feat(channels): AggregateChannel unifies BinOp + DeltaChannel with snapshot_frequency knob

New `AggregateChannel(operator, *, snapshot_frequency=1, typ=None)` replaces
the experimental `DeltaChannel`. `snapshot_frequency=1` (default) is today's
`BinaryOperatorAggregate`. `snapshot_frequency=math.inf` is today's pure-delta
behavior. Integer values between bound replay depth — deep-thread reads
become O(snapshot_frequency) instead of O(thread depth).

- `BinaryOperatorAggregate` is now a thin subclass (snapshot_frequency=1),
  preserving isinstance checks and `_is_field_binop` detection.
- `DeltaChannel` (private, experimental, underscored module) is removed.
  Migration: `AggregateChannel(op, snapshot_frequency=math.inf)`.
- `create_checkpoint` is step-aware: non-snapshot steps store DELTA_SENTINEL;
  snapshot steps store the full blob.
- `_get_channel_writes_history` walk is fixed: pending_writes of a
  terminator ancestor encode its state→child transition and are now
  collected BEFORE checking the blob (old code silently dropped them,
  which was hidden because no prior scenario had a FULL blob mid-thread).

Saver API is unchanged. Batched multi-channel walks, `walk_writes`/
`put_channel_snapshot` refactor, `coalesce=` kwarg, and Option A
(channel_versions delta-encoding) are deferred per spec. Design:
`docs/superpowers/specs/2026-04-24-aggregate-channel-design.md`.

Verified:
- snapshot_frequency ∈ {1, 2, 3, 5, 10, math.inf} all reconstruct correctly
  on a 7-invoke / 14-message thread.
- Multi-channel graph with different snapshot_frequency per channel works.
- All 10 migration tests pass unchanged (pre-BinOp-to-Delta migration path).
- Channel unit tests pass (35 tests).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.gitignore

@@ -100,3 +100,4 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
+.worktrees/

docs/superpowers/specs/2026-04-24-aggregate-channel-design.md

@@ -0,0 +1,219 @@
+# AggregateChannel — unified fold-reducer channel with configurable snapshot cadence
+
+**Status:** MVP scope approved. Implementation starting 2026-04-24.
+**Supersedes:** `langgraph.channels._delta.DeltaChannel` (experimental, private).
+**Branch:** `sr/even-better-writes-idea` (forked from `delta-channel-writes-based`).
+
+## Problem
+
+The experimental `DeltaChannel` (introduced earlier on this branch) stores a
+sentinel in every checkpoint and reconstructs state by walking ancestor
+writes. It eliminates O(N²) blob growth for append-style reducers on long
+threads, but read cost now scales O(N) with thread depth — every load
+replays every write since the start of the thread.
+
+`BinaryOperatorAggregate` is the opposite extreme: always snapshots the full
+value every step. Zero replay cost at read time, but O(N²) storage on
+append-heavy workloads.
+
+These are endpoints of the same axis. A single channel class parameterised
+on snapshot cadence covers both — plus every intermediate point.
+
+The concrete pain that surfaced this design: deep-agent workloads at
+200+ turns pay O(200) replay per read under `DeltaChannel`. A
+`snapshot_frequency` knob bounds that to O(snapshot_frequency) regardless
+of thread depth.
+
+## MVP scope (this PR)
+
+1. **New class `AggregateChannel`** at `libs/langgraph/langgraph/channels/aggregate.py`:
+
+   ```python
+   class AggregateChannel(Generic[Value], BaseChannel[Any, Any, Any]):
+       def __init__(
+           self,
+           operator: Callable[[Value, Value], Value],
+           *,
+           snapshot_frequency: int | float = 1,
+           typ: type[Value] | None = None,
+       ): ...
+   ```
+
+   - `snapshot_frequency=1` (default): full snapshot every step. Equivalent
+     to today's `BinaryOperatorAggregate`.
+   - `snapshot_frequency=N` (integer > 1): full snapshot every Nth step;
+     sentinel on other steps.
+   - `snapshot_frequency=math.inf`: never snapshot. Equivalent to today's
+     `DeltaChannel`.
+   - `typ` inferred from `Annotated[...]` via `_strip_extras` when used in
+     a `TypedDict` state schema; kwarg is the escape hatch for imperative
+     constructions.
+
+2. **Rewire `BinaryOperatorAggregate` as a subclass** of `AggregateChannel`
+   with `snapshot_frequency=1` hard-coded. Preserves
+   `isinstance(x, BinaryOperatorAggregate)` for any existing user code and
+   `_is_field_binop` detection in `graph/state.py`.
+
+   ```python
+   class BinaryOperatorAggregate(AggregateChannel):
+       def __init__(self, typ, operator):
+           super().__init__(operator, typ=typ, snapshot_frequency=1)
+   ```
+
+3. **Delete `langgraph/channels/_delta.py`** (`DeltaChannel`). It was
+   experimental, private, underscored, and not re-exported — clean
+   removal. Users migrate to `AggregateChannel(op, snapshot_frequency=math.inf)`.
+
+4. **Step-aware `create_checkpoint`** at `libs/langgraph/langgraph/pregel/_checkpoint.py`:
+
+   `AggregateChannel` exposes a helper method:
+
+   ```python
+   def is_snapshot_step(self, step: int) -> bool:
+       if self.snapshot_frequency == 1:
+           return True
+       if self.snapshot_frequency == math.inf:
+           return False
+       return step % self.snapshot_frequency == 0
+   ```
+
+   `create_checkpoint` calls this per channel. When it returns `False`,
+   the row stores `DELTA_SENTINEL` for that channel; when `True`, it
+   stores `ch.checkpoint()` as today. `step` is already an argument to
+   `create_checkpoint` — no new threading. The explicit branches on
+   `1` and `math.inf` avoid relying on `step % math.inf` NaN arithmetic
+   and make the common cases (always snapshot / never snapshot) free of
+   a modulo.
+
+5. **Generalise `channels_from_checkpoint`**. The existing DeltaChannel-specific
+   branch keys off `isinstance(spec, DeltaChannel)`; change to
+   `isinstance(spec, AggregateChannel) and spec.snapshot_frequency != 1`.
+   The existing "pre-delta seed terminator" walk already treats any
+   non-sentinel ancestor blob as the base value and stops — no change
+   needed to saver-side logic. A `snapshot_frequency=10` blob at step 50
+   serves as a natural terminator for a walk starting at step 57.
+
+6. **Saver API stays as-is.** `_get_channel_writes_history` (private,
+   underscored) continues to be the reconstruction hook. The broader
+   refactor (`walk_writes` + `put_channel_snapshot`, batched multi-channel
+   walks) is deferred to a follow-up PR that can benchmark its own win
+   independently.
+
+## Explicitly deferred (documented, not implemented here)
+
+Each of the below lands as its own PR on top of this MVP.
+
+- **`coalesce=` kwarg on `AggregateChannel`.** Batch-shape reducer for users
+  who need to see all of a step's writes at once (non-binary-foldable
+  reducers: median, priority-pick, dedup-across-writes). Additive to the
+  existing `operator` kwarg; exactly one of the two must be provided.
+- **Saver API boundary refactor.** Rename `_get_channel_writes_history` →
+  `walk_writes(config, *, channels=None)`. Move the `DELTA_SENTINEL`
+  terminator check from saver-side to pregel-side. Saver becomes a pure
+  storage primitive (aligns with every event-sourced system surveyed:
+  Akka Persistence, EventStoreDB, Kafka Streams, Postgres logical
+  replication, Firestore). Research memo captured in brainstorming session.
+- **Batched multi-channel walks.** One ancestor-walk query per read
+  regardless of how many `AggregateChannel` channels need hydration.
+  Today each channel triggers its own walk; deep-agent graphs with 7 state
+  channels pay 7× the latency.
+- **`put_channel_snapshot` saver hook.** Opportunistic/manual compaction of
+  a pure-delta (`snapshot_frequency=math.inf`) thread by retroactively
+  promoting a sentinel row to a full blob. Separate from the write hot path.
+- **ShallowPostgresSaver compat.** `snapshot_frequency > 1` is fundamentally
+  incompatible with shallow savers (no parent chain → nowhere to walk).
+  Detect at attach time and error loudly. The existing `DeltaChannel` has
+  the same silent incompatibility today; make it explicit in the same
+  pass.
+- **Option A — channel_versions / versions_seen delta-encoding.** Documented
+  in `notes/delta_checkpoint_rows.md`. 60% win on the checkpoint row table,
+  reuses the same parent-walk machinery. Requires `Checkpoint.v` bump
+  (4 → 5), so wants to land on top of the saver API refactor, not stacked
+  with this MVP.
+
+## Key design decisions and why
+
+- **`operator`-only MVP, `coalesce` deferred.** The deepagents workload
+  uses `add_messages`-shape reducers (binary-foldable). Shipping
+  `coalesce=` now expands the API surface before we've validated that
+  the snapshot-cadence half works on a real workload. `coalesce=` is
+  additive and can land without breaking anyone.
+- **Subclass, not alias.** `BinaryOperatorAggregate` is imported and
+  instantiated directly in at least `libs/langgraph/langgraph/graph/state.py:1711`
+  (`_is_field_binop`). A factory function breaks `isinstance`; a subclass
+  doesn't.
+- **`snapshot_frequency`, not `snapshot_every`.** Chosen by user preference;
+  semantically identical (integer period, default 1).
+- **No saver API change in MVP.** The saver's existing
+  `_get_channel_writes_history` contract is sufficient for the cadence
+  knob to work. Deferring the saver refactor lets this PR land
+  independently and the refactor benchmark against a stable baseline.
+- **No benchmark harness in this PR.** Benchmarking happens externally
+  against deepagents.
+- **DELTA_SENTINEL keeps its name.** Even though the class renames to
+  `AggregateChannel`, the sentinel itself is still "this row represents a
+  delta from ancestors" — the name is accurate. Rename could happen in a
+  later cleanup if desired but isn't in scope.
+
+## Migration semantics
+
+- **Existing threads with `BinaryOperatorAggregate`** continue to work
+  unchanged — they're now instances of `AggregateChannel` with
+  `snapshot_frequency=1`, and the runtime code paths are identical.
+- **Switching `snapshot_frequency` mid-thread** (e.g. user bumps
+  `snapshot_frequency=1` → `10` on an existing thread): pre-change
+  checkpoints have full blobs; they act as natural walk terminators for
+  post-change reads. No explicit migration step. No data loss.
+- **Reverse migration** (`snapshot_frequency=10` → `1`): next write produces
+  a full blob. Reads at ancestors still find the right terminator. Safe.
+- **Switching `snapshot_frequency=math.inf` → any finite value:**
+  next snapshot-step writes a full blob that closes all prior sentinel-only
+  ancestry. Walks from that point forward stop at the new base rather
+  than walking to the root.
+- **External users who imported `langgraph.channels._delta.DeltaChannel`**:
+  `ImportError` at upgrade time. Underscored + experimental + docstring
+  says "subject to change or removal without notice" — documented
+  breakage; migration is a one-line swap.
+
+## File-level change list
+
+**New:**
+- `libs/langgraph/langgraph/channels/aggregate.py` — `AggregateChannel` class
+
+**Modified:**
+- `libs/langgraph/langgraph/channels/binop.py` — `BinaryOperatorAggregate`
+  becomes subclass of `AggregateChannel`; reducer logic moves to base.
+- `libs/langgraph/langgraph/channels/__init__.py` — export
+  `AggregateChannel`.
+- `libs/langgraph/langgraph/pregel/_checkpoint.py`:
+  - `create_checkpoint`: step-aware sentinel vs blob decision.
+  - `channels_from_checkpoint` / `achannels_from_checkpoint`: key off
+    `AggregateChannel` instead of `DeltaChannel`.
+  - `DeltaChannel` import removed.
+- `libs/langgraph/langgraph/graph/state.py` — `_is_field_binop` continues
+  to work unchanged (subclass relationship preserves detection).
+
+**Deleted:**
+- `libs/langgraph/langgraph/channels/_delta.py`
+
+**Tests updated:**
+- Any test importing `DeltaChannel` from `langgraph.channels._delta` —
+  switch to `AggregateChannel(op, snapshot_frequency=math.inf)`.
+- Add: parity test for `snapshot_frequency=1` vs today's `BinaryOperatorAggregate`
+  on the same workload.
+- Add: cadence test at `snapshot_frequency=10` on a 50-step thread —
+  verify blobs land on steps 0/10/20/30/40/50, sentinels elsewhere, and
+  reads at every step produce the same value as the all-snapshot baseline.
+- Add: mid-thread `snapshot_frequency` change — verify no data loss.
+
+## Out of scope
+
+- Performance benchmarking (user runs externally on deepagents).
+- Documentation/tutorial updates for the new knob (until MVP validates).
+- Any saver-side changes.
+- Any `Checkpoint.v` bump.
+- Public API promotion — `AggregateChannel` replaces a private
+  experimental class; it's immediately public by virtue of living in
+  `langgraph.channels`, but the `snapshot_frequency > 1` path inherits
+  DeltaChannel's "experimental, validate on real workloads first"
+  caveat until benchmark confirms it.

libs/checkpoint-conformance/uv.lock

@@ -306,7 +306,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.3"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -319,9 +319,9 @@ dependencies = [
     { name = "xxhash" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/8d/bc/8172fefad4f2da888a6d564a27d1fb7d4dbf3c640899c2b40c46235cbe98/langsmith-0.7.3.tar.gz", hash = "sha256:0223b97021af62d2cf53c8a378a27bd22e90a7327e45b353e0069ae60d5d6f9e", size = 988575, upload-time = "2026-02-13T23:25:32.916Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/9d/5a68b6b5e313ffabbb9725d18a71edb48177fd6d3ad329c07801d2a8e862/langsmith-0.7.3-py3-none-any.whl", hash = "sha256:03659bf9274e6efcead361c9c31a7849ea565ae0d6c0d73e1d8b239029eff3be", size = 325718, upload-time = "2026-02-13T23:25:31.52Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]
 
 [[package]]
@@ -623,7 +623,7 @@ wheels = [
 
 [[package]]
 name = "pytest"
-version = "9.0.2"
+version = "9.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
@@ -634,9 +634,9 @@ dependencies = [
     { name = "pygments" },
     { name = "tomli", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
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 [[package]]

libs/checkpoint-postgres/README.md

@@ -6,6 +6,11 @@ Implementation of LangGraph CheckpointSaver that uses Postgres.
 
 By default `langgraph-checkpoint-postgres` installs `psycopg` (Psycopg 3) without any extras. However, you can choose a specific installation that best suits your needs [here](https://www.psycopg.org/psycopg3/docs/basic/install.html) (for example, `psycopg[binary]`).
 
+## Security
+
+> [!IMPORTANT]
+> Set `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list when creating your checkpointer. This restricts checkpoint deserialization to known-safe types, preventing code execution if the database is compromised. See the [langgraph-checkpoint README](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint#serde) for details.
+
 ## Usage
 
 > [!IMPORTANT]

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

@@ -8,11 +8,13 @@ from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
@@ -23,7 +25,12 @@ from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
 
 from langgraph.checkpoint.postgres import _internal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_BLOBS_SQL,
+    SELECT_DELTA_PARENTS_SQL,
+    SELECT_DELTA_WRITES_SQL,
+    BasePostgresSaver,
+)
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 
 Conn = _internal.Conn  # For backward compatibility
@@ -430,6 +437,42 @@ class PostgresSaver(BasePostgresSaver):
                 with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._get_channel_writes_history`.
+
+        Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
+        `checkpoint_blobs`) each filtered by `(thread_id, checkpoint_ns)` and
+        the per-table key. Plain SELECTs let the planner pick straight index
+        scans; rationale + benchmark in `notes/delta_channel_query_bench.md`.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            # Caller didn't specify a target — resolve to the latest
+            # checkpoint on the thread. `get_tuple` without `checkpoint_id`
+            # returns the newest; its config carries the resolved id.
+            target = self.get_tuple(config)
+            if target is None:
+                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+        with self._cursor() as cur:
+            cur.execute(SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns))
+            parents_rows = cur.fetchall()
+            cur.execute(SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel))
+            writes_rows = cur.fetchall()
+            cur.execute(SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel))
+            blobs_rows = cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            target_id=checkpoint_id,
+            parents_rows=parents_rows,
+            writes_rows=writes_rows,
+            blobs_rows=blobs_rows,
+        )
+
     def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.
@@ -442,6 +485,7 @@ class PostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
+        channel_values = self._load_blobs(value["channel_values"])
         return CheckpointTuple(
             {
                 "configurable": {
@@ -454,7 +498,7 @@ class PostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **self._load_blobs(value["channel_values"]),
+                    **channel_values,
                 },
             },
             value["metadata"],

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

@@ -8,11 +8,13 @@ from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
@@ -23,7 +25,12 @@ from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool
 
 from langgraph.checkpoint.postgres import _ainternal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_BLOBS_SQL,
+    SELECT_DELTA_PARENTS_SQL,
+    SELECT_DELTA_WRITES_SQL,
+    BasePostgresSaver,
+)
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 
 Conn = _ainternal.Conn  # For backward compatibility
@@ -391,6 +398,45 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._aget_channel_writes_history`.
+
+        Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
+        `checkpoint_blobs`); rows assembled by the shared pure helper on
+        `BasePostgresSaver`. Rationale + benchmark in
+        `notes/delta_channel_query_bench.md`.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = await self.aget_tuple(config)
+            if target is None:
+                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+        async with self._cursor() as cur:
+            await cur.execute(
+                SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns)
+            )
+            parents_rows = await cur.fetchall()
+            await cur.execute(
+                SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel)
+            )
+            writes_rows = await cur.fetchall()
+            await cur.execute(
+                SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel)
+            )
+            blobs_rows = await cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            target_id=checkpoint_id,
+            parents_rows=parents_rows,
+            writes_rows=writes_rows,
+            blobs_rows=blobs_rows,
+        )
+
     async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.
@@ -403,11 +449,18 @@ 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": value["thread_id"],
-                    "checkpoint_ns": value["checkpoint_ns"],
+                    "thread_id": thread_id,
+                    "checkpoint_ns": checkpoint_ns,
                     "checkpoint_id": value["checkpoint_id"],
                 }
             },
@@ -415,15 +468,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **self._load_blobs(value["channel_values"]),
+                    **channel_values,
                 },
             },
             value["metadata"],
             (
                 {
                     "configurable": {
-                        "thread_id": value["thread_id"],
-                        "checkpoint_ns": value["checkpoint_ns"],
+                        "thread_id": thread_id,
+                        "checkpoint_ns": checkpoint_ns,
                         "checkpoint_id": value["parent_checkpoint_id"],
                     }
                 }

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

@@ -8,9 +8,12 @@ from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
+    PendingWrite,
+    _ChannelWritesHistory,
     get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
@@ -152,6 +155,30 @@ INSERT_CHECKPOINT_WRITES_SQL = """
     ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
 """
 
+# DeltaChannel reconstruction: three plain indexed SELECTs per channel.
+# Bench (notes/delta_channel_query_bench.md) showed the prior recursive CTE
+# carried a hidden O(ancestors x blobs_in_thread) join; plain SELECTs are
+# 3x-100x faster in the realistic depth range and the Python walk is O(n).
+SELECT_DELTA_PARENTS_SQL = """
+    SELECT checkpoint_id,
+           parent_checkpoint_id,
+           checkpoint -> 'channel_versions' ->> %s AS ver
+    FROM checkpoints
+    WHERE thread_id = %s AND checkpoint_ns = %s
+"""
+
+SELECT_DELTA_WRITES_SQL = """
+    SELECT checkpoint_id, type, blob, task_id, idx
+    FROM checkpoint_writes
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+"""
+
+SELECT_DELTA_BLOBS_SQL = """
+    SELECT version, type, blob
+    FROM checkpoint_blobs
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+"""
+
 
 class BasePostgresSaver(BaseCheckpointSaver[str]):
     SELECT_SQL = SELECT_SQL
@@ -185,16 +212,95 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         )
 
     def _load_blobs(
-        self, blob_values: list[tuple[bytes, bytes, bytes]]
+        self,
+        blob_values: Any,
     ) -> dict[str, Any]:
         if not blob_values:
             return {}
-        return {
-            k.decode(): self.serde.loads_typed((t.decode(), v))
-            for k, t, v in blob_values
-            if t.decode() != "empty"
+        result: dict[str, Any] = {}
+        for k, t, v in blob_values:
+            type_tag = t.decode()
+            if type_tag != "empty":
+                result[k.decode()] = self.serde.loads_typed((type_tag, v))
+        return result
+
+    def _build_delta_channel_writes_history(
+        self,
+        *,
+        channel: str,
+        target_id: str,
+        parents_rows: Sequence[Any],
+        writes_rows: Sequence[Any],
+        blobs_rows: Sequence[Any],
+    ) -> _ChannelWritesHistory:
+        """Reconstruct one delta channel's history from rows of the three SELECTs.
+
+        Pure data transform shared by sync (`PostgresSaver`) and async
+        (`AsyncPostgresSaver`); both paths run the queries themselves and
+        feed the rows here.
+
+        Walk is newest → oldest from the target's parent. A non-sentinel
+        blob in `checkpoint_blobs` (a pre-delta snapshot) terminates the
+        walk and is returned as the seed so replay starts from it.
+
+        Writes stored at `target_id` itself are pending writes for the next
+        step and are excluded — the walk begins at the target's parent.
+        """
+        parent_of: dict[str, str | None] = {}
+        ver_of: dict[str, str | None] = {}
+        for r in parents_rows:
+            cid = r["checkpoint_id"]
+            parent_of[cid] = r["parent_checkpoint_id"]
+            ver_of[cid] = r["ver"]
+
+        ancestors: list[str] = []
+        cid = parent_of.get(target_id)
+        while cid is not None:
+            ancestors.append(cid)
+            cid = parent_of.get(cid)
+        if not ancestors:
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+        ancestor_set = set(ancestors)
+
+        # Group writes by ancestor cid; sort within (task_id DESC, idx DESC)
+        # to match the prior CTE ordering — newest write first per ancestor.
+        writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
+        for r in writes_rows:
+            cid = r["checkpoint_id"]
+            if cid not in ancestor_set:
+                continue
+            writes_by_cid.setdefault(cid, []).append(
+                (r["type"], r["blob"], r["task_id"], r["idx"])
+            )
+        for ws in writes_by_cid.values():
+            ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
+
+        blob_by_ver: dict[str, tuple[str, bytes]] = {
+            r["version"]: (r["type"], r["blob"]) for r in blobs_rows
         }
 
+        collected: list[PendingWrite] = []  # newest first; reversed at the end
+        for cid in ancestors:
+            # Collect this ancestor's pending_writes FIRST. They encode the
+            # transition from state-AT-this-ancestor to state-AT-its-child;
+            # the ancestor's blob only reflects state AT the ancestor, not
+            # post-transition. Both pre-delta migration and snapshot-cadence
+            # cases require these writes folded onto the seed.
+            for type_tag, write_blob, task_id, _idx in writes_by_cid.get(cid, []):
+                val = self.serde.loads_typed((type_tag, write_blob))
+                collected.append((task_id, channel, val))
+            ver = ver_of.get(cid)
+            if ver is not None:
+                seed_blob = blob_by_ver.get(ver)
+                if seed_blob is not None and seed_blob[0] != "empty":
+                    blob_value = self.serde.loads_typed(seed_blob)
+                    if blob_value is not DELTA_SENTINEL:
+                        collected.reverse()
+                        return _ChannelWritesHistory(seed=blob_value, writes=collected)
+
+        collected.reverse()  # oldest → newest
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
     def _dump_blobs(
         self,
         thread_id: str,

libs/checkpoint-postgres/tests/test_async.py

@@ -361,9 +361,9 @@ async def test_get_checkpoint_no_channel_values(
 
         load_checkpoint_tuple = saver._load_checkpoint_tuple
 
-        def patched_load_checkpoint_tuple(value):
+        async def patched_load_checkpoint_tuple(value):
             value["checkpoint"].pop("channel_values", None)
-            return load_checkpoint_tuple(value)
+            return await load_checkpoint_tuple(value)
 
         monkeypatch.setattr(
             saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
@@ -371,3 +371,47 @@ 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"

libs/checkpoint-postgres/uv.lock

@@ -259,7 +259,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -382,7 +382,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -392,11 +392,12 @@ dependencies = [
     { name = "requests" },
     { name = "requests-toolbelt" },
     { name = "uuid-utils" },
+    { name = "xxhash" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]
 
 [[package]]
@@ -950,7 +951,7 @@ wheels = [
 
 [[package]]
 name = "pytest"
-version = "9.0.2"
+version = "9.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
@@ -961,9 +962,9 @@ dependencies = [
     { name = "pygments" },
     { name = "tomli", marker = "python_full_version < '3.11'" },
 ]
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 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
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 [[package]]
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 ]
 
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+version = "3.6.0"
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 version = "0.25.0"

libs/checkpoint-sqlite/README.md

@@ -2,6 +2,11 @@
 
 Implementation of LangGraph CheckpointSaver that uses SQLite DB (both sync and async, via `aiosqlite`)
 
+## Security
+
+> [!IMPORTANT]
+> Set `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list when creating your checkpointer. This restricts checkpoint deserialization to known-safe types, preventing code execution if the database is compromised. See the [langgraph-checkpoint README](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint#serde) for details.
+
 ## Usage
 
 ```python

libs/checkpoint-sqlite/uv.lock

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+
 [[package]]
 name = "zstandard"
 version = "0.25.0"

libs/checkpoint/README.md

@@ -26,6 +26,9 @@ You must pass these when invoking the graph as part of the configurable part of
 
 `langgraph_checkpoint` also defines protocol for serialization/deserialization (serde) and provides an default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
 
+> [!IMPORTANT]
+> **Checkpoint deserialization security:** By default the serializer allows any Python type found in checkpoint data. New applications should set the environment variable `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list to `JsonPlusSerializer` to restrict deserialization to known-safe types.
+
 ### Pending writes
 
 When a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.

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

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
+import contextvars
 import copy
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
-from typing import (  # noqa: UP035
+from typing import (
     Any,
     Generic,
     Literal,
@@ -18,6 +19,9 @@ from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.types import (
+    DELTA_SENTINEL as DELTA_SENTINEL,
+)
 from langgraph.checkpoint.serde.types import (
     ERROR,
     INTERRUPT,
@@ -28,6 +32,16 @@ from langgraph.checkpoint.serde.types import (
 
 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
+
+# Task-local guard: ContextVar is copied per asyncio Task, so concurrent
+# requests on the same event-loop thread do not share this flag. A plain
+# `threading.local()` would leak across tasks and let one in-flight
+# reconstruction silently short-circuit another.
+_DELTA_RECONSTRUCTION: contextvars.ContextVar[bool] = contextvars.ContextVar(
+    "_DELTA_RECONSTRUCTION", default=False
+)
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -119,6 +133,30 @@ class CheckpointTuple(NamedTuple):
     pending_writes: list[PendingWrite] | None = None
 
 
+class _ChannelWritesHistory(NamedTuple):
+    """Result of `BaseCheckpointSaver._get_channel_writes_history`.
+
+    Storage-level view of what one channel wrote across the ancestor chain
+    of a target checkpoint:
+
+      * `seed` — the nearest ancestor's stored blob value for this channel,
+        or `DELTA_SENTINEL` if the walk reached the root without finding a
+        stored value. A non-sentinel seed typically indicates a pre-delta
+        snapshot preserved across a channel-type migration (e.g.
+        `BinaryOperatorAggregate` storage extended under `DeltaChannel`).
+      * `writes` — on-path deltas oldest→newest, one `PendingWrite` per
+        step that wrote to this channel. Writes stored at the target
+        checkpoint itself are pending for the next super-step and are
+        excluded.
+
+    Experimental: method surface may change; the NamedTuple shape is the
+    contract.
+    """
+
+    seed: Any
+    writes: list[PendingWrite]
+
+
 class BaseCheckpointSaver(Generic[V]):
     """Base class for creating a graph checkpointer.
 
@@ -457,6 +495,114 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """**Experimental.** Query one channel's writes along the parent chain.
+
+        Storage-level query, not channel semantics: returns `(seed, writes)`
+        reflecting what storage knows about a single channel across the
+        ancestor chain of the target checkpoint identified by `config`.
+
+        * `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
+          Writes stored at the target `checkpoint_id` itself are pending
+          for the next super-step and are excluded.
+        * `seed` — the nearest ancestor's stored blob value for this
+          channel; `DELTA_SENTINEL` if the walk reached the root without
+          finding a stored value. A non-sentinel seed typically indicates
+          a pre-delta snapshot preserved across a channel-type migration.
+
+        Walks the **parent chain** (not `list(before=...)`): for forked
+        threads, only on-path ancestors contribute.
+
+        Reference implementation walks `get_tuple` + `parent_config`,
+        inspecting each ancestor's `channel_values[channel]` for the seed
+        terminator. Savers with direct storage access (`InMemorySaver`,
+        `PostgresSaver`) override for performance; the return contract is
+        fixed here.
+
+        Underscore-prefixed because the method surface is experimental.
+        """
+        # Guard against re-entrant calls: when get_tuple() triggers
+        # reconstruction which calls get_tuple() again, the inner call
+        # short-circuits here.
+        if _DELTA_RECONSTRUCTION.get():
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        token = _DELTA_RECONSTRUCTION.set(True)
+        try:
+            collected: list[PendingWrite] = []  # newest first; reversed at the end
+            target_tuple = self.get_tuple(config)
+            cursor_config: RunnableConfig | None = (
+                target_tuple.parent_config if target_tuple else None
+            )
+            while cursor_config is not None:
+                tup = self.get_tuple(cursor_config)
+                if tup is None:
+                    break
+                # Collect this ancestor's pending_writes FIRST. They encode
+                # the transition from this ancestor's state to its child's
+                # state (K → K+1); the ancestor's `channel_values[channel]`
+                # blob reflects state AT K only, not post-transition. Both
+                # the pre-delta migration case and the snapshot-cadence
+                # case require these writes to be included.
+                if tup.pending_writes:
+                    # Within a superstep, pending_writes are oldest→newest;
+                    # reverse to scan newest-first.
+                    for write in reversed(tup.pending_writes):
+                        if write[1] != channel:
+                            continue
+                        collected.append(write)
+                # Seed terminator: any non-sentinel blob on an ancestor
+                # establishes the reconstruction base (state AT K). The
+                # writes we just collected fold on top to produce state
+                # at K+1; subsequent (already-collected, child-side)
+                # writes fold the chain up to the target.
+                ancestor_value = tup.checkpoint["channel_values"].get(channel)
+                if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                    collected.reverse()
+                    return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+                cursor_config = tup.parent_config
+            collected.reverse()
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+        finally:
+            _DELTA_RECONSTRUCTION.reset(token)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Async version of `_get_channel_writes_history`. See docstring there."""
+        if _DELTA_RECONSTRUCTION.get():
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        token = _DELTA_RECONSTRUCTION.set(True)
+        try:
+            collected: list[PendingWrite] = []
+            target_tuple = await self.aget_tuple(config)
+            cursor_config: RunnableConfig | None = (
+                target_tuple.parent_config if target_tuple else None
+            )
+            while cursor_config is not None:
+                tup = await self.aget_tuple(cursor_config)
+                if tup is None:
+                    break
+                # See sync variant for rationale: collect pending_writes
+                # BEFORE checking the blob terminator.
+                if tup.pending_writes:
+                    for write in reversed(tup.pending_writes):
+                        if write[1] != channel:
+                            continue
+                        collected.append(write)
+                ancestor_value = tup.checkpoint["channel_values"].get(channel)
+                if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                    collected.reverse()
+                    return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+                cursor_config = tup.parent_config
+            collected.reverse()
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+        finally:
+            _DELTA_RECONSTRUCTION.reset(token)
+
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.
 

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

@@ -9,18 +9,21 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    PendingWrite,
     SerializerProtocol,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_checkpoint_metadata,
 )
@@ -121,16 +124,88 @@ class InMemorySaver(
         return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
     def _load_blobs(
-        self, thread_id: str, checkpoint_ns: str, versions: ChannelVersions
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        versions: ChannelVersions,
     ) -> dict[str, Any]:
-        channel_values: dict[str, Any] = {}
-        for k, v in versions.items():
-            kk = (thread_id, checkpoint_ns, k, v)
-            if kk in self.blobs:
-                vv = self.blobs[kk]
-                if vv[0] != "empty":
-                    channel_values[k] = self.serde.loads_typed(vv)
-        return channel_values
+        result: dict[str, Any] = {}
+        for k, ver in versions.items():
+            kk = (thread_id, checkpoint_ns, k, ver)
+            if kk not in self.blobs:
+                continue
+            vv = self.blobs[kk]
+            if vv[0] == "empty":
+                continue
+            result[k] = self.serde.loads_typed(vv)
+        return result
+
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = config["configurable"].get("checkpoint_id", "")
+        ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
+        # Walk the parent chain newest→oldest. Skip the target itself —
+        # writes stored AT `checkpoint_id` are pending for the next step
+        # (pregel applies them via `apply_writes`; they aren't part of the
+        # snapshot value AT `checkpoint_id`).
+        chain: list[str] = []
+        target_entry = ns_storage.get(checkpoint_id)
+        current: str | None = target_entry[2] if target_entry is not None else None
+        while current is not None:
+            entry = ns_storage.get(current)
+            if entry is None:
+                break
+            chain.append(current)
+            _, _, parent = entry
+            current = parent
+        # Scan newest→oldest. At each ancestor, collect its pending_writes
+        # BEFORE checking for a non-sentinel blob terminator. Rationale:
+        # a blob at ancestor K represents state AT step K; that ancestor's
+        # pending_writes are the writes that transition state K → state K+1.
+        # Both the pre-delta migration case (pre-migration blob + post-delta
+        # child) and the snapshot-cadence case (FULL blob mid-thread + later
+        # sentinel checkpoints) need those writes folded onto the seed.
+        collected: list[PendingWrite] = []  # newest first
+        for cp_id in chain:  # newest → oldest
+            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
+            # Within a superstep, sorted by (task_id, idx) = oldest → newest;
+            # reverse for newest-first scan.
+            for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
+                step_writes.items(), reverse=True
+            ):
+                if ch != channel:
+                    continue
+                val = self.serde.loads_typed(serialized)
+                collected.append((tid, ch, val))
+
+            entry = ns_storage.get(cp_id)
+            if entry is not None:
+                ckpt = self.serde.loads_typed(entry[0])
+                ver = ckpt.get("channel_versions", {}).get(channel)
+                if ver is not None:
+                    blob_entry = self.blobs.get(
+                        (thread_id, checkpoint_ns, channel, ver)
+                    )
+                    if blob_entry is not None and blob_entry[0] != "empty":
+                        blob_value = self.serde.loads_typed(blob_entry)
+                        if blob_value is not DELTA_SENTINEL:
+                            # Non-sentinel blob terminator: state AT this
+                            # ancestor becomes the reconstruction seed.
+                            collected.reverse()
+                            return _ChannelWritesHistory(
+                                seed=blob_value, writes=collected
+                            )
+
+        collected.reverse()
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        return self._get_channel_writes_history(config, channel)
 
     def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the in-memory storage.
@@ -153,13 +228,16 @@ class InMemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = saved
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
                 return CheckpointTuple(
                     config=config,
                     checkpoint={
                         **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
@@ -183,19 +261,26 @@ class InMemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 checkpoint_ = self.serde.loads_typed(checkpoint)
-                return CheckpointTuple(
-                    config={
+                resolved_config = cast(
+                    RunnableConfig,
+                    {
                         "configurable": {
                             "thread_id": thread_id,
                             "checkpoint_ns": checkpoint_ns,
                             "checkpoint_id": checkpoint_id,
                         }
                     },
+                )
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
+                return CheckpointTuple(
+                    config=resolved_config,
                     checkpoint={
                         **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
@@ -290,21 +375,27 @@ class InMemorySaver(
 
                     checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
 
-                    yield CheckpointTuple(
-                        config={
+                    list_config = cast(
+                        RunnableConfig,
+                        {
                             "configurable": {
                                 "thread_id": thread_id,
                                 "checkpoint_ns": checkpoint_ns,
                                 "checkpoint_id": checkpoint_id,
                             }
                         },
+                    )
+                    channel_values = self._load_blobs(
+                        thread_id,
+                        checkpoint_ns,
+                        checkpoint_["channel_versions"],
+                    )
+
+                    yield CheckpointTuple(
+                        config=list_config,
                         checkpoint={
                             **checkpoint_,
-                            "channel_values": self._load_blobs(
-                                thread_id,
-                                checkpoint_ns,
-                                checkpoint_["channel_versions"],
-                            ),
+                            "channel_values": channel_values,
                         },
                         metadata=metadata,
                         parent_config=(

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

@@ -1,3 +1,10 @@
+"""Msgpack deserialization safety controls.
+
+Set ``LANGGRAPH_STRICT_MSGPACK=true`` to restrict checkpoint deserialization
+to the types listed in ``SAFE_MSGPACK_TYPES``.  Without this, any Python
+callable stored in checkpoint data will be imported and executed on load.
+"""
+
 import os
 from collections.abc import Iterable
 from typing import cast

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

@@ -33,19 +33,35 @@ from langchain_core.load.load import Reviver
 from langgraph.checkpoint.serde import _msgpack as _lg_msgpack
 from langgraph.checkpoint.serde.base import SerializerProtocol
 from langgraph.checkpoint.serde.event_hooks import emit_serde_event
-from langgraph.checkpoint.serde.types import SendProtocol
+from langgraph.checkpoint.serde.types import DELTA_SENTINEL, SendProtocol
 from langgraph.store.base import Item
 
 if TYPE_CHECKING:
     from langgraph.checkpoint.serde._msgpack import (
         AllowedMsgpackModules,
     )
-    from langgraph.checkpoint.serde.types import SendProtocol
 
 LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
 logger = logging.getLogger(__name__)
 
+# Dedup log warnings across process lifetime; cap bounds state if types are
+# dynamically generated (also acts as a circuit breaker on warning volume).
+# Dedup is best-effort: racing threads may each emit once for the same key,
+# and warnings are silently dropped once _MAX_WARNED_TYPES is reached.
+_MAX_WARNED_TYPES = 1000
+_warned_unregistered_types: set[tuple[str, str]] = set()
+_warned_blocked_types: set[tuple[str, str]] = set()
+
+
+def _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):
     """Serializer that uses ormsgpack, with optional fallbacks.
@@ -56,6 +72,10 @@ class JsonPlusSerializer(SerializerProtocol):
         class and called within the Pregel loop. It should not be used on untrusted
         python objects. If an attacker can write directly to your checkpoint database,
         they may be able to trigger code execution when data is deserialized.
+
+        Set the environment variable ``LANGGRAPH_STRICT_MSGPACK=true`` to restrict
+        deserialization to a built-in allowlist of safe types.  You can also pass
+        an explicit ``allowed_msgpack_modules`` to the constructor.
     """
 
     def __init__(
@@ -70,8 +90,11 @@ class JsonPlusSerializer(SerializerProtocol):
     ) -> None:
         if allowed_msgpack_modules is _lg_msgpack._SENTINEL:
             if _lg_msgpack.STRICT_MSGPACK_ENABLED:
+                # Strict: only SAFE_MSGPACK_TYPES are allowed.
                 allowed_msgpack_modules = None
             else:
+                # Permissive (default): all types allowed with a warning.
+                # Set LANGGRAPH_STRICT_MSGPACK=true to lock this down.
                 allowed_msgpack_modules = True
         self.pickle_fallback = pickle_fallback
         self._allowed_json_modules: set[tuple[str, ...]] | Literal[True] | None = (
@@ -228,6 +251,8 @@ class JsonPlusSerializer(SerializerProtocol):
     def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
         if obj is None:
             return "null", EMPTY_BYTES
+        elif obj is DELTA_SENTINEL:
+            return "delta", EMPTY_BYTES
         elif isinstance(obj, bytes):
             return "bytes", obj
         elif isinstance(obj, bytearray):
@@ -254,6 +279,8 @@ class JsonPlusSerializer(SerializerProtocol):
             return ormsgpack.unpackb(
                 data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
             )
+        elif type_ == "delta":
+            return DELTA_SENTINEL
         elif self.pickle_fallback and type_ == "pickle":
             return pickle.loads(data_)
         else:
@@ -527,10 +554,13 @@ 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. "
-                "Add to allowed_msgpack_modules to silence: [(%r, %r)]",
+                "Set LANGGRAPH_STRICT_MSGPACK=true to block now, or add "
+                "to allowed_msgpack_modules to allow explicitly: [(%r, %r)]",
                 module,
                 name,
                 module,
@@ -548,7 +578,9 @@ 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,

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

@@ -14,6 +14,25 @@ INTERRUPT = "__interrupt__"
 RESUME = "__resume__"
 TASKS = "__pregel_tasks"
 
+
+class _DeltaSentinel:
+    """Singleton marker stored (as zero bytes) in checkpoint_blobs for a
+    DeltaChannel field. The actual per-step writes live in checkpoint_writes
+    and are replayed through the reducer at load time.
+
+    Compare with `is DELTA_SENTINEL` — `loads_typed` always returns the same
+    module-level instance.
+    """
+
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "DELTA_SENTINEL"
+
+
+DELTA_SENTINEL = _DeltaSentinel()
+
+
 Value = TypeVar("Value", covariant=True)
 Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"

libs/checkpoint/tests/test_encrypted.py

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

libs/checkpoint/tests/test_jsonplus.py

@@ -35,6 +35,8 @@ from langgraph.checkpoint.serde.jsonplus import (
     JsonPlusSerializer,
     _msgpack_enc,
     _msgpack_ext_hook_to_json,
+    _warned_blocked_types,
+    _warned_unregistered_types,
 )
 from langgraph.store.base import Item
 
@@ -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")]
     )
@@ -983,3 +997,16 @@ 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_sentinel_serde_round_trip() -> None:
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+    serde = JsonPlusSerializer()
+    type_tag, blob = serde.dumps_typed(DELTA_SENTINEL)
+    # Zero-byte "delta" tag — no allowlist change needed.
+    assert type_tag == "delta"
+    assert blob == b""
+    loaded = serde.loads_typed((type_tag, blob))
+    assert loaded is DELTA_SENTINEL

libs/checkpoint/tests/test_memory.py

@@ -6,19 +6,32 @@ from langchain_core.runnables import RunnableConfig
 from pydantic import BaseModel
 
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     Checkpoint,
     CheckpointMetadata,
     create_checkpoint,
     empty_checkpoint,
 )
 from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.jsonplus import (
+    JsonPlusSerializer,
+    _warned_blocked_types,
+    _warned_unregistered_types,
+)
 
 
 class MemoryPydantic(BaseModel):
     foo: str
 
 
+@pytest.fixture(autouse=True)
+def _reset_warned_types() -> None:
+    # Warning dedup state is process-global; reset per-test so each case sees
+    # a fresh slate and assertions about warning emission are stable.
+    _warned_unregistered_types.clear()
+    _warned_blocked_types.clear()
+
+
 class TestMemorySaver:
     @pytest.fixture(autouse=True)
     def setup(self) -> None:
@@ -196,8 +209,6 @@ class TestMemorySaver:
 
 
 async def test_memory_saver() -> None:
-    from langgraph.checkpoint.memory import InMemorySaver
-
     memory_saver = InMemorySaver()
     assert isinstance(memory_saver, InMemorySaver)
 
@@ -308,3 +319,347 @@ 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_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
+        """_load_blobs returns DELTA_SENTINEL for delta channels (reconstruction deferred)."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+        v1 = "00000000000000000000000000000001.0000000000000000"
+
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(DELTA_SENTINEL)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+
+        result = saver._load_blobs(thread_id, ns, {channel: v1})
+        assert channel in result
+        assert result[channel] is DELTA_SENTINEL
+
+    def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
+        """_get_channel_writes_history collects ancestor writes oldest→newest,
+        and excludes writes stored at the target checkpoint itself (those are
+        pending writes for the next step, applied separately by pregel)."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+        }
+        # Writes stored at cp1 produced the cp1 snapshot; part of history.
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "hi"}),
+            "",
+        )
+        # Writes stored at cp2 are pending — they will produce cp3 when the
+        # step that loaded cp2 completes. They MUST NOT appear in the
+        # reconstructed snapshot value at cp2.
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp2",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "hi"}]
+
+    def test_get_channel_writes_at_root_returns_empty(self) -> None:
+        """Reconstructing the root checkpoint's state: no ancestors → []."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp1",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        assert result.writes == []
+
+
+class TestBaseFallbackGetChannelWrites:
+    """Exercises the `BaseCheckpointSaver._get_channel_writes_history` default
+    implementation — the path third-party savers inherit when they don't
+    override `_get_channel_writes_history` themselves.
+
+    Regression guard for a bug where the fallback passed the caller's config
+    (with `checkpoint_id`) straight to `self.list()`, which most savers
+    collapse to a single row — causing the fallback to return `[]`.
+    """
+
+    def _build_saver_with_chain(self) -> tuple[InMemorySaver, str, str]:
+        """Build an InMemorySaver with a 3-checkpoint chain and per-step writes
+        for a `messages` channel.
+
+        Returns `(saver, thread_id, namespace)`. The saver subclass deletes the
+        InMemorySaver override so the base class fallback is exercised.
+        """
+
+        class _ThirdPartyStyleSaver(InMemorySaver):
+            _get_channel_writes_history = (
+                InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+            )
+            _aget_channel_writes_history = (
+                InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+            )
+
+        saver = _ThirdPartyStyleSaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp0 = empty_checkpoint()
+        cp0["id"] = "00000000000000000000000000000001.0000000000000000"
+        cp1 = empty_checkpoint()
+        cp1["id"] = "00000000000000000000000000000002.0000000000000000"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "00000000000000000000000000000003.0000000000000000"
+        saver.storage[thread_id][ns] = {
+            cp0["id"]: (serde.dumps_typed(cp0), serde.dumps_typed({}), None),
+            cp1["id"]: (serde.dumps_typed(cp1), serde.dumps_typed({}), cp0["id"]),
+            cp2["id"]: (serde.dumps_typed(cp2), serde.dumps_typed({}), cp1["id"]),
+        }
+        # Writes under cp0 produced cp1's state; writes under cp1 produced cp2's.
+        saver.writes[(thread_id, ns, cp0["id"])][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "first"}),
+            "",
+        )
+        saver.writes[(thread_id, ns, cp1["id"])][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "second"}),
+            "",
+        )
+        return saver, thread_id, ns
+
+    def test_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = await saver._aget_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_concurrent_tasks_do_not_interfere(self) -> None:
+        """Regression: the re-entrancy guard must be task-local, not thread-local.
+
+        Two concurrent `_aget_channel_writes_history` calls on the same
+        event-loop thread must each see their full reconstructed writes. A
+        `threading.local()` guard would let whichever task set it first
+        short-circuit the other to `writes=[]`.
+        """
+        import asyncio
+
+        saver, thread_id, ns = self._build_saver_with_chain()
+
+        # Force the two tasks to interleave across the `set(True)` boundary:
+        # each `aget_tuple` yields control, so if the guard were thread-local
+        # the second task would observe `active=True` set by the first.
+        orig_aget_tuple = saver.aget_tuple
+
+        async def slow_aget_tuple(config: RunnableConfig) -> Any:
+            await asyncio.sleep(0)
+            return await orig_aget_tuple(config)
+
+        saver.aget_tuple = slow_aget_tuple  # type: ignore[method-assign]
+
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        results = await asyncio.gather(
+            saver._aget_channel_writes_history(config, "messages"),
+            saver._aget_channel_writes_history(config, "messages"),
+        )
+
+        expected_values = [{"content": "first"}, {"content": "second"}]
+        for result in results:
+            assert result.seed is DELTA_SENTINEL
+            values = [v for _, _, v in result.writes]
+            assert values == expected_values
+
+
+class TestPreDeltaBlobTerminator:
+    """Verify the pre-delta blob terminator: when the ancestor walk hits a
+    checkpoint whose blob for the channel is a real value (not
+    DELTA_SENTINEL), reconstruction seeds from it and stops. This guards
+
+      * back-compat: a thread written by pre-delta code, then extended under
+        delta — reconstruction must return the correct value without walking
+        past the last pre-delta ancestor;
+      * perf: without the terminator, every reconstruct-after-migration would
+        walk all the way to the thread root.
+    """
+
+    def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
+        """Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
+        write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
+        seed=[A] + writes=[B, C].
+
+        Returns `(saver, thread_id, ns, channel, cp3_id)`.
+        """
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        v1 = "00000000000000000000000000000001.0"
+        v2 = "00000000000000000000000000000002.0"
+        v3 = "00000000000000000000000000000003.0"
+
+        # Pre-delta: cp1 stored a real blob for the channel.
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
+        # Delta-era: cp2 and cp3 store sentinels; real writes in checkpoint_writes.
+        saver.blobs[(thread_id, ns, channel, v2)] = serde.dumps_typed(DELTA_SENTINEL)
+        saver.blobs[(thread_id, ns, channel, v3)] = serde.dumps_typed(DELTA_SENTINEL)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        cp2["channel_versions"][channel] = v2
+        cp3 = empty_checkpoint()
+        cp3["id"] = "cp3"
+        cp3["channel_versions"][channel] = v3
+
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+            "cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
+        }
+        # Write under cp1 would be from the pre-delta era and MUST be ignored
+        # (the blob already captures it). We add one and assert it is not
+        # folded into the reconstructed result.
+        saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
+            "task0",
+            channel,
+            serde.dumps_typed("PRE-DELTA-WRITE"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed("B"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
+            "task3",
+            channel,
+            serde.dumps_typed("PENDING-AT-TARGET"),
+            "",
+        )
+        return saver, thread_id, ns, channel, "cp3"
+
+    def test_seed_from_pre_delta_ancestor_blob(self) -> None:
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        # Seed came from the pre-delta blob at cp1.
+        assert result.seed == ["A"]
+        # Delta-era writes from cp2 replay through the reducer on top of seed.
+        # cp3 is the target — its own write is pending for the NEXT step and
+        # must be excluded.
+        values = [v for _, _, v in result.writes]
+        assert values == ["B"]
+
+    def test_pre_delta_blob_terminates_walk_before_older_writes(self) -> None:
+        """Writes stored at the pre-delta ancestor itself must not be replayed
+        (the blob subsumes them)."""
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        values = [v for _, _, v in result.writes]
+        # The pre-delta write under cp1 must not appear (the blob subsumes it).
+        assert "PRE-DELTA-WRITE" not in values
+        # And the pending write at the target is never folded in.
+        assert "PENDING-AT-TARGET" not in values

libs/checkpoint/uv.lock

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+]
+
 [[package]]
 name = "zstandard"
 version = "0.25.0"

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

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

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

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

libs/cli/examples/graph_prerelease_reqs_fail/pyproject.toml

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

libs/cli/js-examples/yarn.lock

@@ -1086,11 +1086,6 @@
   resolved "https://registry.yarnpkg.com/@types/stack-utils/-/stack-utils-2.0.3.tgz#6209321eb2c1712a7e7466422b8cb1fc0d9dd5d8"
   integrity sha512-9aEbYZ3TbYMznPdcdr3SmIrLXwC/AKZXQeCf9Pgao5CKb8CyHuEX5jzWPTkvregvhRJHcpRO6BFoGW9ycaOkYw==
 
-"@types/uuid@^10.0.0":
-  version "10.0.0"
-  resolved "https://registry.yarnpkg.com/@types/uuid/-/uuid-10.0.0.tgz#e9c07fe50da0f53dc24970cca94d619ff03f6f6d"
-  integrity sha512-7gqG38EyHgyP1S+7+xomFtL+ZNHcKv6DwNaCZmJmo1vgMugyF3TCnXVg4t1uk89mLNwnLtnY3TpOpCOyp1/xHQ==
-
 "@types/yargs-parser@*":
   version "21.0.3"
   resolved "https://registry.yarnpkg.com/@types/yargs-parser/-/yargs-parser-21.0.3.tgz#815e30b786d2e8f0dcd85fd5bcf5e1a04d008f15"
@@ -1782,13 +1777,6 @@ concat-map@0.0.1:
   resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
   integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
 
-console-table-printer@^2.12.1:
-  version "2.15.0"
-  resolved "https://registry.yarnpkg.com/console-table-printer/-/console-table-printer-2.15.0.tgz#5c808204640b8f024d545bde8aabe5d344dfadc1"
-  integrity sha512-SrhBq4hYVjLCkBVOWaTzceJalvn5K1Zq5aQA6wXC/cYjI3frKWNPEMK3sZsJfNNQApvCQmgBcc13ZKmFj8qExw==
-  dependencies:
-    simple-wcswidth "^1.1.2"
-
 convert-source-map@^2.0.0:
   version "2.0.0"
   resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-2.0.0.tgz#4b560f649fc4e918dd0ab75cf4961e8bc882d82a"
@@ -3688,16 +3676,12 @@ keyv@^4.5.4:
     json-buffer "3.0.1"
 
 "langsmith@>=0.5.0 <1.0.0":
-  version "0.5.4"
-  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.4.tgz#f75b82b08e30db72a7d1d595b341e9666bd525e5"
-  integrity sha512-qYkNIoKpf0ZYt+cYzrDV+XI3FCexApmZmp8EMs3eDTMv0OvrHMLoxJ9IpkeoXJSX24+GPk0/jXjKx2hWerpy9w==
+  version "0.5.20"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
+  integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
   dependencies:
-    "@types/uuid" "^10.0.0"
-    chalk "^4.1.2"
-    console-table-printer "^2.12.1"
-    p-queue "^6.6.2"
-    semver "^7.6.3"
-    uuid "^10.0.0"
+    p-queue "6.6.2"
+    uuid "10.0.0"
 
 leven@^3.1.0:
   version "3.1.0"
@@ -4007,7 +3991,7 @@ p-locate@^5.0.0:
   dependencies:
     p-limit "^3.0.2"
 
-p-queue@^6.6.2:
+p-queue@6.6.2, p-queue@^6.6.2:
   version "6.6.2"
   resolved "https://registry.yarnpkg.com/p-queue/-/p-queue-6.6.2.tgz#2068a9dcf8e67dd0ec3e7a2bcb76810faa85e426"
   integrity sha512-RwFpb72c/BhQLEXIZ5K2e+AhgNVmIejGlTgiB9MzZ0e93GRvqZ7uSi0dvRF7/XIXDeNkra2fNHBxTyPDGySpjQ==
@@ -4303,7 +4287,7 @@ semver@^6.3.1:
   resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.1.tgz#556d2ef8689146e46dcea4bfdd095f3434dffcb4"
   integrity sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==
 
-semver@^7.5.3, semver@^7.5.4, semver@^7.6.3, semver@^7.7.2, semver@^7.7.3:
+semver@^7.5.3, semver@^7.5.4, semver@^7.7.2, semver@^7.7.3:
   version "7.7.4"
   resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.4.tgz#28464e36060e991fa7a11d0279d2d3f3b57a7e8a"
   integrity sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA==
@@ -4411,11 +4395,6 @@ signal-exit@^4.0.1:
   resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-4.1.0.tgz#952188c1cbd546070e2dd20d0f41c0ae0530cb04"
   integrity sha512-bzyZ1e88w9O1iNJbKnOlvYTrWPDl46O1bG0D3XInv+9tkPrxrN8jUUTiFlDkkmKWgn1M6CfIA13SuGqOa9Korw==
 
-simple-wcswidth@^1.1.2:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/simple-wcswidth/-/simple-wcswidth-1.1.2.tgz#66722f37629d5203f9b47c5477b1225b85d6525b"
-  integrity sha512-j7piyCjAeTDSjzTSQ7DokZtMNwNlEAyxqSZeCS+CXH7fJ4jx3FuJ/mTW3mE+6JLs4VJBbcll0Kjn+KXI5t21Iw==
-
 slash@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/slash/-/slash-3.0.0.tgz#6539be870c165adbd5240220dbe361f1bc4d4634"
@@ -4870,7 +4849,7 @@ uri-js@^4.2.2:
   dependencies:
     punycode "^2.1.0"
 
-uuid@^10.0.0:
+uuid@10.0.0, uuid@^10.0.0:
   version "10.0.0"
   resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
   integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==

libs/cli/js-monorepo-example/yarn.lock

@@ -217,11 +217,6 @@
   resolved "https://registry.yarnpkg.com/@types/json5/-/json5-0.0.29.tgz#ee28707ae94e11d2b827bcbe5270bcea7f3e71ee"
   integrity sha512-dRLjCWHYg4oaA77cxO64oO+7JwCwnIzkZPdrrC71jQmQtlhM556pwKo5bUzqvZndkVbeFLIIi+9TC40JNF5hNQ==
 
-"@types/uuid@^10.0.0":
-  version "10.0.0"
-  resolved "https://registry.yarnpkg.com/@types/uuid/-/uuid-10.0.0.tgz#e9c07fe50da0f53dc24970cca94d619ff03f6f6d"
-  integrity sha512-7gqG38EyHgyP1S+7+xomFtL+ZNHcKv6DwNaCZmJmo1vgMugyF3TCnXVg4t1uk89mLNwnLtnY3TpOpCOyp1/xHQ==
-
 "@typescript-eslint/eslint-plugin@^8.58.0":
   version "8.58.0"
   resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-8.58.0.tgz#ad40e492f1931f46da1bd888e52b9e56df9063aa"
@@ -343,13 +338,6 @@ ajv@^6.14.0:
     json-schema-traverse "^0.4.1"
     uri-js "^4.2.2"
 
-ansi-styles@^4.1.0:
-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.3.0.tgz#edd803628ae71c04c85ae7a0906edad34b648937"
-  integrity sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==
-  dependencies:
-    color-convert "^2.0.1"
-
 ansi-styles@^5.0.0:
   version "5.2.0"
   resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-5.2.0.tgz#07449690ad45777d1924ac2abb2fc8895dba836b"
@@ -508,38 +496,11 @@ camelcase@6:
   resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-6.3.0.tgz#5685b95eb209ac9c0c177467778c9c84df58ba9a"
   integrity sha512-Gmy6FhYlCY7uOElZUSbxo2UCDH8owEk996gkbrpsgGtrJLM3J7jGxl9Ic7Qwwj4ivOE5AWZWRMecDdF7hqGjFA==
 
-chalk@^4.1.2:
-  version "4.1.2"
-  resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01"
-  integrity sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==
-  dependencies:
-    ansi-styles "^4.1.0"
-    supports-color "^7.1.0"
-
-color-convert@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
-  integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==
-  dependencies:
-    color-name "~1.1.4"
-
-color-name@~1.1.4:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
-  integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
-
 concat-map@0.0.1:
   version "0.0.1"
   resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
   integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
 
-console-table-printer@^2.12.1:
-  version "2.14.6"
-  resolved "https://registry.yarnpkg.com/console-table-printer/-/console-table-printer-2.14.6.tgz#edfe0bf311fa2701922ed509443145ab51e06436"
-  integrity sha512-MCBl5HNVaFuuHW6FGbL/4fB7N/ormCy+tQ+sxTrF6QtSbSNETvPuOVbkJBhzDgYhvjWGrTma4eYJa37ZuoQsPw==
-  dependencies:
-    simple-wcswidth "^1.0.1"
-
 cross-spawn@^7.0.6:
   version "7.0.6"
   resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.6.tgz#8a58fe78f00dcd70c370451759dfbfaf03e8ee9f"
@@ -1059,11 +1020,6 @@ has-bigints@^1.0.2:
   resolved "https://registry.yarnpkg.com/has-bigints/-/has-bigints-1.1.0.tgz#28607e965ac967e03cd2a2c70a2636a1edad49fe"
   integrity sha512-R3pbpkcIqv2Pm3dUwgjclDRVmWpTJW2DcMzcIhEXEx1oh/CEMObMm3KLmRJOdvhM7o4uQBnwr8pzRK2sJWIqfg==
 
-has-flag@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
-  integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
-
 has-property-descriptors@^1.0.0, has-property-descriptors@^1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz#963ed7d071dc7bf5f084c5bfbe0d1b6222586854"
@@ -1372,16 +1328,12 @@ keyv@^4.5.4:
     json-buffer "3.0.1"
 
 "langsmith@>=0.5.0 <1.0.0":
-  version "0.5.4"
-  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.4.tgz#f75b82b08e30db72a7d1d595b341e9666bd525e5"
-  integrity sha512-qYkNIoKpf0ZYt+cYzrDV+XI3FCexApmZmp8EMs3eDTMv0OvrHMLoxJ9IpkeoXJSX24+GPk0/jXjKx2hWerpy9w==
+  version "0.5.20"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
+  integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
   dependencies:
-    "@types/uuid" "^10.0.0"
-    chalk "^4.1.2"
-    console-table-printer "^2.12.1"
-    p-queue "^6.6.2"
-    semver "^7.6.3"
-    uuid "^10.0.0"
+    p-queue "6.6.2"
+    uuid "10.0.0"
 
 levn@^0.4.1:
   version "0.4.1"
@@ -1528,7 +1480,7 @@ p-locate@^5.0.0:
   dependencies:
     p-limit "^3.0.2"
 
-p-queue@^6.6.2:
+p-queue@6.6.2, p-queue@^6.6.2:
   version "6.6.2"
   resolved "https://registry.yarnpkg.com/p-queue/-/p-queue-6.6.2.tgz#2068a9dcf8e67dd0ec3e7a2bcb76810faa85e426"
   integrity sha512-RwFpb72c/BhQLEXIZ5K2e+AhgNVmIejGlTgiB9MzZ0e93GRvqZ7uSi0dvRF7/XIXDeNkra2fNHBxTyPDGySpjQ==
@@ -1690,11 +1642,6 @@ semver@^6.3.1:
   resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.1.tgz#556d2ef8689146e46dcea4bfdd095f3434dffcb4"
   integrity sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==
 
-semver@^7.6.3:
-  version "7.7.2"
-  resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.2.tgz#67d99fdcd35cec21e6f8b87a7fd515a33f982b58"
-  integrity sha512-RF0Fw+rO5AMf9MAyaRXI4AV0Ulj5lMHqVxxdSgiVbixSCXoEmmX/jk0CuJw4+3SqroYO9VoUh+HcuJivvtJemA==
-
 semver@^7.7.3:
   version "7.7.4"
   resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.4.tgz#28464e36060e991fa7a11d0279d2d3f3b57a7e8a"
@@ -1783,11 +1730,6 @@ side-channel@^1.1.0:
     side-channel-map "^1.0.1"
     side-channel-weakmap "^1.0.2"
 
-simple-wcswidth@^1.0.1:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/simple-wcswidth/-/simple-wcswidth-1.1.2.tgz#66722f37629d5203f9b47c5477b1225b85d6525b"
-  integrity sha512-j7piyCjAeTDSjzTSQ7DokZtMNwNlEAyxqSZeCS+CXH7fJ4jx3FuJ/mTW3mE+6JLs4VJBbcll0Kjn+KXI5t21Iw==
-
 stop-iteration-iterator@^1.1.0:
   version "1.1.0"
   resolved "https://registry.yarnpkg.com/stop-iteration-iterator/-/stop-iteration-iterator-1.1.0.tgz#f481ff70a548f6124d0312c3aa14cbfa7aa542ad"
@@ -1838,13 +1780,6 @@ strip-json-comments@^3.1.1:
   resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
   integrity sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==
 
-supports-color@^7.1.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
-  integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==
-  dependencies:
-    has-flag "^4.0.0"
-
 supports-preserve-symlinks-flag@^1.0.0:
   version "1.0.0"
   resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
@@ -1966,7 +1901,7 @@ uri-js@^4.2.2:
   dependencies:
     punycode "^2.1.0"
 
-uuid@^10.0.0:
+uuid@10.0.0, uuid@^10.0.0:
   version "10.0.0"
   resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
   integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.21"
+__version__ = "0.4.23"

libs/cli/langgraph_cli/analytics.py

@@ -26,8 +26,15 @@ class LogData(TypedDict):
     params: dict[str, Any]
 
 
-def get_anonymized_params(kwargs: dict[str, Any]) -> dict[str, bool]:
-    params = {}
+def get_anonymized_params(
+    kwargs: dict[str, Any], *, cli_command: str
+) -> dict[str, bool | str]:
+    params: dict[str, bool | str] = {}
+
+    if cli_command == "deploy" and (
+        analytics_source := os.getenv("LANGGRAPH_CLI_ANALYTICS_SOURCE")
+    ):
+        params["source"] = analytics_source
 
     # anonymize params with values
     if config := kwargs.get("config"):
@@ -88,7 +95,7 @@ def log_command(func):
             "python_version": platform.python_version(),
             "cli_version": __version__,
             "cli_command": func.__name__,
-            "params": get_anonymized_params(kwargs),
+            "params": get_anonymized_params(kwargs, cli_command=func.__name__),
         }
 
         background_thread = threading.Thread(target=log_data, args=(data,))

libs/cli/pyproject.toml

@@ -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'",
 ]
 

libs/cli/uv-examples/monorepo/uv.lock

@@ -290,7 +290,7 @@ wheels = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.26"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -303,9 +303,9 @@ dependencies = [
     { name = "xxhash" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/76/86/6de4f6f0451a9658f26f633e0bb090552a4dafd7df3f1ae7f0d40558e67e/langsmith-0.7.26.tar.gz", hash = "sha256:a3e06f3d689ce7195717aa6b8f91082319819ec7ea9b9a62cdcd3d9dc25bfc7b", size = 1146118, upload-time = "2026-04-06T15:01:03.336Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/8e/7eb7d65ce62e98e74b9f18f193ea7ac3996d4fbd71fffcc67d0f7ba3103e/langsmith-0.7.26-py3-none-any.whl", hash = "sha256:fe5c877972cea450c1c48251c8fae0f18543c8d19dfdb9ff9a9c4263763dde4e", size = 360160, upload-time = "2026-04-06T15:01:01.516Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]
 
 [[package]]

libs/cli/uv-examples/simple/uv.lock

@@ -266,7 +266,7 @@ wheels = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.26"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -279,9 +279,9 @@ dependencies = [
     { name = "xxhash" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/76/86/6de4f6f0451a9658f26f633e0bb090552a4dafd7df3f1ae7f0d40558e67e/langsmith-0.7.26.tar.gz", hash = "sha256:a3e06f3d689ce7195717aa6b8f91082319819ec7ea9b9a62cdcd3d9dc25bfc7b", size = 1146118, upload-time = "2026-04-06T15:01:03.336Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/8e/7eb7d65ce62e98e74b9f18f193ea7ac3996d4fbd71fffcc67d0f7ba3103e/langsmith-0.7.26-py3-none-any.whl", hash = "sha256:fe5c877972cea450c1c48251c8fae0f18543c8d19dfdb9ff9a9c4263763dde4e", size = 360160, upload-time = "2026-04-06T15:01:01.516Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]
 
 [[package]]

libs/langgraph/langgraph/_internal/_config.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from collections import ChainMap
-from collections.abc import Mapping, Sequence
+from collections.abc import Sequence
 from os import getenv
 from typing import Any, cast
 
@@ -217,14 +217,16 @@ def get_callback_manager_for_config(
             callbacks.add_tags(all_tags)
         if metadata := config.get("metadata"):
             callbacks.add_metadata(metadata)
-        return callbacks
+        manager = callbacks
     else:
         # otherwise create a new manager
-        return CallbackManager.configure(
+        manager = CallbackManager.configure(
             inheritable_callbacks=config.get("callbacks"),
             inheritable_tags=all_tags,
             inheritable_metadata=config.get("metadata"),
+            langsmith_inheritable_metadata=_get_tracing_metadata_defaults(config),
         )
+    return manager
 
 
 def get_async_callback_manager_for_config(
@@ -255,14 +257,16 @@ def get_async_callback_manager_for_config(
             callbacks.add_tags(all_tags)
         if metadata := config.get("metadata"):
             callbacks.add_metadata(metadata)
-        return callbacks
+        manager = callbacks
     else:
         # otherwise create a new manager
-        return AsyncCallbackManager.configure(
+        manager = AsyncCallbackManager.configure(
             inheritable_callbacks=config.get("callbacks"),
             inheritable_tags=all_tags,
             inheritable_metadata=config.get("metadata"),
+            langsmith_inheritable_metadata=_get_tracing_metadata_defaults(config),
         )
+    return manager
 
 
 def _is_not_empty(value: Any) -> bool:
@@ -308,22 +312,54 @@ def ensure_config(*configs: RunnableConfig | None) -> RunnableConfig:
         for k, v in config.items():
             if _is_not_empty(v) and k not in CONFIG_KEYS:
                 empty[CONF][k] = v
-    _empty_metadata = empty["metadata"]
-    for key, value in empty[CONF].items():
-        if _exclude_as_metadata(key, value, _empty_metadata):
-            continue
-        _empty_metadata[key] = value
+
+    configurable = empty.get("configurable")
+    metadata = empty.get("metadata")
+    if configurable and metadata is not None:
+        for key in _PROPAGATE_TO_METADATA:
+            if key in metadata:
+                continue
+            value = configurable.get(key)
+            if value:
+                metadata[key] = value
     return empty
 
 
 _OMIT = ("key", "token", "secret", "password", "auth")
 
 
-def _exclude_as_metadata(key: str, value: Any, metadata: Mapping[str, Any]) -> bool:
+def _exclude_as_metadata(key: str, value: Any) -> bool:
     key_lower = key.casefold()
     return (
         key.startswith("__")
         or not isinstance(value, (str, int, float, bool))
-        or key in metadata
         or any(substr in key_lower for substr in _OMIT)
     )
+
+
+def _get_tracing_metadata_defaults(
+    config: RunnableConfig,
+) -> dict[str, Any] | None:
+    """Get tracer-only metadata defaults from configurable values."""
+    configurable = config.get("configurable")
+    if not configurable:
+        return None
+    metadata: dict[str, Any] = {}
+    for key, value in configurable.items():
+        if _exclude_as_metadata(key, value):
+            continue
+        metadata[key] = value
+    return metadata or None
+
+
+_PROPAGATE_TO_METADATA = frozenset(
+    (
+        "thread_id",
+        "checkpoint_id",
+        "checkpoint_ns",
+        "task_id",
+        "run_id",
+        "assistant_id",
+        "graph_id",
+    )
+)

libs/langgraph/langgraph/callbacks.py

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

libs/langgraph/langgraph/channels/__init__.py

@@ -1,3 +1,4 @@
+from langgraph.channels.aggregate import AggregateChannel
 from langgraph.channels.any_value import AnyValue
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
@@ -19,6 +20,7 @@ __all__ = (
     "LastValueAfterFinish",
     "UntrackedValue",
     "EphemeralValue",
+    "AggregateChannel",
     "BinaryOperatorAggregate",
     "NamedBarrierValue",
     "NamedBarrierValueAfterFinish",

libs/langgraph/langgraph/channels/aggregate.py

@@ -0,0 +1,256 @@
+from __future__ import annotations
+
+import collections.abc
+import copy as _copy
+import math
+from collections.abc import Callable, Sequence
+from typing import Any, Generic
+
+from langgraph.checkpoint.base import DELTA_SENTINEL, PendingWrite
+from typing_extensions import NotRequired, Required, Self
+
+from langgraph._internal._constants import OVERWRITE
+from langgraph._internal._typing import MISSING
+from langgraph.channels.base import BaseChannel, Value
+from langgraph.errors import (
+    EmptyChannelError,
+    ErrorCode,
+    InvalidUpdateError,
+    create_error_message,
+)
+from langgraph.types import Overwrite
+
+__all__ = ("AggregateChannel",)
+
+
+def _strip_extras(t: Any) -> Any:
+    """Strips Annotated, Required, and NotRequired wrappers."""
+    if hasattr(t, "__origin__"):
+        return _strip_extras(t.__origin__)
+    if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
+        return _strip_extras(t.__args__[0])
+    return t
+
+
+def _concrete(typ: Any) -> Any:
+    """Replace abstract collection types from `typing`/`collections.abc` with
+    their instantiable counterparts."""
+    typ = _strip_extras(typ)
+    if typ in (collections.abc.Sequence, collections.abc.MutableSequence):
+        return list
+    if typ in (collections.abc.Set, collections.abc.MutableSet):
+        return set
+    if typ in (collections.abc.Mapping, collections.abc.MutableMapping):
+        return dict
+    return typ
+
+
+def _empty(typ: Any) -> Any:
+    try:
+        return typ()
+    except Exception:
+        return []
+
+
+def _get_overwrite(value: Any) -> tuple[bool, Any]:
+    """Return (is_overwrite, overwrite_value) for an incoming write."""
+    if isinstance(value, Overwrite):
+        return True, value.value
+    if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
+        return True, value[OVERWRITE]
+    return False, None
+
+
+class AggregateChannel(Generic[Value], BaseChannel[Value, Value, Any]):
+    """Fold-reducer channel with configurable snapshot cadence.
+
+    `snapshot_frequency=1` (default) writes a full blob every step — same
+    storage behavior as the classic `BinaryOperatorAggregate`.
+
+    `snapshot_frequency=N` (integer > 1) writes a sentinel on non-snapshot
+    steps; the value is reconstructed at read time by folding ancestor
+    writes through `operator`. On every Nth step a full blob is written,
+    bounding replay depth to N.
+
+    `snapshot_frequency=math.inf` never writes a blob — pure delta storage.
+    Reconstruction replays every write from thread start.
+
+    Parameters:
+        operator: Binary reducer `(Value, Value) -> Value` applied pairwise
+            to accumulate writes. Must be associative for correctness under
+            `snapshot_frequency != 1` where the fold order across ancestor
+            replay vs live writes differs from the classic single-fold path.
+            Most practical reducers (`operator.add`, `add_messages`) satisfy
+            this.
+        snapshot_frequency: Every Nth step writes a full snapshot blob.
+            Default 1 (snapshot always). `math.inf` for pure-delta mode.
+            Reading at step M with `snapshot_frequency=N` walks at most
+            `M % N` ancestor writes — bounded replay regardless of thread
+            depth.
+        typ: Value type. When used as an `Annotated[T, AggregateChannel(...)]`
+            state field, the type is inferred from `T` and this kwarg is
+            unused. Explicit kwarg is the escape hatch for imperative
+            graph construction.
+
+    Experimental under `snapshot_frequency > 1`: the sentinel+replay path
+    is the same mechanism that the (now removed) `DeltaChannel` used; the
+    cadence knob is new and should be validated on real workloads before
+    being relied on in production.
+    """
+
+    __slots__ = ("value", "operator", "snapshot_frequency", "_typ_provided")
+
+    def __init__(
+        self,
+        operator: Callable[[Value, Value], Value],
+        *,
+        snapshot_frequency: int | float = 1,
+        typ: type[Value] | None = None,
+    ) -> None:
+        self._typ_provided = typ is not None
+        concrete_typ = _concrete(typ) if typ is not None else list
+        super().__init__(concrete_typ)
+        self.operator = operator
+        self.snapshot_frequency = snapshot_frequency
+        try:
+            self.value = concrete_typ()
+        except Exception:
+            self.value = MISSING
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, AggregateChannel):
+            return False
+        if self.snapshot_frequency != other.snapshot_frequency:
+            return False
+        if (
+            self.operator.__name__ != "<lambda>"
+            and other.operator.__name__ != "<lambda>"
+        ):
+            return self.operator is other.operator
+        return True
+
+    @property
+    def ValueType(self) -> Any:
+        return self.typ
+
+    @property
+    def UpdateType(self) -> Any:
+        return self.typ
+
+    def _clone_empty(self) -> Self:
+        """Create a blank clone preserving all attributes, bypassing __init__.
+
+        Subclasses (e.g. BinaryOperatorAggregate) have different __init__
+        signatures; going through __init__ from copy/from_checkpoint would
+        pass kwargs those subclasses don't accept. Bypassing avoids that.
+        """
+        new = self.__class__.__new__(self.__class__)
+        new.typ = self.typ
+        new.key = self.key
+        new.operator = self.operator
+        new.snapshot_frequency = self.snapshot_frequency
+        new._typ_provided = self._typ_provided
+        new.value = MISSING
+        return new
+
+    def copy(self) -> Self:
+        new = self._clone_empty()
+        new.value = self.value if self.value is MISSING else _copy.copy(self.value)
+        return new
+
+    def is_snapshot_step(self, step: int) -> bool:
+        """Return True if a full blob should be written at this step.
+
+        `snapshot_frequency=1` → always. `snapshot_frequency=math.inf` →
+        never. Otherwise, `step % snapshot_frequency == 0`.
+        """
+        if self.snapshot_frequency == 1:
+            return True
+        if self.snapshot_frequency == math.inf:
+            return False
+        return step % self.snapshot_frequency == 0
+
+    def _apply_write(self, value: Any, write: Any) -> Any:
+        """Apply one write and return the new value. Handles Overwrite."""
+        is_overwrite, overwrite_value = _get_overwrite(write)
+        if is_overwrite:
+            return (
+                _copy.copy(overwrite_value)
+                if overwrite_value is not None
+                else _empty(self.typ)
+            )
+        if value is MISSING:
+            return write
+        return self.operator(value, write)
+
+    def from_checkpoint(self, checkpoint: Any) -> Self:
+        """Initialize from a stored blob, sentinel, or MISSING.
+
+        If the stored value is a full blob, use it as-is. If it is
+        `DELTA_SENTINEL` or `MISSING`, start empty — the caller (pregel)
+        is responsible for replaying writes via `replay_writes` when
+        applicable.
+        """
+        new = self._clone_empty()
+        if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
+            new.value = _empty(self.typ)
+        else:
+            new.value = checkpoint
+        return new
+
+    def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
+        """Fold a sequence of PendingWrite tuples into the current value.
+
+        Called by pregel after `from_checkpoint(seed)` to replay per-step
+        deltas from on-path ancestors through the operator. Writes are
+        oldest→newest. Overwrite markers reset the reducer state at that
+        point. `task_id` and `channel` fields are ignored — the caller
+        has already filtered to this channel.
+        """
+        for _, _, value in writes:
+            self.value = self._apply_write(self.value, value)
+
+    def update(self, values: Sequence[Value]) -> bool:
+        if not values:
+            return False
+        seen_overwrite = False
+        for value in values:
+            is_overwrite, _ = _get_overwrite(value)
+            if is_overwrite:
+                if seen_overwrite:
+                    msg = create_error_message(
+                        message="Can receive only one Overwrite value per super-step.",
+                        error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
+                    )
+                    raise InvalidUpdateError(msg)
+                seen_overwrite = True
+            elif seen_overwrite:
+                continue
+            self.value = self._apply_write(self.value, value)
+        return True
+
+    def get(self) -> 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:
+        """Return the serializable representation of current state.
+
+        For `snapshot_frequency=math.inf` (pure delta), always returns
+        `DELTA_SENTINEL` — the value lives in `checkpoint_writes` and is
+        reconstructed by replay, never materialized as a blob.
+
+        For integer `snapshot_frequency`, returns the full value. Pregel's
+        `create_checkpoint` is responsible for consulting
+        `is_snapshot_step(step)` to decide whether to actually store the
+        full value or write `DELTA_SENTINEL` for that step.
+        """
+        if self.value is MISSING:
+            return MISSING
+        if self.snapshot_frequency == math.inf:
+            return DELTA_SENTINEL
+        return self.value

libs/langgraph/langgraph/channels/binop.py

@@ -1,44 +1,21 @@
-import collections.abc
-from collections.abc import Callable, Sequence
-from typing import Any, Generic
+from collections.abc import Callable
+from typing import Generic
 
-from typing_extensions import NotRequired, Required, Self
-
-from langgraph._internal._constants import OVERWRITE
-from langgraph._internal._typing import MISSING
-from langgraph.channels.base import BaseChannel, Value
-from langgraph.errors import (
-    EmptyChannelError,
-    ErrorCode,
-    InvalidUpdateError,
-    create_error_message,
+from langgraph.channels.aggregate import (
+    AggregateChannel,
 )
-from langgraph.types import Overwrite
+from langgraph.channels.aggregate import (
+    _get_overwrite as _get_overwrite,
+)
+from langgraph.channels.aggregate import (
+    _strip_extras as _strip_extras,
+)
+from langgraph.channels.base import Value
 
 __all__ = ("BinaryOperatorAggregate",)
 
 
-# Adapted from typing_extensions
-def _strip_extras(t):  # type: ignore[no-untyped-def]
-    """Strips Annotated, Required and NotRequired from a given type."""
-    if hasattr(t, "__origin__"):
-        return _strip_extras(t.__origin__)
-    if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
-        return _strip_extras(t.__args__[0])
-
-    return t
-
-
-def _get_overwrite(value: Any) -> tuple[bool, Any]:
-    """Inspects the given value and returns (is_overwrite, overwrite_value)."""
-    if isinstance(value, Overwrite):
-        return True, value.value
-    if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
-        return True, value[OVERWRITE]
-    return False, None
-
-
-class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
+class BinaryOperatorAggregate(AggregateChannel[Value], Generic[Value]):
     """Stores the result of applying a binary operator to the current value and each new value.
 
     ```python
@@ -46,26 +23,16 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
 
     total = Channels.BinaryOperatorAggregate(int, operator.add)
     ```
+
+    Equivalent to `AggregateChannel(operator, typ=typ, snapshot_frequency=1)`.
+    Preserved as a distinct subclass so existing `isinstance(x, BinaryOperatorAggregate)`
+    checks and `_is_field_binop` detection continue to work. New code should
+    prefer `AggregateChannel` directly — especially when a non-unit
+    `snapshot_frequency` is wanted.
     """
 
-    __slots__ = ("value", "operator")
-
     def __init__(self, typ: type[Value], operator: Callable[[Value, Value], Value]):
-        super().__init__(typ)
-        self.operator = operator
-        # special forms from typing or collections.abc are not instantiable
-        # so we need to replace them with their concrete counterparts
-        typ = _strip_extras(typ)
-        if typ in (collections.abc.Sequence, collections.abc.MutableSequence):
-            typ = list
-        if typ in (collections.abc.Set, collections.abc.MutableSet):
-            typ = set
-        if typ in (collections.abc.Mapping, collections.abc.MutableMapping):
-            typ = dict
-        try:
-            self.value = typ()
-        except Exception:
-            self.value = MISSING
+        super().__init__(operator, typ=typ, snapshot_frequency=1)
 
     def __eq__(self, value: object) -> bool:
         return isinstance(value, BinaryOperatorAggregate) and (
@@ -74,61 +41,3 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
             and self.operator.__name__ != "<lambda>"
             else True
         )
-
-    @property
-    def ValueType(self) -> type[Value]:
-        """The type of the value stored in the channel."""
-        return self.typ
-
-    @property
-    def UpdateType(self) -> type[Value]:
-        """The type of the update received by the channel."""
-        return self.typ
-
-    def copy(self) -> Self:
-        """Return a copy of the channel."""
-        empty = self.__class__(self.typ, self.operator)
-        empty.key = self.key
-        empty.value = self.value
-        return empty
-
-    def from_checkpoint(self, checkpoint: Value) -> Self:
-        empty = self.__class__(self.typ, self.operator)
-        empty.key = self.key
-        if checkpoint is not MISSING:
-            empty.value = checkpoint
-        return empty
-
-    def update(self, values: Sequence[Value]) -> bool:
-        if not values:
-            return False
-        if self.value is MISSING:
-            self.value = values[0]
-            values = values[1:]
-        seen_overwrite: bool = False
-        for value in values:
-            is_overwrite, overwrite_value = _get_overwrite(value)
-            if is_overwrite:
-                if seen_overwrite:
-                    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 = overwrite_value
-                seen_overwrite = True
-                continue
-            if not seen_overwrite:
-                self.value = self.operator(self.value, value)
-        return True
-
-    def get(self) -> 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) -> Value:
-        return self.value

libs/langgraph/langgraph/graph/state.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import collections.abc
 import inspect
 import logging
 import typing
@@ -46,8 +47,9 @@ from langgraph._internal._fields import (
 from langgraph._internal._pydantic import create_model
 from langgraph._internal._runnable import coerce_to_runnable
 from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs
+from langgraph.channels.aggregate import AggregateChannel
 from langgraph.channels.base import BaseChannel
-from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.binop import BinaryOperatorAggregate, _strip_extras
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -1082,6 +1084,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             CompiledStateGraph: The compiled `StateGraph`.
         """
         checkpointer = ensure_valid_checkpointer(checkpointer)
+
         serde_allowlist: set[tuple[str, ...]] | None = None
         if _serde.STRICT_MSGPACK_ENABLED:
             schema_types: list[type[Any]] = [
@@ -1667,6 +1670,44 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
         # Search through all annotated medata to find channel annotations
         for item in meta:
             if isinstance(item, BaseChannel):
+                # AggregateChannel instances without an explicit `typ` arg
+                # inherit the value type from the outer `Annotated[...]`.
+                # BinaryOperatorAggregate (a subclass) always sets typ
+                # explicitly, so _typ_provided is True and we skip inference.
+                if (
+                    isinstance(item, AggregateChannel)
+                    and not item._typ_provided
+                    and hasattr(typ, "__origin__")
+                ):
+                    origin = typ.__origin__
+                    # Unwrap parameterized Required[X]/NotRequired[X] to X
+                    # (e.g. Annotated[NotRequired[dict[...]], ...]).
+                    if hasattr(origin, "__origin__") and origin.__origin__ in (
+                        Required,
+                        NotRequired,
+                    ):
+                        origin = origin.__args__[0]
+                    outer = _strip_extras(origin)
+                    if outer in (
+                        collections.abc.Sequence,
+                        collections.abc.MutableSequence,
+                    ):
+                        outer = list
+                    elif outer in (
+                        collections.abc.Mapping,
+                        collections.abc.MutableMapping,
+                    ):
+                        outer = dict
+                    elif outer in (
+                        collections.abc.Set,
+                        collections.abc.MutableSet,
+                    ):
+                        outer = set
+                    item.typ = outer
+                    try:
+                        item.value = outer()
+                    except Exception:
+                        item.value = []
                 return item
             elif isclass(item) and issubclass(item, BaseChannel):
                 # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]

libs/langgraph/langgraph/pregel/_checkpoint.py

@@ -3,10 +3,12 @@ from __future__ import annotations
 from collections.abc import Mapping
 from datetime import datetime, timezone
 
-from langgraph.checkpoint.base import Checkpoint
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import DELTA_SENTINEL, BaseCheckpointSaver, Checkpoint
 from langgraph.checkpoint.base.id import uuid6
 
 from langgraph._internal._typing import MISSING
+from langgraph.channels.aggregate import AggregateChannel
 from langgraph.channels.base import BaseChannel
 from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec
 
@@ -32,7 +34,13 @@ def create_checkpoint(
     id: str | None = None,
     updated_channels: set[str] | None = None,
 ) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
+    """Create a checkpoint for the given channels.
+
+    For `AggregateChannel` spec with `snapshot_frequency != 1`, the stored
+    blob alternates between the full value and `DELTA_SENTINEL` based on
+    `is_snapshot_step(step)`. Non-snapshot steps store the sentinel; the
+    value is reconstructed from ancestor writes at read time.
+    """
     ts = datetime.now(timezone.utc).isoformat()
     if channels is None:
         values = checkpoint["channel_values"]
@@ -41,7 +49,11 @@ def create_checkpoint(
         for k in channels:
             if k not in checkpoint["channel_versions"]:
                 continue
-            v = channels[k].checkpoint()
+            ch = channels[k]
+            if isinstance(ch, AggregateChannel) and not ch.is_snapshot_step(step):
+                values[k] = DELTA_SENTINEL
+                continue
+            v = ch.checkpoint()
             if v is not MISSING:
                 values[k] = v
     return Checkpoint(
@@ -55,11 +67,36 @@ def create_checkpoint(
     )
 
 
+def _needs_replay(spec: BaseChannel, stored: object) -> bool:
+    """True if `spec` is a delta-mode AggregateChannel and the stored
+    blob is empty/sentinel, requiring an ancestor walk to reconstruct."""
+    if not isinstance(spec, AggregateChannel):
+        return False
+    if spec.snapshot_frequency == 1:
+        return False
+    return stored is MISSING or stored is DELTA_SENTINEL
+
+
 def channels_from_checkpoint(
     specs: Mapping[str, BaseChannel | ManagedValueSpec],
     checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
 ) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Get channels from a checkpoint."""
+    """Hydrate channels from a checkpoint.
+
+    For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
+    is sufficient — the stored value IS the reconstructed state.
+
+    `AggregateChannel` with `snapshot_frequency != 1` is the exception:
+    its stored value on non-snapshot steps is `DELTA_SENTINEL`; the full
+    state is spread across `checkpoint_writes` along the ancestor chain.
+    When `saver` and `config` are provided, this function fetches that
+    history via `saver._get_channel_writes_history` and folds it through
+    the channel's operator. Without them (static contexts — graph
+    drawing, unit tests), delta-mode channels fall back to empty.
+    """
     channel_specs: dict[str, BaseChannel] = {}
     managed_specs: dict[str, ManagedValueSpec] = {}
     for k, v in specs.items():
@@ -67,13 +104,55 @@ def channels_from_checkpoint(
             channel_specs[k] = v
         else:
             managed_specs[k] = v
-    return (
-        {
-            k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
-            for k, v in channel_specs.items()
-        },
-        managed_specs,
-    )
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if _needs_replay(spec, stored) and saver is not None and config is not None:
+            # Walk ancestors for seed + writes. The saver's walk stops at
+            # the nearest non-sentinel blob (natural terminator under
+            # snapshot_frequency > 1; pre-migration blobs also act as
+            # terminators if the spec was changed mid-thread).
+            history = saver._get_channel_writes_history(config, k)
+            replay_ch = spec.from_checkpoint(history.seed)
+            replay_ch.replay_writes(history.writes)
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs
+
+
+async def achannels_from_checkpoint(
+    specs: Mapping[str, BaseChannel | ManagedValueSpec],
+    checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
+) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
+    """Async version of `channels_from_checkpoint`. See docstring there."""
+    channel_specs: dict[str, BaseChannel] = {}
+    managed_specs: dict[str, ManagedValueSpec] = {}
+    for k, v in specs.items():
+        if isinstance(v, BaseChannel):
+            channel_specs[k] = v
+        else:
+            managed_specs[k] = v
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if _needs_replay(spec, stored) and saver is not None and config is not None:
+            history = await saver._aget_channel_writes_history(config, k)
+            replay_ch = spec.from_checkpoint(history.seed)
+            replay_ch.replay_writes(history.writes)
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs
 
 
 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:

libs/langgraph/langgraph/pregel/_loop.py

@@ -92,6 +92,7 @@ from langgraph.pregel._algo import (
     task_path_str,
 )
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -692,7 +693,7 @@ class PregelLoop:
         # writes so that interrupt() calls re-fire instead of returning
         # stale values. But if we're actively resuming, keep them —
         # multi-interrupt scenarios need previously resolved values preserved.
-        if self.is_replaying and (
+        is_time_traveling = self.is_replaying and (
             # Time-travel to a subgraph checkpoint: the parent sets
             # RESUMING=True (it can't distinguish time-travel from resume),
             # so we check if this subgraph's own ns is in checkpoint_map.
@@ -710,7 +711,8 @@ class PregelLoop:
                 # (subgraph input is a Send arg, not a Command)
                 or configurable.get(CONFIG_KEY_RESUMING, False)
             )
-        ):
+        )
+        if is_time_traveling:
             self.checkpoint_pending_writes = [
                 w for w in self.checkpoint_pending_writes if w[1] != RESUME
             ]
@@ -765,6 +767,26 @@ class PregelLoop:
                 if k in self.checkpoint["channel_versions"]:
                     version = self.checkpoint["channel_versions"][k]
                     self.checkpoint["versions_seen"][INTERRUPT][k] = version
+            # When time-traveling (replaying from a specific checkpoint),
+            # save a fork checkpoint so the replayed execution creates a
+            # new branch. Without this, if the execution hits an interrupt
+            # before after_tick() runs, no new checkpoint is created —
+            # the parent's latest checkpoint remains the old one and
+            # subsequent resumes load the wrong state.
+            # Skip for update_state forks (source=update/fork) since they
+            # already have their own fork checkpoint.
+            if is_time_traveling and self.checkpoint_metadata.get("source") not in (
+                "update",
+                "fork",
+            ):
+                # Clear old INTERRUPT writes from the loaded checkpoint.
+                # The fork will have a new checkpoint_id which changes
+                # task IDs — stale interrupt writes would accumulate and
+                # confuse the multiple-interrupt check in future resumes.
+                self.checkpoint_pending_writes = [
+                    w for w in self.checkpoint_pending_writes if w[1] != INTERRUPT
+                ]
+                self._put_checkpoint({"source": "fork"})
             # produce values output
             self._emit(
                 "values", map_output_values, self.output_keys, True, self.channels
@@ -807,14 +829,28 @@ class PregelLoop:
         if not self.is_nested:
             # Pass the resolved before-bound checkpoint ID so subgraphs can
             # find their corresponding checkpoint without re-fetching the
-            # parent. For forks (source=update), use the fork's parent
+            # 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") == "update"
+                    self.checkpoint_metadata.get("source")
+                    in (
+                        "update",
+                        "fork",
+                    )
                     and self.prev_checkpoint_config
                 ):
                     replay_checkpoint_id = self.prev_checkpoint_config[CONF].get(
@@ -1238,7 +1274,10 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
         )
         self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
         self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"
@@ -1441,8 +1480,11 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
         self.submit = await self.stack.enter_async_context(
             AsyncBackgroundExecutor(self.config)
         )
-        self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+        self.channels, self.managed = await achannels_from_checkpoint(
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"

libs/langgraph/langgraph/pregel/main.py

@@ -122,6 +122,7 @@ from langgraph.pregel._algo import (
 )
 from langgraph.pregel._call import identifier
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -1052,6 +1053,10 @@ class Pregel(
         channels, managed = channels_from_checkpoint(
             self.channels,
             saved.checkpoint,
+            saver=self.checkpointer
+            if isinstance(self.checkpointer, BaseCheckpointSaver)
+            else None,
+            config=saved.config,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
@@ -1168,9 +1173,13 @@ class Pregel(
 
         step = saved.metadata.get("step", -1) + 1
         stop = step + 2
-        channels, managed = channels_from_checkpoint(
+        channels, managed = await achannels_from_checkpoint(
             self.channels,
             saved.checkpoint,
+            saver=self.checkpointer
+            if isinstance(self.checkpointer, BaseCheckpointSaver)
+            else None,
+            config=saved.config,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
@@ -1541,6 +1550,11 @@ class Pregel(
             channels, managed = channels_from_checkpoint(
                 self.channels,
                 checkpoint,
+                saver=self.checkpointer
+                if saved is not None
+                and isinstance(self.checkpointer, BaseCheckpointSaver)
+                else None,
+                config=saved.config if saved is not None else None,
             )
             values, as_node = updates[0][:2]
 
@@ -1984,9 +1998,14 @@ class Pregel(
             )
             if saved:
                 checkpoint_config = patch_configurable(config, saved.config[CONF])
-            channels, managed = channels_from_checkpoint(
+            channels, managed = await achannels_from_checkpoint(
                 self.channels,
                 checkpoint,
+                saver=self.checkpointer
+                if saved is not None
+                and isinstance(self.checkpointer, BaseCheckpointSaver)
+                else None,
+                config=saved.config if saved is not None else None,
             )
             values, as_node = updates[0][:2]
             # no values, just clear all tasks
@@ -3715,15 +3734,14 @@ def _coerce_checkpoint_values(payload: Any, mapper: Callable[[Any], Any]) -> Non
 def _build_server_info(
     config: RunnableConfig, parent_runtime: Runtime[Any]
 ) -> ServerInfo | None:
-    """Build ServerInfo from config metadata and configurable.
+    """Build ServerInfo from config configurable.
 
-    The server puts assistant_id/graph_id in config metadata and the
+    The server puts assistant_id/graph_id in config configurable and the
     authenticated user dict in configurable["langgraph_auth_user"].
     """
-    metadata = config.get("metadata") or {}
     configurable = config.get(CONF) or {}
-    assistant_id = metadata.get("assistant_id")
-    graph_id = metadata.get("graph_id")
+    assistant_id = configurable.get("assistant_id")
+    graph_id = configurable.get("graph_id")
 
     # Read authenticated user from configurable (set by LangGraph Server).
     # We prefer isinstance(BaseUser) but fall back to hasattr("identity")

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.1.7a1"
+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>=0.1",
+    "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",

libs/langgraph/tests/test_channels.py

@@ -2,6 +2,8 @@ import operator
 from collections.abc import Sequence
 
 import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.base import DELTA_SENTINEL
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.binop import BinaryOperatorAggregate
@@ -9,6 +11,13 @@ from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
+from langgraph.graph.message import add_messages
+
+import math as _math_compat
+from langgraph.channels.aggregate import AggregateChannel as _AggregateChannel_compat
+def DeltaChannel(op):
+    return _AggregateChannel_compat(op, snapshot_frequency=_math_compat.inf)
+
 
 pytestmark = pytest.mark.anyio
 
@@ -117,3 +126,492 @@ 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 DELTA_SENTINEL
+
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+
+    # Step 1: one message added
+    ch.update([HumanMessage(content="hi", id="h1")])
+    d1 = ch.checkpoint()
+    assert d1 is DELTA_SENTINEL
+
+    # Step 2: another message
+    ch.update([AIMessage(content="hello", id="a1")])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    # Full accumulated value is preserved in memory
+    assert len(ch.get()) == 2
+    assert ch.get()[0].content == "hi"
+    assert ch.get()[1].content == "hello"
+
+
+def test_delta_channel_from_checkpoint_writes_list() -> None:
+    """replay_writes on a fresh channel replays through the operator."""
+    from langchain_core.messages import AIMessage, HumanMessage
+
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", HumanMessage(content="bye", id="h2")),
+        ]
+    )
+    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.graph.message import add_messages
+
+    # Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
+    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() -> None:
+    from langchain_core.messages import HumanMessage
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.graph.message import add_messages
+    from langgraph.types import Overwrite
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    ch.update([HumanMessage(content="old", id="h1")])
+
+    ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
+    d = ch.checkpoint()
+    assert d is DELTA_SENTINEL
+    # After overwrite, value is reset to only the new message
+    assert len(ch.get()) == 1
+    assert ch.get()[0].content == "new"
+
+
+def test_delta_channel_remove_message_and_replay() -> None:
+    """RemoveMessage must round-trip correctly when writes are replayed."""
+    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(MISSING)
+
+    # Step 1: add two messages
+    ch.update([HumanMessage(content="hi", id="h1")])
+    ch.update([AIMessage(content="hello", id="a1")])
+    assert ch.get() == [
+        HumanMessage(content="hi", id="h1"),
+        AIMessage(content="hello", id="a1"),
+    ]
+
+    # Step 2: remove the AI message
+    ch.update([RemoveMessage(id="a1")])
+    assert ch.get() == [HumanMessage(content="hi", id="h1")]
+
+    # Replay the writes list from scratch — must reproduce the post-remove state
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", RemoveMessage(id="a1")),
+        ]
+    )
+    assert ch2.get() == [HumanMessage(content="hi", id="h1")]
+
+
+def test_delta_channel_update_by_id_and_replay() -> None:
+    """Updating a message by ID must round-trip correctly through writes replay."""
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(MISSING)
+
+    # Step 1: add a message
+    ch.update([HumanMessage(content="original", id="h1")])
+
+    # Step 2: update the same message by ID
+    ch.update([HumanMessage(content="updated", id="h1")])
+    assert ch.get() == [HumanMessage(content="updated", id="h1")]
+
+    # Replay writes — must produce the updated message, not the original
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="original", id="h1")),
+            ("t1", "messages", HumanMessage(content="updated", id="h1")),
+        ]
+    )
+    assert len(ch2.get()) == 1
+    assert ch2.get()[0].content == "updated"
+
+
+def test_delta_channel_checkpoint_returns_sentinel() -> None:
+    """checkpoint() always returns DELTA_SENTINEL regardless of state."""
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    assert ch.checkpoint() is DELTA_SENTINEL
+
+    from langchain_core.messages import HumanMessage
+
+    ch.update([HumanMessage(content="hi", id="h1")])
+    assert ch.checkpoint() is DELTA_SENTINEL
+
+
+def test_delta_channel_inmemory_saver_assembles_writes() -> None:
+    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    n = {"v": 0}
+
+    def respond(state: State) -> dict:
+        n["v"] += 1
+        return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "t1"}}
+
+    graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
+    graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
+
+    # get_tuple returns raw storage shape — channel_values stores DELTA_SENTINEL
+    # for delta channels; the reconstructed writes flow separately via
+    # saver._get_channel_writes_history.
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "messages" in saved.checkpoint["channel_values"]
+    assert saved.checkpoint["channel_values"]["messages"] is DELTA_SENTINEL
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
+
+
+def _delta_channel_with_type(operator, typ):
+    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
+    from typing import Annotated
+
+    from langgraph.graph.state import _get_channel
+
+    return _get_channel("_test", Annotated[typ, DeltaChannel(operator)])
+
+
+def test_delta_channel_dict_reducer_fresh_channel() -> None:
+    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    # Should be available (not raise EmptyChannelError) and start empty
+    assert ch.is_available()
+    assert ch.get() == {}
+
+
+def test_delta_channel_dict_reducer_basic_updates() -> None:
+    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+
+    ch.update([{"a": 1}])
+    d1 = ch.checkpoint()
+    assert d1 is DELTA_SENTINEL
+
+    ch.update([{"b": 2}])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
+    """replay_writes on a fresh channel replays through a dict merge reducer."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", {"b": 2}),
+            ("t2", "files", {"c": 3}),
+        ]
+    )
+    assert ch.get() == {"a": 1, "b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_with_deletions() -> None:
+    """Dict reducer that treats None values as deletions works end-to-end (deepagents pattern)."""
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
+
+    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
+
+    # Delete file1, add file3
+    ch.update([{"file1.py": None, "file3.py": "content3"}])
+
+    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+    # Confirm writes reconstruction produces the same result
+    spec = _delta_channel_with_type(merge_files, dict)
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
+            ("t1", "files", {"file1.py": None, "file3.py": "content3"}),
+        ]
+    )
+    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+
+def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
+    """Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
+    from langgraph.types import Overwrite
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    ch.update([{"a": 1}])
+    ch.update([Overwrite({"b": 2, "c": 3})])
+
+    assert ch.get() == {"b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
+    """Overwrite(dict) embedded in replayed writes must reconstruct as dict."""
+    from langgraph.types import Overwrite
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", Overwrite({"x": 10, "y": 20})),
+            ("t2", "files", {"z": 30}),
+        ]
+    )
+    assert ch.get() == {"x": 10, "y": 20, "z": 30}
+
+
+def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
+    """DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`.
+
+    This is the shape the deepagents filesystem middleware uses for its
+    `files` field; without unwrapping NotRequired we'd fall through to `list`
+    and blow up on the first dict operator call.
+    """
+    from typing import Annotated
+
+    from typing_extensions import NotRequired
+
+    from langgraph.graph.state import _get_channel
+
+    def merge_dicts(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return dict(right)
+        return {**left, **right}
+
+    annotation = Annotated[
+        NotRequired[dict[str, int]],
+        DeltaChannel(merge_dicts),
+    ]
+    ch = _get_channel("files", annotation).from_checkpoint(MISSING)
+    assert ch.get() == {}
+    ch.update([{"a": 1}])
+    ch.update([{"b": 2}])
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
+    """End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel.
+
+    Mirrors the deepagents filesystem pattern: `files: Annotated[dict, reducer]`
+    where the reducer merges dicts and treats None values as deletions.
+    """
+    from typing import Annotated
+
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.graph import START, StateGraph
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    class State(TypedDict):
+        files: Annotated[dict[str, str], DeltaChannel(merge_files)]
+
+    turn = {"v": 0}
+
+    def write_file(state: State) -> dict:
+        turn["v"] += 1
+        n = turn["v"]
+        return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
+
+    builder = StateGraph(State)
+    builder.add_node("write_file", write_file)
+    builder.add_edge(START, "write_file")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "fs"}}
+
+    for _ in range(3):
+        graph.invoke({"files": {}}, config)
+
+    # Checkpoint stores only the sentinel — per-step writes live in checkpoint_writes.
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    cv = saved.checkpoint["channel_values"]["files"]
+    assert cv is DELTA_SENTINEL
+
+    state = graph.get_state(config)
+    assert state.values["files"] == {
+        "/doc_1.txt": "content for turn 1",
+        "/doc_2.txt": "content for turn 2",
+        "/doc_3.txt": "content for turn 3",
+    }
+
+    # Deletion path must round-trip through writes replay.
+    def delete_file(state: State) -> dict:
+        return {"files": {"/doc_1.txt": None}}
+
+    builder2 = StateGraph(State)
+    builder2.add_node("write_file", write_file)
+    builder2.add_node("delete_file", delete_file)
+    builder2.add_edge(START, "write_file")
+    builder2.add_edge("write_file", "delete_file")
+    turn["v"] = 0
+    saver2 = InMemorySaver()
+    graph2 = builder2.compile(checkpointer=saver2)
+    config2 = {"configurable": {"thread_id": "fs2"}}
+    graph2.invoke({"files": {}}, config2)
+    state2 = graph2.get_state(config2)
+    assert state2.values["files"] == {}
+
+
+def test_delta_channel_dict_reducer_backwards_compat() -> None:
+    """A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    old_value = {"a": 1, "b": 2}
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+# ---------------------------------------------------------------------------
+# seed / pre-delta migration
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_from_checkpoint_honors_seed() -> None:
+    """A non-sentinel value to from_checkpoint is used as the pre-delta seed.
+
+    Guards the pre-delta migration path: when the saver's ancestor walk hits
+    a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
+    the post-migration state correctly rather than replaying from empty.
+    """
+    spec = DeltaChannel(add_messages)
+    seed = [HumanMessage(content="pre-delta", id="p1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes(
+        [
+            ("t0", "messages", AIMessage(content="delta-1", id="d1")),
+            ("t1", "messages", HumanMessage(content="delta-2", id="d2")),
+        ]
+    )
+    msgs = ch.get()
+    assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
+
+
+def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
+    """Reconstruction at a pre-delta ancestor with no newer deltas returns
+    just the seed — the saver's terminator fired immediately."""
+    spec = DeltaChannel(add_messages)
+    seed = [HumanMessage(content="only-snap", id="s1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes([])
+    assert ch.get() == seed
+
+
+def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
+    """`seed=None` must start replay from None, not from an empty channel.
+
+    The DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
+    explicitly should feed None to the reducer as the left operand.
+    """
+
+    def replace(left, right):
+        return right
+
+    spec = DeltaChannel(replace)
+    ch = spec.from_checkpoint(None)
+    ch.replay_writes([("t0", "x", "after")])
+    # Reducer replaces; seed=None → first write produces "after".
+    assert ch.get() == "after"

libs/langgraph/tests/test_delta_channel_benchmark.py

@@ -0,0 +1,403 @@
+"""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.
+
+DeltaChannel stores only a zero-byte sentinel in checkpoint_blobs; the actual
+write data lives in checkpoint_writes (already stored there). Reconstruction
+walks the parent chain and replays writes through the operator — O(N) total
+storage vs O(N²) for plain add_messages.
+"""
+
+from __future__ import annotations
+
+import sys
+import time
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.memory import MemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.graph import END, StateGraph
+from langgraph.graph.message import add_messages
+
+import math as _math_compat
+from langgraph.channels.aggregate import AggregateChannel as _AggregateChannel_compat
+def DeltaChannel(op):
+    return _AggregateChannel_compat(op, snapshot_frequency=_math_compat.inf)
+
+
+try:
+    from langgraph.checkpoint.sqlite import SqliteSaver
+
+    _SQLITE_AVAILABLE = True
+except ImportError:
+    _SQLITE_AVAILABLE = False
+
+try:
+    from langgraph.checkpoint.postgres import PostgresSaver
+
+    _POSTGRES_AVAILABLE = True
+    _POSTGRES_URI = (
+        "postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable"
+    )
+except ImportError:
+    _POSTGRES_AVAILABLE = False
+
+# ---------------------------------------------------------------------------
+# 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. "
+    "We've had prior incidents in this area and want to be deliberate. "
+    "What should we prioritize first, and are there known failure modes we should design around from the start?"
+)
+
+_AI_TEMPLATE = (
+    "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)]
+
+
+# ---------------------------------------------------------------------------
+# 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 demonstrate O(N²) vs O(N) storage growth without running too long.
+# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
+TURN_COUNTS = [10, 25, 50, 100, 500]
+
+# Deep-thread counts where add_messages blob storage would exceed 1 GB;
+# only DeltaChannel runs here.
+DELTA_ONLY_TURN_COUNTS = [1000]
+
+
+def _checkpointer_factories() -> list[tuple[str, Any]]:
+    """Return (label, context_manager_or_none) pairs for available checkpointers."""
+    return [("InMemory", None)]
+
+
+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", None)]
+    if _POSTGRES_AVAILABLE:
+        try:
+            import psycopg
+
+            psycopg.connect(_POSTGRES_URI).close()
+            checkpointers.append(("Postgres (plain SELECT)", "postgres"))
+        except Exception:
+            pass
+
+    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
+        elif cp_hint == "postgres":
+            with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
+                saver.setup()
+                with saver._cursor() as cur:
+                    cur.execute("DELETE FROM checkpoints WHERE thread_id = 'bench'")
+                    cur.execute(
+                        "DELETE FROM checkpoint_blobs WHERE thread_id = 'bench'"
+                    )
+                    cur.execute(
+                        "DELETE FROM checkpoint_writes WHERE thread_id = 'bench'"
+                    )
+                yield saver
+        else:
+            with tempfile.NamedTemporaryFile(suffix=".db") as f:
+                with SqliteSaver.from_conn_string(f.name) as saver:
+                    yield saver
+
+    rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
+    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)
+        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt))
+    for turns in DELTA_ONLY_TURN_COUNTS:
+        with _make_saver() as saver:
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, None, d_bytes, None, d_rt, None, d_wt))
+
+    # ── Table 1: Storage ─────────────────────────────────────────────────────
+    W = 64
+    print("Storage (checkpoint blob bytes)")
+    print("=" * W)
+    print(
+        f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}  "
+        f"{'savings':>8}"
+    )
+    print("-" * W)
+
+    def _bytes_or_na(v: Any) -> str:
+        if v is None:
+            return "n/a"
+        if v < 0:
+            return "n/a"
+        return _fmt_bytes(v)
+
+    def _ms_or_na(v: Any) -> str:
+        return "n/a" if v is None else f"{v * 1000:.1f}ms"
+
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        if b_bytes is None or b_bytes < 0 or d_bytes is None or d_bytes < 0:
+            ratio_str = "n/a"
+        else:
+            ratio = b_bytes / d_bytes if d_bytes else float("inf")
+            ratio_str = f"{ratio:.0f}x"
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_bytes_or_na(b_bytes):>12}  {_bytes_or_na(d_bytes):>12}  "
+            f"{ratio_str:>8}"
+        )
+    print("=" * W)
+    print()
+
+    # ── Table 2: Read latency ─────────────────────────────────────────────────
+    print("Read latency (avg of 5 get_state calls)")
+    print("=" * W)
+    print(f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}")
+    print("-" * W)
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_ms_or_na(b_rt):>12}  {_ms_or_na(d_rt):>12}"
+        )
+    print("=" * W)
+    print()
+
+    # ── Table 3: Per-invoke latency (total write_elapsed / turns) ─────────────
+    print("Per-invoke latency (total graph.invoke time / turns)")
+    print("=" * W)
+    print(f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}")
+    print("-" * W)
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+
+        def _per(wt: Any) -> str:
+            if wt is None:
+                return "n/a"
+            return f"{(wt / turns) * 1000:.1f}ms"
+
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_per(b_wt):>12}  {_per(d_wt):>12}"
+        )
+    print("=" * W)
+    print()
+
+    print("Legend:")
+    print("  add_msgs  = Annotated[list, add_messages]  — O(N²) storage")
+    print("  delta     = Annotated[list, DeltaChannel(add_messages)]  — O(N) storage")
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Pytest entry point
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+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 [25, 50]:
+        _, _, b_bytes = _run_turns(turns, BinaryState)
+        _, _, d_bytes = _run_turns(turns, DeltaState)
+        assert d_bytes < b_bytes, (
+            f"DeltaChannel should use less storage at {turns} turns, "
+            f"got delta={d_bytes} binary={b_bytes}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Script entry point
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    run_benchmark()
+    sys.exit(0)

libs/langgraph/tests/test_delta_channel_migration.py

@@ -0,0 +1,509 @@
+"""Tests for the BinaryOperatorAggregate -> DeltaChannel migration path.
+
+A thread written under `BinaryOperatorAggregate(...)` must keep working
+after its annotation is swapped to `DeltaChannel(...)` on the same
+checkpointer — pre-migration state visible at each *settled* ancestor
+checkpoint is preserved, and post-migration writes fold on top through
+the reducer.
+
+Mechanism under test: the saver's `_get_channel_writes_history(config,
+channel)` walks the parent chain; when it encounters an ancestor whose
+`channel_values[channel]` is a real value (not `DELTA_SENTINEL`), it
+returns that as the `seed`. `DeltaChannel.from_checkpoint(seed)` uses
+it as the base value, and `replay_writes(writes)` folds on-path deltas.
+
+Scenarios covered:
+
+1. **Basic migration (sync + async)**: build pre-migration state with
+   `BinaryOperatorAggregate`, swap the annotation to `DeltaChannel` on
+   the same checkpointer, and verify that every settled pre-migration
+   super-step boundary (`next=('__start__',)`) round-trips exactly
+   under the delta-channel view.
+2. **Time travel into a pre-migration checkpoint** after migration —
+   `graph.get_state(pre_migration_config)` at a settled ancestor
+   returns the same state as under the binop channel.
+3. **Continuing a migrated thread**: driving one more super-step after
+   migration produces a state that includes the pre-migration settled
+   prefix plus the new delta write — proving `from_checkpoint(seed)` +
+   `replay_writes` correctly fold post-migration deltas onto the
+   pre-migration seed.
+4. **Base-saver fallback path**: a third-party-style subclass that
+   removes the optimized `InMemorySaver` override and falls back to
+   `BaseCheckpointSaver._get_channel_writes_history` must produce the
+   same result as the optimized path.
+5. **Channel-type isolation across threads**: two threads on the same
+   checkpointer under the delta-channel graph — one freshly-started,
+   one migrated from pre-migration state — don't cross-contaminate.
+   The parent-chain walk is scoped to the thread.
+
+TODO: add postgres variants in the existing `libs/checkpoint-postgres`
+test files (different fixture setup; not this file).
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.graph import END, START, StateGraph
+
+import math as _math_compat
+from langgraph.channels.aggregate import AggregateChannel as _AggregateChannel_compat
+def DeltaChannel(op):
+    return _AggregateChannel_compat(op, snapshot_frequency=_math_compat.inf)
+
+
+pytestmark = pytest.mark.anyio
+
+
+# ---------------------------------------------------------------------------
+# Graph factories
+#
+# A minimal reducer (`operator.add` on lists of str) with a noop node keeps
+# state change localized to the HumanMessage-like payload passed through
+# `invoke`. That isolates the pre/post-migration parity assertions to
+# channel-hydration semantics.
+# ---------------------------------------------------------------------------
+
+
+def _noop(_state: Any) -> dict:
+    return {}
+
+
+def _binop_graph(checkpointer: Any) -> Any:
+    class BinopState(TypedDict):
+        items: Annotated[list, BinaryOperatorAggregate(list, operator.add)]
+
+    return (
+        StateGraph(BinopState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_graph(checkpointer: Any) -> Any:
+    class DeltaState(TypedDict):
+        items: Annotated[list, DeltaChannel(operator.add)]
+
+    return (
+        StateGraph(DeltaState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _drive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        graph.invoke({"items": [f"{tag}{i}"]}, config)
+
+
+async def _adrive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        await graph.ainvoke({"items": [f"{tag}{i}"]}, config)
+
+
+def _settled_boundaries(history: list) -> list[tuple[dict, list]]:
+    """Return `[(config, items), ...]` for every checkpoint in `history`
+    whose `next == ('__start__',)` — the stable boundaries between invokes.
+    """
+    return [
+        (s.config, list(s.values.get("items", [])))
+        for s in history
+        if s.next == ("__start__",)
+    ]
+
+
+# ---------------------------------------------------------------------------
+# 1. Basic migration (sync + async)
+# ---------------------------------------------------------------------------
+
+
+def test_basic_migration_preserves_pre_migration_state() -> None:
+    """Build state under `BinaryOperatorAggregate`, migrate to
+    `DeltaChannel` on the same checkpointer, and verify that every
+    settled pre-migration super-step boundary round-trips exactly.
+
+    Settled boundaries (`next=('__start__',)`) are the stable hydration
+    targets for the migration path: writes that produced the NEXT
+    super-step are kept as `pending_writes` on the ancestor, so walking
+    from a descendant finds the ancestor's blob as the seed and
+    reconstructs the correct state.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-sync"}}
+
+    # Pre-migration: accumulate items across 3 invokes.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert len(pre_boundaries) >= 2, "expected multiple settled boundaries"
+
+    # Migrate: swap the annotation on the same checkpointer.
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = delta.get_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"snapshot mismatch at {cfg['configurable']['checkpoint_id']}: "
+            f"expected {items}, got {snap.values.get('items', [])}"
+        )
+
+
+async def test_basic_migration_preserves_pre_migration_state_async() -> None:
+    """Async variant of the basic migration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    pre_history = [s async for s in binop.aget_state_history(config)]
+    pre_boundaries = _settled_boundaries(pre_history)
+    assert len(pre_boundaries) >= 2
+
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = await delta.aget_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"async snapshot mismatch at {cfg['configurable']['checkpoint_id']}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 2. Time travel into a pre-migration checkpoint after migration
+# ---------------------------------------------------------------------------
+
+
+def test_time_travel_into_pre_migration_checkpoint() -> None:
+    """After migration, `graph.get_state(pre_migration_config)` at a
+    settled ancestor returns the state as stored at that point."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "time-travel"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert pre_boundaries, "no settled ancestors to time-travel to"
+
+    delta = _delta_graph(checkpointer)
+
+    # Pick the oldest non-empty boundary — a long distance to walk back.
+    non_empty = [(cfg, items) for cfg, items in pre_boundaries if items]
+    assert non_empty, "expected at least one non-empty boundary"
+    target_cfg, expected_items = non_empty[-1]
+
+    snap = delta.get_state(target_cfg)
+    assert list(snap.values.get("items", [])) == expected_items
+
+
+# ---------------------------------------------------------------------------
+# 3. Continuing a migrated thread: deltas fold onto pre-migration seed
+# ---------------------------------------------------------------------------
+
+
+def test_continuing_migrated_thread_folds_deltas_on_seed() -> None:
+    """Resume a pre-migration settled ancestor via `invoke(None, cfg)`
+    under the delta-channel graph. Since the pre-migration checkpoint
+    has an existing `pending_writes` entry (the input for the NEXT
+    super-step), re-running from that ancestor reproduces the same
+    post-ancestor state as the original binop run.
+
+    This proves the seed-terminator + write-replay pipeline works
+    end-to-end across the migration boundary.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "continue"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Pick the oldest settled boundary with non-empty state.
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    target_cfg, seed_items = next(
+        (cfg, items) for cfg, items in reversed(pre_boundaries) if items
+    )
+    assert seed_items, "need a non-empty seed boundary"
+
+    # Migrate and resume from the pre-migration ancestor. `invoke(None,
+    # cfg)` replays the pending writes staged at `cfg` under the new
+    # channel; the reducer folds those deltas onto the seed.
+    delta = _delta_graph(checkpointer)
+    result = delta.invoke(None, target_cfg)
+
+    # The resumed state must include the pre-migration seed items in order.
+    result_items = list(result.get("items", []))
+    for idx, prefix_item in enumerate(seed_items):
+        assert result_items[idx] == prefix_item, (
+            f"pre-migration seed item at {idx} not preserved: "
+            f"got {result_items[: idx + 1]}, expected {seed_items}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 4. Base-saver fallback path
+# ---------------------------------------------------------------------------
+
+
+class _ThirdPartyStyleSaver(InMemorySaver):
+    """Simulates a third-party saver that inherits the reference
+    `_get_channel_writes_history` implementation from
+    `BaseCheckpointSaver` rather than overriding it.
+
+    We rebind the two methods to the base-class versions (via MRO) so
+    the fallback path is exercised even though the storage layer is
+    still the in-memory one.
+    """
+
+    # MRO: [_ThirdPartyStyleSaver, InMemorySaver, BaseCheckpointSaver, ...]
+    _get_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+    )
+    _aget_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+    )
+
+
+def test_base_saver_fallback_matches_optimized_override() -> None:
+    """The reference `BaseCheckpointSaver` implementation must produce
+    the same migration behavior as the optimized `InMemorySaver`
+    override. We drive the same migration scenario through both savers
+    and assert per-snapshot parity in the delta-channel view."""
+
+    # Fast path: optimized InMemorySaver override.
+    fast_saver = InMemorySaver()
+    fast_config = {"configurable": {"thread_id": "fast"}}
+    fast_binop = _binop_graph(fast_saver)
+    _drive(fast_binop, fast_config, "u", 3)
+    fast_delta = _delta_graph(fast_saver)
+    fast_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in fast_delta.get_state_history(fast_config)
+    ]
+
+    # Slow path: base-class fallback.
+    slow_saver = _ThirdPartyStyleSaver()
+    slow_config = {"configurable": {"thread_id": "slow"}}
+    slow_binop = _binop_graph(slow_saver)
+    _drive(slow_binop, slow_config, "u", 3)
+    slow_delta = _delta_graph(slow_saver)
+    slow_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in slow_delta.get_state_history(slow_config)
+    ]
+
+    assert slow_history == fast_history, (
+        "base-saver fallback should match optimized-override behavior; "
+        f"fast={fast_history}, slow={slow_history}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 5. Thread isolation under mixed-generation storage
+# ---------------------------------------------------------------------------
+
+
+def test_delta_and_migrated_threads_do_not_cross_contaminate() -> None:
+    """Two threads sharing a checkpointer — one migrated from
+    pre-migration state, one freshly-started under DeltaChannel — must
+    maintain independent state. The parent-chain walk in
+    `_get_channel_writes_history` must be scoped to the target thread.
+    """
+
+    checkpointer = InMemorySaver()
+    migrated_cfg = {"configurable": {"thread_id": "migrated"}}
+    fresh_cfg = {"configurable": {"thread_id": "fresh"}}
+
+    # Thread A: pre-migration build-up.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, migrated_cfg, "m", 2)
+
+    # Thread B: fresh delta-channel run.
+    delta = _delta_graph(checkpointer)
+    _drive(delta, fresh_cfg, "f", 2)
+
+    # Thread A: migrate and confirm its state is anchored in its own
+    # thread's pre-migration history (tag 'm'), never mixing in tag 'f'.
+    migrated_boundaries = _settled_boundaries(
+        list(delta.get_state_history(migrated_cfg))
+    )
+    assert migrated_boundaries, "migrated thread has no settled boundaries"
+    for _, items in migrated_boundaries:
+        for it in items:
+            assert it.startswith("m"), (
+                f"migrated thread leaked item from other thread: {it}"
+            )
+
+    # Thread B: settled boundaries must only contain 'f' tags.
+    fresh_boundaries = _settled_boundaries(list(delta.get_state_history(fresh_cfg)))
+    assert fresh_boundaries, "fresh thread has no settled boundaries"
+    for _, items in fresh_boundaries:
+        for it in items:
+            assert it.startswith("f"), (
+                f"fresh thread leaked item from migrated thread: {it}"
+            )
+
+
+# ---------------------------------------------------------------------------
+# 6. Tip-of-pre-migration hydration: the latest checkpoint from a binop-run
+# thread has a real accumulated value in its own `channel_values["items"]`.
+# When hydrated under the delta-channel graph via `get_state(config)` with no
+# `checkpoint_id`, the short-circuit must use that value directly instead of
+# walking ancestors (which would skip the tip's own blob).
+# ---------------------------------------------------------------------------
+
+
+def test_tip_of_pre_migration_hydrates_directly() -> None:
+    """`graph.get_state(config)` at the latest (pre-migration) checkpoint
+    returns the full accumulated list stored in that checkpoint's own
+    `channel_values`. The hydration must not walk ancestors past it."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-sync"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    binop_tip = binop.get_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"], (
+        f"sanity: pre-migration tip should accumulate all 3 items, got {expected_items}"
+    )
+
+    delta = _delta_graph(checkpointer)
+
+    snap = delta.get_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+async def test_tip_of_pre_migration_hydrates_directly_async() -> None:
+    """Async variant of the tip-of-pre-migration hydration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    binop_tip = await binop.aget_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"]
+
+    delta = _delta_graph(checkpointer)
+
+    snap = await delta.aget_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"async tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 7. `update_state` after migration writes a real value to the new
+# checkpoint's `channel_values` (not a sentinel). Hydration must use it
+# directly — the ancestor walk would skip this blob and return stale state.
+# ---------------------------------------------------------------------------
+
+
+def test_update_state_after_migration_uses_written_value() -> None:
+    """After migrating and running at least one post-migration super-step
+    (so the thread's tip has a `DELTA_SENTINEL`), `update_state` writes a
+    concrete value to a new checkpoint's `channel_values`. `get_state`
+    must reflect that concrete value."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "update-state"}}
+
+    # Pre-migration: accumulate a little state.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Migrate and run one more super-step so the tip is a post-migration
+    # checkpoint with `DELTA_SENTINEL` in its own `channel_values`.
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # `update_state` writes a concrete value into a new checkpoint's blob
+    # via the reducer against the hydrated prior state.
+    delta.update_state(config, {"items": ["x", "y"]})
+
+    snap = delta.get_state(config)
+    updated_items = list(snap.values.get("items", []))
+    # Must include the "x","y" update; without the hydration fix, the
+    # update_state-written blob would be skipped in favor of an ancestor
+    # walk, and the update values would disappear.
+    assert "x" in updated_items and "y" in updated_items, (
+        f"update_state values missing from snapshot: {updated_items}"
+    )
+    # The "x","y" items should be folded onto the prior accumulated state,
+    # not stand alone. This verifies the update-written blob is used
+    # directly by `get_state` (no ancestor walk past it).
+    assert len(updated_items) >= 4, (
+        f"update_state snapshot should preserve pre-update state, got {updated_items}"
+    )
+    assert updated_items[-2:] == ["x", "y"], (
+        f"update_state deltas should be at the tail, got {updated_items}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 8. Fork from an `update_state` checkpoint: a new run branched off the
+# update_state-produced checkpoint must see that checkpoint's concrete
+# `channel_values` as its base, with new deltas folded on top.
+# ---------------------------------------------------------------------------
+
+
+def test_fork_from_update_state_checkpoint() -> None:
+    """Branching a new run from the checkpoint produced by `update_state`
+    must use that checkpoint's concrete blob as the base. Additional
+    deltas from the forked run fold onto it through the reducer."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "fork"}}
+
+    # Pre-migration build-up, then migrate and add one post-migration step.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # Apply `update_state` and capture the returned config (references
+    # the new checkpoint produced by the update).
+    update_cfg = delta.update_state(config, {"items": ["x", "y"]})
+
+    update_snap = delta.get_state(update_cfg)
+    base_items = list(update_snap.values.get("items", []))
+    assert "x" in base_items and "y" in base_items, (
+        f"update_state values missing from snapshot: {base_items}"
+    )
+    assert base_items[-2:] == ["x", "y"], (
+        f"sanity: update_state deltas should be at the tail, got {base_items}"
+    )
+
+    # Fork: invoke from the update_state checkpoint with a new delta.
+    forked = delta.invoke({"items": ["fork0"]}, update_cfg)
+    forked_items = list(forked.get("items", []))
+    # The fork must see the update_state-written blob as its base (not
+    # walk past it), and the new delta must fold on top of it.
+    assert forked_items[: len(base_items)] == base_items, (
+        f"fork lost update_state base: base={base_items}, forked={forked_items}"
+    )
+    assert forked_items[-1] == "fork0", f"fork delta not appended: {forked_items}"

libs/langgraph/tests/test_graph_callbacks.py

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

libs/langgraph/tests/test_large_cases.py

@@ -1396,7 +1396,6 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -1459,7 +1458,6 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -1512,7 +1510,6 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -6884,7 +6881,6 @@ def test_weather_subgraph(
                     "langgraph_path": ("__pregel_pull", "router_node"),
                     "langgraph_checkpoint_ns": AnyStr("router_node:"),
                     "checkpoint_ns": AnyStr("router_node:"),
-                    "_type": "fake-messages-list-chat-model",
                     "ls_provider": "fakemessageslistchatmodel",
                     "ls_model_type": "chat",
                     "ls_integration": "langchain_chat_model",
@@ -6912,7 +6908,6 @@ def test_weather_subgraph(
                     "langgraph_path": ("__pregel_pull", "model_node"),
                     "langgraph_checkpoint_ns": AnyStr("weather_graph:"),
                     "checkpoint_ns": AnyStr("weather_graph:"),
-                    "_type": "fake-messages-list-chat-model",
                     "ls_provider": "fakemessageslistchatmodel",
                     "ls_model_type": "chat",
                     "ls_integration": "langchain_chat_model",
@@ -6949,7 +6944,6 @@ def test_weather_subgraph(
                 "langgraph_path": ("__pregel_pull", "router_node"),
                 "langgraph_checkpoint_ns": AnyStr("router_node:"),
                 "checkpoint_ns": AnyStr("router_node:"),
-                "_type": "fake-messages-list-chat-model",
                 "ls_provider": "fakemessageslistchatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",

libs/langgraph/tests/test_large_cases_async.py

@@ -1147,7 +1147,6 @@ async def test_prebuilt_tool_chat() -> None:
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -1210,7 +1209,6 @@ async def test_prebuilt_tool_chat() -> None:
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -1263,7 +1261,6 @@ async def test_prebuilt_tool_chat() -> None:
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -3981,7 +3978,6 @@ async def test_weather_subgraph(
                     "langgraph_path": ("__pregel_pull", "router_node"),
                     "langgraph_checkpoint_ns": AnyStr("router_node:"),
                     "checkpoint_ns": AnyStr("router_node:"),
-                    "_type": "fake-messages-list-chat-model",
                     "ls_provider": "fakemessageslistchatmodel",
                     "ls_model_type": "chat",
                     "ls_integration": "langchain_chat_model",
@@ -4009,7 +4005,6 @@ async def test_weather_subgraph(
                     "langgraph_path": ("__pregel_pull", "model_node"),
                     "langgraph_checkpoint_ns": AnyStr("weather_graph:"),
                     "checkpoint_ns": AnyStr("weather_graph:"),
-                    "_type": "fake-messages-list-chat-model",
                     "ls_provider": "fakemessageslistchatmodel",
                     "ls_model_type": "chat",
                     "ls_integration": "langchain_chat_model",
@@ -4046,7 +4041,6 @@ async def test_weather_subgraph(
                 "langgraph_path": ("__pregel_pull", "router_node"),
                 "langgraph_checkpoint_ns": AnyStr("router_node:"),
                 "checkpoint_ns": AnyStr("router_node:"),
-                "_type": "fake-messages-list-chat-model",
                 "ls_provider": "fakemessageslistchatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",

libs/langgraph/tests/test_pregel.py

@@ -79,6 +79,14 @@ from tests.messages import (
     _AnyIdToolMessage,
 )
 
+import math as _math_compat
+from langgraph.channels.aggregate import AggregateChannel as _AggregateChannel_compat
+
+
+def DeltaChannel(op):
+    return _AggregateChannel_compat(op, snapshot_frequency=_math_compat.inf)
+
+
 pytestmark = pytest.mark.anyio
 
 logger = logging.getLogger(__name__)
@@ -615,8 +623,11 @@ def test_run_from_checkpoint_id_retains_previous_writes(
         )
     ]
 
-    assert len(new_history) == len(history) + 1
-    for original, new in zip(history, new_history[1:]):
+    # +2: one fork checkpoint from time travel, one from the new execution
+    assert len(new_history) == len(history) + 2
+    # new_history[0] is the new execution result, new_history[1] is the fork
+    assert new_history[1].metadata["source"] == "fork"
+    for original, new in zip(history, new_history[2:]):
         assert original.values == new.values
         assert original.next == new.next
         assert original.metadata["step"] == new.metadata["step"]
@@ -624,7 +635,7 @@ def test_run_from_checkpoint_id_retains_previous_writes(
     def _get_tasks(hist: list, start: int):
         return [h.tasks for h in hist[start:]]
 
-    assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
+    assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
 
 
 def test_batch_two_processes_in_out() -> None:
@@ -6893,7 +6904,6 @@ def test_tags_stream_mode_messages() -> None:
                 "langgraph_path": ("__pregel_pull", "call_model"),
                 "langgraph_checkpoint_ns": AnyStr("call_model:"),
                 "checkpoint_ns": AnyStr("call_model:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "genericfakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -6903,6 +6913,60 @@ def test_tags_stream_mode_messages() -> None:
     ]
 
 
+def test_configurable_propagates_to_stream_metadata() -> None:
+    """Regression: thread_id, run_id, assistant_id, graph_id,
+    and langgraph_auth_user_id from configurable must appear
+    in stream_mode='messages' metadata."""
+
+    def my_node(state):
+        return {"messages": HumanMessage(content="hello")}
+
+    graph = (
+        StateGraph(MessagesState)
+        .add_node("my_node", my_node)
+        .add_edge(START, "my_node")
+        .compile()
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "th-123",
+            "checkpoint_id": "ckpt-1",
+            "checkpoint_ns": "ns-1",
+            "task_id": "task-1",
+            "run_id": "run-456",
+            "assistant_id": "asst-789",
+            "graph_id": "graph-0",
+            "model": "gpt-4o",
+            "user_id": "uid-1",
+            "cron_id": "cron-1",
+            "langgraph_auth_user_id": "user-1",
+            # these should NOT be propagated into metadata
+            "some_api_key": "secret",
+            "custom_setting": {"nested": True},
+        },
+    }
+    results = list(graph.stream({"messages": []}, config, stream_mode="messages"))
+    assert len(results) == 1
+    _, metadata = results[0]
+    # propagated keys
+    assert metadata["thread_id"] == "th-123"
+    assert metadata["checkpoint_id"] == "ckpt-1"
+    assert metadata["checkpoint_ns"] == "ns-1"
+    assert metadata["task_id"] == "task-1"
+    assert metadata["run_id"] == "run-456"
+    assert metadata["assistant_id"] == "asst-789"
+    assert metadata["graph_id"] == "graph-0"
+    # These are only present in trace metadata by default as of langgraph 1.2
+    # assert metadata["model"] == "gpt-4o"
+    # assert metadata["user_id"] == "uid-1"
+    # assert metadata["cron_id"] == "cron-1"
+    # assert metadata["langgraph_auth_user_id"] == "user-1"
+    # non-allowlisted keys must not appear
+    assert "some_api_key" not in metadata
+    assert "custom_setting" not in metadata
+
+
 def test_stream_mode_messages_command() -> None:
     from langchain_core.messages import HumanMessage
 
@@ -9344,3 +9408,186 @@ 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.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.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.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.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"

libs/langgraph/tests/test_pregel_async.py

@@ -20,6 +20,7 @@ from uuid import UUID
 
 import pytest
 from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.messages import HumanMessage
 from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnablePassthrough
 from langchain_core.utils.aiter import aclosing
 from langgraph.cache.base import BaseCache
@@ -2085,8 +2086,11 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
         )
     ]
 
-    assert len(new_history) == len(history) + 1
-    for original, new in zip(history, new_history[1:]):
+    # +2: one fork checkpoint from time travel, one from the new execution
+    assert len(new_history) == len(history) + 2
+    # new_history[0] is the new execution result, new_history[1] is the fork
+    assert new_history[1].metadata["source"] == "fork"
+    for original, new in zip(history, new_history[2:]):
         assert original.values == new.values
         assert original.next == new.next
         assert original.metadata["step"] == new.metadata["step"]
@@ -2094,7 +2098,7 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
     def _get_tasks(hist: list, start: int):
         return [h.tasks for h in hist[start:]]
 
-    assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
+    assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
 
 
 async def test_cond_edge_after_send() -> None:
@@ -7541,7 +7545,6 @@ async def test_tags_stream_mode_messages() -> None:
                 "langgraph_path": ("__pregel_pull", "call_model"),
                 "langgraph_checkpoint_ns": AnyStr("call_model:"),
                 "checkpoint_ns": AnyStr("call_model:"),
-                "_type": "generic-fake-chat-model",
                 "ls_provider": "genericfakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -7551,6 +7554,67 @@ async def test_tags_stream_mode_messages() -> None:
     ]
 
 
+async def test_configurable_propagates_to_stream_metadata() -> None:
+    """Regression: thread_id, run_id, assistant_id, graph_id,
+    and langgraph_auth_user_id from configurable must appear
+    in stream_mode='messages' metadata."""
+
+    def my_node(state):
+        return {"messages": HumanMessage(content="hello")}
+
+    graph = (
+        StateGraph(MessagesState)
+        .add_node("my_node", my_node)
+        .add_edge(START, "my_node")
+        .compile()
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "th-123",
+            "checkpoint_id": "ckpt-1",
+            "checkpoint_ns": "ns-1",
+            "task_id": "task-1",
+            "run_id": "run-456",
+            "assistant_id": "asst-789",
+            "graph_id": "graph-0",
+            "model": "gpt-4o",
+            "user_id": "uid-1",
+            "cron_id": "cron-1",
+            "langgraph_auth_user_id": "user-1",
+            # these should NOT be propagated into metadata
+            "some_api_key": "secret",
+            "custom_setting": {"nested": True},
+        },
+    }
+    results = [
+        chunk
+        async for chunk in graph.astream(
+            {"messages": []}, config, stream_mode="messages"
+        )
+    ]
+    assert len(results) == 1
+    _, metadata = results[0]
+    # propagated keys
+    assert metadata["thread_id"] == "th-123"
+    assert metadata["checkpoint_id"] == "ckpt-1"
+    assert metadata["checkpoint_ns"] == "ns-1"
+    assert metadata["task_id"] == "task-1"
+    assert metadata["run_id"] == "run-456"
+    assert metadata["assistant_id"] == "asst-789"
+    assert metadata["graph_id"] == "graph-0"
+
+    # These will only be traced as of langgraph 1.2 and not present by default in
+    # metadata
+    # assert metadata["model"] == "gpt-4o"
+    # assert metadata["user_id"] == "uid-1"
+    # assert metadata["cron_id"] == "cron-1"
+    # assert metadata["langgraph_auth_user_id"] == "user-1"
+    # non-allowlisted keys must not appear
+    assert "some_api_key" not in metadata
+    assert "custom_setting" not in metadata
+
+
 async def test_stream_mode_messages_command() -> None:
     from langchain_core.messages import HumanMessage
 

libs/langgraph/tests/test_runtime.py

@@ -501,13 +501,13 @@ async def test_execution_info_populated_in_graph_async() -> None:
     assert isinstance(info.node_first_attempt_time, float)
 
 
-def test_server_info_from_metadata() -> None:
-    """server_info is built from assistant_id/graph_id in config metadata."""
+def test_server_info_from_configurable() -> None:
+    """server_info is built from assistant_id/graph_id in config configurable."""
     captured: dict[str, Any] = {}
     compiled = _make_capture_graph(captured)
     compiled.invoke(
         {"message": "hi"},
-        config={"metadata": {"assistant_id": "asst-abc", "graph_id": "my-graph"}},
+        config={"configurable": {"assistant_id": "asst-abc", "graph_id": "my-graph"}},
     )
     si = captured["server_info"]
     assert si is not None
@@ -516,8 +516,8 @@ def test_server_info_from_metadata() -> None:
     assert si.user is None
 
 
-def test_server_info_none_without_metadata() -> None:
-    """server_info is None when no assistant_id/graph_id in metadata."""
+def test_server_info_none_without_configurable() -> None:
+    """server_info is None when no assistant_id/graph_id in configurable."""
     captured: dict[str, Any] = {}
     compiled = _make_capture_graph(captured)
     compiled.invoke({"message": "hi"})
@@ -579,8 +579,11 @@ def test_server_info_user_from_auth_user() -> None:
     compiled.invoke(
         {"message": "hi"},
         config={
-            "configurable": {"langgraph_auth_user": proxy},
-            "metadata": {"assistant_id": "asst-proxy", "graph_id": "graph-proxy"},
+            "configurable": {
+                "langgraph_auth_user": proxy,
+                "assistant_id": "asst-proxy",
+                "graph_id": "graph-proxy",
+            },
         },
     )
     si = captured["server_info"]

libs/langgraph/tests/test_time_travel.py

@@ -37,6 +37,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
     Returns a list of dicts (newest-first, matching get_state_history order) with:
       - id: short checkpoint id suffix (last 6 chars)
       - parent_id: short parent checkpoint id suffix or None
+      - source: checkpoint metadata source (input, loop, fork, update)
       - next: tuple of next node names
       - values: channel values snapshot
     """
@@ -52,6 +53,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
             {
                 "id": cid[-6:],
                 "parent_id": pid[-6:] if pid else None,
+                "source": s.metadata.get("source"),
                 "next": s.next,
                 "values": s.values,
             }
@@ -280,6 +282,116 @@ def test_replay_from_before_interrupt_refires(
     assert call_count["node_b"] == 1  # NOT re-executed (after interrupt)
 
 
+def test_replay_from_before_interrupt_then_resume(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Replay from checkpoint before interrupt node, then resume with a new
+    answer and verify the graph completes with the new value.
+
+    Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
+
+    Original run:
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(node_a,)     values=[]
+      source=loop   next=(ask_human,)  values=[a]            <-- replay from here
+      source=loop   next=(node_b,)     values=[a, human:old_answer]
+      source=loop   next=()            values=[a, human:old_answer, b]
+
+    After replay (fork created) + resume with "new_answer":
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(node_a,)     values=[]
+      source=loop   next=(ask_human,)  values=[a]            <-- branch point
+      source=loop   next=(node_b,)     values=[a, human:old_answer]
+      source=loop   next=()            values=[a, human:old_answer, b]  (old branch)
+      source=fork   next=(ask_human,)  values=[a]            <-- fork from branch point
+      source=loop   next=(node_b,)     values=[a, human:new_answer]
+      source=loop   next=()            values=[a, human:new_answer, b]  (new branch)
+    """
+
+    called: list[str] = []
+
+    def node_a(state: State) -> State:
+        called.append("node_a")
+        return {"value": ["a"]}
+
+    def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("What is your input?")
+        return {"value": [f"human:{answer}"]}
+
+    def node_b(state: State) -> State:
+        called.append("node_b")
+        return {"value": ["b"]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("ask_human", ask_human)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "ask_human")
+        .add_edge("ask_human", "node_b")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: invoke until interrupt, then resume to complete ---
+    graph.invoke({"value": []}, config)
+    graph.invoke(Command(resume="old_answer"), config)
+
+    original_history = list(graph.get_state_history(config))
+    original = _checkpoint_summary(original_history)
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Replay from checkpoint before ask_human ---
+    before_ask = next(s for s in original_history if s.next == ("ask_human",))
+
+    called.clear()
+    replay_result = graph.invoke(None, before_ask.config)
+    assert replay_result["__interrupt__"][0].value == "What is your input?"
+    assert "ask_human" in called
+    assert "node_a" not in called  # before the replay point, not re-executed
+
+    # A fork checkpoint is now the latest — it branches from the replay point
+    post_replay = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"]) for s in post_replay] == [
+        ("fork", ("ask_human",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("node_b",)),
+        ("loop", ("ask_human",)),  # branch point
+        ("loop", ("node_a",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer ---
+    called.clear()
+    final_result = graph.invoke(Command(resume="new_answer"), config)
+    assert final_result["value"] == ["a", "human:new_answer", "b"]
+    assert "ask_human" in called
+    assert "node_b" in called
+
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from fork)
+        ("loop", (), {"value": ["a", "human:new_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
+        ("fork", ("ask_human",), {"value": ["a"]}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
 def test_replay_interrupt_stable_across_replays(
     sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -320,8 +432,14 @@ def test_replay_interrupt_stable_across_replays(
         r = graph.invoke(None, before_ask.config)
         results.append(r)
 
-    assert all(r == results[0] for r in results)
-    assert "__interrupt__" in results[0]
+    # Each replay creates a fork with a unique interrupt ID, so we compare
+    # interrupt values and state values rather than full equality.
+    assert all("__interrupt__" in r for r in results)
+    assert all(
+        r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
+        for r in results
+    )
+    assert all(r["value"] == results[0]["value"] for r in results)
 
 
 def test_fork_from_before_interrupt_refires(
@@ -854,6 +972,354 @@ def test_subgraph_interrupt_replay_from_interrupt_checkpoint(
     assert "step_b" not in called
 
 
+def test_subgraph_interrupt_replay_from_parent_then_resume(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Replay from the parent checkpoint where a subgraph interrupt fired,
+    then resume with a new answer. Verifies that a fork is created and the
+    full graph completes. Checks full checkpoint history at each stage."""
+
+    called: list[str] = []
+
+    def router(state: State) -> State:
+        called.append("router")
+        return {"value": ["routed"]}
+
+    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)
+    )
+
+    def post_process(state: State) -> State:
+        called.append("post_process")
+        return {"value": ["post"]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("router", router)
+        .add_node("subgraph_node", subgraph)
+        .add_node("post_process", post_process)
+        .add_edge(START, "router")
+        .add_edge("router", "subgraph_node")
+        .add_edge("subgraph_node", "post_process")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt, then resume to complete
+    graph.invoke({"value": []}, config)
+    graph.invoke(Command(resume="old_answer"), config)
+
+    # Original parent history (newest first)
+    original_history = list(graph.get_state_history(config))
+    assert [s.next for s in original_history] == [
+        (),  # done
+        ("post_process",),
+        ("subgraph_node",),  # subgraph ran, interrupt fired here
+        ("router",),
+        ("__start__",),
+    ]
+
+    # Find the parent checkpoint where the interrupt fired
+    interrupt_checkpoint = next(
+        s for s in original_history if s.next == ("subgraph_node",)
+    )
+
+    # Replay from parent checkpoint — subgraph re-executes, interrupt re-fires
+    called.clear()
+    replay_result = graph.invoke(None, interrupt_checkpoint.config)
+    assert "__interrupt__" in replay_result
+    assert replay_result["__interrupt__"][0].value == "Provide input:"
+    assert "step_a" in called
+    assert "ask_human" in called
+    assert "step_b" not in called
+
+    # Verify fork checkpoint was created
+    post_replay_history = list(graph.get_state_history(config))
+    assert [s.next for s in post_replay_history] == [
+        ("subgraph_node",),  # fork (interrupt pending)
+        (),  # original done
+        ("post_process",),
+        ("subgraph_node",),
+        ("router",),
+        ("__start__",),
+    ]
+    assert [s.metadata["source"] for s in post_replay_history] == [
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+    fork = post_replay_history[0]
+    assert (
+        fork.parent_config["configurable"]["checkpoint_id"]
+        == interrupt_checkpoint.config["configurable"]["checkpoint_id"]
+    )
+
+    # Resume with a new answer — full graph should complete
+    called.clear()
+    final_result = graph.invoke(Command(resume="new_answer"), config)
+    assert "__interrupt__" not in final_result
+    assert "human:new_answer" in final_result["value"]
+    assert "sub_b" in final_result["value"]
+    assert "post" in final_result["value"]
+    assert "ask_human" in called
+    assert "step_b" in called
+    assert "post_process" in called
+
+    # Final checkpoint history
+    final_history = list(graph.get_state_history(config))
+    assert [s.next for s in final_history] == [
+        (),  # new branch done
+        ("post_process",),  # new branch post_process
+        ("subgraph_node",),  # fork
+        (),  # original done
+        ("post_process",),
+        ("subgraph_node",),
+        ("router",),
+        ("__start__",),
+    ]
+    assert [s.metadata["source"] for s in final_history] == [
+        "loop",
+        "loop",
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+
+
+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:
+    """Two parent invocations, then replay from before the subgraph in the
+    2nd invocation. The subgraph (checkpointer=True) should load its
+    accumulated state from the 1st invocation via ReplayState, re-fire
+    the interrupt, and then resume + complete.
+
+    This tests the ReplayState path: the parent is replaying and the
+    subgraph uses list(before=parent_checkpoint_id) to find its
+    corresponding checkpoint from the original execution.
+    """
+
+    class SubState(TypedDict):
+        value: Annotated[list[str], operator.add]
+
+    class ParentState(TypedDict):
+        results: Annotated[list[str], operator.add]
+
+    started_state: list[dict] = []
+
+    def step_a(state: SubState) -> SubState:
+        started_state.append(dict(state))
+        answer = interrupt("question_a")
+        return {"value": [f"a:{answer}"]}
+
+    subgraph = (
+        StateGraph(SubState)
+        .add_node("step_a", step_a)
+        .add_edge(START, "step_a")
+        .compile(checkpointer=True)
+    )
+
+    def parent_node(state: ParentState) -> ParentState:
+        return {"results": ["p"]}
+
+    graph = (
+        StateGraph(ParentState)
+        .add_node("parent_node", parent_node)
+        .add_node("sub_node", subgraph)
+        .add_edge(START, "parent_node")
+        .add_edge("parent_node", "sub_node")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # === 1st invocation: complete with answer "a1" ===
+    graph.invoke({"results": []}, config)
+    graph.invoke(Command(resume="a1"), config)
+
+    # step_a saw empty state (fresh subgraph)
+    assert started_state[0] == {"value": []}
+
+    # === 2nd invocation: complete with answer "a2" ===
+    started_state.clear()
+    graph.invoke({"results": []}, config)
+    graph.invoke(Command(resume="a2"), config)
+
+    # Stateful subgraph retained state from 1st invocation
+    assert started_state[0] == {"value": ["a:a1"]}
+
+    # Original history (newest first)
+    original_history = list(graph.get_state_history(config))
+    assert [s.next for s in original_history] == [
+        (),  # 2nd done
+        ("sub_node",),  # 2nd sub_node
+        ("parent_node",),  # 2nd parent_node
+        ("__start__",),  # 2nd input
+        (),  # 1st done
+        ("sub_node",),  # 1st sub_node
+        ("parent_node",),  # 1st parent_node
+        ("__start__",),  # 1st input
+    ]
+
+    # Replay from before sub_node in 2nd invocation (newest match)
+    before_sub_2nd = [s for s in original_history if s.next == ("sub_node",)][0]
+    started_state.clear()
+    replay = graph.invoke(None, before_sub_2nd.config)
+    assert "__interrupt__" in replay
+
+    # Subgraph should see accumulated state from END of 1st invocation
+    assert started_state[0] == {"value": ["a:a1"]}
+
+    # Verify fork was created
+    post_replay_history = list(graph.get_state_history(config))
+    assert [s.next for s in post_replay_history] == [
+        ("sub_node",),  # fork (interrupt pending)
+        (),  # 2nd done
+        ("sub_node",),  # 2nd sub_node
+        ("parent_node",),  # 2nd parent_node
+        ("__start__",),  # 2nd input
+        (),  # 1st done
+        ("sub_node",),  # 1st sub_node
+        ("parent_node",),  # 1st parent_node
+        ("__start__",),  # 1st input
+    ]
+    assert [s.metadata["source"] for s in post_replay_history] == [
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+
+    # Resume with a new answer
+    started_state.clear()
+    final = graph.invoke(Command(resume="a3"), config)
+    assert "__interrupt__" not in final
+    assert final["results"] == ["p", "p"]
+
+    # Final history
+    final_history = list(graph.get_state_history(config))
+    assert [s.next for s in final_history] == [
+        (),  # new branch done
+        ("sub_node",),  # fork
+        (),  # 2nd done
+        ("sub_node",),  # 2nd sub_node
+        ("parent_node",),  # 2nd parent_node
+        ("__start__",),  # 2nd input
+        (),  # 1st done
+        ("sub_node",),  # 1st sub_node
+        ("parent_node",),  # 1st parent_node
+        ("__start__",),  # 1st input
+    ]
+    assert [s.metadata["source"] for s in final_history] == [
+        "loop",
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+
+
 def test_subgraph_interrupt_full_flow(
     sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -1290,6 +1756,321 @@ def test_subgraph_time_travel_to_second_interrupt(
     assert "ask_1" not in called
 
 
+def test_subgraph_time_travel_resume_from_first_interrupt(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the first interrupt, then
+    resume through both interrupts with new answers.
+
+    This verifies the key bug fix: after time-traveling to a subgraph
+    checkpoint with an interrupt, a fork checkpoint is created so that
+    subsequent resumes find the correct state (not the old branch tip).
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+
+    Parent history after original run completes:
+      source=input  next=(__start__,)   values=[]
+      source=loop   next=(executor,)    values=[]
+      source=loop   next=()             values=[step_a_done, ask_1:answer_1, ask_2:answer_2]
+
+    After time-traveling to 1st interrupt + resuming with new answers:
+      source=input  next=(__start__,)   values=[]
+      source=loop   next=(executor,)    values=[]          <-- branch point
+      source=loop   next=()             values=[..., ask_2:answer_2]  (old branch)
+      source=fork   next=(executor,)    values=[]          <-- fork from time travel
+      source=loop   next=()             values=[..., ask_2:new_answer_2]  (new branch)
+    """
+
+    called: list[str] = []
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    graph.invoke({"value": []}, config)
+    sub_config_at_first = graph.get_state(config, subgraphs=True).tasks[0].state.config
+    graph.invoke(Command(resume="answer_1"), config)
+    graph.invoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to first interrupt's subgraph checkpoint ---
+    called.clear()
+    replay_result = graph.invoke(None, sub_config_at_first)
+    assert replay_result["__interrupt__"][0].value == "Question 1?"
+    assert "step_a" not in called  # before interrupt, not re-executed
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume both interrupts with new answers ---
+    called.clear()
+    resume_1 = graph.invoke(Command(resume="new_answer_1"), config)
+    assert resume_1["__interrupt__"][0].value == "Question 2?"
+    assert "ask_1" in called
+
+    called.clear()
+    resume_2 = graph.invoke(Command(resume="new_answer_2"), config)
+    assert resume_2["value"] == [
+        "step_a_done",
+        "ask_1:new_answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history: original branch preserved, new branch appended
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+def test_subgraph_time_travel_resume_from_second_interrupt(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the second interrupt, then
+    resume with a new answer. The first interrupt's answer should be preserved.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+
+    Key assertion: after resuming from a time-travel to the 2nd interrupt,
+    the final state keeps ask_1's original answer but uses the new ask_2 answer.
+    """
+
+    called: list[str] = []
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    graph.invoke({"value": []}, config)
+    graph.invoke(Command(resume="answer_1"), config)
+    sub_config_at_second = graph.get_state(config, subgraphs=True).tasks[0].state.config
+    graph.invoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to second interrupt ---
+    called.clear()
+    replay_result = graph.invoke(None, sub_config_at_second)
+    assert replay_result["__interrupt__"][0].value == "Question 2?"
+    assert "step_a" not in called
+    assert "ask_1" not in called  # already resolved, not re-executed
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer for ask_2 only ---
+    called.clear()
+    resume_result = graph.invoke(Command(resume="new_answer_2"), config)
+    # ask_1's original answer preserved, ask_2 uses the new answer
+    assert resume_result["value"] == [
+        "step_a_done",
+        "ask_1:answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history: original branch preserved, new branch appended
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+def test_subgraph_time_travel_checkpoint_pattern(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Verify the checkpoint pattern created by time travel to a subgraph
+    interrupt. A fork checkpoint should branch from the replay point and
+    become the latest parent checkpoint.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> ask (interrupt) --> END
+
+    Original run (after completing):
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(executor,)   values=[]          <-- replay point
+      source=loop   next=()            values=[a:first]
+
+    After time travel to interrupt + resume with "second":
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(executor,)   values=[]          <-- branch point
+      source=loop   next=()            values=[a:first]   (old branch)
+      source=fork   next=(executor,)   values=[]          <-- fork
+      source=loop   next=()            values=[a:second]  (new branch)
+    """
+
+    def ask(state: State) -> State:
+        answer = interrupt("Q?")
+        return {"value": [f"a:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("ask", ask)
+        .add_edge(START, "ask")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt, then complete
+    graph.invoke({"value": []}, config)
+    sub_config = graph.get_state(config, subgraphs=True).tasks[0].state.config
+    graph.invoke(Command(resume="first"), config)
+
+    original = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # Time travel to the interrupt
+    graph.invoke(None, sub_config)
+
+    # Fork is now the latest, branching from the original replay point
+    post_tt = list(graph.get_state_history(config))
+    post_tt_summary = _checkpoint_summary(post_tt)
+    assert [(s["source"], s["next"]) for s in post_tt_summary] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),
+        ("loop", ("executor",)),  # <-- replay point / fork parent
+        ("input", ("__start__",)),
+    ]
+    # Verify the fork's parent is the original replay point
+    replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
+    assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
+
+    # Resume from the fork — graph completes with new answer
+    result = graph.invoke(Command(resume="second"), config)
+    assert result["value"] == ["a:second"]
+
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch
+        ("loop", (), {"value": ["a:second"]}),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
 def test_subgraph_time_travel_after_completion(
     sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -2283,14 +3064,16 @@ def test_replay_creates_branch_preserving_old_checkpoints(
     # -- Post-replay checkpoint history (newest first) --
     post_replay_history = list(graph.get_state_history(config))
     post_summary = _checkpoint_summary(post_replay_history)
-    assert len(post_summary) == 7  # 5 original + 2 new branch checkpoints
+    # 5 original + 1 fork + 2 new branch checkpoints = 8
+    assert len(post_summary) == 8
 
     # Verify the full shape after replay
     assert [s["next"] for s in post_summary] == [
-        (),  # new branch tip (C6)
-        ("node_c",),  # new branch (C5)
-        (),  # old branch tip (C4)
-        ("node_c",),  # old (C3)
+        (),  # new branch tip
+        ("node_c",),  # new branch
+        ("node_b",),  # fork from replay point
+        (),  # old branch tip
+        ("node_c",),  # old
         ("node_b",),  # branch point (C2)
         ("node_a",),  # old (C1)
         ("__start__",),  # old (C0)
@@ -2298,6 +3081,7 @@ def test_replay_creates_branch_preserving_old_checkpoints(
     assert [s["values"] for s in post_summary] == [
         {"value": ["a", "b2", "c"]},  # new branch tip
         {"value": ["a", "b2"]},  # new: node_b re-ran with call_count=2
+        {"value": ["a"]},  # fork from replay point
         {"value": ["a", "b1", "c"]},  # old branch tip preserved
         {"value": ["a", "b1"]},  # old
         {"value": ["a"]},  # branch point

libs/langgraph/tests/test_time_travel_async.py

@@ -46,6 +46,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
     Returns a list of dicts (newest-first, matching get_state_history order) with:
       - id: short checkpoint id suffix (last 6 chars)
       - parent_id: short parent checkpoint id suffix or None
+      - source: checkpoint metadata source (input, loop, fork, update)
       - next: tuple of next node names
       - values: channel values snapshot
     """
@@ -61,6 +62,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
             {
                 "id": cid[-6:],
                 "parent_id": pid[-6:] if pid else None,
+                "source": s.metadata.get("source"),
                 "next": s.next,
                 "values": s.values,
             }
@@ -335,8 +337,14 @@ async def test_replay_interrupt_stable_across_replays(
         r = await graph.ainvoke(None, before_ask.config)
         results.append(r)
 
-    assert all(r == results[0] for r in results)
-    assert "__interrupt__" in results[0]
+    # Each replay creates a fork with a unique interrupt ID, so we compare
+    # interrupt values and state values rather than full equality.
+    assert all("__interrupt__" in r for r in results)
+    assert all(
+        r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
+        for r in results
+    )
+    assert all(r["value"] == results[0]["value"] for r in results)
 
 
 @NEEDS_CONTEXTVARS
@@ -1261,6 +1269,391 @@ async def test_subgraph_time_travel_after_completion_async(
     assert "ask_2:answer_2" in replay_result["value"]
 
 
+@NEEDS_CONTEXTVARS
+async def test_replay_from_before_interrupt_then_resume_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Replay from checkpoint before interrupt node, then resume with a new
+    answer and verify the graph completes with the new value.
+
+    Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
+    """
+
+    called: list[str] = []
+
+    async def node_a(state: State) -> State:
+        called.append("node_a")
+        return {"value": ["a"]}
+
+    async def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("What is your input?")
+        return {"value": [f"human:{answer}"]}
+
+    async def node_b(state: State) -> State:
+        called.append("node_b")
+        return {"value": ["b"]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("ask_human", ask_human)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "ask_human")
+        .add_edge("ask_human", "node_b")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: invoke until interrupt, then resume to complete ---
+    await graph.ainvoke({"value": []}, config)
+    await graph.ainvoke(Command(resume="old_answer"), config)
+
+    original_history = [s async for s in graph.aget_state_history(config)]
+    original = _checkpoint_summary(original_history)
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Replay from checkpoint before ask_human ---
+    before_ask = next(s for s in original_history if s.next == ("ask_human",))
+
+    called.clear()
+    replay_result = await graph.ainvoke(None, before_ask.config)
+    assert replay_result["__interrupt__"][0].value == "What is your input?"
+    assert "ask_human" in called
+    assert "node_a" not in called
+
+    # A fork checkpoint is now the latest
+    post_replay = _checkpoint_summary(
+        [s async for s in graph.aget_state_history(config)]
+    )
+    assert [(s["source"], s["next"]) for s in post_replay] == [
+        ("fork", ("ask_human",)),
+        ("loop", ()),
+        ("loop", ("node_b",)),
+        ("loop", ("ask_human",)),
+        ("loop", ("node_a",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer ---
+    called.clear()
+    final_result = await graph.ainvoke(Command(resume="new_answer"), config)
+    assert final_result["value"] == ["a", "human:new_answer", "b"]
+    assert "ask_human" in called
+    assert "node_b" in called
+
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from fork)
+        ("loop", (), {"value": ["a", "human:new_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
+        ("fork", ("ask_human",), {"value": ["a"]}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_subgraph_time_travel_resume_from_first_interrupt_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the first interrupt, then
+    resume through both interrupts with new answers.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+    """
+
+    called: list[str] = []
+
+    async def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    async def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    async def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    await graph.ainvoke({"value": []}, config)
+    sub_config_at_first = (
+        (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
+    )
+    await graph.ainvoke(Command(resume="answer_1"), config)
+    await graph.ainvoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to first interrupt's subgraph checkpoint ---
+    called.clear()
+    replay_result = await graph.ainvoke(None, sub_config_at_first)
+    assert replay_result["__interrupt__"][0].value == "Question 1?"
+    assert "step_a" not in called
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume both interrupts with new answers ---
+    called.clear()
+    resume_1 = await graph.ainvoke(Command(resume="new_answer_1"), config)
+    assert resume_1["__interrupt__"][0].value == "Question 2?"
+    assert "ask_1" in called
+
+    called.clear()
+    resume_2 = await graph.ainvoke(Command(resume="new_answer_2"), config)
+    assert resume_2["value"] == [
+        "step_a_done",
+        "ask_1:new_answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history: original branch preserved, new branch appended
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_subgraph_time_travel_resume_from_second_interrupt_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the second interrupt, then
+    resume with a new answer. The first interrupt's answer should be preserved.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+    """
+
+    called: list[str] = []
+
+    async def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    async def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    async def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    await graph.ainvoke({"value": []}, config)
+    await graph.ainvoke(Command(resume="answer_1"), config)
+    sub_config_at_second = (
+        (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
+    )
+    await graph.ainvoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to second interrupt ---
+    called.clear()
+    replay_result = await graph.ainvoke(None, sub_config_at_second)
+    assert replay_result["__interrupt__"][0].value == "Question 2?"
+    assert "step_a" not in called
+    assert "ask_1" not in called
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer for ask_2 only ---
+    called.clear()
+    resume_result = await graph.ainvoke(Command(resume="new_answer_2"), config)
+    assert resume_result["value"] == [
+        "step_a_done",
+        "ask_1:answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_subgraph_time_travel_checkpoint_pattern_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Verify the checkpoint pattern created by time travel to a subgraph
+    interrupt. A fork checkpoint should branch from the replay point.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> ask (interrupt) --> END
+    """
+
+    async def ask(state: State) -> State:
+        answer = interrupt("Q?")
+        return {"value": [f"a:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("ask", ask)
+        .add_edge(START, "ask")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt, then complete
+    await graph.ainvoke({"value": []}, config)
+    sub_config = (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
+    await graph.ainvoke(Command(resume="first"), config)
+
+    original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # Time travel to the interrupt
+    await graph.ainvoke(None, sub_config)
+
+    # Fork is now the latest, branching from the original replay point
+    post_tt = [s async for s in graph.aget_state_history(config)]
+    post_tt_summary = _checkpoint_summary(post_tt)
+    assert [(s["source"], s["next"]) for s in post_tt_summary] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),
+        ("loop", ("executor",)),  # <-- replay point / fork parent
+        ("input", ("__start__",)),
+    ]
+    # Verify the fork's parent is the original replay point
+    replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
+    assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
+
+    # Resume from the fork
+    result = await graph.ainvoke(Command(resume="second"), config)
+    assert result["value"] == ["a:second"]
+
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch
+        ("loop", (), {"value": ["a:second"]}),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
 @NEEDS_CONTEXTVARS
 async def test_3_levels_deep_time_travel_to_first_interrupt_async(
     async_checkpointer: BaseCheckpointSaver,
@@ -2088,13 +2481,15 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
     # -- Post-replay checkpoint history (newest first) --
     post_replay_history = [s async for s in graph.aget_state_history(config)]
     post_summary = _checkpoint_summary(post_replay_history)
-    assert len(post_summary) == 7  # 5 original + 2 new branch checkpoints
+    # 5 original + 1 fork + 2 new branch checkpoints = 8
+    assert len(post_summary) == 8
 
     assert [s["next"] for s in post_summary] == [
-        (),  # new branch tip (C6)
-        ("node_c",),  # new branch (C5)
-        (),  # old branch tip (C4)
-        ("node_c",),  # old (C3)
+        (),  # new branch tip
+        ("node_c",),  # new branch
+        ("node_b",),  # fork from replay point
+        (),  # old branch tip
+        ("node_c",),  # old
         ("node_b",),  # branch point (C2)
         ("node_a",),  # old (C1)
         ("__start__",),  # old (C0)
@@ -2102,6 +2497,7 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
     assert [s["values"] for s in post_summary] == [
         {"value": ["a", "b2", "c"]},  # new branch tip
         {"value": ["a", "b2"]},  # new: node_b re-ran with call_count=2
+        {"value": ["a"]},  # fork from replay point
         {"value": ["a", "b1", "c"]},  # old branch tip preserved
         {"value": ["a", "b1"]},  # old
         {"value": ["a"]},  # branch point

libs/langgraph/tests/test_utils.py

@@ -11,13 +11,19 @@ from typing import (
     TypeVar,
     Union,
 )
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import langsmith
 import pytest
+from langchain_core.runnables import RunnableConfig
+from langchain_core.tracers import LangChainTracer
 from typing_extensions import NotRequired, Required, TypedDict
 
-from langgraph._internal._config import _is_not_empty, ensure_config
+from langgraph._internal._config import (
+    _is_not_empty,
+    ensure_config,
+    get_callback_manager_for_config,
+)
 from langgraph._internal._fields import (
     _is_optional_type,
     get_enhanced_type_hints,
@@ -298,7 +304,7 @@ def test_is_not_empty() -> None:
     assert not _is_not_empty({})
 
 
-def test_configurable_metadata():
+def test_configurable_metadata() -> None:
     config = {
         "configurable": {
             "a-key": "foo",
@@ -309,11 +315,115 @@ def test_configurable_metadata():
             "andme": 42,
             "nested": {"foo": "bar"},
             "nooverride": -2,
+            "thread_id": "th-123",
+            "checkpoint_id": "ckpt-1",
+            "checkpoint_ns": "ns-1",
+            "task_id": "task-1",
+            "run_id": "run-456",
+            "assistant_id": "asst-789",
+            "graph_id": "graph-0",
+            "model": "gpt-4o",
+            "user_id": "uid-1",
+            "cron_id": "cron-1",
+            "langgraph_auth_user_id": "user-1",
         },
         "metadata": {"nooverride": 18},
     }
-    expected = {"includeme", "andme", "nooverride"}
     merged = ensure_config(config)
     metadata = merged["metadata"]
-    assert metadata.keys() == expected
+    assert set(metadata) == {
+        "nooverride",
+        "assistant_id",
+        "thread_id",
+        "checkpoint_id",
+        "run_id",
+        "graph_id",
+        "checkpoint_ns",
+        "task_id",
+    }
     assert metadata["nooverride"] == 18
+
+
+def test_callback_manager_copies_whitelisted_configurable_ids_to_metadata() -> None:
+    config = {
+        "configurable": {
+            "thread_id": "th-123",
+            "checkpoint_id": "ckpt-1",
+            "checkpoint_ns": "ns-1",
+            "task_id": "task-1",
+            "run_id": "run-456",
+            "assistant_id": "asst-789",
+            "graph_id": "graph-0",
+            "model": "gpt-4o",
+            "user_id": "uid-1",
+            "cron_id": "cron-1",
+            "langgraph_auth_user_id": "user-1",
+        },
+        "metadata": {
+            "thread_id": "from-metadata",
+            "nooverride": 18,
+        },
+    }
+    manager = ensure_config(config)
+    callback_manager = get_callback_manager_for_config(manager)
+    assert callback_manager.metadata == {
+        "thread_id": "from-metadata",
+        "nooverride": 18,
+        "checkpoint_id": "ckpt-1",
+        "checkpoint_ns": "ns-1",
+        "task_id": "task-1",
+        "run_id": "run-456",
+        "assistant_id": "asst-789",
+        "graph_id": "graph-0",
+    }
+
+
+def test_callback_manager_copies_configurable_ids_to_tracing_metadata() -> None:
+    tracer = LangChainTracer(client=MagicMock())
+    config: RunnableConfig = {
+        "configurable": {
+            "thread_id": "th-123",
+            "checkpoint_id": "ckpt-1",
+            "checkpoint_ns": "ns-1",
+            "task_id": "task-1",
+            "run_id": "run-456",
+            "assistant_id": "asst-789",
+            "graph_id": "graph-0",
+            "model": "gpt-4o",
+            "user_id": "uid-1",
+            "cron_id": "cron-1",
+            "langgraph_auth_user_id": "user-1",
+            "includeme": "hi",
+            "andme": 42,
+            "__dontinclude": "bar",
+            "some_api_key": "secret",
+            "custom_setting": {"nested": True},
+        },
+        "metadata": {
+            "thread_id": "from-metadata",
+            "user_id": "from-metadata-user",
+            "includeme": "from-metadata",
+        },
+        "callbacks": [tracer],
+    }
+
+    manager = ensure_config(config)
+    callback_manager = get_callback_manager_for_config(manager)
+    handlers = callback_manager.handlers
+    tracers = [handler for handler in handlers if isinstance(handler, LangChainTracer)]
+    assert len(tracers) == 1
+    tracer = tracers[0]
+    assert tracer.tracing_metadata == {
+        "checkpoint_id": "ckpt-1",
+        "checkpoint_ns": "ns-1",
+        "task_id": "task-1",
+        "run_id": "run-456",
+        "assistant_id": "asst-789",
+        "graph_id": "graph-0",
+        "model": "gpt-4o",
+        "cron_id": "cron-1",
+        "andme": 42,
+        "includeme": "hi",
+        "thread_id": "th-123",
+        "user_id": "uid-1",
+    }

libs/langgraph/uv.lock

@@ -1348,7 +1348,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.2.22"
+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/b1/a3/c4cd6827a1df46c821e7214b7f7b7a28b189e6c9b84ef15c6d629c5e3179/langchain_core-1.2.22.tar.gz", hash = "sha256:8d8f726d03d3652d403da915126626bb6250747e8ba406537d849e68b9f5d058", size = 842487, upload-time = "2026-03-24T18:48:44.9Z" }
+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/c7/a6/2ffacf0f1a3788f250e75d0b52a24896c413be11be3a6d42bcdf46fbea48/langchain_core-1.2.22-py3-none-any.whl", hash = "sha256:7e30d586b75918e828833b9ec1efc25465723566845dd652c277baf751e9c04b", size = 506829, upload-time = "2026-03-24T18:48:43.286Z" },
+    { 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 = "1.1.7a1"
+version = "1.1.9"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1439,7 +1439,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=0.1" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "../prebuilt" },
     { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1548,7 +1548,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -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" },
@@ -1852,7 +1852,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -1862,11 +1862,12 @@ dependencies = [
     { name = "requests" },
     { name = "requests-toolbelt" },
     { name = "uuid-utils" },
+    { name = "xxhash" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]
 
 [package.optional-dependencies]
@@ -2905,7 +2906,7 @@ wheels = [
 
 [[package]]
 name = "pytest"
-version = "9.0.2"
+version = "9.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
@@ -2916,9 +2917,9 @@ dependencies = [
     { name = "pygments" },
     { name = "tomli", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
 ]
 
 [[package]]

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -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,
     )

libs/prebuilt/pyproject.toml

@@ -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"

libs/prebuilt/tests/test_injected_state_not_required.py

@@ -0,0 +1,285 @@
+"""Test InjectedState with NotRequired state fields.
+
+This tests the fix for https://github.com/langchain-ai/langchain/issues/35585
+
+When using InjectedState(<field>) on a tool parameter, and the referenced field is
+declared as NotRequired in the custom state schema, the ToolNode should gracefully
+handle missing fields by injecting None instead of raising KeyError.
+"""
+
+import sys
+from typing import Annotated
+
+import pytest
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage
+from langchain_core.tools import tool
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from typing_extensions import NotRequired
+
+from langgraph.prebuilt import InjectedState, ToolNode, create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+
+from .model import FakeToolCallingModel
+
+
+class CustomAgentStateWithNotRequired(AgentState):
+    """Custom state with a NotRequired field (TypedDict style)."""
+
+    city: NotRequired[str]
+
+
+class CustomAgentStatePydanticWithDefault(BaseModel):
+    """Custom state with Optional field and default (Pydantic style)."""
+
+    messages: Annotated[list[AnyMessage], add_messages]
+    remaining_steps: int = Field(default=10)
+    city: str | None = Field(default=None)
+
+
+@tool
+def get_weather(city: Annotated[str | None, InjectedState("city")] = None) -> str:
+    """Get weather for a given city."""
+    if city is None:
+        return "No city provided"
+    return f"It's always sunny in {city}!"
+
+
+def _create_mock_runtime(
+    state: dict | None = None,
+    store=None,
+):
+    """Create a mock Runtime for testing ToolNode directly."""
+    from unittest.mock import Mock
+
+    from langgraph.runtime import Runtime
+
+    mock_runtime = Mock(spec=Runtime)
+    mock_runtime.context = {}
+    return mock_runtime
+
+
+def _create_config_with_runtime(store=None, state=None):
+    """Create a RunnableConfig with mocked runtime for direct ToolNode testing."""
+    from langgraph.prebuilt.tool_node import ToolRuntime
+
+    tool_runtime = ToolRuntime(
+        state=state or {},
+        config={},
+        context={},
+        store=store,
+        stream_writer=None,
+        tools=[],
+        tool_call_id="test_id",
+    )
+    return {
+        "configurable": {
+            "__pregel_runtime": _create_mock_runtime(),
+            "__tool_runtime__": tool_runtime,
+        }
+    }
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_injected_state_not_required_field_missing_injects_none():
+    """Test that InjectedState with NotRequired field injects None when field is missing.
+
+    This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
+    """
+    tool_node = ToolNode([get_weather])
+
+    tool_call = {
+        "name": "get_weather",
+        "args": {},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+    ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
+
+    # State WITHOUT the "city" field - should inject None instead of raising KeyError
+    state_without_city: CustomAgentStateWithNotRequired = {
+        "messages": [HumanMessage("What's the weather?"), ai_msg],
+    }
+
+    result = tool_node.invoke(
+        state_without_city,
+        config=_create_config_with_runtime(state=state_without_city),
+    )
+
+    assert len(result["messages"]) == 1
+    tool_msg = result["messages"][0]
+    assert isinstance(tool_msg, ToolMessage)
+    assert "No city provided" in tool_msg.content
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_injected_state_not_required_field_present_works():
+    """Test that InjectedState with NotRequired field works when field IS present."""
+    tool_node = ToolNode([get_weather])
+
+    tool_call = {
+        "name": "get_weather",
+        "args": {},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+    ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
+
+    # State WITH the "city" field - this should work
+    state_with_city: CustomAgentStateWithNotRequired = {
+        "messages": [HumanMessage("What's the weather?"), ai_msg],
+        "city": "San Francisco",
+    }
+
+    result = tool_node.invoke(
+        state_with_city,
+        config=_create_config_with_runtime(state=state_with_city),
+    )
+
+    assert len(result["messages"]) == 1
+    tool_msg = result["messages"][0]
+    assert isinstance(tool_msg, ToolMessage)
+    assert "San Francisco" in tool_msg.content
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_create_react_agent_injected_state_not_required_field_missing():
+    """Test create_react_agent with InjectedState using NotRequired field that is missing.
+
+    This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
+    """
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather],
+        state_schema=CustomAgentStateWithNotRequired,
+    )
+
+    # Invoke WITHOUT the city field - should work, injecting None
+    result = agent.invoke(
+        {"messages": [HumanMessage("What's the weather?")]},
+    )
+
+    # Check that the tool was called successfully with None injected
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "No city provided" in tool_messages[0].content
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_create_react_agent_injected_state_not_required_field_present():
+    """Test create_react_agent with InjectedState using NotRequired field that IS present."""
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather],
+        state_schema=CustomAgentStateWithNotRequired,
+    )
+
+    # Invoke WITH the city field
+    result = agent.invoke(
+        {
+            "messages": [HumanMessage("What's the weather?")],
+            "city": "San Francisco",
+        },
+    )
+
+    # Check that the tool was called successfully
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "San Francisco" in tool_messages[0].content
+
+
+@tool
+def get_weather_optional(city: Annotated[str | None, InjectedState("city")]) -> str:
+    """Get weather for a given city (accepts None)."""
+    if city is None:
+        return "Please provide a city!"
+    return f"It's always sunny in {city}!"
+
+
+def test_pydantic_state_with_default_field_missing_works():
+    """Test that Pydantic state with Optional field and default=None works when field is missing.
+
+    This is the workaround suggested in the issue comments - using Pydantic BaseModel
+    with `city: Optional[str] = Field(default=None)` instead of TypedDict with NotRequired.
+    """
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather_optional],
+        state_schema=CustomAgentStatePydanticWithDefault,
+    )
+
+    # Invoke WITHOUT the city field - should work because Pydantic provides default
+    result = agent.invoke(
+        {"messages": [HumanMessage("What's the weather?")]},
+    )
+
+    # Check that the tool was called successfully with None
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "Please provide a city!" in tool_messages[0].content
+
+
+def test_pydantic_state_with_default_field_present_works():
+    """Test that Pydantic state with Optional field works when field IS present."""
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather_optional],
+        state_schema=CustomAgentStatePydanticWithDefault,
+    )
+
+    # Invoke WITH the city field
+    result = agent.invoke(
+        {
+            "messages": [HumanMessage("What's the weather?")],
+            "city": "San Francisco",
+        },
+    )
+
+    # Check that the tool was called successfully
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "San Francisco" in tool_messages[0].content

libs/prebuilt/tests/test_tool_node.py

@@ -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 ---

libs/prebuilt/uv.lock

@@ -249,7 +249,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.2.25"
+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/86/2a/d65de24fc9b7989137253da8973f850f3e39b4ce3e0377bc8200d6b3c189/langchain_core-1.2.25.tar.gz", hash = "sha256:77e032b96509d0eb1f6875042fdf97b7e2334a815314700c6894d9d078909b9c", size = 842347, upload-time = "2026-04-02T22:39:11.528Z" }
+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/3d/0e/7b31b0249f9b9b0fc7829d5b0ee484b8f8d43c78e376e9951e2ef3eac70c/langchain_core-1.2.25-py3-none-any.whl", hash = "sha256:0c05bf395aec6d2dfa14488fd006f7bcd0540e7e89287e04f92203532a82c828", size = 506866, upload-time = "2026-04-02T22:39:10.137Z" },
+    { 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 = "1.1.7a1"
+version = "1.1.9"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -281,7 +281,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=0.1" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "." },
     { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -352,7 +352,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -490,7 +490,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -619,7 +619,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -629,11 +629,12 @@ dependencies = [
     { name = "requests" },
     { name = "requests-toolbelt" },
     { name = "uuid-utils" },
+    { name = "xxhash" },
     { name = "zstandard" },
 ]
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notes/delta_channel_query_bench.md

@@ -0,0 +1,137 @@
+# Delta-channel reconstruction: query strategy benchmark
+
+**Branch:** `delta-channel-writes-based`
+**Question (Nuno):** Is the recursive CTE the right query shape for reconstructing a delta channel inside `get_tuple`, or would a plain `SELECT WHERE` be cheaper even though it returns more rows?
+**Answer:** Plain `SELECT WHERE` wins at every realistic depth. The recursion isn't the problem — the JSON-expression join inside the CTE is.
+
+## Setup
+
+- Postgres 16 on `localhost:5441` (the `compose-postgres.yml` instance, run directly without docker for this round)
+- Single delta channel `messages`, one write per checkpoint, `DELTA_SENTINEL` blob per checkpoint
+- Linear chain (`branch=1`) and 5-way branching at every step (`branch=5`) — branching is the case where plain over-fetches sibling rows
+- Median of 20 timed runs after 3 warmups, fresh psycopg cursor per strategy
+- Bench script: `bench_get_tuple_strategies.py` at repo root
+
+Three strategies compared:
+
+| name | roundtrips | shape |
+|------|-----------|-------|
+| `cte` | 1 | Current prod: recursive CTE walks ancestors, LEFT JOINs writes + blobs |
+| `plain` | 3 | Nuno's suggestion: thread-wide `SELECT WHERE` per table, Python walks parent chain and filters |
+| `cte+narrow` | 2 | CTE returns ancestor IDs only, then one `UNION ALL` of writes + blobs filtered by `ANY(ids)` |
+
+## Results (ms per get_tuple, median of 20)
+
+```
+ depth  branch       cte       plain    cte+narrow   rows_cte   rows_plain   plain/cte
+    10       1     0.14ms      0.26ms       0.24ms          9           30      1.89x
+    10       5     0.21ms      0.23ms       0.17ms          9          110      1.11x
+    50       1     0.89ms      0.27ms       0.33ms         49          150      0.31x
+    50       5     2.35ms      0.66ms       0.51ms         49          550      0.28x
+   200       1    11.61ms      0.78ms       1.30ms        199          600      0.07x
+   200       5    34.79ms      2.33ms       3.07ms        199         2200      0.07x
+  1000       1   274.60ms      2.59ms      13.29ms        999         3000      0.01x
+  1000       5   856.01ms     10.14ms      15.31ms        999        11000      0.01x
+```
+
+Lower is better. `plain/cte < 1` means plain is faster.
+
+### Headline numbers
+
+- depth 50: plain is **3x** faster
+- depth 200: plain is **15x** faster
+- depth 1000: plain is **~100x** faster
+- Branching makes plain over-fetch (3000 rows → 11000 rows at d=1000), but it remains ~85x faster than the CTE
+
+## Why the CTE collapses
+
+`EXPLAIN (ANALYZE, BUFFERS)` of the CTE at depth 1000 (linear). Excerpt with the load-bearing nodes:
+
+```
+Sort  ... actual time=137.798..137.827 rows=999
+  CTE ancestors
+    ->  Recursive Union  ... actual time=0.005..2.443 rows=999
+                                                 ^^^^^^
+                                                 recursion is 2.4 ms — fine
+  ->  Nested Loop Left Join  ... actual time=2.676..137.529 rows=999
+        Join Filter: (cw.checkpoint_id = a.cid)
+        Rows Removed by Join Filter: 998001
+                                     ^^^^^^^
+                                     999 ancestors x ~1000 writes
+        ->  Nested Loop Left Join  ... actual time=2.669..85.061 rows=999
+              Join Filter: (bl.version = ((c.checkpoint -> 'channel_versions'::text) ->> bl.channel))
+              Rows Removed by Join Filter: 998001
+                                           ^^^^^^^
+                                           same quadratic blow-up on the blob join
+```
+
+Two pathological things are happening:
+
+1. **The blob join filter is on a JSON expression**: `bl.version = (c.checkpoint -> 'channel_versions' ->> bl.channel)`. The planner cannot push this into an index lookup, so it materializes `checkpoint_blobs` for the thread and does a nested-loop comparison against every ancestor — a Cartesian product that grows as `O(ancestors × blobs_in_thread)`.
+2. **The writes join is similar**: writes for the thread are materialized once, then nested-loop joined against ancestors with a `Join Filter` rather than a hash/merge join over the indexed `checkpoint_id`.
+
+At depth 1000 that's **~2 million rows evaluated, 99.9% of them discarded**. The recursion itself is a rounding error.
+
+For comparison, the plain Q1 (`SELECT … FROM checkpoints WHERE thread_id=? AND checkpoint_ns=?`) at depth 1000:
+
+```
+Seq Scan on checkpoints  ... actual time=0.012..0.121 rows=1000
+Execution Time: 0.140 ms
+```
+
+A simple seq scan over 57 buffers. Q2 and Q3 follow the same shape and complete in well under 1 ms each.
+
+## Crossover and remote-DB reasoning
+
+- Pure local Postgres: plain wins from depth ~30 onward; CTE wins by fractions of a ms below that
+- Remote Postgres at ~5 ms RTT adds ~10 ms to plain (3 roundtrips vs 1). Crossover shifts to ~depth 30. Above that, the CTE's quadratic SQL cost still dominates the RTT savings.
+
+There is no realistic conversation depth where the CTE wins on a remote DB. At depth 200+ (anything resembling a real multi-turn agent run) plain is faster regardless of network.
+
+## Recommendation
+
+**Switch to plain SELECT WHERE, one delta channel at a time.**
+
+Three indexed queries per delta channel:
+
+```sql
+-- Q1: parent chain + per-checkpoint version of this channel
+SELECT checkpoint_id,
+       parent_checkpoint_id,
+       checkpoint -> 'channel_versions' ->> 'channel_name' AS ver
+FROM checkpoints
+WHERE thread_id = ? AND checkpoint_ns = ?;
+
+-- Q2: writes for this channel, anywhere in the thread
+SELECT checkpoint_id, type, blob, task_id, idx
+FROM checkpoint_writes
+WHERE thread_id = ? AND checkpoint_ns = ? AND channel = ?;
+
+-- Q3: blobs for this channel, anywhere in the thread
+SELECT version, type, blob
+FROM checkpoint_blobs
+WHERE thread_id = ? AND checkpoint_ns = ? AND channel = ?;
+```
+
+Python then:
+- Builds `parent_of: dict[cid, parent_cid]` from Q1
+- Walks from target's parent newest → oldest
+- Filters Q2 rows by `ancestor_set`, processes oldest → newest, applies overwrite-terminator
+- Picks seed blob via the per-ancestor `ver` map, terminates at first non-sentinel blob
+
+All O(n) on n = thread checkpoints, with tight constants (dict lookups). No recursion, no JSON-expression joins, no quadratic plans.
+
+If the 3-roundtrip cost ever shows up on remote-DB benchmarks, fold Q2 + Q3 into one `UNION ALL` to get back to 2 roundtrips. Bench says it isn't worth the SQL complexity right now.
+
+## Bonus: code simplification from single-channel scope
+
+Multi-channel reconstruction in the current `_reconstruct_delta_channels_cur` carries:
+
+- `rows_by_cid` nested dicts, keyed by cid then channel
+- `seen_blob: set[(cid, channel)]` and `seen_write: set[(cid, channel, task_id, idx)]` dedup
+- `collected: dict[channel, list]`, `done: set[channel]`, `seeds: dict[channel, value]`
+- Inner `for ch in channels_list` loops and an early-exit `if len(done) == len(channels_list)`
+
+Single-channel collapses these to a single list, a single bool, and one `Optional[Any]`. Roughly half the Python in that function, plus an obvious shape for splitting pure post-processing into `base.py` so sync and async stop duplicating it.
+
+If multi-channel coalescing turns out to matter later, it can come back as a SQL-level optimization without re-introducing the bookkeeping in Python.