test(add_messages): cover fast-path guards and format handling

Nine new tests pin the behavioral boundaries introduced by the optimization: chunk / dict / tuple / missing-id left inputs must fall through to full conversion, duplicate right ids and None right ids must still be handled correctly, format="langchain-openai" and invalid format must round-trip through the fast path, and the fast path must return a fresh list rather than aliasing left. Also picks up a ruff-format reflow in test_time_travel.py. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
fix(add_messages): guard fast path against None IDs and intra-right duplicates
2026-08-27 01:52:25 +02:00 · 2026-04-23 09:21:24 -04:00 · 2026-04-22 08:27:34 -04:00 · 2026-04-22 08:13:24 -04:00 · 2026-04-21 09:38:58 -04:00 · 2026-04-20 21:29:18 -04:00
46 changed files with 1248 additions and 4205 deletions
@@ -100,4 +100,3 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
-.worktrees/
@@ -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` |
@@ -430,43 +430,6 @@ 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."""
-        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
-                  )
-                """,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    channel,
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint_id,
-                ),
-            )
-            row = cur.fetchone()
-        if row is None:
-            return NotImplemented
-        return self.serde.loads_typed((row["type"], row["blob"]))
-
    def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.
@@ -479,13 +442,6 @@ 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 +454,7 @@ class PostgresSaver(BasePostgresSaver):
                **value["checkpoint"],
                "channel_values": {
                    **(value["checkpoint"].get("channel_values") or {}),
-                    **channel_values,
+                    **self._load_blobs(value["channel_values"]),
                },
            },
            value["metadata"],
@@ -391,43 +391,6 @@ 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 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
-                  )
-                """,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    channel,
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint_id,
-                ),
-            )
-            row = await cur.fetchone()
-        if row is None:
-            return NotImplemented
-        return self.serde.loads_typed((row["type"], row["blob"]))
-
    async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.
@@ -440,19 +403,11 @@ 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 +415,15 @@ 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"],
                    }
                }
@@ -185,22 +185,15 @@ 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"
+        }

    def _dump_blobs(
        self,
@@ -371,47 +371,3 @@ async def test_get_checkpoint_no_channel_values(

        checkpoint = await saver.aget_tuple(config)
        assert checkpoint.checkpoint["channel_values"] == {}
-
-
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
-async def test_delta_channel_chain_reconstruction(saver_name: str) -> None:
-    """AsyncPostgresSaver reconstructs DeltaChannel chain via point-lookup traversal."""
-    pytest.importorskip(
-        "langgraph.channels.delta", reason="langgraph core not installed"
-    )
-
-    from typing import Annotated
-
-    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 typing_extensions import TypedDict
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    def respond(state: State) -> dict:
-        n = len(state["messages"])
-        return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-
-    async with _saver(saver_name) as saver:
-        graph = builder.compile(checkpointer=saver)
-        config = {"configurable": {"thread_id": "diff-channel-test-1"}}
-
-        await graph.ainvoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
-        await graph.ainvoke(
-            {"messages": [HumanMessage(content="there", id="h2")]}, config
-        )
-
-        state = await graph.aget_state(config)
-        msgs = state.values["messages"]
-        assert len(msgs) == 4, f"expected 4, got {len(msgs)}: {msgs}"
-        assert msgs[0].content == "hi"
-        assert msgs[1].content == "reply-1"
-        assert msgs[2].content == "there"
-        assert msgs[3].content == "reply-3"
@@ -1,7 +1,6 @@
 from __future__ import annotations

 import copy
-import dataclasses
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
 from typing import (  # noqa: UP035
@@ -29,26 +28,6 @@ from langgraph.checkpoint.serde.types import (

 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__)


@@ -478,42 +457,6 @@ 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.
-
-        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.
-        """
-        return NotImplemented
-
-    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).
-
-        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.
-
-        Savers with a dedicated blob store (InMemorySaver, PostgresSaver)
-        should override this for O(1) performance.
-        """
-        return NotImplemented
-
    def get_next_version(self, current: V | None, channel: None) -> V:
        """Generate the next version ID for a channel.

@@ -126,46 +126,12 @@ class InMemorySaver(
        channel_values: dict[str, Any] = {}
        for k, v in versions.items():
            kk = (thread_id, checkpoint_ns, k, v)
-            if kk not in self.blobs:
-                continue
-            vv = self.blobs[kk]
-            if vv[0] != "empty":
-                channel_values[k] = self.serde.loads_typed(vv)
+            if kk in self.blobs:
+                vv = self.blobs[kk]
+                if vv[0] != "empty":
+                    channel_values[k] = self.serde.loads_typed(vv)
        return channel_values

-    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."""
-        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)
-
-    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)
-
    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
        """Get a checkpoint tuple from the in-memory storage.

@@ -80,8 +80,6 @@ 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"),
    }
 )

@@ -46,11 +46,22 @@ 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 _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):
@@ -245,8 +256,6 @@ 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 +278,6 @@ 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:
@@ -549,7 +551,9 @@ 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 +575,9 @@ 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,
@@ -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()
@@ -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

@@ -580,6 +582,14 @@ 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 +605,12 @@ 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 +655,6 @@ 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 +672,6 @@ 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 +997,3 @@ 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
@@ -12,13 +12,25 @@ from langgraph.checkpoint.base import (
    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:
@@ -308,36 +320,3 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
    assert direct is not None
    expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
    assert direct.checkpoint["channel_values"]["foo"] == expected
-
-
-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
-
-        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
-        saver.storage[thread_id][ns] = {
-            "cp1": (serde.dumps_typed(cp), serde.dumps_typed({}), None)
-        }
-
-        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
-
-    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
-        )
@@ -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"
 ]
@@ -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"
 ]
@@ -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",
 ]
@@ -1 +1 @@
-__version__ = "0.4.22"
+__version__ = "0.4.23"
@@ -23,7 +23,7 @@ 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'",
 ]

@@ -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,
        *,
@@ -1,7 +1,6 @@
 from langgraph.channels.any_value import AnyValue
 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 (
@@ -21,7 +20,6 @@ __all__ = (
    "UntrackedValue",
    "EphemeralValue",
    "BinaryOperatorAggregate",
-    "DeltaChannel",
    "NamedBarrierValue",
    "NamedBarrierValueAfterFinish",
    # topics
@@ -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
@@ -1,206 +0,0 @@
-from __future__ import annotations
-
-import collections.abc
-from collections.abc import Callable, Sequence
-from typing import Any, Generic
-
-from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
-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
-
-__all__ = ("DeltaChannel",)
-
-
-class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
-    """A channel that stores only per-step write deltas in checkpoints.
-
-    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).
-
-    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.
-
-    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.
-
-    Usage::
-
-        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)]
-    """
-
-    __slots__ = (
-        "value",
-        "operator",
-        "snapshot_every",
-        "_pending",
-        "_base_version",
-        "_last_checkpoint_id",
-        "_overwritten",
-        "_steps_since_snapshot",
-    )
-
-    def __init__(
-        self,
-        operator: Callable[[list[Value], Any], list[Value]],
-        typ: type = list,
-        *,
-        snapshot_every: int | None = None,
-    ) -> None:
-        typ = _strip_extras(typ)
-        if typ in (
-            collections.abc.Sequence,
-            collections.abc.MutableSequence,
-        ):
-            typ = list
-        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
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, DeltaChannel):
-            return False
-        if self.snapshot_every != other.snapshot_every:
-            return False
-        if (
-            self.operator.__name__ != "<lambda>"
-            and other.operator.__name__ != "<lambda>"
-        ):
-            return self.operator is other.operator
-        return True
-
-    @property
-    def ValueType(self) -> Any:
-        return list[self.typ]  # type: ignore[name-defined]
-
-    @property
-    def UpdateType(self) -> Any:
-        return self.typ | list[self.typ]  # type: ignore[name-defined]
-
-    def copy(self) -> Self:
-        new = DeltaChannel(self.operator, self.typ, snapshot_every=self.snapshot_every)
-        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
-        return new
-
-    def from_checkpoint(self, checkpoint: Any) -> Self:
-        new = DeltaChannel(self.operator, self.typ, snapshot_every=self.snapshot_every)
-        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."
-            )
-        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
-        return new
-
-    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,
-                    )
-
-                    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)
-        return True
-
-    def get(self) -> list[Value]:
-        if self.value is MISSING:
-            raise EmptyChannelError()
-        return self.value
-
-    def is_available(self) -> bool:
-        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,
-        )
-
-    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
@@ -184,39 +184,72 @@ def add_messages(
        ```

    """
-    remove_all_idx = None
    # coerce to list
    if not isinstance(left, list):
        left = [left]  # type: ignore[assignment]
    if not isinstance(right, list):
        right = [right]  # type: ignore[assignment]
-    # coerce to message
-    left = [
-        message_chunk_to_message(cast(BaseMessageChunk, m))
-        for m in convert_to_messages(left)
-    ]
-    right = [
+
+    # Optimization 1: skip conversion + ID assignment on left when it already
+    # contains fully-resolved BaseMessage objects (the common case after the
+    # first call, since add_messages always returns list[BaseMessage] with IDs).
+    left_msgs: list[BaseMessage]
+    left_seq = cast(list, left)
+    if (
+        left_seq
+        and isinstance(left_seq[0], BaseMessage)
+        and left_seq[0].id is not None
+        and not isinstance(left_seq[0], BaseMessageChunk)
+    ):
+        left_msgs = left_seq
+    else:
+        left_msgs = [
+            message_chunk_to_message(cast(BaseMessageChunk, m))
+            for m in convert_to_messages(left)
+        ]
+        for m in left_msgs:
+            if m.id is None:
+                m.id = str(uuid.uuid4())
+
+    # always normalise right — it's fresh external input
+    right_msgs: list[BaseMessage] = [
        message_chunk_to_message(cast(BaseMessageChunk, m))
        for m in convert_to_messages(right)
    ]
-    # assign missing ids
-    for m in left:
+    remove_all_idx = None
+    has_remove = False
+    for idx, m in enumerate(right_msgs):
        if m.id is None:
            m.id = str(uuid.uuid4())
-    for idx, m in enumerate(right):
-        if m.id is None:
-            m.id = str(uuid.uuid4())
-        if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
-            remove_all_idx = idx
+        if isinstance(m, RemoveMessage):
+            has_remove = True
+            if m.id == REMOVE_ALL_MESSAGES:
+                remove_all_idx = idx

    if remove_all_idx is not None:
-        return right[remove_all_idx + 1 :]
+        return right_msgs[remove_all_idx + 1 :]

-    # merge
-    merged = left.copy()
+    # Optimization 2: pure-append fast path — no removals, no ID overlaps with
+    # left, and no duplicate IDs within right (all imply a dedup/update is needed).
+    if not has_remove:
+        left_ids = {m.id for m in left_msgs}
+        right_id_set = {m.id for m in right_msgs}
+        if len(right_id_set) == len(right_msgs) and not (right_id_set & left_ids):
+            result = left_msgs + right_msgs
+            if format == "langchain-openai":
+                return _format_messages(result)
+            elif format:
+                msg = (
+                    f"Unrecognized {format=}. Expected one of 'langchain-openai', None."
+                )
+                raise ValueError(msg)
+            return result
+
+    # slow path: updates or removals present — full indexed merge
+    merged = left_msgs.copy()
    merged_by_id = {m.id: i for i, m in enumerate(merged)}
    ids_to_remove = set()
-    for m in right:
+    for m in right_msgs:
        if (existing_idx := merged_by_id.get(m.id)) is not None:
            if isinstance(m, RemoveMessage):
                ids_to_remove.add(m.id)
@@ -228,7 +261,6 @@ def add_messages(
                raise ValueError(
                    f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
                )
-
            merged_by_id[m.id] = len(merged)
            merged.append(m)
    merged = [m for m in merged if m.id not in ids_to_remove]
@@ -238,8 +270,6 @@ def add_messages(
    elif format:
        msg = f"Unrecognized {format=}. Expected one of 'langchain-openai', None."
        raise ValueError(msg)
-    else:
-        pass

    return merged

@@ -1,17 +1,9 @@
 from __future__ import annotations

-import logging
 from collections.abc import Mapping
 from datetime import datetime, timezone
-from typing import Any

-from langchain_core.runnables import RunnableConfig
-from langgraph.checkpoint.base import (
-    BaseCheckpointSaver,
-    Checkpoint,
-    DeltaChainValue,
-    DeltaValue,
-)
+from langgraph.checkpoint.base import Checkpoint
 from langgraph.checkpoint.base.id import uuid6

 from langgraph._internal._typing import MISSING
@@ -20,171 +12,6 @@ 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
-

 def empty_checkpoint() -> Checkpoint:
    return Checkpoint(
@@ -240,12 +67,13 @@ 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"))
-        channels[k] = ch
-    return channels, managed_specs
+    return (
+        {
+            k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
+            for k, v in channel_specs.items()
+        },
+        managed_specs,
+    )


 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -92,8 +92,6 @@ from langgraph.pregel._algo import (
    task_path_str,
 )
 from langgraph.pregel._checkpoint import (
-    _aassemble_delta_channels,
-    _assemble_delta_channels,
    channels_from_checkpoint,
    copy_checkpoint,
    create_checkpoint,
@@ -833,8 +831,18 @@ 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")
@@ -883,12 +891,6 @@ class PregelLoop:
            id=self.checkpoint["id"] if exiting else None,
            updated_channels=self.updated_channels,
        )
-        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()
@@ -1270,19 +1272,6 @@ 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
        )
@@ -1487,18 +1476,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
        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
        )
@@ -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
@@ -132,23 +132,15 @@ 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,
@@ -122,8 +122,6 @@ from langgraph.pregel._algo import (
 )
 from langgraph.pregel._call import identifier
 from langgraph.pregel._checkpoint import (
-    _aassemble_delta_channels,
-    _assemble_delta_channels,
    channels_from_checkpoint,
    copy_checkpoint,
    create_checkpoint,
@@ -1051,23 +1049,13 @@ class Pregel(

        step = saved.metadata.get("step", -1) + 1
        stop = step + 2
-        checkpoint = saved.checkpoint
-        if isinstance(self.checkpointer, BaseCheckpointSaver):
-            assembled = _assemble_delta_channels(
-                checkpoint, saved.config, self.checkpointer
-            )
-            if assembled:
-                checkpoint = {
-                    **checkpoint,
-                    "channel_values": {**checkpoint["channel_values"], **assembled},
-                }
        channels, managed = channels_from_checkpoint(
            self.channels,
-            checkpoint,
+            saved.checkpoint,
        )
        # tasks for this checkpoint
        next_tasks = prepare_next_tasks(
-            checkpoint,
+            saved.checkpoint,
            saved.pending_writes or [],
            self.nodes,
            channels,
@@ -1180,23 +1168,13 @@ class Pregel(

        step = saved.metadata.get("step", -1) + 1
        stop = step + 2
-        checkpoint = saved.checkpoint
-        if isinstance(self.checkpointer, BaseCheckpointSaver):
-            assembled = await _aassemble_delta_channels(
-                checkpoint, saved.config, self.checkpointer
-            )
-            if assembled:
-                checkpoint = {
-                    **checkpoint,
-                    "channel_values": {**checkpoint["channel_values"], **assembled},
-                }
        channels, managed = channels_from_checkpoint(
            self.channels,
-            checkpoint,
+            saved.checkpoint,
        )
        # tasks for this checkpoint
        next_tasks = prepare_next_tasks(
-            checkpoint,
+            saved.checkpoint,
            saved.pending_writes or [],
            self.nodes,
            channels,
@@ -1542,20 +1520,9 @@ class Pregel(
            saved = checkpointer.get_tuple(config)
            if saved is not None:
                self._migrate_checkpoint(saved.checkpoint)
-            base_checkpoint = saved.checkpoint if saved else empty_checkpoint()
-            if saved:
-                assembled = _assemble_delta_channels(
-                    base_checkpoint, saved.config, checkpointer
-                )
-                if assembled:
-                    base_checkpoint = {
-                        **base_checkpoint,
-                        "channel_values": {
-                            **base_checkpoint["channel_values"],
-                            **assembled,
-                        },
-                    }
-            checkpoint = copy_checkpoint(base_checkpoint) if saved else base_checkpoint
+            checkpoint = (
+                copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
+            )
            checkpoint_previous_versions = (
                saved.checkpoint["channel_versions"].copy() if saved else {}
            )
@@ -1999,20 +1966,9 @@ class Pregel(
            saved = await checkpointer.aget_tuple(config)
            if saved is not None:
                self._migrate_checkpoint(saved.checkpoint)
-            base_checkpoint = saved.checkpoint if saved else empty_checkpoint()
-            if saved:
-                assembled = await _aassemble_delta_channels(
-                    base_checkpoint, saved.config, checkpointer
-                )
-                if assembled:
-                    base_checkpoint = {
-                        **base_checkpoint,
-                        "channel_values": {
-                            **base_checkpoint["channel_values"],
-                            **assembled,
-                        },
-                    }
-            checkpoint = copy_checkpoint(base_checkpoint) if saved else base_checkpoint
+            checkpoint = (
+                copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
+            )
            checkpoint_previous_versions = (
                saved.checkpoint["channel_versions"].copy() if saved else {}
            )
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -24,7 +24,7 @@ classifiers = [
    'Programming Language :: Python :: 3.13',
 ]
 dependencies = [
-    "langchain-core==1.3.0a2",
+    "langchain-core>=1.3.0,<2",
    "langgraph-checkpoint>=2.1.0,<5.0.0",
    "langgraph-sdk>=0.3.0,<0.4.0",
    "langgraph-prebuilt>=1.0.9,<1.1.0",
@@ -0,0 +1,290 @@
+"""Benchmark: add_messages fast-path optimizations.
+
+Both implementations are inlined so the benchmark is self-contained and
+immune to import-cache or installed-vs-local confusion.
+
+Run directly:
+    python tests/test_add_messages_benchmark.py
+
+Or via pytest (correctness only, numbers printed to stdout):
+    pytest tests/test_add_messages_benchmark.py -s -v
+"""
+
+import statistics
+import time
+import tracemalloc
+import uuid
+from typing import cast
+
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    BaseMessageChunk,
+    HumanMessage,
+    RemoveMessage,
+    convert_to_messages,
+    message_chunk_to_message,
+)
+
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
+
+# ── original implementation (pre-optimisation) ────────────────────────────────
+
+
+def _add_messages_original(left, right):
+    remove_all_idx = None
+    if not isinstance(left, list):
+        left = [left]
+    if not isinstance(right, list):
+        right = [right]
+    left = [
+        message_chunk_to_message(cast(BaseMessageChunk, m))
+        for m in convert_to_messages(left)
+    ]
+    right = [
+        message_chunk_to_message(cast(BaseMessageChunk, m))
+        for m in convert_to_messages(right)
+    ]
+    for m in left:
+        if m.id is None:
+            m.id = str(uuid.uuid4())
+    for idx, m in enumerate(right):
+        if m.id is None:
+            m.id = str(uuid.uuid4())
+        if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
+            remove_all_idx = idx
+    if remove_all_idx is not None:
+        return right[remove_all_idx + 1 :]
+    merged = left.copy()
+    merged_by_id = {m.id: i for i, m in enumerate(merged)}
+    ids_to_remove = set()
+    for m in right:
+        if (existing_idx := merged_by_id.get(m.id)) is not None:
+            if isinstance(m, RemoveMessage):
+                ids_to_remove.add(m.id)
+            else:
+                ids_to_remove.discard(m.id)
+                merged[existing_idx] = m
+        else:
+            if isinstance(m, RemoveMessage):
+                raise ValueError(
+                    f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
+                )
+            merged_by_id[m.id] = len(merged)
+            merged.append(m)
+    return [m for m in merged if m.id not in ids_to_remove]
+
+
+# ── optimised implementation ──────────────────────────────────────────────────
+
+
+def _add_messages_optimized(left, right):
+    if not isinstance(left, list):
+        left = [left]
+    if not isinstance(right, list):
+        right = [right]
+
+    # Optimisation 1: skip conversion + ID assignment on left when it already
+    # contains fully-resolved BaseMessage objects (the common case after the
+    # first call, since add_messages always returns list[BaseMessage] with IDs).
+    if (
+        left
+        and isinstance(left[0], BaseMessage)
+        and not isinstance(left[0], BaseMessageChunk)
+    ):
+        left = cast(list[BaseMessage], left)
+    else:
+        left = [
+            message_chunk_to_message(cast(BaseMessageChunk, m))
+            for m in convert_to_messages(left)
+        ]
+        for m in left:
+            if m.id is None:
+                m.id = str(uuid.uuid4())
+
+    # always normalise right — it's fresh external input
+    right = [
+        message_chunk_to_message(cast(BaseMessageChunk, m))
+        for m in convert_to_messages(right)
+    ]
+    remove_all_idx = None
+    has_remove = False
+    for idx, m in enumerate(right):
+        if m.id is None:
+            m.id = str(uuid.uuid4())
+        if isinstance(m, RemoveMessage):
+            has_remove = True
+            if m.id == REMOVE_ALL_MESSAGES:
+                remove_all_idx = idx
+
+    if remove_all_idx is not None:
+        return right[remove_all_idx + 1 :]
+
+    # Optimisation 2: pure-append fast path — no removals and no ID overlaps.
+    # Builds one set over left instead of copying left + building a full dict.
+    if not has_remove:
+        left_ids = {m.id for m in left}
+        if not any(m.id in left_ids for m in right):
+            return left + right
+
+    # slow path: updates or removals present — full indexed merge
+    merged = left.copy()
+    merged_by_id = {m.id: i for i, m in enumerate(merged)}
+    ids_to_remove = set()
+    for m in right:
+        if (existing_idx := merged_by_id.get(m.id)) is not None:
+            if isinstance(m, RemoveMessage):
+                ids_to_remove.add(m.id)
+            else:
+                ids_to_remove.discard(m.id)
+                merged[existing_idx] = m
+        else:
+            if isinstance(m, RemoveMessage):
+                raise ValueError(
+                    f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
+                )
+            merged_by_id[m.id] = len(merged)
+            merged.append(m)
+    return [m for m in merged if m.id not in ids_to_remove]
+
+
+# ── helpers ───────────────────────────────────────────────────────────────────
+
+
+def _make_messages(n: int) -> list[BaseMessage]:
+    return [
+        (HumanMessage if i % 2 == 0 else AIMessage)(
+            content=f"message {i}", id=str(uuid.uuid4())
+        )
+        for i in range(n)
+    ]
+
+
+def _bench_time(fn, left, right, *, iters: int = 2_000) -> float:
+    """Return median latency in microseconds."""
+    for _ in range(100):
+        fn(list(left), list(right))
+    times = []
+    for _ in range(iters):
+        left_copy, right_copy = list(left), list(right)
+        t0 = time.perf_counter()
+        fn(left_copy, right_copy)
+        times.append(time.perf_counter() - t0)
+    return statistics.median(times) * 1e6
+
+
+def _bench_memory(fn, left, right) -> int:
+    """Return peak memory allocated during a single call (bytes)."""
+    # one warm-up so any lazy init is excluded
+    fn(list(left), list(right))
+    left_copy, right_copy = list(left), list(right)
+    tracemalloc.start()
+    tracemalloc.clear_traces()
+    fn(left_copy, right_copy)
+    _, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    return peak
+
+
+# ── scenarios ─────────────────────────────────────────────────────────────────
+
+SCENARIOS = [
+    ("pure append  1 → 1  msg", 1, 1, "append"),
+    ("pure append  10 → 1  msg", 10, 1, "append"),
+    ("pure append  100 → 1  msg", 100, 1, "append"),
+    ("pure append  1000 → 1  msg", 1000, 1, "append"),
+    ("pure append  1000 → 5  msgs", 1000, 5, "append"),
+    ("update existing  100 → 1  msg", 100, 1, "update"),
+    ("remove message  100 → 1  msg", 100, 1, "remove"),
+]
+
+
+def _make_inputs(n_left, n_right, mode):
+    left = _make_messages(n_left)
+    right = _make_messages(n_right)
+    if mode == "update":
+        right[0] = AIMessage(content="updated", id=left[0].id)
+    elif mode == "remove":
+        right = [RemoveMessage(id=left[0].id)]
+    return left, right
+
+
+# ── main output ───────────────────────────────────────────────────────────────
+
+COL = 36
+
+
+def run_benchmarks() -> None:
+    print()
+    print("=" * 88)
+    print("add_messages benchmark — time (µs, median of 2 000 iterations)")
+    print("=" * 88)
+    print(f"{'Scenario':<{COL}} {'Original':>10} {'Optimized':>11} {'Speedup':>8}")
+    print("-" * 88)
+
+    for label, n_left, n_right, mode in SCENARIOS:
+        left, right = _make_inputs(n_left, n_right, mode)
+        t_orig = _bench_time(_add_messages_original, left, right)
+        t_opt = _bench_time(_add_messages_optimized, left, right)
+        print(f"{label:<{COL}} {t_orig:>10.2f} {t_opt:>11.2f} {t_orig / t_opt:>7.2f}x")
+
+    print()
+    print("=" * 88)
+    print("add_messages benchmark — peak memory allocated per call (bytes)")
+    print("=" * 88)
+    print(f"{'Scenario':<{COL}} {'Original':>10} {'Optimized':>11} {'Reduction':>10}")
+    print("-" * 88)
+
+    for label, n_left, n_right, mode in SCENARIOS:
+        left, right = _make_inputs(n_left, n_right, mode)
+        m_orig = _bench_memory(_add_messages_original, left, right)
+        m_opt = _bench_memory(_add_messages_optimized, left, right)
+        reduction = (1 - m_opt / m_orig) * 100 if m_orig else 0.0
+        print(f"{label:<{COL}} {m_orig:>10,} {m_opt:>11,} {reduction:>9.1f}%")
+
+    print()
+    print("=" * 88)
+    print("Simulated long thread — 200 steps × 2 msgs appended per step")
+    print("=" * 88)
+    for name, fn in [
+        ("original", _add_messages_original),
+        ("optimized", _add_messages_optimized),
+    ]:
+        state: list = []
+        t0 = time.perf_counter()
+        for step in range(200):
+            new_msgs = [
+                HumanMessage(content=f"step {step} human", id=str(uuid.uuid4())),
+                AIMessage(content=f"step {step} ai", id=str(uuid.uuid4())),
+            ]
+            state = fn(state, new_msgs)
+        elapsed = (time.perf_counter() - t0) * 1_000
+        print(f"  {name:<12}  {elapsed:.2f} ms  ({len(state)} messages)")
+    print()
+
+
+# ── pytest entry-points ───────────────────────────────────────────────────────
+
+
+def test_add_messages_correctness():
+    """Optimised implementation must match original output for every scenario."""
+    for label, n_left, n_right, mode in SCENARIOS:
+        left, right = _make_inputs(n_left, n_right, mode)
+        expected = _add_messages_original(list(left), list(right))
+        actual = _add_messages_optimized(list(left), list(right))
+        assert len(actual) == len(expected), f"[{label}] length mismatch"
+        for a, b in zip(actual, expected):
+            assert type(a) is type(b), f"[{label}] type mismatch"
+            assert a.id == b.id, f"[{label}] id mismatch"
+            assert a.content == b.content, f"[{label}] content mismatch"
+
+
+def test_add_messages_benchmark(capsys):
+    run_benchmarks()
+    out = capsys.readouterr().out
+    assert "Speedup" in out
+    assert "Optimized" in out
+
+
+if __name__ == "__main__":
+    run_benchmarks()
@@ -117,408 +117,3 @@ def test_untracked_value() -> None:
    new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
    with pytest.raises(EmptyChannelError):
        new_channel.get()
-
-
-def test_delta_channel_basic_two_steps() -> None:
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.base import DeltaValue
-
-    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")
-
-    # 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")
-
-    # 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")],
-        ],
-    )
-    ch = spec.from_checkpoint(chain)
-    msgs = ch.get()
-    assert len(msgs) == 3
-    assert msgs[0].content == "hi"
-    assert msgs[1].content == "hello"
-    assert msgs[2].content == "bye"
-
-
-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)
-    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)
-    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"
-
-
-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)
-    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)
-    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)
-    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)
-    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
-
-    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"
-
-
-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
-
-    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
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages, snapshot_every=2)]
-
-    counter = {"n": 0}
-
-    def respond(state: State) -> dict:
-        counter["n"] += 1
-        return {
-            "messages": [
-                AIMessage(content=f"ai-{counter['n']}", id=f"ai-{counter['n']}")
-            ]
-        }
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-    graph = builder.compile(checkpointer=InMemorySaver())
-    config = {"configurable": {"thread_id": "snap-test"}}
-
-    # 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)
-
-    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}"
-
-
-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
-
-    from langgraph.checkpoint.base import (
-        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": "one"}
-    msg2 = {"type": "ai", "content": "two"}
-    msg3 = {"type": "human", "content": "three"}
-
-    # 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"
-    )
-
-    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"
-
-
-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
-
-    from langgraph.checkpoint.base import DeltaValue, empty_checkpoint
-
-    from langgraph.pregel._checkpoint import _assemble_delta_channels
-
-    cp = empty_checkpoint()
-    cp["id"] = "cp2"
-    cp["channel_values"]["messages"] = DeltaValue(
-        delta=["msg2"], prev_checkpoint_id="cp-missing"
-    )
-
-    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": ""}}
-
-    assembled = _assemble_delta_channels(cp, config, saver)
-
-    # Should still assemble — with partial chain (just the current delta, base=None)
-    assert "messages" in assembled
-    from langgraph.checkpoint.base import DeltaChainValue
-
-    chain = assembled["messages"]
-    assert isinstance(chain, DeltaChainValue)
-    assert chain.base is None
-    assert chain.deltas == [["msg2"]]
@@ -1,356 +0,0 @@
-"""Benchmark: DeltaChannel vs BinaryOperatorAggregate storage and time.
-
-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.
-
-Token estimates: 1 token ≈ 4 chars; each turn ≈ 200 tokens (human + AI).
-A 1M-token conversation ≈ 5,000 turns of realistic messages.
-"""
-
-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
-
-try:
-    from langgraph.checkpoint.sqlite import SqliteSaver
-
-    _SQLITE_AVAILABLE = True
-except ImportError:
-    _SQLITE_AVAILABLE = False
-
-SNAPSHOT_EVERY = 50
-
-# ---------------------------------------------------------------------------
-# Realistic message payload (~100 tokens / ~400 chars each)
-# ---------------------------------------------------------------------------
-
-_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."
-)
-
-_AI_TEMPLATE = (
-    "Great question about {topic}. The key insight here is that {concern} introduces "
-    "a subtle ordering dependency that most teams overlook until they hit it in production. "
-    "For your {component} layer specifically, I'd recommend starting with a careful audit "
-    "of the interface boundaries before making any structural changes. This will give you "
-    "a clear picture of the blast radius and let you sequence the migration safely."
-)
-
-_TOPICS = [
-    "distributed tracing",
-    "eventual consistency",
-    "schema migration",
-    "backpressure handling",
-    "idempotency guarantees",
-    "cache invalidation",
-    "connection pooling",
-    "rate limiting",
-    "circuit breaking",
-    "observability pipelines",
-]
-
-_CONCERNS = [
-    "concurrency model",
-    "retry semantics",
-    "state management",
-    "error propagation",
-    "latency budget",
-]
-
-_COMPONENTS = [
-    "persistence",
-    "routing",
-    "ingestion",
-    "aggregation",
-    "serialization",
-]
-
-
-def _human_content(i: int) -> str:
-    return _HUMAN_TEMPLATE.format(
-        topic=_TOPICS[i % len(_TOPICS)],
-        concern=_CONCERNS[i % len(_CONCERNS)],
-        component=_COMPONENTS[i % len(_COMPONENTS)],
-    )
-
-
-def _ai_content(i: int) -> str:
-    return _AI_TEMPLATE.format(
-        topic=_TOPICS[i % len(_TOPICS)],
-        concern=_CONCERNS[i % len(_CONCERNS)],
-        component=_COMPONENTS[i % len(_COMPONENTS)],
-    )
-
-
-# ---------------------------------------------------------------------------
-# State definitions
-# ---------------------------------------------------------------------------
-
-
-class BinaryState(TypedDict):
-    messages: Annotated[list, add_messages]
-
-
-class DeltaState(TypedDict):
-    messages: Annotated[list, DeltaChannel(add_messages)]
-
-
-class DeltaSnapshotState(TypedDict):
-    messages: Annotated[list, DeltaChannel(add_messages, snapshot_every=SNAPSHOT_EVERY)]
-
-
-# ---------------------------------------------------------------------------
-# Graph factory
-# ---------------------------------------------------------------------------
-
-
-def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
-    def human_node(state: Any) -> dict:
-        return {}
-
-    def ai_node(state: Any) -> dict:
-        i = len(state["messages"]) // 2
-        return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
-
-    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=checkpointer or MemorySaver())
-
-
-# ---------------------------------------------------------------------------
-# Measurement helpers
-# ---------------------------------------------------------------------------
-
-
-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 _run_turns(
-    n_turns: int,
-    state_cls: type,
-    checkpointer: Any = None,
-) -> tuple[float, float, int]:
-    """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.
-    """
-    graph = _make_graph(state_cls, checkpointer)
-    config = {"configurable": {"thread_id": "bench"}}
-
-    t0 = time.perf_counter()
-    for i in range(n_turns):
-        graph.invoke(
-            {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
-            config,
-        )
-    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
-    return write_elapsed, read_elapsed, blob_bytes
-
-
-def _fmt_bytes(n: int) -> str:
-    if n >= 1_000_000:
-        return f"{n / 1_000_000:.1f} MB"
-    if n >= 1_000:
-        return f"{n / 1_000:.1f} KB"
-    return f"{n} B"
-
-
-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"
-    if tokens >= 1_000:
-        return f"~{tokens / 1_000:.0f}K tok"
-    return f"~{tokens} tok"
-
-
-# ---------------------------------------------------------------------------
-# Benchmark matrix
-# ---------------------------------------------------------------------------
-
-# 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]
-
-
-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
-
-        factories.append(("SQLite", tempfile.NamedTemporaryFile(suffix=".db")))
-    return factories
-
-
-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()
-
-    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)
-
-
-def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
-    import contextlib
-    import tempfile
-
-    @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
-
-    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:
-        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)
-        with _make_saver() as saver:
-            s_wt, s_rt, s_bytes = _run_turns(turns, DeltaSnapshotState, saver)
-
-        # 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"
-        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))
-
-        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"
-        )
-
-    print("=" * W)
-    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()
-    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"
-    )
-    print()
-
-
-# ---------------------------------------------------------------------------
-# Pytest entry point
-# ---------------------------------------------------------------------------
-
-
-def test_delta_channel_benchmark(capsys: Any) -> None:
-    """Storage grows O(N²) for add_messages, O(N) for DeltaChannel."""
-    with capsys.disabled():
-        run_benchmark()
-
-    # Correctness assertion: DeltaChannel must use less storage at scale.
-    for turns in [100, 200]:
-        _, _, 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}"
-        )
-
-
-# ---------------------------------------------------------------------------
-# Script entry point
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    run_benchmark()
-    sys.exit(0)
@@ -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 == []
@@ -5,6 +5,7 @@ import langchain_core
 import pytest
 from langchain_core.messages import (
    AIMessage,
+    AIMessageChunk,
    AnyMessage,
    HumanMessage,
    RemoveMessage,
@@ -338,6 +339,123 @@ def test_remove_all_messages():
    ]


+def test_fast_path_preserves_format_openai():
+    """Pure-append fast path must still apply the `langchain-openai` formatter."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [
+        AIMessage(
+            content=[
+                {
+                    "type": "tool_use",
+                    "name": "foo",
+                    "input": {"bar": "baz"},
+                    "id": "t1",
+                }
+            ],
+            id="2",
+        )
+    ]
+    result = add_messages(left, right, format="langchain-openai")
+    assert isinstance(result[0], HumanMessage)
+    assert result[0].content == "prior"
+    assert isinstance(result[1], AIMessage)
+    # formatter collapses the tool_use content block into `tool_calls`
+    assert result[1].content == ""
+    assert len(result[1].tool_calls) == 1
+    assert result[1].tool_calls[0]["name"] == "foo"
+    assert result[1].tool_calls[0]["args"] == {"bar": "baz"}
+    assert result[1].tool_calls[0]["id"] == "t1"
+
+
+def test_fast_path_rejects_invalid_format():
+    """Pure-append fast path must validate the `format` arg like the slow path."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [AIMessage(content="new", id="2")]
+    with pytest.raises(ValueError, match="Unrecognized format="):
+        add_messages(left, right, format="bogus")  # type: ignore[arg-type]
+
+
+def test_left_starting_with_chunk_is_normalized():
+    """Opt-1 guard: a `BaseMessageChunk` at left[0] must trigger full conversion."""
+    chunk = AIMessageChunk(content="chunk", id="c1")
+    result = add_messages([chunk], [HumanMessage(content="h", id="h1")])
+    assert len(result) == 2
+    # chunk must be converted to a non-chunk message
+    assert type(result[0]).__name__ == "AIMessage"
+    assert result[0].id == "c1"
+    assert result[1].id == "h1"
+
+
+def test_left_as_dicts_is_normalized():
+    """Opt-1 guard: dicts at left[0] must trigger full conversion."""
+    left = [{"role": "user", "content": "hi", "id": "d1"}]
+    right = [AIMessage(content="reply", id="a1")]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage)
+    assert result[0].id == "d1"
+    assert result[0].content == "hi"
+
+
+def test_left_as_tuples_is_normalized():
+    """Opt-1 guard: tuple-form messages must trigger full conversion."""
+    left = [("user", "hi")]
+    right = [AIMessage(content="reply", id="a1")]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage)
+    # id is auto-assigned
+    assert isinstance(result[0].id, str) and UUID(result[0].id, version=4)
+
+
+def test_left_first_msg_missing_id_is_normalized():
+    """Opt-1 guard: a BaseMessage without an id at left[0] falls to the else branch."""
+    left = [HumanMessage(content="hi")]  # no id
+    right = [AIMessage(content="reply", id="a1")]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    # left's id must have been auto-assigned
+    assert isinstance(result[0].id, str) and UUID(result[0].id, version=4)
+
+
+def test_duplicate_ids_in_right_with_nonempty_left():
+    """Opt-2 guard: intra-right duplicate ids must take slow path (dedup kept)."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [
+        AIMessage(content="first", id="2"),
+        AIMessage(content="second", id="2"),
+    ]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    assert result[0].id == "1"
+    assert result[1].id == "2"
+    assert result[1].content == "second"
+
+
+def test_right_with_none_ids_pure_append():
+    """Fast path still correct when right entries start with id=None (fresh uuids assigned)."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [AIMessage(content="a"), AIMessage(content="b")]
+    result = add_messages(left, right)
+    assert len(result) == 3
+    assert result[0].id == "1"
+    for m in result[1:]:
+        assert isinstance(m.id, str) and UUID(m.id, version=4)
+    # fresh uuids must be distinct
+    assert result[1].id != result[2].id
+
+
+def test_fast_path_returns_fresh_list():
+    """Fast path must return a new list object (not mutate or alias left)."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [AIMessage(content="new", id="2")]
+    result = add_messages(left, right)
+    assert result is not left
+    # left must be untouched
+    assert len(left) == 1
+    assert left[0].id == "1"
+
+
 def test_push_messages_in_graph():
    class MessagesState(TypedDict):
        messages: Annotated[list[AnyMessage], add_messages]
@@ -9400,190 +9400,3 @@ def test_fork_does_not_apply_pending_writes(

    # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
    assert result == {"value": 121}
-
-
-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)]
-
-    def respond(state: State) -> dict:
-        n = len(state["messages"])
-        return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-    graph = builder.compile(checkpointer=InMemorySaver())
-
-    config = {"configurable": {"thread_id": "diff-test-1"}}
-
-    # Turn 1
-    graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
-    # Turn 2
-    graph.invoke({"messages": [HumanMessage(content="world", id="h2")]}, config)
-    # Turn 3
-    graph.invoke({"messages": [HumanMessage(content="bye", id="h3")]}, config)
-
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # 3 human + 3 AI = 6 total
-    assert len(msgs) == 6, f"expected 6 messages, got {len(msgs)}: {msgs}"
-    assert msgs[0].content == "hello"
-    assert msgs[2].content == "world"
-    assert msgs[4].content == "bye"
-    assert msgs[1].content == "reply-1"
-    assert msgs[3].content == "reply-3"
-    assert msgs[5].content == "reply-5"
-
-
-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)]
-
-    counter = {"n": 0}
-
-    def respond(state: State) -> dict:
-        counter["n"] += 1
-        return {
-            "messages": [
-                AIMessage(content=f"ai-{counter['n']}", id=f"ai-{counter['n']}")
-            ]
-        }
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-    saver = InMemorySaver()
-    graph = builder.compile(checkpointer=saver)
-
-    config = {"configurable": {"thread_id": "diff-time-travel"}}
-
-    # Run 2 turns: h1→ai-1, h2→ai-2
-    graph.invoke({"messages": [HumanMessage(content="h1", id="h1")]}, config)
-    graph.invoke({"messages": [HumanMessage(content="h2", id="h2")]}, config)
-
-    # Find the checkpoint after turn 1 (2 messages: h1 + ai-1)
-    history = list(graph.get_state_history(config))
-    after_turn1 = next(h for h in history if len(h.values.get("messages", [])) == 2)
-
-    assert len(after_turn1.values["messages"]) == 2
-    assert after_turn1.values["messages"][0].content == "h1"
-    assert after_turn1.values["messages"][1].content == "ai-1"
-
-    # Resume from turn-1 checkpoint: inject h3, expect 3 messages total (h1, ai-1, ai-N)
-    # NOT 5 messages (turn-2 deltas must not bleed into the resumed run)
-    result = graph.invoke(
-        {"messages": [HumanMessage(content="h3", id="h3")]},
-        after_turn1.config,
-    )
-    msgs = result["messages"]
-    # Should be: h1, ai-1, h3, ai-N — 4 messages total
-    assert len(msgs) == 4, (
-        f"expected 4 messages after time-travel resume, got {len(msgs)}: {msgs}"
-    )
-    assert msgs[0].content == "h1"
-    assert msgs[1].content == "ai-1"
-    assert msgs[2].content == "h3"
-
-
-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)]
-
-    def respond(state: State) -> dict:
-        return {"messages": [AIMessage(content="reply", id="ai-1")]}
-
-    def delete_first(state: State) -> dict:
-        # removes the first message
-        return {"messages": [RemoveMessage(id=state["messages"][0].id)]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_node("delete_first", delete_first)
-    builder.add_edge(START, "respond")
-    builder.add_edge("respond", "delete_first")
-    graph = builder.compile(checkpointer=InMemorySaver())
-
-    config = {"configurable": {"thread_id": "diff-remove-test"}}
-    graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
-
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # h1 was removed, only ai-1 should remain
-    assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
-    assert msgs[0].id == "ai-1"
-
-    # A subsequent turn must reconstruct from the checkpoint correctly
-    graph.invoke({"messages": [HumanMessage(content="again", id="h2")]}, config)
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # ai-1 + h2 + ai-1(second reply, same id overwrites) + h2 removed
-    # more simply: after second run we expect ai-1 updated + h2 remaining minus deleted h2
-    # just assert h1 is still gone
-    assert all(m.id != "h1" for m in msgs), (
-        "h1 should still be absent after second turn"
-    )
-
-
-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)]
-
-    def update_msg(state: State) -> dict:
-        # re-send h1 with updated content
-        return {"messages": [HumanMessage(content="updated", id="h1")]}
-
-    builder = StateGraph(State)
-    builder.add_node("update_msg", update_msg)
-    builder.add_edge(START, "update_msg")
-    graph = builder.compile(checkpointer=InMemorySaver())
-
-    config = {"configurable": {"thread_id": "diff-update-id-test"}}
-    graph.invoke({"messages": [HumanMessage(content="original", id="h1")]}, config)
-
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
-    assert msgs[0].content == "updated"
-    assert msgs[0].id == "h1"
-
-    # Second turn: verify the updated state is the base for further accumulation
-    graph.invoke({"messages": [HumanMessage(content="new", id="h2")]}, config)
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    ids = [m.id for m in msgs]
-    assert "h1" in ids  # h1 persists (updated, not duplicated)
-    assert "h2" in ids
-    assert ids.count("h1") == 1, "h1 must not be duplicated"
@@ -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)
@@ -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:
@@ -1348,7 +1348,7 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
@@ -1360,14 +1360,14 @@ 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/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
 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/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -1439,7 +1439,7 @@ test = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-prebuilt", editable = "../prebuilt" },
    { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1706,7 +1706,7 @@ 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 +1742,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "../prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -614,6 +614,7 @@ class _InjectedArgs:
    store: str | None
    runtime: str | None
    all_injected_keys: set[str]
+    _optional_state_args: set[str]


 class ToolNode(RunnableCallable):
@@ -807,6 +808,7 @@ 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,
            )
@@ -841,6 +843,7 @@ 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,
            )
@@ -1333,7 +1336,7 @@ 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 +1364,20 @@ 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:
@@ -1569,6 +1578,7 @@ 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.
@@ -1611,6 +1621,7 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
    context: ContextT
    config: RunnableConfig
    stream_writer: StreamWriter
+    tools: list[BaseTool]
    tool_call_id: str | None
    store: BaseStore | None
    execution_info: ExecutionInfo | None = None
@@ -1859,6 +1870,7 @@ 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 +1885,9 @@ 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 +1904,5 @@ 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,
    )
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -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 {
@@ -2016,8 +2016,8 @@ 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_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 +2043,15 @@ 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 +2060,21 @@ 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 +2100,15 @@ 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 +2117,17 @@ 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 ---
@@ -249,7 +249,7 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
@@ -261,14 +261,14 @@ 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/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
 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/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -281,7 +281,7 @@ dependencies = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-prebuilt", editable = "." },
    { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -490,7 +490,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -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)
@@ -262,7 +262,7 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
@@ -274,14 +274,14 @@ 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/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
 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/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -294,7 +294,7 @@ dependencies = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-prebuilt", editable = "../prebuilt" },
    { name = "langgraph-sdk", editable = "." },
@@ -413,7 +413,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "../prebuilt" }
 dependencies = [
    { name = "langchain-core" },