
+            and input[-1].get("type") == "tool_call"
+        ):
+            read = config.get(CONF, {}).get(CONFIG_KEY_READ)
+            if read is None:
+                return {}
+            # Pregel installs CONFIG_KEY_READ as
+            # `functools.partial(local_read, scratchpad, channels, managed, task)`.
+            # Match the previous inlined-state contract by reading channels only;
+            # managed values have their own injection path (`ToolRuntime.context`).
+            channels = read.args[1]
+            return cast("dict[str, Any]", read(list(channels), True))
         return input
 
     def _inject_tool_args(
@@ -1333,7 +1361,7 @@ class ToolNode(RunnableCallable):
             return tool_call
 
         tool_call_copy: ToolCall = copy(tool_call)
-        injected_args = {}
+        injected_args: dict[str, Any] = {}
 
         # Inject state
         if injected.state:
@@ -1361,14 +1389,20 @@ class ToolNode(RunnableCallable):
             # Extract state values
             if isinstance(state, dict):
                 for tool_arg, state_field in injected.state.items():
-                    injected_args[tool_arg] = (
-                        state[state_field] if state_field else state
-                    )
+                    if not state_field:
+                        injected_args[tool_arg] = state
+                    elif state_field in state:
+                        injected_args[tool_arg] = state[state_field]
+                    elif tool_arg not in injected._optional_state_args:
+                        raise KeyError(state_field)
             else:
                 for tool_arg, state_field in injected.state.items():
-                    injected_args[tool_arg] = (
-                        getattr(state, state_field) if state_field else state
-                    )
+                    if not state_field:
+                        injected_args[tool_arg] = state
+                    elif hasattr(state, state_field):
+                        injected_args[tool_arg] = getattr(state, state_field)
+                    elif tool_arg not in injected._optional_state_args:
+                        raise AttributeError(state_field)
 
         # Inject store
         if injected.store:
@@ -1395,11 +1429,84 @@ class ToolNode(RunnableCallable):
         tool_call_copy["args"] = {**stripped_args, **injected_args}
         return tool_call_copy
 
+    def _normalize_tool_response(
+        self,
+        response: Any,
+        tool_call: ToolCall,
+        input_type: Literal["list", "dict", "tool_calls"],
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
+        """Validate and normalize a tool's raw return value."""
+        if isinstance(response, Command):
+            return self._validate_tool_command(response, tool_call, input_type)
+        if isinstance(response, ToolMessage):
+            response.content = cast("str | list", msg_content_output(response.content))
+            return response
+        if isinstance(response, list):
+            if all(isinstance(r, (Command, ToolMessage)) for r in response):
+                return self._validate_tool_command_list(response, tool_call, input_type)
+            msg = (
+                f"Tool {tool_call['name']} returned a list with invalid element "
+                "types: expected all Command or ToolMessage"
+            )
+            raise TypeError(msg)
+        msg = f"Tool {tool_call['name']} returned unexpected type: {type(response)}"
+        raise TypeError(msg)
+
+    def _validate_tool_command_list(
+        self,
+        response: list[Command | ToolMessage],
+        tool_call: ToolCall,
+        input_type: Literal["list", "dict", "tool_calls"],
+    ) -> list[Command | ToolMessage]:
+        """Validate a list of Command/ToolMessage returned by a single tool call.
+
+        Requires exactly one terminating ToolMessage (matching the outer tool_call_id)
+        across the list — either as a top-level element or nested in a
+        Command.update["messages"].
+        """
+        expected_id = tool_call["id"]
+
+        terminator_count = 0
+        for item in response:
+            if isinstance(item, ToolMessage):
+                if item.tool_call_id == expected_id:
+                    terminator_count += 1
+            elif isinstance(item, Command) and isinstance(item.update, dict):
+                for msg in item.update.get(self._messages_key, []):
+                    if isinstance(msg, ToolMessage) and msg.tool_call_id == expected_id:
+                        terminator_count += 1
+
+        if terminator_count != 1:
+            msg = (
+                f"Tool {tool_call['name']} returned a list with "
+                f"{terminator_count} messages bound to tool_call_id "
+                f"{expected_id!r}; expected exactly one terminating ToolMessage."
+            )
+            raise ValueError(msg)
+
+        # Per-Command normalization still runs, but the list-level count above
+        # already guarantees exactly one terminator, so individual Commands may
+        # lack one.
+        validated: list[Command | ToolMessage] = []
+        for item in response:
+            if isinstance(item, Command):
+                validated.append(
+                    self._validate_tool_command(
+                        item, tool_call, input_type, require_terminator=False
+                    )
+                )
+            else:
+                item.content = cast("str | list", msg_content_output(item.content))
+                validated.append(item)
+        return validated
+
     def _validate_tool_command(
         self,
         command: Command,
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
+        *,
+        require_terminator: bool = True,
     ) -> Command:
         if isinstance(command.update, dict):
             # input type is dict when ToolNode is invoked with a dict input
@@ -1449,7 +1556,11 @@ class ToolNode(RunnableCallable):
 
         # validate that we always have a ToolMessage matching the tool call in
         # Command.update if command is sent to the CURRENT graph
-        if updated_command.graph is None and not has_matching_tool_message:
+        if (
+            require_terminator
+            and updated_command.graph is None
+            and not has_matching_tool_message
+        ):
             example_update = (
                 '`Command(update={"messages": '
                 '[ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`'
@@ -1569,6 +1680,7 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
     - `context`: Runtime context (shared with `Runtime`)
     - `store`: `BaseStore` instance for persistent storage (shared with `Runtime`)
     - `stream_writer`: `StreamWriter` for streaming output (shared with `Runtime`)
+    - `tools`: List of all available `BaseTool` instances
 
     No `Annotated` wrapper is needed - just use `runtime: ToolRuntime`
     as a parameter.
@@ -1613,9 +1725,30 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
     stream_writer: StreamWriter
     tool_call_id: str | None
     store: BaseStore | None
+    tools: list[BaseTool] = field(default_factory=list)
     execution_info: ExecutionInfo | None = None
     server_info: ServerInfo | None = None
 
+    def emit_output_delta(self, delta: Any) -> None:
+        """Stream a partial output chunk on the `tools` stream channel.
+
+        Reads the per-tool-call writer that `StreamToolCallHandler`
+        installs on a ContextVar at `on_tool_start` and forwards `delta`
+        through it. Silent no-op when the graph was not run with
+        `"tools"` in `stream_mode` (no writer is set), so tool authors
+        can leave `emit_output_delta` calls in place without gating
+        them on stream mode.
+
+        Args:
+            delta: Partial output chunk. Any JSON-serializable value;
+                surfaced as-is on the `tools` channel's
+                `tool-output-delta` payload under `"delta"`.
+        """
+        writer = _tool_call_writer.get()
+        if writer is None:
+            return
+        writer(delta)
+
 
 class InjectedState(InjectedToolArg):
     """Annotation for injecting graph state into tool arguments.
@@ -1859,6 +1992,7 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
     store_arg: str | None = None
     runtime_arg: str | None = None
     all_injected_keys: set[str] = set()
+    _optional_state_args: set[str] = set()
 
     for name, type_ in all_annotations.items():
         # Track all InjectedToolArg-annotated params (including custom subclasses)
@@ -1873,6 +2007,9 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
         if state_inj := _get_injection_from_type(type_, InjectedState):
             if isinstance(state_inj, InjectedState) and state_inj.field:
                 state_args[name] = state_inj.field
+                field_info = full_schema.model_fields.get(name)
+                if field_info and not field_info.is_required():
+                    _optional_state_args.add(name)
             else:
                 state_args[name] = None
 
@@ -1889,4 +2026,5 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
         store=store_arg,
         runtime=runtime_arg,
         all_injected_keys=all_injected_keys,
+        _optional_state_args=_optional_state_args,
     )

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.1.0a1"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -25,12 +25,12 @@ classifiers = [
 ]
 dependencies = [
     "langgraph-checkpoint>=2.1.0,<5.0.0",
-    "langchain-core>=1.0.0",
+    "langchain-core>=1.3.1",
 ]
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/prebuilt"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/prebuilt/tests/test_injected_state_not_required.py

@@ -69,6 +69,7 @@ def _create_config_with_runtime(store=None, state=None):
         context={},
         store=store,
         stream_writer=None,
+        tools=[],
         tool_call_id="test_id",
     )
     return {

libs/prebuilt/tests/test_on_tool_call.py

@@ -1320,6 +1320,98 @@ async def test_state_extraction_with_tool_call_with_context_async() -> None:
     assert "tool_call" not in state_seen[0]
 
 
+def _config_with_channel_read(
+    channel_values: dict[str, object],
+    store: BaseStore | None = None,
+) -> RunnableConfig:
+    """Build a config that mimics `CONFIG_KEY_READ` as Pregel installs it.
+
+    Pregel always installs a `functools.partial(local_read, scratchpad,
+    channels, managed, task)`, and `ToolNode` introspects that partial to
+    learn channel names. The stub matches the shape: partial whose second and
+    third positional args are `channels` and `managed` mappings.
+    """
+    import functools
+
+    channels_stub = {k: None for k in channel_values}
+    managed_stub: dict[str, object] = {}
+
+    # Shape matches pregel's real partial:
+    # functools.partial(local_read, scratchpad, channels, managed, task)
+    def _read(scratchpad, channels, managed, task, select, fresh):  # noqa: ARG001
+        if isinstance(select, str):
+            return channel_values[select]
+        return {k: channel_values[k] for k in select if k in channel_values}
+
+    read = functools.partial(_read, None, channels_stub, managed_stub, None)
+    cfg = _create_config_with_runtime(store)
+    cfg["configurable"]["__pregel_read"] = read
+    return cfg
+
+
+def test_list_form_send_hydrates_state_from_channel_read() -> None:
+    """Send('tools', [tool_call]) with no inlined state should hydrate
+    ToolRuntime.state from CONFIG_KEY_READ (full state read)."""
+    state_seen = []
+
+    def state_inspector_handler(
+        request: ToolCallRequest,
+        execute: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        state_seen.append(request.state)
+        return execute(request)
+
+    channel_values = {
+        "messages": [AIMessage("from channels")],
+        "files": {"/a.md": "body"},
+    }
+
+    tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
+
+    tool_call: ToolCall = {
+        "name": "add",
+        "args": {"a": 1, "b": 2},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+
+    tool_node.invoke([tool_call], config=_config_with_channel_read(channel_values))
+
+    assert len(state_seen) == 1
+    got = state_seen[0]
+    assert got == channel_values
+    assert "messages" in got and "files" in got
+
+
+async def test_list_form_send_hydrates_state_async() -> None:
+    state_seen = []
+
+    def state_inspector_handler(
+        request: ToolCallRequest,
+        execute: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        state_seen.append(request.state)
+        return execute(request)
+
+    channel_values = {"messages": [AIMessage("from channels")], "files": {}}
+
+    tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
+
+    tool_call: ToolCall = {
+        "name": "add",
+        "args": {"a": 1, "b": 2},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+
+    await tool_node.ainvoke(
+        [tool_call], config=_config_with_channel_read(channel_values)
+    )
+
+    assert len(state_seen) == 1
+    assert state_seen[0] == channel_values
+
+
 def test_tool_call_request_is_frozen() -> None:
     """Test that ToolCallRequest raises deprecation warnings on direct attribute reassignment."""
     tool_call: ToolCall = {"name": "add", "args": {"a": 1, "b": 2}, "id": "call_1"}

libs/prebuilt/tests/test_tool_call_transformer.py

@@ -0,0 +1,321 @@
+"""Tests for ToolCallTransformer and the ToolCallStream projection."""
+
+from __future__ import annotations
+
+import time
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    MessagesTransformer,
+    ValuesTransformer,
+)
+from typing_extensions import TypedDict
+
+from langgraph.prebuilt import (
+    ToolCallTransformer,
+    ToolNode,
+    ToolRuntime,
+)
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
+
+TS = int(time.time() * 1000)
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _tool_event(
+    event: str,
+    tool_call_id: str,
+    *,
+    tool_name: str = "",
+    input: dict[str, Any] | None = None,
+    delta: Any = None,
+    output: Any = None,
+    message: str = "",
+    namespace: list[str] | None = None,
+) -> ProtocolEvent:
+    data: dict[str, Any] = {"event": event, "tool_call_id": tool_call_id}
+    if event == "tool-started":
+        data["tool_name"] = tool_name
+        if input is not None:
+            data["input"] = input
+    elif event == "tool-output-delta":
+        data["delta"] = delta
+    elif event == "tool-finished":
+        data["output"] = output
+    elif event == "tool-error":
+        data["message"] = message
+    return {
+        "type": "event",
+        "method": "tools",
+        "params": {
+            "namespace": namespace or [],
+            "timestamp": TS,
+            "data": data,
+        },
+    }
+
+
+def _subscribe(log: StreamChannel) -> None:
+    log._subscribed = True
+
+
+def _mux() -> tuple[StreamMux, ToolCallTransformer]:
+    transformer = ToolCallTransformer()
+    mux = StreamMux(
+        [
+            ValuesTransformer(),
+            MessagesTransformer(),
+            transformer,
+        ],
+        is_async=False,
+    )
+    _subscribe(transformer._log)
+    return mux, transformer
+
+
+class TestToolCallTransformerUnit:
+    def test_required_stream_modes_declares_tools(self) -> None:
+        assert ToolCallTransformer.required_stream_modes == ("tools",)
+
+    def test_tool_started_yields_handle(self) -> None:
+        mux, transformer = _mux()
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc1",
+                tool_name="echo",
+                input={"text": "hi"},
+            )
+        )
+        handles = _unstamped(transformer._log._items)
+        assert len(handles) == 1
+        h = handles[0]
+        assert isinstance(h, ToolCallStream)
+        assert h.tool_call_id == "tc1"
+        assert h.tool_name == "echo"
+        assert h.input == {"text": "hi"}
+        assert h.completed is False
+
+    def test_delta_accumulates_on_active_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        _subscribe(transformer._active["tc1"]._output_deltas)
+        mux.push(_tool_event("tool-output-delta", "tc1", delta="a"))
+        mux.push(_tool_event("tool-output-delta", "tc1", delta="b"))
+        stream = transformer._active["tc1"]
+        assert _unstamped(stream._output_deltas._items) == ["a", "b"]
+
+    def test_finish_closes_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        stream = transformer._active["tc1"]
+        mux.push(_tool_event("tool-finished", "tc1", output="done"))
+        assert stream.completed is True
+        assert stream.output == "done"
+        assert stream.error is None
+        assert "tc1" not in transformer._active
+
+    def test_error_closes_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="boom"))
+        stream = transformer._active["tc1"]
+        mux.push(_tool_event("tool-error", "tc1", message="nope"))
+        assert stream.completed is True
+        assert stream.output is None
+        assert stream.error == "nope"
+        assert "tc1" not in transformer._active
+
+    def test_concurrent_tool_calls_do_not_bleed(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "a", tool_name="t"))
+        mux.push(_tool_event("tool-started", "b", tool_name="t"))
+        for tc in ("a", "b"):
+            _subscribe(transformer._active[tc]._output_deltas)
+        mux.push(_tool_event("tool-output-delta", "a", delta="A1"))
+        mux.push(_tool_event("tool-output-delta", "b", delta="B1"))
+        mux.push(_tool_event("tool-output-delta", "a", delta="A2"))
+        assert _unstamped(transformer._active["a"]._output_deltas._items) == [
+            "A1",
+            "A2",
+        ]
+        assert _unstamped(transformer._active["b"]._output_deltas._items) == ["B1"]
+
+    def test_tools_event_passes_through_main_log(self) -> None:
+        mux, transformer = _mux()
+        _subscribe(mux._events)
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        kept = [e for e in _unstamped(mux._events._items) if e["method"] == "tools"]
+        assert len(kept) == 1
+
+
+# ---------------------------------------------------------------------------
+# End-to-end tests with a real graph
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def _build_graph(caller, tools):
+    sg = StateGraph(_State)
+    sg.add_node("caller", caller)
+    sg.add_node("tools", ToolNode(tools))
+    sg.add_edge(START, "caller")
+    sg.add_edge("caller", "tools")
+    sg.add_edge("tools", END)
+    return sg.compile()
+
+
+class TestToolCallTransformerEndToEnd:
+    def test_sync_streaming_tool_populates_tool_calls(self) -> None:
+        @tool
+        def streamer(text: str, runtime: ToolRuntime) -> str:
+            """streams chunks."""
+            for chunk in ("one", "two"):
+                runtime.emit_output_delta(chunk)
+            return text
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "streamer", "args": {"text": "x"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [streamer])
+        run = graph.stream_events(
+            {"messages": []}, transformers=[ToolCallTransformer], version="v3"
+        )
+
+        tool_calls: list[ToolCallStream] = []
+        for tc in run.tool_calls:
+            tool_calls.append(tc)
+            deltas = list(tc.output_deltas)
+            assert deltas == ["one", "two"]
+        assert len(tool_calls) == 1
+        tc = tool_calls[0]
+        assert tc.tool_call_id == "tc1"
+        assert tc.tool_name == "streamer"
+        assert tc.completed is True
+        assert tc.error is None
+
+    def test_stream_modes_union_includes_tools(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return text
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "echo", "args": {"text": "x"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [echo])
+        # Without ToolCallTransformer, no tool_calls projection is
+        # exposed and no `tools` events flow through (required_stream_modes
+        # omits it).
+        run_no_tc = graph.stream_events({"messages": []}, version="v3")
+        assert "tool_calls" not in run_no_tc._mux.extensions  # type: ignore[attr-defined]
+
+        # With ToolCallTransformer, the projection is present.
+        run = graph.stream_events(
+            {"messages": []}, transformers=[ToolCallTransformer], version="v3"
+        )
+        assert "tool_calls" in run._mux.extensions  # type: ignore[attr-defined]
+        # Drain so the run closes cleanly.
+        list(run.tool_calls)
+
+    @pytest.mark.anyio
+    async def test_async_streaming_tool_populates_tool_calls(self) -> None:
+        @tool
+        async def astreamer(text: str, runtime: ToolRuntime) -> str:
+            """async streams."""
+            runtime.emit_output_delta(text)
+            runtime.emit_output_delta(text + "!")
+            return text
+
+        async def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "astreamer", "args": {"text": "hi"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [astreamer])
+        run = await graph.astream_events(
+            {"messages": []}, version="v3", transformers=[ToolCallTransformer]
+        )
+
+        collected: list[ToolCallStream] = []
+        async for tc in run.tool_calls:
+            collected.append(tc)
+            deltas = [d async for d in tc.output_deltas]
+            assert deltas == ["hi", "hi!"]
+        assert len(collected) == 1
+        assert collected[0].completed is True
+        assert collected[0].error is None
+
+    def test_tool_error_populates_error_field(self) -> None:
+        @tool
+        def boom() -> str:
+            """raises."""
+            raise ValueError("nope")
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[{"name": "boom", "args": {}, "id": "tc1"}],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [boom])
+        run = graph.stream_events(
+            {"messages": []}, transformers=[ToolCallTransformer], version="v3"
+        )
+
+        collected: list[ToolCallStream] = []
+        with pytest.raises(ValueError, match="nope"):
+            for tc in run.tool_calls:
+                collected.append(tc)
+                # Drain deltas so the error field is populated before we
+                # inspect it below.
+                list(tc.output_deltas)
+
+        assert len(collected) == 1
+        assert collected[0].error == "nope"
+        assert collected[0].output is None
+        assert collected[0].completed is True

libs/prebuilt/tests/test_tool_node.py

@@ -2016,8 +2016,21 @@ async def test_tool_node_inject_runtime_dynamic_tool_via_wrap_tool_call_async()
     assert tool_message.tool_call_id == "call_dynamic_2"
 
 
-def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
-    """Test that execution_info and server_info are forwarded from Runtime to ToolRuntime."""
+def test_tool_runtime_defaults_tools_to_empty_list() -> None:
+    runtime = ToolRuntime(
+        state={},
+        context=None,
+        config={},
+        stream_writer=lambda *args, **kwargs: None,
+        tool_call_id=None,
+        store=None,
+    )
+
+    assert runtime.tools == []
+
+
+def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
+    """Test that execution_info, server_info, and tools are forwarded from Runtime to ToolRuntime."""
     from langgraph.runtime import ExecutionInfo, ServerInfo
 
     exec_info = ExecutionInfo(
@@ -2043,9 +2056,15 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
         """Tool that captures runtime info."""
         captured["execution_info"] = runtime.execution_info
         captured["server_info"] = runtime.server_info
+        captured["tools"] = runtime.tools
         return "ok"
 
-    node = ToolNode([info_tool])
+    @dec_tool
+    def other_tool(y: int) -> str:
+        """Another tool available to the runtime."""
+        return str(y)
+
+    node = ToolNode([info_tool, other_tool])
     tool_call = {
         "name": "info_tool",
         "args": {"x": 1},
@@ -2054,17 +2073,21 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
     }
     msg = AIMessage("", tool_calls=[tool_call])
     config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
-    node.invoke({"messages": [msg]}, config=config)
+    result = node.invoke({"messages": [msg]}, config=config)
 
+    assert result["messages"][-1].content == "ok"
     assert captured["execution_info"] is exec_info
     assert captured["execution_info"].thread_id == "t-1"
     assert captured["execution_info"].task_id == "tk-1"
     assert captured["server_info"] is server_info
     assert captured["server_info"].assistant_id == "asst-1"
+    assert [tool.name for tool in captured["tools"]] == ["info_tool", "other_tool"]
 
 
-async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> None:
-    """Test that execution_info and server_info are forwarded in async path."""
+async def test_tool_runtime_forwards_execution_info_server_info_and_tools_async() -> (
+    None
+):
+    """Test that execution_info, server_info, and tools are forwarded in async path."""
     from langgraph.runtime import ExecutionInfo, ServerInfo
 
     exec_info = ExecutionInfo(
@@ -2090,9 +2113,15 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
         """Async tool that captures runtime info."""
         captured["execution_info"] = runtime.execution_info
         captured["server_info"] = runtime.server_info
+        captured["tools"] = runtime.tools
         return "ok"
 
-    node = ToolNode([info_tool_async])
+    @dec_tool
+    async def other_tool_async(y: int) -> str:
+        """Another async tool available to the runtime."""
+        return str(y)
+
+    node = ToolNode([info_tool_async, other_tool_async])
     tool_call = {
         "name": "info_tool_async",
         "args": {"x": 1},
@@ -2101,12 +2130,17 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
     }
     msg = AIMessage("", tool_calls=[tool_call])
     config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
-    await node.ainvoke({"messages": [msg]}, config=config)
+    result = await node.ainvoke({"messages": [msg]}, config=config)
 
+    assert result["messages"][-1].content == "ok"
     assert captured["execution_info"] is exec_info
     assert captured["execution_info"].thread_id == "t-2"
     assert captured["server_info"] is server_info
     assert captured["server_info"].graph_id == "graph-2"
+    assert [tool.name for tool in captured["tools"]] == [
+        "info_tool_async",
+        "other_tool_async",
+    ]
 
 
 # --- InjectedToolArg security tests ---
@@ -2202,3 +2236,195 @@ def test_tool_node_injected_state_overwrites_llm_value() -> None:
     )
     tool_message = result["messages"][-1]
     assert tool_message.content == "PUBLIC_DATA"
+
+
+class _ReturningTool(BaseTool):
+    """A tool that returns a configured value verbatim."""
+
+    name: str = "list_tool"
+    description: str = "Returns a configured value"
+    return_value: Any = None
+
+    def _run(self, **kwargs: Any) -> Any:
+        return self.return_value
+
+    async def _arun(self, **kwargs: Any) -> Any:
+        return self.return_value
+
+
+def _list_tool_call(outer_id: str = "call-1") -> dict[str, Any]:
+    return {"name": "list_tool", "args": {}, "id": outer_id, "type": "tool_call"}
+
+
+def _invoke_returning(
+    return_value: Any,
+    *,
+    outer_id: str = "call-1",
+    handle_tool_errors: bool = True,
+) -> Any:
+    node = ToolNode(
+        [_ReturningTool(return_value=return_value)],
+        handle_tool_errors=handle_tool_errors,
+    )
+    return node.invoke(
+        {"messages": [AIMessage("", tool_calls=[_list_tool_call(outer_id)])]},
+        config=_create_config_with_runtime(),
+    )
+
+
+def test_tool_node_list_return_command_and_tool_message() -> None:
+    """Valid: tool returns [Command(update={...}), ToolMessage(...)]."""
+    outer_id = "call-1"
+    result = _invoke_returning(
+        [
+            Command(update={"foo": "bar"}),
+            ToolMessage(content="done", tool_call_id=outer_id),
+        ]
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 1
+    assert commands[0].update == {"foo": "bar"}
+    non_commands = [r for r in result if not isinstance(r, Command)]
+    assert len(non_commands) == 1
+    assert isinstance(non_commands[0], dict)
+    msgs = non_commands[0]["messages"]
+    assert len(msgs) == 1
+    assert isinstance(msgs[0], ToolMessage)
+    assert msgs[0].content == "done"
+    assert msgs[0].tool_call_id == outer_id
+
+
+def test_tool_node_list_return_nested_terminator() -> None:
+    """Valid: terminator nested inside Command.update['messages']."""
+    outer_id = "call-1"
+    result = _invoke_returning(
+        [
+            Command(update={"foo": "bar"}),
+            Command(
+                update={
+                    "messages": [ToolMessage(content="done", tool_call_id=outer_id)]
+                }
+            ),
+        ]
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 2
+    updates = [c.update for c in commands]
+    assert {"foo": "bar"} in updates
+    msgs_update = next(u for u in updates if "messages" in (u or {}))
+    assert any(
+        isinstance(m, ToolMessage) and m.tool_call_id == outer_id
+        for m in msgs_update["messages"]
+    )
+
+
+def test_tool_node_list_return_parent_goto_with_terminator() -> None:
+    """Valid: [Command(graph=PARENT, goto=[Send(...)]), ToolMessage(...)]."""
+    outer_id = "call-1"
+    result = _invoke_returning(
+        [
+            Command(graph=Command.PARENT, goto=[Send("child", {})]),
+            ToolMessage(content="ok", tool_call_id=outer_id),
+        ]
+    )
+    assert isinstance(result, list)
+    parent_cmds = [
+        r for r in result if isinstance(r, Command) and r.graph is Command.PARENT
+    ]
+    assert len(parent_cmds) == 1
+    assert isinstance(parent_cmds[0].goto, list)
+    assert any(isinstance(s, Send) for s in parent_cmds[0].goto)
+    non_commands = [r for r in result if not isinstance(r, Command)]
+    assert len(non_commands) == 1
+
+
+def test_tool_node_list_return_no_terminator_raises() -> None:
+    """Invalid: list with no terminating ToolMessage."""
+    with pytest.raises(ValueError, match="0 messages bound to tool_call_id"):
+        _invoke_returning([Command(update={"foo": "bar"})], handle_tool_errors=False)
+
+
+def test_tool_node_list_return_multiple_terminators_raises() -> None:
+    """Invalid: list with two terminating ToolMessages."""
+    outer_id = "call-1"
+    with pytest.raises(ValueError, match="2 messages bound to tool_call_id"):
+        _invoke_returning(
+            [
+                ToolMessage(content="a", tool_call_id=outer_id),
+                ToolMessage(content="b", tool_call_id=outer_id),
+            ],
+            handle_tool_errors=False,
+        )
+
+
+def test_tool_node_list_return_validation_error_handled() -> None:
+    """handle_tool_errors=True converts validation errors to an error ToolMessage."""
+    result = _invoke_returning([Command(update={"foo": "bar"})])
+    assert isinstance(result, dict)
+    msg = result["messages"][0]
+    assert isinstance(msg, ToolMessage)
+    assert msg.status == "error"
+    assert "0 messages bound to tool_call_id" in msg.content
+
+
+async def test_tool_node_list_return_async_smoke() -> None:
+    """Async path parallels sync for the happy case."""
+    outer_id = "call-1"
+    node = ToolNode(
+        [
+            _ReturningTool(
+                return_value=[
+                    Command(update={"foo": "bar"}),
+                    ToolMessage(content="done", tool_call_id=outer_id),
+                ]
+            )
+        ]
+    )
+    result = await node.ainvoke(
+        {"messages": [AIMessage("", tool_calls=[_list_tool_call(outer_id)])]},
+        config=_create_config_with_runtime(),
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 1 and commands[0].update == {"foo": "bar"}
+
+
+def test_tool_node_list_return_mixed_with_regular_tool() -> None:
+    """List-returning tool and a regular tool dispatched from the same AIMessage."""
+    list_tool_id = "call-list"
+    regular_tool_id = "call-regular"
+    list_tool = _ReturningTool(
+        return_value=[
+            Command(update={"foo": "bar"}),
+            ToolMessage(content="list done", tool_call_id=list_tool_id),
+        ]
+    )
+
+    def regular_tool(x: int) -> str:
+        """A normal tool."""
+        return f"regular: {x}"
+
+    tool_calls = [
+        {"name": "list_tool", "args": {}, "id": list_tool_id, "type": "tool_call"},
+        {
+            "name": "regular_tool",
+            "args": {"x": 7},
+            "id": regular_tool_id,
+            "type": "tool_call",
+        },
+    ]
+    node = ToolNode([list_tool, regular_tool])
+    result = node.invoke(
+        {"messages": [AIMessage("", tool_calls=tool_calls)]},
+        config=_create_config_with_runtime(),
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 1
+    assert commands[0].update == {"foo": "bar"}
+    all_msgs = [m for r in result if isinstance(r, dict) for m in r["messages"]]
+    tool_call_ids = {m.tool_call_id for m in all_msgs}
+    assert list_tool_id in tool_call_ids
+    assert regular_tool_id in tool_call_ids

libs/prebuilt/uv.lock

@@ -249,10 +249,11 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.4.0a2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
+    { name = "langchain-protocol" },
     { name = "langsmith" },
     { name = "packaging" },
     { name = "pydantic" },
@@ -261,14 +262,26 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3c/93/68bafa047f8e1770d0cf0f61d6c70889f1dec42ef6bd263540d916c421b9/langchain_core-1.4.0a2.tar.gz", hash = "sha256:b723c7961b615c7f2180ce2bcf352fdad8247bc51a60adecd3d97088235c120d", size = 916486, upload-time = "2026-05-01T15:02:19.029Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/4e/8e/933e0ba7ba0430ce264e36b178d581b255239ad45093872483142d93478c/langchain_core-1.4.0a2-py3-none-any.whl", hash = "sha256:a5c689f8404357df797120c012da7704144a953b2ae18f258df263301e7badd5", size = 546297, upload-time = "2026-05-01T15:02:17.731Z" },
+]
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/bf/efb5e2ed832e4d6d45590e25a9e5191986b291b543bc6a807b48bee070b0/langchain_protocol-0.0.14.tar.gz", hash = "sha256:bc1e8553122e6ede310280462d5813023a172ff2785ccbbdec54d43f3a15e5f2", size = 5862, upload-time = "2026-04-29T16:40:18.657Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/e9/06c47ecb2aff08f83dfa30058da3bf86be64862c19569043ed5331bbeecd/langchain_protocol-0.0.14-py3-none-any.whl", hash = "sha256:ffc35089779bd8ca217015180cef5e660fc3b074efdaa0f2e95df73583f1a047", size = 6984, upload-time = "2026-04-29T16:40:17.841Z" },
 ]
 
 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.2.0a4"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -281,7 +294,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.4.0a2,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "." },
     { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -352,7 +365,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -400,7 +413,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.5"
+version = "3.1.0a3"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -490,7 +503,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.1.0a1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -535,7 +548,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", specifier = ">=1.3.1" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
 ]
 
@@ -593,20 +606,20 @@ requires-dist = [
 dev = [
     { name = "codespell" },
     { name = "langgraph", editable = "../langgraph" },
-    { name = "mypy", specifier = "==1.19.1" },
+    { name = "mypy", specifier = "==1.20.2" },
     { name = "pydantic", specifier = ">=2.12.4" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watch" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "ruff", specifier = "==0.15.12" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
 lint = [
     { name = "codespell" },
-    { name = "mypy", specifier = "==1.19.1" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "mypy", specifier = "==1.20.2" },
+    { name = "ruff", specifier = "==0.15.12" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]

libs/sdk-py/langgraph_sdk/__init__.py

@@ -1,30 +1,8 @@
-from __future__ import annotations
-
-import importlib
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from langgraph_sdk.auth import Auth
-    from langgraph_sdk.client import get_client, get_sync_client
-    from langgraph_sdk.encryption import Encryption
-    from langgraph_sdk.encryption.types import EncryptionContext
+from langgraph_sdk.auth import Auth
+from langgraph_sdk.client import get_client, get_sync_client
+from langgraph_sdk.encryption import Encryption
+from langgraph_sdk.encryption.types import EncryptionContext
 
 __version__ = "0.3.13"
 
 __all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]
-
-_LAZY: dict[str, str] = {
-    "Auth": "langgraph_sdk.auth",
-    "get_client": "langgraph_sdk.client",
-    "get_sync_client": "langgraph_sdk.client",
-    "Encryption": "langgraph_sdk.encryption",
-    "EncryptionContext": "langgraph_sdk.encryption.types",
-}
-
-
-def __getattr__(name: str) -> object:
-    if name in _LAZY:
-        mod = importlib.import_module(_LAZY[name])
-        return getattr(mod, name)
-    msg = f"module {__name__!r} has no attribute {name!r}"
-    raise AttributeError(msg)

libs/sdk-py/pyproject.toml

@@ -18,7 +18,7 @@ path = "langgraph_sdk/__init__.py"
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-py"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 
@@ -30,9 +30,9 @@ test = [
     "pytest-watch",
 ]
 lint = [
-    "ruff==0.15.6",
+    "ruff==0.15.12",
     "codespell",
-    "mypy==1.19.1",
+    "mypy==1.20.2",
     "ty==0.0.23",
     "starlette",
 ]

libs/sdk-py/uv.lock

@@ -1,6 +1,10 @@
 version = 1
 revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]
 
 [[package]]
 name = "annotated-types"
@@ -262,10 +266,11 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.4.0a2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
+    { name = "langchain-protocol" },
     { name = "langsmith" },
     { name = "packaging" },
     { name = "pydantic" },
@@ -274,14 +279,26 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3c/93/68bafa047f8e1770d0cf0f61d6c70889f1dec42ef6bd263540d916c421b9/langchain_core-1.4.0a2.tar.gz", hash = "sha256:b723c7961b615c7f2180ce2bcf352fdad8247bc51a60adecd3d97088235c120d", size = 916486, upload-time = "2026-05-01T15:02:19.029Z" }
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+    { url = "https://files.pythonhosted.org/packages/4e/8e/933e0ba7ba0430ce264e36b178d581b255239ad45093872483142d93478c/langchain_core-1.4.0a2-py3-none-any.whl", hash = "sha256:a5c689f8404357df797120c012da7704144a953b2ae18f258df263301e7badd5", size = 546297, upload-time = "2026-05-01T15:02:17.731Z" },
+]
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/bf/efb5e2ed832e4d6d45590e25a9e5191986b291b543bc6a807b48bee070b0/langchain_protocol-0.0.14.tar.gz", hash = "sha256:bc1e8553122e6ede310280462d5813023a172ff2785ccbbdec54d43f3a15e5f2", size = 5862, upload-time = "2026-04-29T16:40:18.657Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/e9/06c47ecb2aff08f83dfa30058da3bf86be64862c19569043ed5331bbeecd/langchain_protocol-0.0.14-py3-none-any.whl", hash = "sha256:ffc35089779bd8ca217015180cef5e660fc3b074efdaa0f2e95df73583f1a047", size = 6984, upload-time = "2026-04-29T16:40:17.841Z" },
 ]
 
 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.2.0a4"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -294,7 +311,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.4.0a2,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "../prebuilt" },
     { name = "langgraph-sdk", editable = "." },
@@ -365,7 +382,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a3"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -413,7 +430,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.1.0a1"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -422,7 +439,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", specifier = ">=1.3.1" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
 ]
 
@@ -508,20 +525,20 @@ requires-dist = [
 dev = [
     { name = "codespell" },
     { name = "langgraph", editable = "../langgraph" },
-    { name = "mypy", specifier = "==1.19.1" },
+    { name = "mypy", specifier = "==1.20.2" },
     { name = "pydantic", specifier = ">=2.12.4" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watch" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "ruff", specifier = "==0.15.12" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
 lint = [
     { name = "codespell" },
-    { name = "mypy", specifier = "==1.19.1" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "mypy", specifier = "==1.20.2" },
+    { name = "ruff", specifier = "==0.15.12" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
@@ -639,7 +656,7 @@ wheels = [
 
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-version = "1.19.1"
+version = "1.20.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -648,39 +665,51 @@ dependencies = [
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     { name = "typing-extensions" },
 ]
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