release(sdk-py): 0.3.14 (#7712 )

Cuts a release for langgraph-sdk to publish #7704 (`return_minimal` on `threads.update`). Needed by langchain-ai/langchainplus#24203 to swap Fleet Python `threads.update` callsites from raw `Prefer` headers to the first-class `return_minimal=True` parameter. Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
docs: update README.md (#7710 )
2026-08-25 09:02:25 +02:00 · 2026-05-05 10:47:08 -07:00 · 2026-05-05 17:58:37 +02:00 · 2026-05-04 22:41:05 -07:00 · 2026-05-04 22:39:39 -07:00 · 2026-05-05 03:46:33 +00:00
105 changed files with 20197 additions and 1633 deletions
@@ -33,7 +33,7 @@ jobs:
          cache-suffix: test-${{ inputs.working-directory }}
          working-directory: ${{ inputs.working-directory }}
      - name: Login to Docker Hub
-        uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
+        uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4
        if: ${{ !github.event.pull_request.head.repo.fork }}
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -31,7 +31,7 @@ jobs:
          cache-suffix: "test-langgraph"
          working-directory: libs/langgraph
      - name: Login to Docker Hub
-        uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
+        uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4
        if: ${{ !github.event.pull_request.head.repo.fork }}
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -82,7 +82,7 @@ jobs:
          path: ${{ inputs.working-directory }}/dist/

      - name: Publish to test PyPI
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1
+        uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # release/v1
        with:
          packages-dir: ${{ inputs.working-directory }}/dist/
          verbose: true
@@ -57,7 +57,7 @@ jobs:
            echo EOF
          } >> "$GITHUB_OUTPUT"
      - name: Annotation
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          script: |
            const file = JSON.parse(`${{ steps.files.outputs.added_modified_renamed }}`)[0]
@@ -40,7 +40,7 @@ jobs:
        uses: actions/configure-pages@v6

      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v4
+        uses: actions/upload-pages-artifact@v5
        with:
          path: 'docs/_site'

@@ -276,7 +276,7 @@ jobs:
          path: ${{ inputs.working-directory }}/dist/

      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1
+        uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # release/v1
        with:
          packages-dir: ${{ inputs.working-directory }}/dist/
          verbose: true
@@ -26,7 +26,7 @@ jobs:

    steps:
      - name: Find and reopen matching PRs
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -50,7 +50,7 @@ jobs:
    steps:
      - name: Check for issue link and assignee
        id: check-link
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -283,7 +283,7 @@ jobs:
        if: >-
          env.ENFORCE_ISSUE_LINK == 'true' &&
          (steps.check-link.outputs.has-link != 'true' || steps.check-link.outputs.is-assigned != 'true')
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -312,7 +312,7 @@ jobs:
        if: >-
          env.ENFORCE_ISSUE_LINK == 'true' &&
          steps.check-link.outputs.has-link == 'true' && steps.check-link.outputs.is-assigned == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -366,7 +366,7 @@ jobs:
        if: >-
          env.ENFORCE_ISSUE_LINK == 'true' &&
          (steps.check-link.outputs.has-link != 'true' || steps.check-link.outputs.is-assigned != 'true')
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -63,7 +63,7 @@ jobs:
      - name: Check if contributor is external
        if: steps.app-token.outcome == 'success'
        id: check-membership
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ steps.app-token.outputs.token }}
          script: |
@@ -102,7 +102,7 @@ jobs:

      - name: Apply contributor tier label
        if: steps.check-membership.outputs.is-external == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          script: |
@@ -155,7 +155,7 @@ jobs:

      - name: Add external label
        if: steps.check-membership.outputs.is-external == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          script: |
@@ -168,7 +168,7 @@ jobs:

      - name: Add internal label
        if: steps.check-membership.outputs.is-external == 'false'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          script: |
@@ -196,7 +196,7 @@ jobs:
          private-key: ${{ secrets.ORG_MEMBERSHIP_APP_PRIVATE_KEY }}

      - name: Backfill labels
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ steps.app-token.outputs.token }}
          script: |
@@ -48,7 +48,7 @@ jobs:
      - name: Check if contributor is external
        if: steps.app-token.outcome == 'success'
        id: check-membership
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ steps.app-token.outputs.token }}
          script: |
@@ -90,7 +90,7 @@ jobs:
      # event fires and triggers require_issue_link.yml.
      - name: Apply contributor tier label
        if: steps.check-membership.outputs.is-external == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          # Use App token so the "labeled" event propagates to downstream
          # workflows (e.g. require_issue_link.yml).
@@ -145,7 +145,7 @@ jobs:

      - name: Add external label
        if: steps.check-membership.outputs.is-external == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          # Use App token so the "labeled" event propagates to downstream
          # workflows (e.g. require_issue_link.yml). Events created by the
@@ -161,7 +161,7 @@ jobs:

      - name: Add internal label
        if: steps.check-membership.outputs.is-external == 'false'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v9
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          script: |
@@ -28,7 +28,7 @@ jobs:
        run: make lock-upgrade
          
      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@c0f553fe549906ede9cf27b5156039d195d2ece0 # v8
+        uses: peter-evans/create-pull-request@5f6978faf089d4d20b00c7766989d076bb2fc7f1 # v8
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
          commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
@@ -100,3 +100,4 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
+.worktrees/
@@ -16,7 +16,7 @@
  <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
  <a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
  <a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
-  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
+  <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
 </div>

 <br>
@@ -27,10 +27,10 @@ Trusted by companies shaping the future of agents – including Klarna, Replit,
 pip install -U langgraph
 ```

-If you're looking to quickly build agents with LangChain's `create_agent` (built on LangGraph), check out the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
+> [!TIP]
+> If you're looking to quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.

-> [!NOTE]
-> Looking for the JS/TS library? Check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
+For an equivalent JS/TS library, check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).

 ## Why use LangGraph?

@@ -51,7 +51,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L

 To improve your LLM application development, pair LangGraph with:

- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
+- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 - [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
 - [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
@@ -250,15 +250,15 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "langchain-core" },
    { name = "ormsgpack" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/7c/e1/885e49cdafceb4c74dae4573bc5dd6054c6c640382ee73104532f33dca46/langgraph_checkpoint-4.0.3.tar.gz", hash = "sha256:a7b5e2ca18fb79b55edf19396d4ee446f8a53dcb7a4ec62ce6f1c7e00bb5af7f", size = 174009, upload-time = "2026-04-27T14:34:02.777Z" }
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+    { url = "https://files.pythonhosted.org/packages/19/ee/ecd3fa2e893746dde3b768daca2a4935208bc77d09445437ccfffb4a8c9b/langgraph_checkpoint-4.0.3-py3-none-any.whl", hash = "sha256:b91b765712a2311a5b198760f714b7ab9b376d01c047ed78d9b9a3e80df802a3", size = 51682, upload-time = "2026-04-27T14:34:01.51Z" },
 ]

 [[package]]
@@ -746,27 +746,27 @@ wheels = [

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+version = "0.15.12"
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+    { url = "https://files.pythonhosted.org/packages/3c/a0/a2938b23ae3e1a09a2d7c189e2ac5f7113676bae4e0e23948b568e18e5f8/ty-0.0.33-py3-none-win_amd64.whl", hash = "sha256:c1c3aec62c44de610c6e95f0a4e97ac3dbc07934bfdbf1fd90d758c9ff72f48e", size = 11342470, upload-time = "2026-04-28T10:45:26.455Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/62/7fb948aace38d2f6329261bb33c035a8484549c74f1db28649c7a4c6fed9/ty-0.0.33-py3-none-win_arm64.whl", hash = "sha256:0d44f99ba1b441e55e2aa301b2ac0a21112784931b46a5f66f4ea9efe5620d97", size = 10742673, upload-time = "2026-04-28T10:45:35.555Z" },
 ]

 [[package]]
@@ -2,9 +2,9 @@ from __future__ import annotations

 import threading
 from collections import defaultdict
-from collections.abc import Iterator, Sequence
+from collections.abc import Iterator, Mapping, Sequence
 from contextlib import contextmanager
-from typing import Any
+from typing import Any, cast

 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
@@ -13,17 +13,25 @@ from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
+    DeltaChannelHistory,
    get_checkpoint_id,
    get_serializable_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool

 from langgraph.checkpoint.postgres import _internal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    _DELTA_PAGE_SIZE,
+    BasePostgresSaver,
+    _build_delta_stage1_sql,
+    _build_delta_stage2_sql,
+    _DeltaStage2Row,
+)
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver

 Conn = _internal.Conn  # For backward compatibility
@@ -302,7 +310,10 @@ class PostgresSaver(BasePostgresSaver):
        # others are stored in blobs table
        blob_values = {}
        for k, v in checkpoint["channel_values"].items():
-            if v is None or isinstance(v, (str, int, float, bool)):
+            if isinstance(v, _DeltaSnapshot):
+                blob_values[k] = copy["channel_values"].pop(k)
+                copy["channel_values"][k] = True
+            elif v is None or isinstance(v, (str, int, float, bool)):
                pass
            else:
                blob_values[k] = copy["channel_values"].pop(k)
@@ -430,6 +441,114 @@ class PostgresSaver(BasePostgresSaver):
                with conn.cursor(binary=True, row_factory=dict_row) as cur:
                    yield cur

+    def get_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Fast-path override of `BaseCheckpointSaver.get_delta_channel_history`.
+
+        Two-stage query, both stages cover ALL requested channels:
+
+        * Stage 1 (paged): dynamic SELECT over `checkpoints` with K parallel
+          JSONB key lookups (one column pair per channel) — no subquery, no
+          aggregation. Pages newest-first by `checkpoint_id` with a cursor;
+          page size is `_DELTA_PAGE_SIZE`. Stops paging when every channel
+          has found its seed or the chain is exhausted.
+
+        * Stage 2 (per-channel UNION ALL): one branch per channel reading
+          `checkpoint_writes` filtered to that channel's specific
+          `chain_cids`, plus one branch per channel that has a seed reading
+          `checkpoint_blobs` for that channel + version. Avoids the
+          over-fetch of a single `channel = ANY(channels)` filter when
+          channels have different chain depths.
+        """
+        if not channels:
+            return {}
+        channels = list(channels)
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = self.get_tuple(config)
+            if target is None:
+                return {ch: {"writes": []} for ch in channels}
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
+        # Stage 1: paged K-JSONB-lookup scan, walking the parent chain in
+        # Python after each page. Stops as soon as every channel has its seed.
+        stage1_sql = _build_delta_stage1_sql(channels, paged=True)
+        parent_of: dict[str, str | None] = {}
+        ver_by_i_by_cid: list[dict[str, str | None]] = [{} for _ in channels]
+        hs_by_i_by_cid: list[dict[str, bool]] = [{} for _ in channels]
+        chain_by_ch: dict[str, list[str]] = {ch: [] for ch in channels}
+        seed_ver_by_ch: dict[str, str | None] = {ch: None for ch in channels}
+        walk_cursor_by_ch: dict[str, str | None] = {}
+        seeded: set[str] = set()
+        cursor: str | None = None
+
+        with self._cursor() as cur:
+            while True:
+                stage1_params: list[Any] = []
+                for ch in channels:
+                    stage1_params.extend([ch, ch])
+                stage1_params.extend(
+                    [thread_id, checkpoint_ns, cursor, cursor, _DELTA_PAGE_SIZE]
+                )
+                cur.execute(stage1_sql, stage1_params)
+                page = cur.fetchall()
+                if not page:
+                    break
+                oldest = self._ingest_stage1_page(
+                    cast("list[Mapping[str, Any]]", page),
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                )
+                self._try_advance_walks(
+                    checkpoint_id,
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                    chain_by_ch,
+                    seed_ver_by_ch,
+                    walk_cursor_by_ch,
+                    seeded,
+                )
+                # Stop if every channel is seeded, or the page was short
+                # (chain exhausted — no more rows to fetch).
+                if len(seeded) == len(channels) or len(page) < _DELTA_PAGE_SIZE:
+                    break
+                cursor = oldest
+
+        # Stage 2: per-channel UNION ALL — one writes branch per channel
+        # with non-empty chain, plus one blob branch per seeded channel.
+        channels_with_chain = [ch for ch in channels if chain_by_ch[ch]]
+        channels_with_seed = [ch for ch in channels if seed_ver_by_ch[ch] is not None]
+        stage2_sql = _build_delta_stage2_sql(
+            channels_with_chain=channels_with_chain,
+            channels_with_seed=channels_with_seed,
+        )
+
+        if stage2_sql:
+            stage2_params: list[Any] = []
+            for ch in channels_with_chain:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, chain_by_ch[ch]])
+            for ch in channels_with_seed:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, seed_ver_by_ch[ch]])
+            with self._cursor() as cur:
+                cur.execute(stage2_sql, stage2_params)
+                stage2_rows = cur.fetchall()
+        else:
+            stage2_rows = []
+
+        return self._build_delta_channels_writes_history(
+            channels=channels,
+            chain_by_ch=chain_by_ch,
+            seed_ver_by_ch=seed_ver_by_ch,
+            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
+        )
+
    def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.
@@ -2,9 +2,9 @@ from __future__ import annotations

 import asyncio
 from collections import defaultdict
-from collections.abc import AsyncIterator, Iterator, Sequence
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from contextlib import asynccontextmanager
-from typing import Any
+from typing import Any, cast

 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
@@ -13,17 +13,25 @@ from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
+    DeltaChannelHistory,
    get_checkpoint_id,
    get_serializable_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool

 from langgraph.checkpoint.postgres import _ainternal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    _DELTA_PAGE_SIZE,
+    BasePostgresSaver,
+    _build_delta_stage1_sql,
+    _build_delta_stage2_sql,
+    _DeltaStage2Row,
+)
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver

 Conn = _ainternal.Conn  # For backward compatibility
@@ -261,7 +269,10 @@ class AsyncPostgresSaver(BasePostgresSaver):
        # others are stored in blobs table
        blob_values = {}
        for k, v in checkpoint["channel_values"].items():
-            if v is None or isinstance(v, (str, int, float, bool)):
+            if isinstance(v, _DeltaSnapshot):
+                blob_values[k] = copy["channel_values"].pop(k)
+                copy["channel_values"][k] = True
+            elif v is None or isinstance(v, (str, int, float, bool)):
                pass
            else:
                blob_values[k] = copy["channel_values"].pop(k)
@@ -391,6 +402,97 @@ class AsyncPostgresSaver(BasePostgresSaver):
                async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                    yield cur

+    async def aget_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Fast-path override of `BaseCheckpointSaver.aget_delta_channel_history`.
+
+        See `PostgresSaver.get_delta_channel_history` for design notes; this is
+        the async equivalent with internal stage-1 paging and per-channel
+        UNION ALL stage-2.
+        """
+        if not channels:
+            return {}
+        channels = list(channels)
+        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 {ch: {"writes": []} for ch in channels}
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
+        stage1_sql = _build_delta_stage1_sql(channels, paged=True)
+        parent_of: dict[str, str | None] = {}
+        ver_by_i_by_cid: list[dict[str, str | None]] = [{} for _ in channels]
+        hs_by_i_by_cid: list[dict[str, bool]] = [{} for _ in channels]
+        chain_by_ch: dict[str, list[str]] = {ch: [] for ch in channels}
+        seed_ver_by_ch: dict[str, str | None] = {ch: None for ch in channels}
+        walk_cursor_by_ch: dict[str, str | None] = {}
+        seeded: set[str] = set()
+        cursor: str | None = None
+
+        async with self._cursor() as cur:
+            while True:
+                stage1_params: list[Any] = []
+                for ch in channels:
+                    stage1_params.extend([ch, ch])
+                stage1_params.extend(
+                    [thread_id, checkpoint_ns, cursor, cursor, _DELTA_PAGE_SIZE]
+                )
+                await cur.execute(stage1_sql, stage1_params)
+                page = await cur.fetchall()
+                if not page:
+                    break
+                oldest = self._ingest_stage1_page(
+                    cast("list[Mapping[str, Any]]", page),
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                )
+                self._try_advance_walks(
+                    checkpoint_id,
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                    chain_by_ch,
+                    seed_ver_by_ch,
+                    walk_cursor_by_ch,
+                    seeded,
+                )
+                if len(seeded) == len(channels) or len(page) < _DELTA_PAGE_SIZE:
+                    break
+                cursor = oldest
+
+        channels_with_chain = [ch for ch in channels if chain_by_ch[ch]]
+        channels_with_seed = [ch for ch in channels if seed_ver_by_ch[ch] is not None]
+        stage2_sql = _build_delta_stage2_sql(
+            channels_with_chain=channels_with_chain,
+            channels_with_seed=channels_with_seed,
+        )
+
+        if stage2_sql:
+            stage2_params: list[Any] = []
+            for ch in channels_with_chain:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, chain_by_ch[ch]])
+            for ch in channels_with_seed:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, seed_ver_by_ch[ch]])
+            async with self._cursor() as cur:
+                await cur.execute(stage2_sql, stage2_params)
+                stage2_rows = await cur.fetchall()
+        else:
+            stage2_rows = []
+
+        return self._build_delta_channels_writes_history(
+            channels=channels,
+            chain_by_ch=chain_by_ch,
+            seed_ver_by_ch=seed_ver_by_ch,
+            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
+        )
+
    async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.
@@ -2,20 +2,26 @@ from __future__ import annotations

 import random
 import warnings
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from importlib.metadata import version as get_version
-from typing import Any, cast
+from typing import Any, TypedDict, cast

 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
    ChannelVersions,
+    DeltaChannelHistory,
+    PendingWrite,
    get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
 from psycopg.types.json import Jsonb

+# Page size for stage-1 paged scan in `get_delta_channel_history`. Internal
+# constant — exposing this as a kwarg is left as a follow-up.
+_DELTA_PAGE_SIZE = 1024
+
 MetadataInput = dict[str, Any] | None

 try:
@@ -153,6 +159,141 @@ INSERT_CHECKPOINT_WRITES_SQL = """
 """


+class _DeltaStage2Row(TypedDict, total=False):
+    """One row from `_build_delta_stage2_sql` (a UNION ALL of writes and blobs)."""
+
+    _kind: str  # "w" or "b"
+    checkpoint_id: str | None  # "w" rows only
+    channel: str | None  # set on both "w" and "b" rows
+    type: str | None
+    blob: bytes | None
+    task_id: str | None  # "w" rows only
+    idx: int | None  # "w" rows only
+    version: str | None  # "b" rows only
+
+
+# Multi-channel two-stage DeltaChannel reconstruction.
+#
+# Stage 1 scans checkpoint metadata (no blob bytes) and emits one row per
+# checkpoint with K parallel JSONB key lookups (one column pair per
+# requested delta channel: ver_i / hs_i).  No subqueries, no aggregation.
+# Python walks the parent chain once across all channels.
+#
+# Stage 2 fetches all writes and the seed blobs for ALL channels in a
+# single roundtrip via `channel = ANY(%s)` and chain/seed-version
+# filtering.
+#
+# Empirical comparison vs an alternative "ship full channel_versions /
+# channel_values JSONB and let Python pick" form (1000 checkpoints,
+# 8 total channels in graph, 3 delta channels requested):
+#
+#   Postgres execution:    A=0.24ms vs B=0.38ms   (both negligible)
+#   End-to-end latency:    A=6.83ms vs B=2.28ms   (B is 3.0x faster)
+#   Wire payload:          A=836KB  vs B=330KB    (61% smaller)
+#   Buffer hits:           identical (167 blocks)
+#
+# B (this dynamic-columns design) wins because it avoids JSONB
+# serialization on the wire and JSONB-to-dict deserialization in
+# psycopg.  Even at K=8 (8 delta channels = 16 dynamic columns), B
+# still beats A end-to-end (4.2ms vs 6.8ms).
+
+
+def _build_delta_stage1_sql(channels: Sequence[str], *, paged: bool) -> str:
+    """Build stage 1 SQL with 2K parallel JSONB key lookups.
+
+    For channels=["messages", "files"] (with `paged=True`) the result is::
+
+        SELECT checkpoint_id, parent_checkpoint_id,
+               checkpoint -> 'channel_versions' ->> %s AS ver_0,
+               (checkpoint -> 'channel_values' -> %s) IS NOT NULL AS hs_0,
+               checkpoint -> 'channel_versions' ->> %s AS ver_1,
+               (checkpoint -> 'channel_values' -> %s) IS NOT NULL AS hs_1
+        FROM checkpoints
+        WHERE thread_id = %s AND checkpoint_ns = %s
+          AND (%s::text IS NULL OR checkpoint_id < %s)
+        ORDER BY checkpoint_id DESC
+        LIMIT %s
+
+    Channel names are passed as `%s` parameters (safe from SQL injection).
+    Only the column aliases `ver_i` / `hs_i` are interpolated into the
+    SQL string (i is bounded by len(channels) and uses safe identifiers).
+
+    Caller must extend params with `[ch_0, ch_0, ch_1, ch_1, ...,
+    thread_id, ns, cursor, cursor, page_size]` when `paged=True`.
+
+    When `paged=False`, the WHERE has no cursor predicate and there's no
+    LIMIT/ORDER BY — kept as a non-public helper for tests/diagnostics.
+    """
+    cols = []
+    for i in range(len(channels)):
+        cols.append(
+            f"checkpoint -> 'channel_versions' ->> %s AS ver_{i}, "
+            f"(checkpoint -> 'channel_values' -> %s) IS NOT NULL AS hs_{i}"
+        )
+    sql = (
+        "SELECT checkpoint_id, parent_checkpoint_id, "
+        + ", ".join(cols)
+        + " FROM checkpoints WHERE thread_id = %s AND checkpoint_ns = %s"
+    )
+    if paged:
+        sql += (
+            " AND (%s::text IS NULL OR checkpoint_id < %s)"
+            " ORDER BY checkpoint_id DESC LIMIT %s"
+        )
+    return sql
+
+
+def _build_delta_stage2_sql(
+    *,
+    channels_with_chain: Sequence[str],
+    channels_with_seed: Sequence[str],
+) -> str:
+    """Build stage 2 SQL as a per-channel UNION ALL.
+
+    For each channel with a non-empty chain, emit one branch reading
+    `checkpoint_writes` for that specific channel + chain_cids. For each
+    channel with a seed_version, emit one branch reading `checkpoint_blobs`
+    for that channel + version. This avoids the over-fetch of the prior
+    `channel = ANY(channels) AND checkpoint_id = ANY(union)` form when
+    channels have different chain depths.
+
+    The caller must pass parameters in matching order:
+
+        for ch in channels_with_chain:
+            params += [thread_id, checkpoint_ns, ch, chain_cids[ch]]
+        for ch in channels_with_seed:
+            params += [thread_id, checkpoint_ns, ch, seed_version[ch]]
+
+    Returns an empty SQL string if both channel lists are empty (caller
+    must skip executing in that case).
+    """
+    branches: list[str] = []
+    for _ in channels_with_chain:
+        branches.append(
+            "SELECT 'w'::text AS _kind, "
+            "checkpoint_id, channel, "
+            "type, blob, task_id, idx, NULL::text AS version "
+            "FROM checkpoint_writes "
+            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
+            "AND checkpoint_id = ANY(%s)"
+        )
+    for _ in channels_with_seed:
+        branches.append(
+            "SELECT 'b'::text, NULL, channel, "
+            "type, blob, NULL, NULL, version "
+            "FROM checkpoint_blobs "
+            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
+            "AND version = %s"
+        )
+    return " UNION ALL ".join(branches)
+
+
+# Stage 1 rows are dynamic-shape dicts: {checkpoint_id, parent_checkpoint_id,
+# ver_0, hs_0, ver_1, hs_1, ...}.  Walking is parameterized by the channel
+# list to map indices back to channel names — no static TypedDict here.
+# `dict[str, Any]` is the practical signature.
+
+
 class BasePostgresSaver(BaseCheckpointSaver[str]):
    SELECT_SQL = SELECT_SQL
    SELECT_PENDING_SENDS_SQL = SELECT_PENDING_SENDS_SQL
@@ -195,6 +336,146 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
            if t.decode() != "empty"
        }

+    @staticmethod
+    def _ingest_stage1_page(
+        stage1_rows: Sequence[Mapping[str, Any]],
+        channels: Sequence[str],
+        parent_of: dict[str, str | None],
+        ver_by_i_by_cid: list[dict[str, str | None]],
+        hs_by_i_by_cid: list[dict[str, bool]],
+    ) -> str | None:
+        """Fold one stage-1 page into the running walk-state mappings.
+
+        Returns the oldest checkpoint_id seen on this page (smallest, since
+        pages come back DESC). Caller uses it as the cursor for the next
+        page (`AND checkpoint_id < cursor`).
+        """
+        oldest: str | None = None
+        for r in stage1_rows:
+            cid = cast(str, r["checkpoint_id"])
+            parent_of[cid] = cast("str | None", r["parent_checkpoint_id"])
+            for i in range(len(channels)):
+                ver_by_i_by_cid[i][cid] = cast("str | None", r.get(f"ver_{i}"))
+                hs_by_i_by_cid[i][cid] = bool(r.get(f"hs_{i}"))
+            # Rows are DESC; the last one is the smallest cid in the page.
+            oldest = cid
+        return oldest
+
+    @staticmethod
+    def _try_advance_walks(
+        target_id: str,
+        channels: Sequence[str],
+        parent_of: Mapping[str, str | None],
+        ver_by_i_by_cid: Sequence[Mapping[str, str | None]],
+        hs_by_i_by_cid: Sequence[Mapping[str, bool]],
+        chain_by_ch: dict[str, list[str]],
+        seed_ver_by_ch: dict[str, str | None],
+        walk_cursor_by_ch: dict[str, str | None],
+        seeded: set[str],
+    ) -> None:
+        """Advance each not-yet-seeded channel's walk as far as possible.
+
+        Uses the partial `parent_of` map accumulated so far. A walk stops
+        either because:
+          (a) it found a snapshot for its channel (channel becomes seeded),
+          (b) it reached a real root (parent_of[cid] is None — fully
+              materialized at this point), or
+          (c) the next ancestor cid isn't in `parent_of` yet (waiting for
+              a later page; the cursor stays put).
+
+        Mutates `chain_by_ch`, `seed_ver_by_ch`, `walk_cursor_by_ch`, and
+        `seeded` in place.
+        """
+        for i, ch in enumerate(channels):
+            if ch in seeded:
+                continue
+            # First-time entry: cursor starts at the target's parent.
+            if ch not in walk_cursor_by_ch:
+                walk_cursor_by_ch[ch] = parent_of.get(target_id)
+            cur_cid = walk_cursor_by_ch[ch]
+            ch_chain = chain_by_ch[ch]
+            hs_i = hs_by_i_by_cid[i]
+            ver_i = ver_by_i_by_cid[i]
+            while cur_cid is not None:
+                if cur_cid not in parent_of:
+                    # Need more pages to continue this walk.
+                    break
+                ch_chain.append(cur_cid)
+                if hs_i.get(cur_cid, False):
+                    seed_ver_by_ch[ch] = ver_i.get(cur_cid)
+                    seeded.add(ch)
+                    cur_cid = None
+                    break
+                cur_cid = parent_of[cur_cid]
+            walk_cursor_by_ch[ch] = cur_cid
+
+    def _build_delta_channels_writes_history(
+        self,
+        *,
+        channels: Sequence[str],
+        chain_by_ch: Mapping[str, list[str]],
+        seed_ver_by_ch: Mapping[str, str | None],
+        stage2_rows: Sequence[_DeltaStage2Row],
+    ) -> dict[str, DeltaChannelHistory]:
+        """Demux stage 2 rows per channel; produce per-channel histories.
+
+        stage2_rows carry `channel` on every row. We build per-channel
+        `writes_by_cid` and per-channel `seed_blob` dicts, then assemble
+        a `DeltaChannelHistory` per requested channel. The `seed` key is omitted
+        when the walk reached root with no snapshot found, or when the
+        seed blob is sentinel "empty" — in both cases the consumer treats
+        absence as "start empty".
+        """
+        # writes_by_ch_by_cid[channel][cid] = list of (type, blob, task_id, idx)
+        writes_by_ch_by_cid: dict[str, dict[str, list[tuple[str, bytes, str, int]]]] = {
+            ch: {} for ch in channels
+        }
+        # seed_blob_by_ver[(channel, version)] = (type, blob)
+        seed_blob_by_ver: dict[tuple[str, str], tuple[str, bytes]] = {}
+
+        for r in stage2_rows:
+            ch = cast(str, r["channel"])
+            kind = r["_kind"]
+            if kind == "w":
+                cid = cast(str, r["checkpoint_id"])
+                writes_by_ch_by_cid.setdefault(ch, {}).setdefault(cid, []).append(
+                    cast(
+                        "tuple[str, bytes, str, int]",
+                        (r["type"], r["blob"], r["task_id"], r["idx"]),
+                    )
+                )
+            else:  # kind == "b"
+                ver = cast(str, r["version"])
+                seed_blob_by_ver[(ch, ver)] = cast(
+                    "tuple[str, bytes]", (r["type"], r["blob"])
+                )
+
+        # Sort writes per (channel, cid) newest-first by (task_id, idx)
+        for cid_map in writes_by_ch_by_cid.values():
+            for ws in cid_map.values():
+                ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
+
+        result: dict[str, DeltaChannelHistory] = {}
+        for ch in channels:
+            chain_cids = chain_by_ch.get(ch, [])
+            seed_version = seed_ver_by_ch.get(ch)
+
+            collected: list[PendingWrite] = []
+            cid_writes = writes_by_ch_by_cid.get(ch, {})
+            for cid in chain_cids:
+                for type_tag, write_blob, task_id, _idx in cid_writes.get(cid, []):
+                    val = self.serde.loads_typed((type_tag, write_blob))
+                    collected.append((task_id, ch, val))
+            collected.reverse()
+
+            entry: DeltaChannelHistory = {"writes": collected}
+            if seed_version is not None:
+                blob = seed_blob_by_ver.get((ch, seed_version))
+                if blob is not None and blob[0] != "empty":
+                    entry["seed"] = self.serde.loads_typed(blob)
+            result[ch] = entry
+        return result
+
    def _dump_blobs(
        self,
        thread_id: str,
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.5"
+version = "3.1.0a4"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -12,7 +12,7 @@ readme = "README.md"
 license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
-  "langgraph-checkpoint>=2.1.2,<5.0.0",
+  "langgraph-checkpoint>=4.1.0a4,<5.0.0",
  "orjson>=3.11.5",
  "psycopg>=3.2.0",
  "psycopg-pool>=3.2.0",
@@ -20,7 +20,7 @@ dependencies = [

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

@@ -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 _messages_delta_reducer
+    from typing_extensions import TypedDict
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    def respond(state: State) -> dict:
+        n = len(state["messages"])
+        return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+
+    async with _saver(saver_name) as saver:
+        graph = builder.compile(checkpointer=saver)
+        config = {"configurable": {"thread_id": "diff-channel-test-1"}}
+
+        await graph.ainvoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
+        await graph.ainvoke(
+            {"messages": [HumanMessage(content="there", id="h2")]}, config
+        )
+
+        state = await graph.aget_state(config)
+        msgs = state.values["messages"]
+        assert len(msgs) == 4, f"expected 4, got {len(msgs)}: {msgs}"
+        assert msgs[0].content == "hi"
+        assert msgs[1].content == "reply-1"
+        assert msgs[2].content == "there"
+        assert msgs[3].content == "reply-3"
@@ -1,6 +1,10 @@
 version = 1
 revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]

 [[package]]
 name = "annotated-types"
@@ -240,10 +244,11 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.2.28"
+version = "1.3.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -252,14 +257,26 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f8/a4/317a1a3ac1df33a64adb3670bf88bbe3b3d5baa274db6863a979db472897/langchain_core-1.2.28.tar.gz", hash = "sha256:271a3d8bd618f795fdeba112b0753980457fc90537c46a0c11998516a74dc2cb", size = 846119, upload-time = "2026-04-08T18:19:34.867Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a8/03/7219502e8ca728d65eb44d7a3eb60239230742a70dbfc9241b9bfd61c4ab/langchain_core-1.3.2.tar.gz", hash = "sha256:fd7a50b2f28ba561fd9d7f5d2760bc9e06cf00cdf820a3ccafe88a94ffa8d5b7", size = 911813, upload-time = "2026-04-24T15:49:23.699Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a8/92/32f785f077c7e898da97064f113c73fbd9ad55d1e2169cf3a391b183dedb/langchain_core-1.2.28-py3-none-any.whl", hash = "sha256:80764232581eaf8057bcefa71dbf8adc1f6a28d257ebd8b95ba9b8b452e8c6ac", size = 508727, upload-time = "2026-04-08T18:19:32.823Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/d5/8fa4431007cbb7cfed7590f4d6a5dea3ad724f4174d248f6642ef5ce7d05/langchain_core-1.3.2-py3-none-any.whl", hash = "sha256:d44a66127f9f8db735bdfd0ab9661bccb47a97113cfd3f2d89c74864422b7274", size = 542390, upload-time = "2026-04-24T15:49:21.991Z" },
+]
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.15"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4f/24/9777489d6fbbee64af0c8f96d4f840239c408cf694f3394672807dafc490/langchain_protocol-0.0.15.tar.gz", hash = "sha256:9ab2d11ee73944754f10e037e717098d3a6796f0e58afa9cadda6154e7655ade", size = 5862, upload-time = "2026-05-01T22:30:04.748Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1d/7a/9c97a7b9cbe4c5dc6a44cdb1545450c28f0c8ce89b9c1f0ee7fbad896263/langchain_protocol-0.0.15-py3-none-any.whl", hash = "sha256:461eb794358f83d5e42635a5797799ffec7b4702314e34edf73ac21e75d3ef79", size = 6982, upload-time = "2026-05-01T22:30:03.877Z" },
 ]

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a4"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -307,7 +324,7 @@ test = [

 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.5"
+version = "3.1.0a4"
 source = { editable = "." }
 dependencies = [
    { name = "langgraph-checkpoint" },
@@ -487,7 +504,7 @@ wheels = [

 [[package]]
 name = "mypy"
-version = "1.20.0"
+version = "1.20.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "librt", marker = "platform_python_implementation != 'PyPy'" },
@@ -496,51 +513,51 @@ dependencies = [
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    { name = "typing-extensions" },
 ]
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 [[package]]
@@ -19,7 +19,7 @@ dependencies = [

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

@@ -0,0 +1,271 @@
+"""Smoke tests for `BaseCheckpointSaver.get_delta_channel_history` on sqlite.
+
+`SqliteSaver` (and `AsyncSqliteSaver`) deliberately don't override the
+default implementation in `BaseCheckpointSaver` — these tests pin the
+default impl to behave correctly end-to-end against a real persistent
+saver and a real `DeltaChannel`-backed graph.
+
+Scenarios covered:
+
+* Empty `channels` argument returns an empty mapping (no I/O).
+* On a non-trivial multi-checkpoint thread, per-channel writes come back
+  oldest→newest.
+* When the walk reaches the root without ever finding a stored value,
+  `seed` is omitted from the entry (consumer treats absence as "start
+  empty").
+* When a `_DeltaSnapshot` blob is present at an ancestor, it is returned
+  as the `seed`.
+* The async saver returns the same shape via `aget_delta_channel_history`.
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.runnables import RunnableConfig
+
+# `langgraph` is not a dep of `langgraph-checkpoint-sqlite`. When tests run
+# in the sqlite lib's standalone CI environment without it installed, skip
+# the whole module rather than failing at import.
+pytest.importorskip("langgraph.channels.delta", reason="langgraph core not installed")
+pytest.importorskip("langgraph.graph", reason="langgraph core not installed")
+
+from langgraph.channels.delta import DeltaChannel  # type: ignore[import-untyped]  # noqa: E402,I001
+from langgraph.checkpoint.serde.types import _DeltaSnapshot  # noqa: E402
+from langgraph.graph import END, START, StateGraph  # type: ignore[import-untyped]  # noqa: E402
+from typing_extensions import TypedDict  # noqa: E402
+
+from langgraph.checkpoint.sqlite import SqliteSaver  # noqa: E402
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver  # noqa: E402
+
+pytestmark = pytest.mark.anyio
+
+
+# ---------------------------------------------------------------------------
+# Graph helpers
+# ---------------------------------------------------------------------------
+
+
+def _noop(_state: Any) -> dict[str, Any]:
+    return {}
+
+
+class _DeltaState(TypedDict):
+    items: Annotated[list, DeltaChannel(operator.add)]
+
+
+def _delta_graph(checkpointer: Any) -> Any:
+    return (
+        StateGraph(_DeltaState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _drive(graph: Any, config: RunnableConfig, n: int) -> None:
+    for i in range(n):
+        graph.invoke({"items": [f"v{i}"]}, config)
+
+
+async def _adrive(graph: Any, config: RunnableConfig, n: int) -> None:
+    for i in range(n):
+        await graph.ainvoke({"items": [f"v{i}"]}, config)
+
+
+def _pick_non_root(saver: Any, config: RunnableConfig) -> RunnableConfig:
+    """Return a config pointing at a checkpoint that has at least one ancestor.
+
+    `get_delta_channel_history` walks the parent chain — calling it on the root
+    checkpoint produces `writes=[]` and no `seed`, which is uninteresting
+    for the multi-step assertions below.
+    """
+    history = list(saver.list(config))
+    assert history, "expected non-empty history"
+    # `list` yields newest→oldest; the second entry has the first entry
+    # as its parent, so its parent_config is non-None.
+    for tup in history:
+        if tup.parent_config is not None:
+            return tup.config
+    raise AssertionError("no checkpoint with a parent in history")
+
+
+async def _apick_non_root(saver: Any, config: RunnableConfig) -> RunnableConfig:
+    history = [tup async for tup in saver.alist(config)]
+    assert history, "expected non-empty history"
+    for tup in history:
+        if tup.parent_config is not None:
+            return tup.config
+    raise AssertionError("no checkpoint with a parent in history")
+
+
+# ---------------------------------------------------------------------------
+# Sync: SqliteSaver
+# ---------------------------------------------------------------------------
+
+
+def test_empty_channels_returns_empty_mapping_sync() -> None:
+    """Empty `channels` short-circuits to `{}` without touching storage."""
+    with SqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "empty"}}
+        assert saver.get_delta_channel_history(config=config, channels=[]) == {}
+
+
+def test_writes_history_oldest_to_newest_sync() -> None:
+    """Per-channel writes accumulated across the walk come back oldest→newest."""
+    with SqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "history-sync"}}
+        graph = _delta_graph(saver)
+        _drive(graph, config, 3)
+
+        target_cfg = _pick_non_root(saver, config)
+        result = saver.get_delta_channel_history(config=target_cfg, channels=["items"])
+
+        assert "items" in result
+        entry = result["items"]
+        assert isinstance(entry["writes"], list)
+
+        # If any writes were collected, their values should be in oldest→newest
+        # order — i.e. tagged 'v0', 'v1', ... matching invoke order.
+        write_values: list[Any] = []
+        for _task_id, channel, value in entry["writes"]:
+            assert channel == "items"
+            write_values.extend(value if isinstance(value, list) else [value])
+
+        # `_drive` invokes with payloads ['v0'], ['v1'], ['v2']. Whatever
+        # subset shows up in the chain must be a contiguous prefix in order.
+        for idx, val in enumerate(write_values):
+            assert val == f"v{idx}", (
+                f"writes not in oldest→newest order: {write_values}"
+            )
+
+
+def test_seed_present_when_snapshot_in_ancestor_sync() -> None:
+    """Inserting a `_DeltaSnapshot` blob at an ancestor → walk returns it as `seed`."""
+    with SqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "seed-sync"}}
+        graph = _delta_graph(saver)
+        _drive(graph, config, 2)
+
+        # Find the oldest non-root checkpoint, then walk to its parent and
+        # rewrite that parent's `channel_values["items"]` to a real
+        # `_DeltaSnapshot`. After this surgery, calling `get_delta_channel_history`
+        # at the leaf must return the snapshot value as `seed`.
+        history = list(saver.list(config))
+        assert len(history) >= 2
+        leaf_tup = history[0]
+        # Walk to an ancestor with a parent_config (any non-root will do).
+        ancestor_tup = next(
+            (tup for tup in history if tup.parent_config is not None), None
+        )
+        assert ancestor_tup is not None
+        parent_cfg = ancestor_tup.parent_config
+        assert parent_cfg is not None
+        parent_tup = saver.get_tuple(parent_cfg)
+        assert parent_tup is not None
+
+        snapshot_value = ["seeded", "items"]
+        parent_tup.checkpoint["channel_values"]["items"] = _DeltaSnapshot(
+            snapshot_value
+        )
+        # Make sure the channel has a version so the optimized blob lookup
+        # in any future override has something to hit.
+        parent_tup.checkpoint["channel_versions"].setdefault("items", 1)
+        saver.put(
+            parent_tup.parent_config or {"configurable": parent_cfg["configurable"]},
+            parent_tup.checkpoint,
+            parent_tup.metadata,
+            {},
+        )
+
+        result = saver.get_delta_channel_history(
+            config=leaf_tup.config, channels=["items"]
+        )
+        entry = result["items"]
+        assert "seed" in entry, f"expected seed to be present, got {entry}"
+        seed = entry["seed"]
+        assert isinstance(seed, _DeltaSnapshot), (
+            f"expected _DeltaSnapshot, got {seed!r}"
+        )
+        assert seed.value == snapshot_value
+
+
+def test_seed_omitted_when_walk_reaches_root_sync() -> None:
+    """`get_delta_channel_history` on the root checkpoint → no `seed` key, no writes."""
+    with SqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "root-sync"}}
+        graph = _delta_graph(saver)
+        _drive(graph, config, 1)
+
+        history = list(saver.list(config))
+        # Root is the oldest checkpoint (no parent_config).
+        root_tup = history[-1]
+        assert root_tup.parent_config is None
+
+        result = saver.get_delta_channel_history(
+            config=root_tup.config, channels=["items"]
+        )
+        entry = result["items"]
+        assert "seed" not in entry, f"root-walk should have no seed, got {entry}"
+        assert entry["writes"] == []
+
+
+# ---------------------------------------------------------------------------
+# Async: AsyncSqliteSaver
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_channels_returns_empty_mapping_async() -> None:
+    """Async equivalent of the empty-channels short-circuit."""
+    async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "empty-async"}}
+        assert await saver.aget_delta_channel_history(config=config, channels=[]) == {}
+
+
+async def test_writes_history_oldest_to_newest_async() -> None:
+    """Async equivalent of the oldest→newest ordering check."""
+    async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "history-async"}}
+        graph = _delta_graph(saver)
+        await _adrive(graph, config, 3)
+
+        target_cfg = await _apick_non_root(saver, config)
+        result = await saver.aget_delta_channel_history(
+            config=target_cfg, channels=["items"]
+        )
+
+        assert "items" in result
+        entry = result["items"]
+        assert isinstance(entry["writes"], list)
+
+        write_values: list[Any] = []
+        for _task_id, channel, value in entry["writes"]:
+            assert channel == "items"
+            write_values.extend(value if isinstance(value, list) else [value])
+
+        for idx, val in enumerate(write_values):
+            assert val == f"v{idx}", (
+                f"writes not in oldest→newest order: {write_values}"
+            )
+
+
+async def test_seed_omitted_when_walk_reaches_root_async() -> None:
+    """Async equivalent of the root-walk seed-absence check."""
+    async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
+        config: RunnableConfig = {"configurable": {"thread_id": "root-async"}}
+        graph = _delta_graph(saver)
+        await _adrive(graph, config, 1)
+
+        history = [tup async for tup in saver.alist(config)]
+        root_tup = history[-1]
+        assert root_tup.parent_config is None
+
+        result = await saver.aget_delta_channel_history(
+            config=root_tup.config, channels=["items"]
+        )
+        entry = result["items"]
+        assert "seed" not in entry, f"root-walk should have no seed, got {entry}"
+        assert entry["writes"] == []
@@ -1,6 +1,10 @@
 version = 1
 revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]

 [[package]]
 name = "aiosqlite"
@@ -268,7 +272,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a4"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -490,7 +494,7 @@ wheels = [

 [[package]]
 name = "mypy"
-version = "1.20.0"
+version = "1.20.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "librt", marker = "platform_python_implementation != 'PyPy'" },
@@ -499,51 +503,51 @@ dependencies = [
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    { name = "typing-extensions" },
 ]
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 [[package]]
@@ -3,7 +3,7 @@ from __future__ import annotations
 import copy
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
-from typing import (  # noqa: UP035
+from typing import (
    Any,
    Generic,
    Literal,
@@ -13,6 +13,7 @@ from typing import (  # noqa: UP035
 )

 from langchain_core.runnables import RunnableConfig
+from typing_extensions import NotRequired

 from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
@@ -28,6 +29,8 @@ from langgraph.checkpoint.serde.types import (

 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
+
+
 logger = logging.getLogger(__name__)


@@ -57,6 +60,16 @@ class CheckpointMetadata(TypedDict, total=False):
    """
    run_id: str
    """The ID of the run that created this checkpoint."""
+    delta_updates_since_snapshot: dict[str, int]
+    """Per-channel update count since the last `_DeltaSnapshot` was written.
+
+    Maps channel name → number of supersteps that wrote to this channel
+    since its last snapshot blob. Used by `pregel.create_checkpoint` to
+    decide when to write the next snapshot (when the count reaches the
+    channel's `snapshot_frequency`, snapshot fires and the count resets
+    to 0). Absent on threads that don't use delta channels. Version-format
+    independent — works for int, float, and string version schemes.
+    """


 ChannelVersions = dict[str, str | int | float]
@@ -119,6 +132,28 @@ class CheckpointTuple(NamedTuple):
    pending_writes: list[PendingWrite] | None = None


+class DeltaChannelHistory(TypedDict):
+    """Per-channel result entry from `BaseCheckpointSaver.get_delta_channel_history`.
+
+    Storage-level view of what one channel contributed across the ancestor
+    chain of a target checkpoint:
+
+    * `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
+      Always present; possibly empty. Already filtered to one channel.
+      Writes stored at the target checkpoint itself are pending for the
+      next super-step and are excluded.
+    * `seed` — the stored value at the nearest ancestor whose
+      `channel_values[ch]` is populated. Omitted if the walk reached the
+      root without finding any stored value (consumer treats absence as
+      "start empty"). Typically a `_DeltaSnapshot` for delta channels with
+      finite snapshot frequency, or a plain value for threads migrated
+      from a pre-delta channel type.
+    """
+
+    writes: list[PendingWrite]
+    seed: NotRequired[Any]
+
+
 class BaseCheckpointSaver(Generic[V]):
    """Base class for creating a graph checkpointer.

@@ -457,6 +492,102 @@ class BaseCheckpointSaver(Generic[V]):
        """
        raise NotImplementedError

+    def get_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Walk the parent chain returning per-channel writes + seed.
+
+        For each requested channel, walks ancestors of the checkpoint
+        identified by `config` (following `parent_config`) and accumulates
+        `pending_writes` for that channel. The walk terminates per-channel
+        at the nearest ancestor whose `channel_values[ch]` is populated;
+        that value is returned as `seed`. If the walk reaches the root
+        without finding a stored value, `seed` is omitted from that
+        channel's entry — the consumer treats the absence as "start
+        empty."
+
+        Walks the **parent chain** (not `list(before=...)`): for forked
+        threads, only on-path ancestors contribute.
+
+        The default implementation walks `get_tuple` + `parent_config`
+        once for all channels — each ancestor visited once, not once per
+        channel. Savers with direct storage access (`InMemorySaver`,
+        `PostgresSaver`) override for performance; the return contract is
+        fixed here.
+
+        Args:
+            config: Configuration identifying the target checkpoint.
+            channels: Channel names to walk for. Empty → empty mapping.
+
+        Returns:
+            Per-channel `DeltaChannelHistory` for every name in `channels`.
+        """
+        if not channels:
+            return {}
+        collected_by_ch: dict[str, list[PendingWrite]] = {c: [] for c in channels}
+        seed_by_ch: dict[str, Any] = {}
+        remaining: set[str] = set(channels)
+        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 and remaining:
+            tup = self.get_tuple(cursor_config)
+            if tup is None:
+                break
+            if tup.pending_writes:
+                for write in reversed(tup.pending_writes):
+                    ch = write[1]
+                    if ch in remaining:
+                        collected_by_ch[ch].append(write)
+            for ch in list(remaining):
+                if ch in tup.checkpoint["channel_values"]:
+                    seed_by_ch[ch] = tup.checkpoint["channel_values"][ch]
+                    remaining.discard(ch)
+            cursor_config = tup.parent_config
+        result: dict[str, DeltaChannelHistory] = {}
+        for ch in channels:
+            entry: DeltaChannelHistory = {"writes": list(reversed(collected_by_ch[ch]))}
+            if ch in seed_by_ch:
+                entry["seed"] = seed_by_ch[ch]
+            result[ch] = entry
+        return result
+
+    async def aget_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Async version of `get_delta_channel_history`."""
+        if not channels:
+            return {}
+        collected_by_ch: dict[str, list[PendingWrite]] = {c: [] for c in channels}
+        seed_by_ch: dict[str, Any] = {}
+        remaining: set[str] = set(channels)
+        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 and remaining:
+            tup = await self.aget_tuple(cursor_config)
+            if tup is None:
+                break
+            if tup.pending_writes:
+                for write in reversed(tup.pending_writes):
+                    ch = write[1]
+                    if ch in remaining:
+                        collected_by_ch[ch].append(write)
+            for ch in list(remaining):
+                if ch in tup.checkpoint["channel_values"]:
+                    seed_by_ch[ch] = tup.checkpoint["channel_values"][ch]
+                    remaining.discard(ch)
+            cursor_config = tup.parent_config
+        result: dict[str, DeltaChannelHistory] = {}
+        for ch in channels:
+            entry: DeltaChannelHistory = {"writes": list(reversed(collected_by_ch[ch]))}
+            if ch in seed_by_ch:
+                entry["seed"] = seed_by_ch[ch]
+            result[ch] = entry
+        return result
+
    def get_next_version(self, current: V | None, channel: None) -> V:
        """Generate the next version ID for a channel.

@@ -6,7 +6,7 @@ import pickle
 import random
 import shutil
 from collections import defaultdict
-from collections.abc import AsyncIterator, Iterator, Sequence
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
 from typing import Any
@@ -20,6 +20,8 @@ from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
+    DeltaChannelHistory,
+    PendingWrite,
    SerializerProtocol,
    get_checkpoint_id,
    get_checkpoint_metadata,
@@ -121,16 +123,115 @@ 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_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Override: walk the parent chain ONCE for all requested channels.
+
+        Each channel terminates independently at the nearest ancestor
+        whose stored blob is non-empty. Other channels keep walking until
+        they find their own terminator or hit the root.
+
+        Pre-delta plain-value blobs subsume their ancestor's pending
+        writes (the value already includes them); `_DeltaSnapshot` blobs
+        do not (snapshot is the value AT that ancestor, prior to its own
+        pending writes that produce the child).
+        """
+        if not channels:
+            return {}
+        # Imported lazily to avoid a hard checkpoint→serde-types coupling at
+        # module import; only this override needs the runtime check.
+        from langgraph.checkpoint.serde.types import _DeltaSnapshot
+
+        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, {})
+
+        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
+
+        collected_by_ch: dict[str, list[PendingWrite]] = {c: [] for c in channels}
+        seed_by_ch: dict[str, Any] = {}
+        remaining: set[str] = set(channels)
+
+        for cp_id in chain:
+            if not remaining:
+                break
+            entry = ns_storage.get(cp_id)
+            ckpt = self.serde.loads_typed(entry[0]) if entry is not None else None
+
+            terminated_here: set[str] = set()
+            blob_value_by_ch: dict[str, Any] = {}
+            if ckpt is not None:
+                versions = ckpt.get("channel_versions", {})
+                for ch in remaining:
+                    ver = versions.get(ch)
+                    if ver is None:
+                        continue
+                    blob_entry = self.blobs.get((thread_id, checkpoint_ns, ch, ver))
+                    if blob_entry is None or blob_entry[0] == "empty":
+                        continue
+                    blob_value_by_ch[ch] = self.serde.loads_typed(blob_entry)
+                    terminated_here.add(ch)
+
+            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
+            for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
+                step_writes.items(), reverse=True
+            ):
+                if ch not in remaining:
+                    continue
+                blob_value = blob_value_by_ch.get(ch)
+                if blob_value is not None and not isinstance(
+                    blob_value, _DeltaSnapshot
+                ):
+                    continue
+                collected_by_ch[ch].append(
+                    (tid, ch, self.serde.loads_typed(serialized))
+                )
+
+            for ch in terminated_here:
+                seed_by_ch[ch] = blob_value_by_ch[ch]
+                remaining.discard(ch)
+
+        result: dict[str, DeltaChannelHistory] = {}
+        for ch in channels:
+            entry_h: DeltaChannelHistory = {
+                "writes": list(reversed(collected_by_ch[ch]))
+            }
+            if ch in seed_by_ch:
+                entry_h["seed"] = seed_by_ch[ch]
+            result[ch] = entry_h
+        return result
+
+    async def aget_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        return self.get_delta_channel_history(config=config, channels=channels)

    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
        """Get a checkpoint tuple from the in-memory storage.
@@ -73,6 +73,7 @@ SAFE_MSGPACK_TYPES: frozenset[tuple[str, ...]] = frozenset(
        ("langchain_core.documents.base", "Document"),
        # langgraph
        ("langgraph.types", "Send"),
+        ("langgraph.types", "TimeoutPolicy"),
        ("langgraph.types", "Interrupt"),
        ("langgraph.types", "Command"),
        ("langgraph.types", "StateSnapshot"),
@@ -33,14 +33,16 @@ from langchain_core.load.load import Reviver
 from langgraph.checkpoint.serde import _msgpack as _lg_msgpack
 from langgraph.checkpoint.serde.base import SerializerProtocol
 from langgraph.checkpoint.serde.event_hooks import emit_serde_event
-from langgraph.checkpoint.serde.types import SendProtocol
+from langgraph.checkpoint.serde.types import (
+    SendProtocol,
+    _DeltaSnapshot,
+)
 from langgraph.store.base import Item

 if TYPE_CHECKING:
    from langgraph.checkpoint.serde._msgpack import (
        AllowedMsgpackModules,
    )
-    from langgraph.checkpoint.serde.types import SendProtocol

 LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
@@ -55,6 +57,19 @@ _warned_unregistered_types: set[tuple[str, str]] = set()
 _warned_blocked_types: set[tuple[str, str]] = set()


+def _is_safe_json_type(id_list: list[str]) -> bool:
+    """Return True if an lc=2 id refers to a type in SAFE_MSGPACK_TYPES.
+
+    Safe types bypass the ``allowed_json_modules`` gate so that old "json" format
+    checkpoints (written before the msgpack migration) can be resumed without
+    requiring users to configure an explicit allowlist.
+    """
+    if len(id_list) < 2:
+        return False
+    module_name = ".".join(id_list[:-1])
+    return (module_name, id_list[-1]) in _lg_msgpack.SAFE_MSGPACK_TYPES
+
+
 def _warn_once(
    seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
 ) -> None:
@@ -164,19 +179,23 @@ class JsonPlusSerializer(SerializerProtocol):
        return out

    def _reviver(self, value: dict[str, Any]) -> Any:
-        if self._allowed_json_modules and (
+        if (
            value.get("lc", None) == 2
            and value.get("type", None) == "constructor"
            and value.get("id", None) is not None
        ):
-            try:
-                return self._revive_lc2(value)
-            except InvalidModuleError as e:
-                logger.warning(
-                    "Object %s is not in the deserialization allowlist.\n%s",
-                    value["id"],
-                    e.message,
-                )
+            id_list = value["id"]
+            is_safe = _is_safe_json_type(id_list)
+            if self._allowed_json_modules or is_safe:
+                try:
+                    return self._revive_lc2(value)
+                except InvalidModuleError as e:
+                    if not is_safe:
+                        logger.warning(
+                            "Object %s is not in the deserialization allowlist.\n%s",
+                            value["id"],
+                            e.message,
+                        )

        return LC_REVIVER(value)

@@ -224,6 +243,13 @@ class JsonPlusSerializer(SerializerProtocol):
            method_display = "<init>"

        dotted = ".".join(needed)
+        # Safe types (the same set already allowed for msgpack deserialization) are
+        # permitted without an explicit allowlist — they are known-safe LangGraph and
+        # LangChain types.  This restores backwards-compat for old "json" checkpoints
+        # that pre-date the msgpack migration without reopening the broader security gate.
+        if _is_safe_json_type(list(needed)):
+            return
+
        if not self._allowed_json_modules:
            raise InvalidModuleError(
                f"Refused to deserialize JSON constructor: {dotted} (method: {method_display}). "
@@ -293,10 +319,13 @@ EXT_METHOD_SINGLE_ARG = 3
 EXT_PYDANTIC_V1 = 4
 EXT_PYDANTIC_V2 = 5
 EXT_NUMPY_ARRAY = 6
+EXT_DELTA_SNAPSHOT = 7


 def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
-    if hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
+    if isinstance(obj, _DeltaSnapshot):
+        return ormsgpack.Ext(EXT_DELTA_SNAPSHOT, _msgpack_enc(obj.value))
+    elif hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
        return ormsgpack.Ext(
            EXT_PYDANTIC_V2,
            _msgpack_enc(
@@ -468,10 +497,13 @@ def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
            ),
        )
    elif isinstance(obj, SendProtocol):
+        args: tuple[Any, ...] = (obj.node, obj.arg)
+        if (timeout := getattr(obj, "timeout", None)) is not None:
+            args = (obj.node, obj.arg, timeout)
        return ormsgpack.Ext(
            EXT_CONSTRUCTOR_POS_ARGS,
            _msgpack_enc(
-                (obj.__class__.__module__, obj.__class__.__name__, (obj.node, obj.arg)),
+                (obj.__class__.__module__, obj.__class__.__name__, args),
            ),
        )
    elif dataclasses.is_dataclass(obj):
@@ -522,6 +554,15 @@ def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
        raise TypeError(f"Object of type {obj.__class__.__name__} is not serializable")


+def _send_from_args(args: Sequence[Any]) -> Any:
+    # ya we have a cyclic import here ¯\_(ツ)_/¯
+    from langgraph.types import Send  # type: ignore
+
+    if len(args) == 2:
+        return Send(*args)
+    return Send(args[0], args[1], timeout=args[2])
+
+
 def _create_msgpack_ext_hook(
    allowed_modules: set[tuple[str, ...]] | Literal[True] | None,
 ) -> Callable[[int, bytes], Any]:
@@ -610,7 +651,13 @@ def _create_msgpack_ext_hook(
        return False

    def ext_hook(code: int, data: bytes) -> Any:
-        if code == EXT_CONSTRUCTOR_SINGLE_ARG:
+        if code == EXT_DELTA_SNAPSHOT:
+            return _DeltaSnapshot(
+                ormsgpack.unpackb(
+                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+                )
+            )
+        elif code == EXT_CONSTRUCTOR_SINGLE_ARG:
            try:
                tup = ormsgpack.unpackb(
                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
@@ -631,6 +678,8 @@ def _create_msgpack_ext_hook(
                )
                if not _check_allowed(tup[0], tup[1]):
                    return tup[2]
+                if tup[0] == "langgraph.types" and tup[1] == "Send":
+                    return _send_from_args(tup[2])
                # module, name, args
                return getattr(importlib.import_module(tup[0]), tup[1])(*tup[2])
            except Exception:
@@ -744,9 +793,7 @@ def _msgpack_ext_hook_to_json(code: int, data: bytes) -> Any:
                option=ormsgpack.OPT_NON_STR_KEYS,
            )
            if tup[0] == "langgraph.types" and tup[1] == "Send":
-                from langgraph.types import Send  # type: ignore
-
-                return Send(*tup[2])
+                return _send_from_args(tup[2])
            # module, name, args
            return tup[2]
        except Exception:
@@ -1,6 +1,7 @@
 from collections.abc import Sequence
 from typing import (
    Any,
+    NamedTuple,
    Protocol,
    TypeVar,
    runtime_checkable,
@@ -14,6 +15,22 @@ INTERRUPT = "__interrupt__"
 RESUME = "__resume__"
 TASKS = "__pregel_tasks"

+
+class _DeltaSnapshot(NamedTuple):
+    """Snapshot blob for a DeltaChannel with finite snapshot_frequency.
+
+    Stored in checkpoint_blobs via the `EXT_DELTA_SNAPSHOT` msgpack ext code.
+    The ancestor walk in `BaseCheckpointSaver.get_delta_channel_history` terminates
+    when it encounters this type (any non-empty channel_values entry stops
+    the walk for that channel).
+
+    `from_checkpoint` reconstructs the channel value directly from `.value`
+    without replaying writes — the snapshot IS the accumulated state.
+    """
+
+    value: Any
+
+
 Value = TypeVar("Value", covariant=True)
 Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a4"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"
@@ -18,7 +18,7 @@ dependencies = [

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

@@ -333,6 +333,57 @@ def test_serde_jsonplus_bytes() -> None:
    assert serde.loads_typed(dumped) == some_bytes


+def test_lc2_json_safe_type_revives_without_allowlist() -> None:
+    """Old 'json' blobs with lc=2 for safe types must revive without an explicit allowlist.
+
+    Regression test for: https://github.com/langchain-ai/langgraph/issues/7498
+    Threads checkpointed before v1.0.1 (pre-msgpack) stored messages as lc=2 JSON
+    constructor dicts. Resuming those threads must reconstruct proper BaseMessage objects
+    rather than returning raw dicts that cause MESSAGE_COERCION_FAILURE in add_messages.
+    """
+    from langchain_core.messages import AIMessage
+
+    serde = JsonPlusSerializer()  # default: _allowed_json_modules=None
+
+    human_blob = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["langchain_core", "messages", "human", "HumanMessage"],
+        "kwargs": {"content": "hello", "type": "human"},
+    }
+    ai_blob = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["langchain_core", "messages", "ai", "AIMessage"],
+        "kwargs": {"content": "hi there", "type": "ai"},
+    }
+    result = serde.loads_typed(("json", json.dumps([human_blob, ai_blob]).encode()))
+
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage), (
+        f"Expected HumanMessage, got {type(result[0])}: {result[0]!r}\n"
+        "lc=2 JSON blobs for safe types must deserialize without an explicit allowlist"
+    )
+    assert result[0].content == "hello"
+    assert isinstance(result[1], AIMessage)
+    assert result[1].content == "hi there"
+
+
+def test_lc2_json_unknown_type_stays_blocked_without_allowlist() -> None:
+    """lc=2 JSON blobs for types NOT in SAFE_MSGPACK_TYPES still require an allowlist."""
+    serde = JsonPlusSerializer()
+    load = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["pprint", "pprint"],
+        "kwargs": {"object": "HELLO"},
+    }
+    # No allowlist configured → raw dict returned (not raised, not reconstructed)
+    result = serde.loads_typed(("json", json.dumps(load).encode()))
+    assert isinstance(result, dict), "Unknown lc=2 type must stay as raw dict"
+    assert result.get("lc") == 2
+
+
 def test_deserde_invalid_module() -> None:
    serde = JsonPlusSerializer()
    load = {
@@ -208,8 +208,6 @@ class TestMemorySaver:


 async def test_memory_saver() -> None:
-    from langgraph.checkpoint.memory import InMemorySaver
-
    memory_saver = InMemorySaver()
    assert isinstance(memory_saver, InMemorySaver)

@@ -320,3 +318,363 @@ 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_omits_delta_channel(self) -> None:
+        """_load_blobs omits delta channels (stored as 'empty'); reconstruction deferred."""
+        saver = InMemorySaver()
+
+        thread_id, ns, channel = "t1", "", "messages"
+        v1 = "00000000000000000000000000000001.0000000000000000"
+
+        saver.blobs[(thread_id, ns, channel, v1)] = ("empty", b"")
+
+        result = saver._load_blobs(thread_id, ns, {channel: v1})
+        assert channel not in result
+
+    def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
+        """`get_delta_channel_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).
+
+        When the walk reaches the root without finding any stored value for
+        the channel, the per-channel entry has no `seed` key (TypedDict
+        absence indicates "start empty").
+        """
+        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_delta_channel_history(config=config, channels=[channel])[
+            channel
+        ]
+        # Walk reached the root without finding a stored value → no `seed` key.
+        assert "seed" not in result
+        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_delta_channel_history(config=config, channels=[channel])[
+            channel
+        ]
+        # No ancestors → no seed found, no writes accumulated.
+        assert "seed" not in result
+        assert result["writes"] == []
+
+
+class TestBaseFallbackGetChannelWrites:
+    """Exercises the `BaseCheckpointSaver.get_delta_channel_history` default
+    implementation — the path third-party savers inherit when they don't
+    override `get_delta_channel_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_delta_channel_history = (
+                InMemorySaver.__mro__[1].get_delta_channel_history  # type: ignore[attr-defined]
+            )
+            aget_delta_channel_history = (
+                InMemorySaver.__mro__[1].aget_delta_channel_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_delta_channel_history(config=config, channels=["messages"])[
+            "messages"
+        ]
+
+        # Walk reached root without a stored value → `seed` key absent.
+        assert "seed" not in result
+        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_delta_channel_history(config=config, channels=["messages"])
+        )["messages"]
+
+        assert "seed" not in result
+        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_delta_channel_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_delta_channel_history(config=config, channels=["messages"]),
+            saver.aget_delta_channel_history(config=config, channels=["messages"]),
+        )
+
+        expected_values = [{"content": "first"}, {"content": "second"}]
+        for result_map in results:
+            result = result_map["messages"]
+            assert "seed" not in result
+            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, 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.
+
+    Under the new public API a found seed populates `seed` in the
+    `DeltaChannelHistory` TypedDict; absence of the `seed` key means the walk
+    reached root without finding a stored value.
+    """
+
+    def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
+        """Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
+        write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
+        seed=[A] + writes=[B, C].
+
+        Returns `(saver, thread_id, ns, channel, cp3_id)`.
+        """
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        v1 = "00000000000000000000000000000001.0"
+        v2 = "00000000000000000000000000000002.0"
+        v3 = "00000000000000000000000000000003.0"
+
+        # Pre-delta: cp1 stored a real blob for the channel.
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
+        # Delta-era: cp2 and cp3 store "empty"; real writes in checkpoint_writes.
+        saver.blobs[(thread_id, ns, channel, v2)] = ("empty", b"")
+        saver.blobs[(thread_id, ns, channel, v3)] = ("empty", b"")
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        cp2["channel_versions"][channel] = v2
+        cp3 = empty_checkpoint()
+        cp3["id"] = "cp3"
+        cp3["channel_versions"][channel] = v3
+
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+            "cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
+        }
+        # Write under cp1 would be from the pre-delta era and MUST be ignored
+        # (the blob already captures it). We add one and assert it is not
+        # folded into the reconstructed result.
+        saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
+            "task0",
+            channel,
+            serde.dumps_typed("PRE-DELTA-WRITE"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed("B"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
+            "task3",
+            channel,
+            serde.dumps_typed("PENDING-AT-TARGET"),
+            "",
+        )
+        return saver, thread_id, ns, channel, "cp3"
+
+    def test_seed_from_pre_delta_ancestor_blob(self) -> None:
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver.get_delta_channel_history(config=config, channels=[channel])[
+            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_delta_channel_history(config=config, channels=[channel])[
+            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
@@ -2,7 +2,8 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.15'",
+    "python_full_version >= '3.12' and python_full_version < '3.15'",
    "python_full_version == '3.11.*'",
    "python_full_version < '3.11'",
 ]
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-version = "1.2.28"
+version = "1.3.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
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+
+[[package]]
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+version = "0.0.15"
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 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.1.0a4"
 source = { editable = "." }
 dependencies = [
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+version = "1.20.2"
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-"@eslint/config-array@^0.23.3":
-  version "0.23.3"
-  resolved "https://registry.yarnpkg.com/@eslint/config-array/-/config-array-0.23.3.tgz#3f4a93dd546169c09130cbd10f2415b13a20a219"
-  integrity sha512-j+eEWmB6YYLwcNOdlwQ6L2OsptI/LO6lNBuLIqe5R7RetD658HLoF+Mn7LzYmAWWNNzdC6cqP+L6r8ujeYXWLw==
+"@eslint/config-array@^0.23.5":
+  version "0.23.5"
+  resolved "https://registry.yarnpkg.com/@eslint/config-array/-/config-array-0.23.5.tgz#56e86d243049195d8acc0c06a1b3dfdc3fa3de95"
+  integrity sha512-Y3kKLvC1dvTOT+oGlqNQ1XLqK6D1HU2YXPc52NmAlJZbMMWDzGYXMiPRJ8TYD39muD/OTjlZmNJ4ib7dvSrMBA==
  dependencies:
-    "@eslint/object-schema" "^3.0.3"
+    "@eslint/object-schema" "^3.0.5"
    debug "^4.3.1"
    minimatch "^10.2.4"

-"@eslint/config-helpers@^0.5.3":
-  version "0.5.3"
-  resolved "https://registry.yarnpkg.com/@eslint/config-helpers/-/config-helpers-0.5.3.tgz#721fe6bbb90d74b0c80d6ff2428e5bbcb002becb"
-  integrity sha512-lzGN0onllOZCGroKJmRwY6QcEHxbjBw1gwB8SgRSqK8YbbtEXMvKynsXc3553ckIEBxsbMBU7oOZXKIPGZNeZw==
+"@eslint/config-helpers@^0.5.5":
+  version "0.5.5"
+  resolved "https://registry.yarnpkg.com/@eslint/config-helpers/-/config-helpers-0.5.5.tgz#ae16134e4792ac5fbdc533548a24ac1ea9f7f3ae"
+  integrity sha512-eIJYKTCECbP/nsKaaruF6LW967mtbQbsw4JTtSVkUQc9MneSkbrgPJAbKl9nWr0ZeowV8BfsarBmPpBzGelA2w==
  dependencies:
-    "@eslint/core" "^1.1.1"
+    "@eslint/core" "^1.2.1"

-"@eslint/core@^1.1.1":
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/@eslint/core/-/core-1.1.1.tgz#450f3d2be2d463ccd51119544092256b4e88df32"
-  integrity sha512-QUPblTtE51/7/Zhfv8BDwO0qkkzQL7P/aWWbqcf4xWLEYn1oKjdO0gglQBB4GAsu7u6wjijbCmzsUTy6mnk6oQ==
+"@eslint/core@^1.2.1":
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/@eslint/core/-/core-1.2.1.tgz#c1da7cd1b82fa8787f98b5629fb811848a1b63ce"
+  integrity sha512-MwcE1P+AZ4C6DWlpin/OmOA54mmIZ/+xZuJiQd4SyB29oAJjN30UW9wkKNptW2ctp4cEsvhlLY/CsQ1uoHDloQ==
  dependencies:
    "@types/json-schema" "^7.0.15"

@@ -517,17 +517,17 @@
  resolved "https://registry.yarnpkg.com/@eslint/js/-/js-10.0.1.tgz#1e8a876f50117af8ab67e47d5ad94d38d6622583"
  integrity sha512-zeR9k5pd4gxjZ0abRoIaxdc7I3nDktoXZk2qOv9gCNWx3mVwEn32VRhyLaRsDiJjTs0xq/T8mfPtyuXu7GWBcA==

-"@eslint/object-schema@^3.0.3":
-  version "3.0.3"
-  resolved "https://registry.yarnpkg.com/@eslint/object-schema/-/object-schema-3.0.3.tgz#5bf671e52e382e4adc47a9906f2699374637db6b"
-  integrity sha512-iM869Pugn9Nsxbh/YHRqYiqd23AmIbxJOcpUMOuWCVNdoQJ5ZtwL6h3t0bcZzJUlC3Dq9jCFCESBZnX0GTv7iQ==
+"@eslint/object-schema@^3.0.5":
+  version "3.0.5"
+  resolved "https://registry.yarnpkg.com/@eslint/object-schema/-/object-schema-3.0.5.tgz#88e9bf4d11d2b19c082e78ebe7ce88724a5eb091"
+  integrity sha512-vqTaUEgxzm+YDSdElad6PiRoX4t8VGDjCtt05zn4nU810UIx/uNEV7/lZJ6KwFThKZOzOxzXy48da+No7HZaMw==

-"@eslint/plugin-kit@^0.6.1":
-  version "0.6.1"
-  resolved "https://registry.yarnpkg.com/@eslint/plugin-kit/-/plugin-kit-0.6.1.tgz#eb9e6689b56ce8bc1855bb33090e63f3fc115e8e"
-  integrity sha512-iH1B076HoAshH1mLpHMgwdGeTs0CYwL0SPMkGuSebZrwBp16v415e9NZXg2jtrqPVQjf6IANe2Vtlr5KswtcZQ==
+"@eslint/plugin-kit@^0.7.1":
+  version "0.7.1"
+  resolved "https://registry.yarnpkg.com/@eslint/plugin-kit/-/plugin-kit-0.7.1.tgz#c4125fd015eceeb09b793109fdbcd4dd0a02d346"
+  integrity sha512-rZAP3aVgB9ds9KOeUSL+zZ21hPmo8dh6fnIFwRQj5EAZl9gzR7wxYbYXYysAM8CTqGmUGyp2S4kUdV17MnGuWQ==
  dependencies:
-    "@eslint/core" "^1.1.1"
+    "@eslint/core" "^1.2.1"
    levn "^0.4.1"

 "@humanfs/core@^0.19.1":
@@ -890,10 +890,10 @@
    "@jridgewell/resolve-uri" "^3.1.0"
    "@jridgewell/sourcemap-codec" "^1.4.14"

-"@langchain/core@^1.1.38":
-  version "1.1.38"
-  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-1.1.38.tgz#b5939b2e77af408771c9bb95bf56a5ad8183313e"
-  integrity sha512-C340wH1YL10CiVOFlEpQMp0zQE85/eBLKX/gi1Lv7shAyUmR3CQ0t/mXlCd5RsZa6ntAN1kDJnp64ArWey9XAA==
+"@langchain/core@^1.1.42":
+  version "1.1.42"
+  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-1.1.42.tgz#607bfd34c1414c9cbeb2859fda1b254a14c28085"
+  integrity sha512-d0tN96BrwPMryYyWR9VfyAntSivn7EQrZCe5Kpxum93tcjTXbKKmKvItFec8AluQt88iTcmAJrahUZUNfzGwTA==
  dependencies:
    "@cfworker/json-schema" "^4.0.2"
    "@standard-schema/spec" "^1.1.0"
@@ -904,7 +904,6 @@
    langsmith ">=0.5.0 <1.0.0"
    mustache "^4.2.0"
    p-queue "^6.6.2"
-    uuid "^11.1.0"
    zod "^3.25.76 || ^4"

 "@langchain/langgraph-checkpoint@^1.0.1":
@@ -914,23 +913,23 @@
  dependencies:
    uuid "^10.0.0"

-"@langchain/langgraph-sdk@~1.8.1":
-  version "1.8.3"
-  resolved "https://registry.yarnpkg.com/@langchain/langgraph-sdk/-/langgraph-sdk-1.8.3.tgz#deedc9bddd863693c6ab5f3a74058548b040377d"
-  integrity sha512-Py0S5yVtlOHz410aEsSGLRKjtsK2giDvfPS1JjAjTdcs71khuJufFtUZFwmwdJCbsG4DaGurRLHOAJu9jO4a0g==
+"@langchain/langgraph-sdk@~1.8.9":
+  version "1.8.10"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph-sdk/-/langgraph-sdk-1.8.10.tgz#bd175cda1ab629845ef28b22b45f3bdd1f65b029"
+  integrity sha512-wrB3rkRw5KAmsqezwvKP3midT4qJrV6Hj9XJMYo+cbvXC4HYpSAmyY/VriSyeTFRbLG/OP/pY2Yz+9Z54nSaXQ==
  dependencies:
    "@types/json-schema" "^7.0.15"
    p-queue "^9.0.1"
    p-retry "^7.1.1"
    uuid "^13.0.0"

-"@langchain/langgraph@^1.2.6":
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-1.2.6.tgz#e6a578f96fbbc1c54ba78e0298884671dad5d195"
-  integrity sha512-5cX402dNGN6w9+0mlMU2dgUiKx6Y1tlENp7x05e9ByDbQCHSDc0kyqRWNFLGc7vatQ92S4ylxQrcCJvi8Fr4SQ==
+"@langchain/langgraph@^1.2.9":
+  version "1.2.9"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-1.2.9.tgz#4f0ba159062c3649c83cf5f621ef926b96933e32"
+  integrity sha512-3c7BtGycHC2v9p6w/Hv8L7kEl1YnZYOQTDJtmAp3knk6JOedO7d2bYP3y0SRyhv5orUEGf/KGvx8ZsB/ideP7g==
  dependencies:
    "@langchain/langgraph-checkpoint" "^1.0.1"
-    "@langchain/langgraph-sdk" "~1.8.1"
+    "@langchain/langgraph-sdk" "~1.8.9"
    "@standard-schema/spec" "1.1.0"
    uuid "^10.0.0"

@@ -1098,100 +1097,100 @@
  dependencies:
    "@types/yargs-parser" "*"

-"@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"
-  integrity sha512-RLkVSiNuUP1C2ROIWfqX+YcUfLaSnxGE/8M+Y57lopVwg9VTYYfhuz15Yf1IzCKgZj6/rIbYTmJCUSqr76r0Wg==
+"@typescript-eslint/eslint-plugin@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-8.59.1.tgz#781bc6f9002982cfaf75a185240e24ad7276628a"
+  integrity sha512-BOziFIfE+6osHO9FoJG4zjoHUcvI7fTNBSpdAwrNH0/TLvzjsk2oo8XSSOT2HhqUyhZPfHv4UOffoJ9oEEQ7Ag==
  dependencies:
    "@eslint-community/regexpp" "^4.12.2"
-    "@typescript-eslint/scope-manager" "8.58.0"
-    "@typescript-eslint/type-utils" "8.58.0"
-    "@typescript-eslint/utils" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/scope-manager" "8.59.1"
+    "@typescript-eslint/type-utils" "8.59.1"
+    "@typescript-eslint/utils" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"
    ignore "^7.0.5"
    natural-compare "^1.4.0"
    ts-api-utils "^2.5.0"

-"@typescript-eslint/parser@^8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-8.58.0.tgz#da04ece1967b6c2fe8f10c3473dabf3825795ef7"
-  integrity sha512-rLoGZIf9afaRBYsPUMtvkDWykwXwUPL60HebR4JgTI8mxfFe2cQTu3AGitANp4b9B2QlVru6WzjgB2IzJKiCSA==
+"@typescript-eslint/parser@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-8.59.1.tgz#835d20a62350659a082a1ae2a60b822c40488905"
+  integrity sha512-HDQH9O/47Dxi1ceDhBXdaldtf/WV9yRYMjbjCuNk3qnaTD564qwv61Y7+gTxwxRKzSrgO5uhtw584igXVuuZkA==
  dependencies:
-    "@typescript-eslint/scope-manager" "8.58.0"
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/typescript-estree" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/scope-manager" "8.59.1"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/typescript-estree" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"
    debug "^4.4.3"

-"@typescript-eslint/project-service@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/project-service/-/project-service-8.58.0.tgz#66ceda0aabf7427aec3e2713fa43eb278dead2aa"
-  integrity sha512-8Q/wBPWLQP1j16NxoPNIKpDZFMaxl7yWIoqXWYeWO+Bbd2mjgvoF0dxP2jKZg5+x49rgKdf7Ck473M8PC3V9lg==
+"@typescript-eslint/project-service@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/project-service/-/project-service-8.59.1.tgz#49efe87c37ef84262f23df8bf62fdc56698ca6fe"
+  integrity sha512-+MuHQlHiEr00Of/IQbE/MmEoi44znZHbR/Pz7Opq4HryUOlRi+/44dro9Ycy8Fyo+/024IWtw8m4JUMCGTYxDg==
  dependencies:
-    "@typescript-eslint/tsconfig-utils" "^8.58.0"
-    "@typescript-eslint/types" "^8.58.0"
+    "@typescript-eslint/tsconfig-utils" "^8.59.1"
+    "@typescript-eslint/types" "^8.59.1"
    debug "^4.4.3"

-"@typescript-eslint/scope-manager@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-8.58.0.tgz#e304142775e49a1b7ac3c8bf2536714447c72cab"
-  integrity sha512-W1Lur1oF50FxSnNdGp3Vs6P+yBRSmZiw4IIjEeYxd8UQJwhUF0gDgDD/W/Tgmh73mxgEU3qX0Bzdl/NGuSPEpQ==
+"@typescript-eslint/scope-manager@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-8.59.1.tgz#ed90d054fc3db2d0c81464db3a953a94fb85bb58"
+  integrity sha512-LwuHQI4pDOYVKvmH2dkaJo6YZCSgouVgnS/z7yBPKBMvgtBvyLqiLy9Z6b7+m/TRcX1NFYUqZetI5Y+aT4GEfg==
  dependencies:
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"

-"@typescript-eslint/tsconfig-utils@8.58.0", "@typescript-eslint/tsconfig-utils@^8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/tsconfig-utils/-/tsconfig-utils-8.58.0.tgz#c5a8edb21f31e0fdee565724e1b984171c559482"
-  integrity sha512-doNSZEVJsWEu4htiVC+PR6NpM+pa+a4ClH9INRWOWCUzMst/VA9c4gXq92F8GUD1rwhNvRLkgjfYtFXegXQF7A==
+"@typescript-eslint/tsconfig-utils@8.59.1", "@typescript-eslint/tsconfig-utils@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/tsconfig-utils/-/tsconfig-utils-8.59.1.tgz#ba2a779a444f1d5cb92a606f9b209d239fd4cab1"
+  integrity sha512-/0nEyPbX7gRsk0Uwfe4ALwwgxuA66d/l2mhRDNlAvaj4U3juhUtJNq0DsY8M2AYwwb9rEq2hrC3IcIcEt++iJA==

-"@typescript-eslint/type-utils@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-8.58.0.tgz#ce0e72cd967ffbbe8de322db6089bd4374be352f"
-  integrity sha512-aGsCQImkDIqMyx1u4PrVlbi/krmDsQUs4zAcCV6M7yPcPev+RqVlndsJy9kJ8TLihW9TZ0kbDAzctpLn5o+lOg==
+"@typescript-eslint/type-utils@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-8.59.1.tgz#9c83d3f2ed9187a815e8120f72c08317e513e409"
+  integrity sha512-klWPBR2ciQHS3f++ug/mVnWKPjBUo7icEL3FAO1lhAR1Z1i5NQYZ1EannMSRYcq5qCv5wNALlXr6fksRHyYl7w==
  dependencies:
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/typescript-estree" "8.58.0"
-    "@typescript-eslint/utils" "8.58.0"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/typescript-estree" "8.59.1"
+    "@typescript-eslint/utils" "8.59.1"
    debug "^4.4.3"
    ts-api-utils "^2.5.0"

-"@typescript-eslint/types@8.58.0", "@typescript-eslint/types@^8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-8.58.0.tgz#e94ae7abdc1c6530e71183c1007b61fa93112a5a"
-  integrity sha512-O9CjxypDT89fbHxRfETNoAnHj/i6IpRK0CvbVN3qibxlLdo5p5hcLmUuCCrHMpxiWSwKyI8mCP7qRNYuOJ0Uww==
+"@typescript-eslint/types@8.59.1", "@typescript-eslint/types@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-8.59.1.tgz#c1d014d3f03a97e0113a8899fc9d4e45a7fb0ca9"
+  integrity sha512-ZDCjgccSdYPw5Bxh+my4Z0lJU96ZDN7jbBzvmEn0FZx3RtU1C7VWl6NbDx94bwY3V5YsgwRzJPOgeY2Q/nLG8A==

-"@typescript-eslint/typescript-estree@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-8.58.0.tgz#ed233faa8e2f2a2e1357c3e7d553d6465a0ee59a"
-  integrity sha512-7vv5UWbHqew/dvs+D3e1RvLv1v2eeZ9txRHPnEEBUgSNLx5ghdzjHa0sgLWYVKssH+lYmV0JaWdoubo0ncGYLA==
+"@typescript-eslint/typescript-estree@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-8.59.1.tgz#4391fadf98a22c869c5b6522dbf4e491e53e351a"
+  integrity sha512-OUd+vJS05sSkOip+BkZ/2NS8RMxrAAJemsC6vU3kmfLyeaJT0TftHkV9mcx2107MmsBVXXexhVu4F0TZXyMl4g==
  dependencies:
-    "@typescript-eslint/project-service" "8.58.0"
-    "@typescript-eslint/tsconfig-utils" "8.58.0"
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/project-service" "8.59.1"
+    "@typescript-eslint/tsconfig-utils" "8.59.1"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"
    debug "^4.4.3"
    minimatch "^10.2.2"
    semver "^7.7.3"
    tinyglobby "^0.2.15"
    ts-api-utils "^2.5.0"

-"@typescript-eslint/utils@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-8.58.0.tgz#21a74a7963b0d288b719a4121c7dd555adaab3c3"
-  integrity sha512-RfeSqcFeHMHlAWzt4TBjWOAtoW9lnsAGiP3GbaX9uVgTYYrMbVnGONEfUCiSss+xMHFl+eHZiipmA8WkQ7FuNA==
+"@typescript-eslint/utils@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-8.59.1.tgz#cf6204d69701bbbc5b150f98c18aeef0a42c10bd"
+  integrity sha512-3pIeoXhCeYH9FSCBI8P3iNwJlGuzPlYKkTlen2O9T1DSeeg8UG8jstq6BLk+Mda0qup7mgk4z4XL4OzRaxZ8LA==
  dependencies:
    "@eslint-community/eslint-utils" "^4.9.1"
-    "@typescript-eslint/scope-manager" "8.58.0"
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/typescript-estree" "8.58.0"
+    "@typescript-eslint/scope-manager" "8.59.1"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/typescript-estree" "8.59.1"

-"@typescript-eslint/visitor-keys@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-8.58.0.tgz#2abd55a4be70fd55967aceaba4330b9ba9f45189"
-  integrity sha512-XJ9UD9+bbDo4a4epraTwG3TsNPeiB9aShrUneAVXy8q4LuwowN+qu89/6ByLMINqvIMeI9H9hOHQtg/ijrYXzQ==
+"@typescript-eslint/visitor-keys@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-8.59.1.tgz#b5cba576287a3eeb0b400b62813189abcc3f976a"
+  integrity sha512-LdDNl6C5iJExcM0Yh0PwAIBb9PrSiCsWamF/JyEZawm3kFDnRoaq3LGE4bpyRao/fWeGKKyw7icx0YxrLFC5Cg==
  dependencies:
-    "@typescript-eslint/types" "8.58.0"
+    "@typescript-eslint/types" "8.59.1"
    eslint-visitor-keys "^5.0.0"

 "@ungap/structured-clone@^1.3.0":
@@ -1909,10 +1908,10 @@ doctrine@^2.1.0:
  dependencies:
    esutils "^2.0.2"

-dotenv@^17.3.1:
-  version "17.3.1"
-  resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-17.3.1.tgz#2706f5b0165e45a1503348187b8468f87fe6aae2"
-  integrity sha512-IO8C/dzEb6O3F9/twg6ZLXz164a2fhTnEWb95H23Dm4OuN+92NmEAlTrupP9VW6Jm3sO26tQlqyvyi4CsnY9GA==
+dotenv@^17.4.2:
+  version "17.4.2"
+  resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-17.4.2.tgz#c07e54a746e11eba021dd9e1047ced5afdc1c034"
+  integrity sha512-nI4U3TottKAcAD9LLud4Cb7b2QztQMUEfHbvhTH09bqXTxnSie8WnjPALV/WMCrJZ6UV/qHJ6L03OqO3LcdYZw==

 dunder-proto@^1.0.0, dunder-proto@^1.0.1:
  version "1.0.1"
@@ -2263,17 +2262,17 @@ eslint-visitor-keys@^5.0.0, eslint-visitor-keys@^5.0.1:
  resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-5.0.1.tgz#9e3c9489697824d2d4ce3a8ad12628f91e9f59be"
  integrity sha512-tD40eHxA35h0PEIZNeIjkHoDR4YjjJp34biM0mDvplBe//mB+IHCqHDGV7pxF+7MklTvighcCPPZC7ynWyjdTA==

-eslint@^10.1.0:
-  version "10.1.0"
-  resolved "https://registry.yarnpkg.com/eslint/-/eslint-10.1.0.tgz#9ca98e654e642ab2e1af6d1e9d8613857ac341b4"
-  integrity sha512-S9jlY/ELKEUwwQnqWDO+f+m6sercqOPSqXM5Go94l7DOmxHVDgmSFGWEzeE/gwgTAr0W103BWt0QLe/7mabIvA==
+eslint@^10.2.1:
+  version "10.2.1"
+  resolved "https://registry.yarnpkg.com/eslint/-/eslint-10.2.1.tgz#224b2a6caeb34473eddcf918762363e2e063222a"
+  integrity sha512-wiyGaKsDgqXvF40P8mDwiUp/KQjE1FdrIEJsM8PZ3XCiniTMXS3OHWWUe5FI5agoCnr8x4xPrTDZuxsBlNHl+Q==
  dependencies:
    "@eslint-community/eslint-utils" "^4.8.0"
    "@eslint-community/regexpp" "^4.12.2"
-    "@eslint/config-array" "^0.23.3"
-    "@eslint/config-helpers" "^0.5.3"
-    "@eslint/core" "^1.1.1"
-    "@eslint/plugin-kit" "^0.6.1"
+    "@eslint/config-array" "^0.23.5"
+    "@eslint/config-helpers" "^0.5.5"
+    "@eslint/core" "^1.2.1"
+    "@eslint/plugin-kit" "^0.7.1"
    "@humanfs/node" "^0.16.6"
    "@humanwhocodes/module-importer" "^1.0.1"
    "@humanwhocodes/retry" "^0.4.2"
@@ -2683,7 +2682,7 @@ graceful-fs@^4.2.11:
  resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.11.tgz#4183e4e8bf08bb6e05bbb2f7d2e0c8f712ca40e3"
  integrity sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==

-handlebars@^4.7.8:
+handlebars@^4.7.9:
  version "4.7.9"
  resolved "https://registry.yarnpkg.com/handlebars/-/handlebars-4.7.9.tgz#6f139082ab58dc4e5a0e51efe7db5ae890d56a0f"
  integrity sha512-4E71E0rpOaQuJR2A3xDZ+GM1HyWYv1clR58tC8emQNeQe3RH7MAzSbat+V0wG78LQBo6m6bzSG/L4pBuCsgnUQ==
@@ -4130,10 +4129,10 @@ prettier-linter-helpers@^1.0.1:
  dependencies:
    fast-diff "^1.1.2"

-prettier@^3.8.1:
-  version "3.8.1"
-  resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.8.1.tgz#edf48977cf991558f4fcbd8a3ba6015ba2a3a173"
-  integrity sha512-UOnG6LftzbdaHZcKoPFtOcCKztrQ57WkHDeRD9t/PTQtmT0NHSeWWepj6pS0z/N7+08BHFDQVUrfmfMRcZwbMg==
+prettier@^3.8.3:
+  version "3.8.3"
+  resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.8.3.tgz#560f2de55bf01b4c0503bc629d5df99b9a1d09b0"
+  integrity sha512-7igPTM53cGHMW8xWuVTydi2KO233VFiTNyF5hLJqpilHfmn8C8gPf+PS7dUT64YcXFbiMGZxS9pCSxL/Dxm/Jw==

 pretty-format@30.2.0, pretty-format@^30.0.0:
  version "30.2.0"
@@ -4287,7 +4286,7 @@ 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@^7.7.2, semver@^7.7.3:
+semver@^7.5.3, semver@^7.5.4, semver@^7.7.2, semver@^7.7.3, semver@^7.7.4:
  version "7.7.4"
  resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.4.tgz#28464e36060e991fa7a11d0279d2d3f3b57a7e8a"
  integrity sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA==
@@ -4623,18 +4622,18 @@ ts-api-utils@^2.5.0:
  resolved "https://registry.yarnpkg.com/ts-api-utils/-/ts-api-utils-2.5.0.tgz#4acd4a155e22734990a5ed1fe9e97f113bcb37c1"
  integrity sha512-OJ/ibxhPlqrMM0UiNHJ/0CKQkoKF243/AEmplt3qpRgkW8VG7IfOS41h7V8TjITqdByHzrjcS/2si+y4lIh8NA==

-ts-jest@^29.4.6:
-  version "29.4.6"
-  resolved "https://registry.yarnpkg.com/ts-jest/-/ts-jest-29.4.6.tgz#51cb7c133f227396818b71297ad7409bb77106e9"
-  integrity sha512-fSpWtOO/1AjSNQguk43hb/JCo16oJDnMJf3CdEGNkqsEX3t0KX96xvyX1D7PfLCpVoKu4MfVrqUkFyblYoY4lA==
+ts-jest@^29.4.9:
+  version "29.4.9"
+  resolved "https://registry.yarnpkg.com/ts-jest/-/ts-jest-29.4.9.tgz#47dc33d0f5c36bddcedd16afefae285e0b049d2d"
+  integrity sha512-LTb9496gYPMCqjeDLdPrKuXtncudeV1yRZnF4Wo5l3SFi0RYEnYRNgMrFIdg+FHvfzjCyQk1cLncWVqiSX+EvQ==
  dependencies:
    bs-logger "^0.2.6"
    fast-json-stable-stringify "^2.1.0"
-    handlebars "^4.7.8"
+    handlebars "^4.7.9"
    json5 "^2.2.3"
    lodash.memoize "^4.1.2"
    make-error "^1.3.6"
-    semver "^7.7.3"
+    semver "^7.7.4"
    type-fest "^4.41.0"
    yargs-parser "^21.1.1"

@@ -4854,11 +4853,6 @@ uuid@10.0.0, uuid@^10.0.0:
  resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
  integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==

-uuid@^11.1.0:
-  version "11.1.0"
-  resolved "https://registry.yarnpkg.com/uuid/-/uuid-11.1.0.tgz#9549028be1753bb934fc96e2bca09bb4105ae912"
-  integrity sha512-0/A9rDy9P7cJ+8w1c9WD9V//9Wj15Ce2MPz8Ri6032usz+NfePxx5AcN3bN+r6ZL6jEo066/yNYB3tn4pQEx+A==
-
 uuid@^13.0.0:
  version "13.0.0"
  resolved "https://registry.yarnpkg.com/uuid/-/uuid-13.0.0.tgz#263dc341b19b4d755eb8fe36b78d95a6b65707e8"
@@ -9,8 +9,8 @@
  },
  "dependencies": {
    "@js-monorepo-example/shared": "*",
-    "@langchain/core": "^1.1.38",
-    "@langchain/langgraph": "^1.2.6"
+    "@langchain/core": "^1.1.42",
+    "@langchain/langgraph": "^1.2.9"
  },
  "devDependencies": {
    "typescript": "^5.9.3"
@@ -17,18 +17,18 @@
    "lint": "eslint 'apps/**/*.ts' 'libs/**/*.ts'"
  },
  "devDependencies": {
-    "turbo": "^2.9.3",
+    "turbo": "^2.9.7",
    "typescript": "^5.9.3",
    "@tsconfig/recommended": "^1.0.13",
    "@eslint/eslintrc": "^3.3.5",
    "@eslint/js": "^10.0.1",
-    "eslint": "^10.1.0",
+    "eslint": "^10.2.1",
    "eslint-config-prettier": "^10.1.8",
    "eslint-plugin-import": "^2.27.5",
    "eslint-plugin-no-instanceof": "^1.0.1",
    "eslint-plugin-prettier": "^5.5.5",
-    "@typescript-eslint/eslint-plugin": "^8.58.0",
-    "@typescript-eslint/parser": "^8.58.0",
-    "prettier": "^3.8.1"
+    "@typescript-eslint/eslint-plugin": "^8.59.1",
+    "@typescript-eslint/parser": "^8.59.1",
+    "prettier": "^3.8.3"
  }
 }
@@ -19,26 +19,26 @@
  resolved "https://registry.yarnpkg.com/@eslint-community/regexpp/-/regexpp-4.12.2.tgz#bccdf615bcf7b6e8db830ec0b8d21c9a25de597b"
  integrity sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew==

-"@eslint/config-array@^0.23.3":
-  version "0.23.3"
-  resolved "https://registry.yarnpkg.com/@eslint/config-array/-/config-array-0.23.3.tgz#3f4a93dd546169c09130cbd10f2415b13a20a219"
-  integrity sha512-j+eEWmB6YYLwcNOdlwQ6L2OsptI/LO6lNBuLIqe5R7RetD658HLoF+Mn7LzYmAWWNNzdC6cqP+L6r8ujeYXWLw==
+"@eslint/config-array@^0.23.5":
+  version "0.23.5"
+  resolved "https://registry.yarnpkg.com/@eslint/config-array/-/config-array-0.23.5.tgz#56e86d243049195d8acc0c06a1b3dfdc3fa3de95"
+  integrity sha512-Y3kKLvC1dvTOT+oGlqNQ1XLqK6D1HU2YXPc52NmAlJZbMMWDzGYXMiPRJ8TYD39muD/OTjlZmNJ4ib7dvSrMBA==
  dependencies:
-    "@eslint/object-schema" "^3.0.3"
+    "@eslint/object-schema" "^3.0.5"
    debug "^4.3.1"
    minimatch "^10.2.4"

-"@eslint/config-helpers@^0.5.3":
-  version "0.5.3"
-  resolved "https://registry.yarnpkg.com/@eslint/config-helpers/-/config-helpers-0.5.3.tgz#721fe6bbb90d74b0c80d6ff2428e5bbcb002becb"
-  integrity sha512-lzGN0onllOZCGroKJmRwY6QcEHxbjBw1gwB8SgRSqK8YbbtEXMvKynsXc3553ckIEBxsbMBU7oOZXKIPGZNeZw==
+"@eslint/config-helpers@^0.5.5":
+  version "0.5.5"
+  resolved "https://registry.yarnpkg.com/@eslint/config-helpers/-/config-helpers-0.5.5.tgz#ae16134e4792ac5fbdc533548a24ac1ea9f7f3ae"
+  integrity sha512-eIJYKTCECbP/nsKaaruF6LW967mtbQbsw4JTtSVkUQc9MneSkbrgPJAbKl9nWr0ZeowV8BfsarBmPpBzGelA2w==
  dependencies:
-    "@eslint/core" "^1.1.1"
+    "@eslint/core" "^1.2.1"

-"@eslint/core@^1.1.1":
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/@eslint/core/-/core-1.1.1.tgz#450f3d2be2d463ccd51119544092256b4e88df32"
-  integrity sha512-QUPblTtE51/7/Zhfv8BDwO0qkkzQL7P/aWWbqcf4xWLEYn1oKjdO0gglQBB4GAsu7u6wjijbCmzsUTy6mnk6oQ==
+"@eslint/core@^1.2.1":
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/@eslint/core/-/core-1.2.1.tgz#c1da7cd1b82fa8787f98b5629fb811848a1b63ce"
+  integrity sha512-MwcE1P+AZ4C6DWlpin/OmOA54mmIZ/+xZuJiQd4SyB29oAJjN30UW9wkKNptW2ctp4cEsvhlLY/CsQ1uoHDloQ==
  dependencies:
    "@types/json-schema" "^7.0.15"

@@ -62,17 +62,17 @@
  resolved "https://registry.yarnpkg.com/@eslint/js/-/js-10.0.1.tgz#1e8a876f50117af8ab67e47d5ad94d38d6622583"
  integrity sha512-zeR9k5pd4gxjZ0abRoIaxdc7I3nDktoXZk2qOv9gCNWx3mVwEn32VRhyLaRsDiJjTs0xq/T8mfPtyuXu7GWBcA==

-"@eslint/object-schema@^3.0.3":
-  version "3.0.3"
-  resolved "https://registry.yarnpkg.com/@eslint/object-schema/-/object-schema-3.0.3.tgz#5bf671e52e382e4adc47a9906f2699374637db6b"
-  integrity sha512-iM869Pugn9Nsxbh/YHRqYiqd23AmIbxJOcpUMOuWCVNdoQJ5ZtwL6h3t0bcZzJUlC3Dq9jCFCESBZnX0GTv7iQ==
+"@eslint/object-schema@^3.0.5":
+  version "3.0.5"
+  resolved "https://registry.yarnpkg.com/@eslint/object-schema/-/object-schema-3.0.5.tgz#88e9bf4d11d2b19c082e78ebe7ce88724a5eb091"
+  integrity sha512-vqTaUEgxzm+YDSdElad6PiRoX4t8VGDjCtt05zn4nU810UIx/uNEV7/lZJ6KwFThKZOzOxzXy48da+No7HZaMw==

-"@eslint/plugin-kit@^0.6.1":
-  version "0.6.1"
-  resolved "https://registry.yarnpkg.com/@eslint/plugin-kit/-/plugin-kit-0.6.1.tgz#eb9e6689b56ce8bc1855bb33090e63f3fc115e8e"
-  integrity sha512-iH1B076HoAshH1mLpHMgwdGeTs0CYwL0SPMkGuSebZrwBp16v415e9NZXg2jtrqPVQjf6IANe2Vtlr5KswtcZQ==
+"@eslint/plugin-kit@^0.7.1":
+  version "0.7.1"
+  resolved "https://registry.yarnpkg.com/@eslint/plugin-kit/-/plugin-kit-0.7.1.tgz#c4125fd015eceeb09b793109fdbcd4dd0a02d346"
+  integrity sha512-rZAP3aVgB9ds9KOeUSL+zZ21hPmo8dh6fnIFwRQj5EAZl9gzR7wxYbYXYysAM8CTqGmUGyp2S4kUdV17MnGuWQ==
  dependencies:
-    "@eslint/core" "^1.1.1"
+    "@eslint/core" "^1.2.1"
    levn "^0.4.1"

 "@humanfs/core@^0.19.1":
@@ -103,10 +103,10 @@
  resolved "https://registry.yarnpkg.com/@isaacs/cliui/-/cliui-9.0.0.tgz#4d0a3f127058043bf2e7ee169eaf30ed901302f3"
  integrity sha512-AokJm4tuBHillT+FpMtxQ60n8ObyXBatq7jD2/JA9dxbDDokKQm8KMht5ibGzLVU9IJDIKK4TPKgMHEYMn3lMg==

-"@langchain/core@^1.1.38":
-  version "1.1.38"
-  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-1.1.38.tgz#b5939b2e77af408771c9bb95bf56a5ad8183313e"
-  integrity sha512-C340wH1YL10CiVOFlEpQMp0zQE85/eBLKX/gi1Lv7shAyUmR3CQ0t/mXlCd5RsZa6ntAN1kDJnp64ArWey9XAA==
+"@langchain/core@^1.1.42":
+  version "1.1.42"
+  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-1.1.42.tgz#607bfd34c1414c9cbeb2859fda1b254a14c28085"
+  integrity sha512-d0tN96BrwPMryYyWR9VfyAntSivn7EQrZCe5Kpxum93tcjTXbKKmKvItFec8AluQt88iTcmAJrahUZUNfzGwTA==
  dependencies:
    "@cfworker/json-schema" "^4.0.2"
    "@standard-schema/spec" "^1.1.0"
@@ -117,7 +117,6 @@
    langsmith ">=0.5.0 <1.0.0"
    mustache "^4.2.0"
    p-queue "^6.6.2"
-    uuid "^11.1.0"
    zod "^3.25.76 || ^4"

 "@langchain/langgraph-checkpoint@^1.0.1":
@@ -127,23 +126,23 @@
  dependencies:
    uuid "^10.0.0"

-"@langchain/langgraph-sdk@~1.8.1":
-  version "1.8.3"
-  resolved "https://registry.yarnpkg.com/@langchain/langgraph-sdk/-/langgraph-sdk-1.8.3.tgz#deedc9bddd863693c6ab5f3a74058548b040377d"
-  integrity sha512-Py0S5yVtlOHz410aEsSGLRKjtsK2giDvfPS1JjAjTdcs71khuJufFtUZFwmwdJCbsG4DaGurRLHOAJu9jO4a0g==
+"@langchain/langgraph-sdk@~1.8.9":
+  version "1.8.10"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph-sdk/-/langgraph-sdk-1.8.10.tgz#bd175cda1ab629845ef28b22b45f3bdd1f65b029"
+  integrity sha512-wrB3rkRw5KAmsqezwvKP3midT4qJrV6Hj9XJMYo+cbvXC4HYpSAmyY/VriSyeTFRbLG/OP/pY2Yz+9Z54nSaXQ==
  dependencies:
    "@types/json-schema" "^7.0.15"
    p-queue "^9.0.1"
    p-retry "^7.1.1"
    uuid "^13.0.0"

-"@langchain/langgraph@^1.2.6":
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-1.2.6.tgz#e6a578f96fbbc1c54ba78e0298884671dad5d195"
-  integrity sha512-5cX402dNGN6w9+0mlMU2dgUiKx6Y1tlENp7x05e9ByDbQCHSDc0kyqRWNFLGc7vatQ92S4ylxQrcCJvi8Fr4SQ==
+"@langchain/langgraph@^1.2.9":
+  version "1.2.9"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-1.2.9.tgz#4f0ba159062c3649c83cf5f621ef926b96933e32"
+  integrity sha512-3c7BtGycHC2v9p6w/Hv8L7kEl1YnZYOQTDJtmAp3knk6JOedO7d2bYP3y0SRyhv5orUEGf/KGvx8ZsB/ideP7g==
  dependencies:
    "@langchain/langgraph-checkpoint" "^1.0.1"
-    "@langchain/langgraph-sdk" "~1.8.1"
+    "@langchain/langgraph-sdk" "~1.8.9"
    "@standard-schema/spec" "1.1.0"
    uuid "^10.0.0"

@@ -167,35 +166,35 @@
  resolved "https://registry.yarnpkg.com/@tsconfig/recommended/-/recommended-1.0.13.tgz#269fce3ad04ca70b93269ff44cca81b950f542da"
  integrity sha512-sySRuBfMKyKO/j2ZAhR8kSembhjuPEV4Ra3AHtmWLq51+iGaudr45crPSzNC5b7/Ctrh9dfUpBuTlYrH6rM58Q==

-"@turbo/darwin-64@2.9.3":
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/@turbo/darwin-64/-/darwin-64-2.9.3.tgz#5d782d341ddebc20822d09ba1393238d4467ae7b"
-  integrity sha512-P8foouaP+y/p+hhEGBoZpzMbpVvUMwPjDpcy6wN7EYfvvyISD1USuV27qWkczecihwuPJzQ1lDBuL8ERcavTyg==
+"@turbo/darwin-64@2.9.7":
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/@turbo/darwin-64/-/darwin-64-2.9.7.tgz#46fddae01ea7817192dc6ff36a678cc3879b753b"
+  integrity sha512-wnvOWuVWJ5EUHNKxExEWiGlTeVpLG1L0PCu5MUozyC1P2SHGiWsmpW6/yAuShH91Fa2TAHOvdCRBzriZh4j4Eg==

-"@turbo/darwin-arm64@2.9.3":
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/@turbo/darwin-arm64/-/darwin-arm64-2.9.3.tgz#75cd662d9c2a572532ce82d7cae1a30e2497d693"
-  integrity sha512-SIzEkvtNdzdI50FJDaIQ6kQGqgSSdFPcdn0wqmmONN6iGKjy6hsT+EH99GP65FsfV7DLZTh2NmtTIRl2kdoz5Q==
+"@turbo/darwin-arm64@2.9.7":
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/@turbo/darwin-arm64/-/darwin-arm64-2.9.7.tgz#cd7cf3a024509af0f59ae3885216d62fd090fdb6"
+  integrity sha512-mA0FIPMwwN3lodDkQYaGxj6PeT7ZaN5aCEbkKn/WB+ZB9yJdVWA4J83GH7t43jqDc5dcnVluVN5UFx3plRiXhA==

-"@turbo/linux-64@2.9.3":
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/@turbo/linux-64/-/linux-64-2.9.3.tgz#de693a996071654a0bea51c7ed9dc6fdc3950ae8"
-  integrity sha512-pLRwFmcHHNBvsCySLS6OFabr/07kDT2pxEt/k6eBf/3asiVQZKJ7Rk88AafQx2aYA641qek4RsXvYO3JYpiBug==
+"@turbo/linux-64@2.9.7":
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/@turbo/linux-64/-/linux-64-2.9.7.tgz#25ee9a2cb3042498a3203e14653b14041105020f"
+  integrity sha512-fEbUYpgb5l7P+q+5tsWF2gw+/GSjUsuUTcnfm+f0lozUjgcjLKyOat6PgtAChmIFcTPchCL/8rJ3TvkBy01gfA==

-"@turbo/linux-arm64@2.9.3":
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/@turbo/linux-arm64/-/linux-arm64-2.9.3.tgz#88fc6fe572d83c5488eeae9b564b57c335f4d856"
-  integrity sha512-gy6ApUroC2Nzv+qjGtE/uPNkhHAFU4c8God+zd5Aiv9L9uBgHlxVJpHT3XWl5xwlJZ2KWuMrlHTaS5kmNB+q1Q==
+"@turbo/linux-arm64@2.9.7":
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/@turbo/linux-arm64/-/linux-arm64-2.9.7.tgz#5b19d6249a50cb8153b8f2bc22ad10bbda614fbe"
+  integrity sha512-VkUjulo9ytfHKUHOS5gy0XPoh4CTKPXWCL8nLdrlHVi9fSut31ECeUqnm/dAbETP5D4xo9mH9XkJ+qMzGe/zmg==

-"@turbo/windows-64@2.9.3":
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/@turbo/windows-64/-/windows-64-2.9.3.tgz#a7812e4f33972f74daed93b2ac8845f88a68c775"
-  integrity sha512-d0YelTX6hAsB7kIEtGB3PzIzSfAg3yDoUlHwuwJc3adBXUsyUIs0YLG+1NNtuhcDOUGnWQeKUoJ2pGWvbpRj7w==
+"@turbo/windows-64@2.9.7":
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/@turbo/windows-64/-/windows-64-2.9.7.tgz#900645776fa44ff8333e801d890992736f6dbc72"
+  integrity sha512-/GWdY6/x4aIHqkYJq596Rpdk1x0MkpRPkJcLAoB3yGRwyUms0+u2F1GnV54IbyAZTeKLRWSJKzNC+QwVGdYchA==

-"@turbo/windows-arm64@2.9.3":
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/@turbo/windows-arm64/-/windows-arm64-2.9.3.tgz#1891a89fe87d501e63a7a4367082f3afec18fa6f"
-  integrity sha512-/08CwpKJl3oRY8nOlh2YgilZVJDHsr60XTNxRhuDeuFXONpUZ5X+Nv65izbG/xBew9qxcJFbDX9/sAmAX+ITcQ==
+"@turbo/windows-arm64@2.9.7":
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/@turbo/windows-arm64/-/windows-arm64-2.9.7.tgz#e678d73fdbcbb12a679403fe7ba070fb67f37810"
+  integrity sha512-xBBgxCC5PK2+WZ1PPRZdp+aJ0bMBcEbweXWux3RUHJvX9ZodcoQySkrW6qt+ahb+uk8ZjyQodLfDwtVSoYds1w==

 "@types/esrecurse@^4.3.1":
  version "4.3.1"
@@ -217,100 +216,100 @@
  resolved "https://registry.yarnpkg.com/@types/json5/-/json5-0.0.29.tgz#ee28707ae94e11d2b827bcbe5270bcea7f3e71ee"
  integrity sha512-dRLjCWHYg4oaA77cxO64oO+7JwCwnIzkZPdrrC71jQmQtlhM556pwKo5bUzqvZndkVbeFLIIi+9TC40JNF5hNQ==

-"@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"
-  integrity sha512-RLkVSiNuUP1C2ROIWfqX+YcUfLaSnxGE/8M+Y57lopVwg9VTYYfhuz15Yf1IzCKgZj6/rIbYTmJCUSqr76r0Wg==
+"@typescript-eslint/eslint-plugin@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-8.59.1.tgz#781bc6f9002982cfaf75a185240e24ad7276628a"
+  integrity sha512-BOziFIfE+6osHO9FoJG4zjoHUcvI7fTNBSpdAwrNH0/TLvzjsk2oo8XSSOT2HhqUyhZPfHv4UOffoJ9oEEQ7Ag==
  dependencies:
    "@eslint-community/regexpp" "^4.12.2"
-    "@typescript-eslint/scope-manager" "8.58.0"
-    "@typescript-eslint/type-utils" "8.58.0"
-    "@typescript-eslint/utils" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/scope-manager" "8.59.1"
+    "@typescript-eslint/type-utils" "8.59.1"
+    "@typescript-eslint/utils" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"
    ignore "^7.0.5"
    natural-compare "^1.4.0"
    ts-api-utils "^2.5.0"

-"@typescript-eslint/parser@^8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-8.58.0.tgz#da04ece1967b6c2fe8f10c3473dabf3825795ef7"
-  integrity sha512-rLoGZIf9afaRBYsPUMtvkDWykwXwUPL60HebR4JgTI8mxfFe2cQTu3AGitANp4b9B2QlVru6WzjgB2IzJKiCSA==
+"@typescript-eslint/parser@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-8.59.1.tgz#835d20a62350659a082a1ae2a60b822c40488905"
+  integrity sha512-HDQH9O/47Dxi1ceDhBXdaldtf/WV9yRYMjbjCuNk3qnaTD564qwv61Y7+gTxwxRKzSrgO5uhtw584igXVuuZkA==
  dependencies:
-    "@typescript-eslint/scope-manager" "8.58.0"
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/typescript-estree" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/scope-manager" "8.59.1"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/typescript-estree" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"
    debug "^4.4.3"

-"@typescript-eslint/project-service@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/project-service/-/project-service-8.58.0.tgz#66ceda0aabf7427aec3e2713fa43eb278dead2aa"
-  integrity sha512-8Q/wBPWLQP1j16NxoPNIKpDZFMaxl7yWIoqXWYeWO+Bbd2mjgvoF0dxP2jKZg5+x49rgKdf7Ck473M8PC3V9lg==
+"@typescript-eslint/project-service@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/project-service/-/project-service-8.59.1.tgz#49efe87c37ef84262f23df8bf62fdc56698ca6fe"
+  integrity sha512-+MuHQlHiEr00Of/IQbE/MmEoi44znZHbR/Pz7Opq4HryUOlRi+/44dro9Ycy8Fyo+/024IWtw8m4JUMCGTYxDg==
  dependencies:
-    "@typescript-eslint/tsconfig-utils" "^8.58.0"
-    "@typescript-eslint/types" "^8.58.0"
+    "@typescript-eslint/tsconfig-utils" "^8.59.1"
+    "@typescript-eslint/types" "^8.59.1"
    debug "^4.4.3"

-"@typescript-eslint/scope-manager@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-8.58.0.tgz#e304142775e49a1b7ac3c8bf2536714447c72cab"
-  integrity sha512-W1Lur1oF50FxSnNdGp3Vs6P+yBRSmZiw4IIjEeYxd8UQJwhUF0gDgDD/W/Tgmh73mxgEU3qX0Bzdl/NGuSPEpQ==
+"@typescript-eslint/scope-manager@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-8.59.1.tgz#ed90d054fc3db2d0c81464db3a953a94fb85bb58"
+  integrity sha512-LwuHQI4pDOYVKvmH2dkaJo6YZCSgouVgnS/z7yBPKBMvgtBvyLqiLy9Z6b7+m/TRcX1NFYUqZetI5Y+aT4GEfg==
  dependencies:
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"

-"@typescript-eslint/tsconfig-utils@8.58.0", "@typescript-eslint/tsconfig-utils@^8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/tsconfig-utils/-/tsconfig-utils-8.58.0.tgz#c5a8edb21f31e0fdee565724e1b984171c559482"
-  integrity sha512-doNSZEVJsWEu4htiVC+PR6NpM+pa+a4ClH9INRWOWCUzMst/VA9c4gXq92F8GUD1rwhNvRLkgjfYtFXegXQF7A==
+"@typescript-eslint/tsconfig-utils@8.59.1", "@typescript-eslint/tsconfig-utils@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/tsconfig-utils/-/tsconfig-utils-8.59.1.tgz#ba2a779a444f1d5cb92a606f9b209d239fd4cab1"
+  integrity sha512-/0nEyPbX7gRsk0Uwfe4ALwwgxuA66d/l2mhRDNlAvaj4U3juhUtJNq0DsY8M2AYwwb9rEq2hrC3IcIcEt++iJA==

-"@typescript-eslint/type-utils@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-8.58.0.tgz#ce0e72cd967ffbbe8de322db6089bd4374be352f"
-  integrity sha512-aGsCQImkDIqMyx1u4PrVlbi/krmDsQUs4zAcCV6M7yPcPev+RqVlndsJy9kJ8TLihW9TZ0kbDAzctpLn5o+lOg==
+"@typescript-eslint/type-utils@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-8.59.1.tgz#9c83d3f2ed9187a815e8120f72c08317e513e409"
+  integrity sha512-klWPBR2ciQHS3f++ug/mVnWKPjBUo7icEL3FAO1lhAR1Z1i5NQYZ1EannMSRYcq5qCv5wNALlXr6fksRHyYl7w==
  dependencies:
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/typescript-estree" "8.58.0"
-    "@typescript-eslint/utils" "8.58.0"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/typescript-estree" "8.59.1"
+    "@typescript-eslint/utils" "8.59.1"
    debug "^4.4.3"
    ts-api-utils "^2.5.0"

-"@typescript-eslint/types@8.58.0", "@typescript-eslint/types@^8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-8.58.0.tgz#e94ae7abdc1c6530e71183c1007b61fa93112a5a"
-  integrity sha512-O9CjxypDT89fbHxRfETNoAnHj/i6IpRK0CvbVN3qibxlLdo5p5hcLmUuCCrHMpxiWSwKyI8mCP7qRNYuOJ0Uww==
+"@typescript-eslint/types@8.59.1", "@typescript-eslint/types@^8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-8.59.1.tgz#c1d014d3f03a97e0113a8899fc9d4e45a7fb0ca9"
+  integrity sha512-ZDCjgccSdYPw5Bxh+my4Z0lJU96ZDN7jbBzvmEn0FZx3RtU1C7VWl6NbDx94bwY3V5YsgwRzJPOgeY2Q/nLG8A==

-"@typescript-eslint/typescript-estree@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-8.58.0.tgz#ed233faa8e2f2a2e1357c3e7d553d6465a0ee59a"
-  integrity sha512-7vv5UWbHqew/dvs+D3e1RvLv1v2eeZ9txRHPnEEBUgSNLx5ghdzjHa0sgLWYVKssH+lYmV0JaWdoubo0ncGYLA==
+"@typescript-eslint/typescript-estree@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-8.59.1.tgz#4391fadf98a22c869c5b6522dbf4e491e53e351a"
+  integrity sha512-OUd+vJS05sSkOip+BkZ/2NS8RMxrAAJemsC6vU3kmfLyeaJT0TftHkV9mcx2107MmsBVXXexhVu4F0TZXyMl4g==
  dependencies:
-    "@typescript-eslint/project-service" "8.58.0"
-    "@typescript-eslint/tsconfig-utils" "8.58.0"
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/visitor-keys" "8.58.0"
+    "@typescript-eslint/project-service" "8.59.1"
+    "@typescript-eslint/tsconfig-utils" "8.59.1"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/visitor-keys" "8.59.1"
    debug "^4.4.3"
    minimatch "^10.2.2"
    semver "^7.7.3"
    tinyglobby "^0.2.15"
    ts-api-utils "^2.5.0"

-"@typescript-eslint/utils@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-8.58.0.tgz#21a74a7963b0d288b719a4121c7dd555adaab3c3"
-  integrity sha512-RfeSqcFeHMHlAWzt4TBjWOAtoW9lnsAGiP3GbaX9uVgTYYrMbVnGONEfUCiSss+xMHFl+eHZiipmA8WkQ7FuNA==
+"@typescript-eslint/utils@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-8.59.1.tgz#cf6204d69701bbbc5b150f98c18aeef0a42c10bd"
+  integrity sha512-3pIeoXhCeYH9FSCBI8P3iNwJlGuzPlYKkTlen2O9T1DSeeg8UG8jstq6BLk+Mda0qup7mgk4z4XL4OzRaxZ8LA==
  dependencies:
    "@eslint-community/eslint-utils" "^4.9.1"
-    "@typescript-eslint/scope-manager" "8.58.0"
-    "@typescript-eslint/types" "8.58.0"
-    "@typescript-eslint/typescript-estree" "8.58.0"
+    "@typescript-eslint/scope-manager" "8.59.1"
+    "@typescript-eslint/types" "8.59.1"
+    "@typescript-eslint/typescript-estree" "8.59.1"

-"@typescript-eslint/visitor-keys@8.58.0":
-  version "8.58.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-8.58.0.tgz#2abd55a4be70fd55967aceaba4330b9ba9f45189"
-  integrity sha512-XJ9UD9+bbDo4a4epraTwG3TsNPeiB9aShrUneAVXy8q4LuwowN+qu89/6ByLMINqvIMeI9H9hOHQtg/ijrYXzQ==
+"@typescript-eslint/visitor-keys@8.59.1":
+  version "8.59.1"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-8.59.1.tgz#b5cba576287a3eeb0b400b62813189abcc3f976a"
+  integrity sha512-LdDNl6C5iJExcM0Yh0PwAIBb9PrSiCsWamF/JyEZawm3kFDnRoaq3LGE4bpyRao/fWeGKKyw7icx0YxrLFC5Cg==
  dependencies:
-    "@typescript-eslint/types" "8.58.0"
+    "@typescript-eslint/types" "8.59.1"
    eslint-visitor-keys "^5.0.0"

 acorn-jsx@^5.3.2:
@@ -787,17 +786,17 @@ eslint-visitor-keys@^5.0.0, eslint-visitor-keys@^5.0.1:
  resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-5.0.1.tgz#9e3c9489697824d2d4ce3a8ad12628f91e9f59be"
  integrity sha512-tD40eHxA35h0PEIZNeIjkHoDR4YjjJp34biM0mDvplBe//mB+IHCqHDGV7pxF+7MklTvighcCPPZC7ynWyjdTA==

-eslint@^10.1.0:
-  version "10.1.0"
-  resolved "https://registry.yarnpkg.com/eslint/-/eslint-10.1.0.tgz#9ca98e654e642ab2e1af6d1e9d8613857ac341b4"
-  integrity sha512-S9jlY/ELKEUwwQnqWDO+f+m6sercqOPSqXM5Go94l7DOmxHVDgmSFGWEzeE/gwgTAr0W103BWt0QLe/7mabIvA==
+eslint@^10.2.1:
+  version "10.2.1"
+  resolved "https://registry.yarnpkg.com/eslint/-/eslint-10.2.1.tgz#224b2a6caeb34473eddcf918762363e2e063222a"
+  integrity sha512-wiyGaKsDgqXvF40P8mDwiUp/KQjE1FdrIEJsM8PZ3XCiniTMXS3OHWWUe5FI5agoCnr8x4xPrTDZuxsBlNHl+Q==
  dependencies:
    "@eslint-community/eslint-utils" "^4.8.0"
    "@eslint-community/regexpp" "^4.12.2"
-    "@eslint/config-array" "^0.23.3"
-    "@eslint/config-helpers" "^0.5.3"
-    "@eslint/core" "^1.1.1"
-    "@eslint/plugin-kit" "^0.6.1"
+    "@eslint/config-array" "^0.23.5"
+    "@eslint/config-helpers" "^0.5.5"
+    "@eslint/core" "^1.2.1"
+    "@eslint/plugin-kit" "^0.7.1"
    "@humanfs/node" "^0.16.6"
    "@humanwhocodes/module-importer" "^1.0.1"
    "@humanwhocodes/retry" "^0.4.2"
@@ -1559,10 +1558,10 @@ prettier-linter-helpers@^1.0.1:
  dependencies:
    fast-diff "^1.1.2"

-prettier@^3.8.1:
-  version "3.8.1"
-  resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.8.1.tgz#edf48977cf991558f4fcbd8a3ba6015ba2a3a173"
-  integrity sha512-UOnG6LftzbdaHZcKoPFtOcCKztrQ57WkHDeRD9t/PTQtmT0NHSeWWepj6pS0z/N7+08BHFDQVUrfmfMRcZwbMg==
+prettier@^3.8.3:
+  version "3.8.3"
+  resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.8.3.tgz#560f2de55bf01b4c0503bc629d5df99b9a1d09b0"
+  integrity sha512-7igPTM53cGHMW8xWuVTydi2KO233VFiTNyF5hLJqpilHfmn8C8gPf+PS7dUT64YcXFbiMGZxS9pCSxL/Dxm/Jw==

 punycode@^2.1.0:
  version "2.3.1"
@@ -1815,17 +1814,17 @@ tsconfig-paths@^3.15.0:
    minimist "^1.2.6"
    strip-bom "^3.0.0"

-turbo@^2.9.3:
-  version "2.9.3"
-  resolved "https://registry.yarnpkg.com/turbo/-/turbo-2.9.3.tgz#4f0adf2c8677edd7b46d400629c99e9e26e58e44"
-  integrity sha512-J/VUvsGRykPb9R8Kh8dHVBOqioDexLk9BhLCU/ZybRR+HN9UR3cURdazFvNgMDt9zPP8TF6K73Z+tplfmi0PqQ==
+turbo@^2.9.7:
+  version "2.9.7"
+  resolved "https://registry.yarnpkg.com/turbo/-/turbo-2.9.7.tgz#da6d5a821f0bde5174e1a55af6690dad5c2fb004"
+  integrity sha512-epxzqVO2s0IxcSWcgb+qKrtco8isfe7g3VtiS6hkYnEK4A9XQDZbrtavQ6MtWR1KoQn+1fUomaQth2rfRHlUlg==
  optionalDependencies:
-    "@turbo/darwin-64" "2.9.3"
-    "@turbo/darwin-arm64" "2.9.3"
-    "@turbo/linux-64" "2.9.3"
-    "@turbo/linux-arm64" "2.9.3"
-    "@turbo/windows-64" "2.9.3"
-    "@turbo/windows-arm64" "2.9.3"
+    "@turbo/darwin-64" "2.9.7"
+    "@turbo/darwin-arm64" "2.9.7"
+    "@turbo/linux-64" "2.9.7"
+    "@turbo/linux-arm64" "2.9.7"
+    "@turbo/windows-64" "2.9.7"
+    "@turbo/windows-arm64" "2.9.7"

 type-check@^0.4.0, type-check@~0.4.0:
  version "0.4.0"
@@ -1906,11 +1905,6 @@ uuid@10.0.0, uuid@^10.0.0:
  resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
  integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==

-uuid@^11.1.0:
-  version "11.1.0"
-  resolved "https://registry.yarnpkg.com/uuid/-/uuid-11.1.0.tgz#9549028be1753bb934fc96e2bca09bb4105ae912"
-  integrity sha512-0/A9rDy9P7cJ+8w1c9WD9V//9Wj15Ce2MPz8Ri6032usz+NfePxx5AcN3bN+r6ZL6jEo066/yNYB3tn4pQEx+A==
-
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  resolved "https://registry.yarnpkg.com/uuid/-/uuid-13.0.0.tgz#263dc341b19b4d755eb8fe36b78d95a6b65707e8"
@@ -29,7 +29,7 @@ inmem = [

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

@@ -2,7 +2,8 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.11'",
+    "python_full_version >= '3.15'",
+    "python_full_version >= '3.11' and python_full_version < '3.15'",
    "python_full_version < '3.11'",
 ]

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+version = "8.3.3"
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+version = "1.20.2"
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@@ -18,7 +18,7 @@
  <a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
  <a href="https://github.com/langchain-ai/langgraph/issues" target="_blank"><img src="https://img.shields.io/github/issues-raw/langchain-ai/langgraph" alt="Open Issues"></a>
  <a href="https://docs.langchain.com/oss/python/langgraph/overview" target="_blank"><img src="https://img.shields.io/badge/docs-latest-blue" alt="Docs"></a>
-  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
+  <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
 </div>

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

 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")
@@ -93,6 +105,7 @@ RESERVED = {
    INTERRUPT,
    RESUME,
    ERROR,
+    ERROR_SOURCE_NODE,
    NO_WRITES,
    # reserved config.configurable keys
    CONFIG_KEY_SEND,
@@ -106,7 +119,9 @@ RESERVED = {
    CONFIG_KEY_CHECKPOINT_MAP,
    CONFIG_KEY_CHECKPOINT_ID,
    CONFIG_KEY_CHECKPOINT_NS,
+    CONFIG_KEY_TIMED_ATTEMPT_OBSERVER,
    CONFIG_KEY_RESUME_MAP,
+    CONFIG_KEY_STREAM_MESSAGES_V2,
    # other constants
    PUSH,
    PULL,
@@ -51,9 +51,11 @@ from langgraph._internal._config import (
 )
 from langgraph._internal._constants import (
    CONF,
+    CONFIG_KEY_NODE_ERROR,
    CONFIG_KEY_RUNTIME,
 )
 from langgraph._internal._typing import MISSING
+from langgraph.errors import NodeError
 from langgraph.types import StreamWriter

 try:
@@ -117,6 +119,19 @@ def set_config_context(
        ctx.run(_unset_config_context, config_token, run)


+def create_task_in_config_context(
+    coro_factory: Callable[[], Coroutine[Any, Any, Any]], config: RunnableConfig
+) -> asyncio.Task[Any]:
+    """Create an asyncio.Task that inherits `config` as the child runnable context.
+
+    `asyncio.create_task` snapshots the current contextvars onto the new task,
+    so calling `create_task` while the config context is set ensures the task
+    sees `config` via `var_child_runnable_config` and any tracing parent.
+    """
+    with set_config_context(config) as context:
+        return context.run(lambda: asyncio.create_task(coro_factory()))
+
+
 # Before Python 3.11 native StrEnum is not available
 class StrEnum(str, enum.Enum):
    """A string enum."""
@@ -181,6 +196,15 @@ KWARGS_CONFIG_KEYS: tuple[tuple[str, tuple[Any, ...], str, Any], ...] = (
        "N/A",
        inspect.Parameter.empty,
    ),
+    (
+        "error",
+        (NodeError, "NodeError"),
+        # we never hit this block, we read directly from configurable
+        "N/A",
+        # default to None so non-handler nodes that happen to type a parameter
+        # `error: NodeError` don't blow up; handlers always receive a NodeError.
+        None,
+    ),
 )
 """List of kwargs that can be passed to functions, and their corresponding
 config keys, default values and type annotations.
@@ -354,6 +378,8 @@ class RunnableCallable(Runnable):
            kw_value: Any = MISSING
            if kw == "config":
                kw_value = config
+            elif kw == "error":
+                kw_value = config.get(CONF, {}).get(CONFIG_KEY_NODE_ERROR, MISSING)
            elif runtime:
                if kw == "runtime":
                    kw_value = runtime
@@ -426,6 +452,8 @@ class RunnableCallable(Runnable):
            kw_value: Any = MISSING
            if kw == "config":
                kw_value = config
+            elif kw == "error":
+                kw_value = config.get(CONF, {}).get(CONFIG_KEY_NODE_ERROR, MISSING)
            elif runtime:
                if kw == "runtime":
                    kw_value = runtime
@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from datetime import timedelta
+from typing import Literal
+
+from langgraph.types import TimeoutPolicy
+
+_SYNC_TIMEOUT_PREFIX = (
+    "Node timeouts are only supported for async nodes because sync Python "
+    "execution cannot be safely cancelled in-process."
+)
+
+
+def coerce_timeout_policy(
+    value: float | timedelta | TimeoutPolicy | None,
+) -> TimeoutPolicy | None:
+    """Normalize a timeout value to positive-second policy fields."""
+    return TimeoutPolicy.coerce(value)
+
+
+def sync_timeout_unsupported(
+    name: str, *, kind: Literal["Node", "Task"] = "Node"
+) -> ValueError:
+    """Build the canonical error for using `timeout` with a sync target."""
+    return ValueError(f"{_SYNC_TIMEOUT_PREFIX} {kind} {name!r} is sync.")
@@ -1,6 +1,7 @@
 from langgraph.channels.any_value import AnyValue
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -20,6 +21,7 @@ __all__ = (
    "UntrackedValue",
    "EphemeralValue",
    "BinaryOperatorAggregate",
+    "DeltaChannel",
    "NamedBarrierValue",
    "NamedBarrierValueAfterFinish",
    # topics
@@ -22,10 +22,9 @@ __all__ = ("BinaryOperatorAggregate",)
 def _strip_extras(t):  # type: ignore[no-untyped-def]
    """Strips Annotated, Required and NotRequired from a given type."""
    if hasattr(t, "__origin__"):
+        if t.__origin__ in (Required, NotRequired):
+            return _strip_extras(t.__args__[0])
        return _strip_extras(t.__origin__)
-    if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
-        return _strip_extras(t.__args__[0])
-
    return t


@@ -33,11 +32,22 @@ def _get_overwrite(value: Any) -> tuple[bool, Any]:
    """Inspects the given value and returns (is_overwrite, overwrite_value)."""
    if isinstance(value, Overwrite):
        return True, value.value
-    if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
+    if isinstance(value, dict) and len(value) == 1 and OVERWRITE in value:
        return True, value[OVERWRITE]
    return False, None


+def _operators_equal(a: Callable, b: Callable) -> bool:
+    """Return True if two reducer operators should be considered equal.
+
+    Lambdas all share the name '<lambda>' so identity comparison is
+    unreliable; treat any pairing that includes a lambda as equal.
+    """
+    if a.__name__ == "<lambda>" or b.__name__ == "<lambda>":
+        return True
+    return a is b
+
+
 class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
    """Stores the result of applying a binary operator to the current value and each new value.

@@ -68,11 +78,8 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
            self.value = MISSING

    def __eq__(self, value: object) -> bool:
-        return isinstance(value, BinaryOperatorAggregate) and (
-            value.operator is self.operator
-            if value.operator.__name__ != "<lambda>"
-            and self.operator.__name__ != "<lambda>"
-            else True
+        return isinstance(value, BinaryOperatorAggregate) and _operators_equal(
+            self.operator, value.operator
        )

    @property
@@ -0,0 +1,192 @@
+from __future__ import annotations
+
+import collections.abc
+import copy as _copy
+from collections.abc import Callable, Sequence
+from typing import Any, Generic
+
+from langgraph.checkpoint.base import PendingWrite
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
+from typing_extensions import Self
+
+from langgraph._internal._typing import MISSING
+from langgraph.channels.base import BaseChannel, Value
+from langgraph.channels.binop import _get_overwrite, _operators_equal, _strip_extras
+from langgraph.errors import (
+    EmptyChannelError,
+    ErrorCode,
+    InvalidUpdateError,
+    create_error_message,
+)
+
+__all__ = ("DeltaChannel",)
+
+
+class DeltaChannel(Generic[Value], BaseChannel[Any, Any, Any]):
+    """Reducer channel that stores only a sentinel in checkpoint blobs and
+    reconstructs state by replaying ancestor writes through the reducer.
+
+    The reducer receives the current accumulated value and a batch of writes
+    in one call: `reducer(state, [write1, write2, ...]) -> new_state`.
+
+    Reducers must be deterministic and batching-invariant (associative across
+    folds): applying two consecutive write batches separately must produce the
+    same state as applying their concatenation once:
+
+        reducer(reducer(state, xs), ys) == reducer(state, xs + ys)
+
+    This lets LangGraph replay checkpointed writes in larger batches than they
+    were originally produced without changing reconstructed state.
+
+    Snapshot cadence is driven by per-channel update count. `create_checkpoint`
+    writes a full `_DeltaSnapshot` blob every `snapshot_frequency` updates to
+    this channel, bounding replay depth.
+
+    Parameters:
+        reducer: `(state, list[writes]) -> new_state`. Must be deterministic
+            and batching-invariant as described above.
+        typ: The value type (e.g. `list`, `dict`). Inferred automatically
+            from the outer type when used inside `Annotated[T, DeltaChannel(...)]`.
+        snapshot_frequency: Every Nth update to this channel writes a snapshot
+            blob (default `1000`). Must be a positive int.
+    """
+
+    __slots__ = ("value", "reducer", "snapshot_frequency")
+    value: Value | Any
+
+    def __init__(
+        self,
+        reducer: Callable[[Any, Sequence[Any]], Any],
+        typ: type[Value] | None = None,
+        *,
+        snapshot_frequency: int = 1000,
+    ) -> None:
+        if snapshot_frequency <= 0:
+            raise ValueError(
+                f"snapshot_frequency must be a positive int, got {snapshot_frequency}"
+            )
+        if typ is None:
+            typ = list  # type: ignore[assignment]  # placeholder; overridden by _is_field_channel
+        super().__init__(typ)
+        self.reducer = reducer
+        self.snapshot_frequency = snapshot_frequency
+        typ = _strip_extras(typ)
+        if typ in (collections.abc.Sequence, collections.abc.MutableSequence):
+            typ = list
+        if typ in (collections.abc.Set, collections.abc.MutableSet):
+            typ = set
+        if typ in (collections.abc.Mapping, collections.abc.MutableMapping):
+            typ = dict
+        self.typ = typ
+        self.value: Any = MISSING
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, DeltaChannel):
+            return False
+        if self.snapshot_frequency != other.snapshot_frequency:
+            return False
+        return _operators_equal(self.reducer, other.reducer)
+
+    @property
+    def ValueType(self) -> Any:
+        return self.typ
+
+    @property
+    def UpdateType(self) -> Any:
+        return self.typ
+
+    def copy(self) -> Self:
+        new = self.__class__(
+            self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
+        )
+        new.key = self.key
+        new.value = self.value if self.value is MISSING else _copy.copy(self.value)
+        return new
+
+    def from_checkpoint(self, checkpoint: Any) -> Self:
+        """Initialize from a stored blob.
+
+        Blob types:
+          * `MISSING`: start empty; caller replays writes.
+          * `_DeltaSnapshot(value)`: restore value directly from snapshot.
+          * plain value (migration from old `BinaryOperatorAggregate` blobs):
+            use directly.
+        """
+        new = self.__class__(
+            self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
+        )
+        new.key = self.key
+        if checkpoint is MISSING:
+            new.value = self.typ()
+        elif isinstance(checkpoint, _DeltaSnapshot):
+            new.value = checkpoint.value
+        else:
+            new.value = checkpoint
+        return new
+
+    def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
+        """Apply ancestor writes oldest-to-newest via a single reducer call.
+
+        If any write is an Overwrite, the last one in the sequence acts as
+        the reset point: its value becomes the new base and only writes
+        after it are passed to the reducer.
+        """
+        values = [v for _, _, v in writes]
+        if not values:
+            return
+        base = self.value
+        start = 0
+        for i, v in enumerate(values):
+            is_ow, ow_value = _get_overwrite(v)
+            if is_ow:
+                base = _copy.copy(ow_value) if ow_value is not None else self.typ()
+                start = i + 1
+        remaining = values[start:]
+        self.value = self.reducer(base, remaining) if remaining else base
+
+    def update(self, values: Sequence[Any]) -> bool:
+        if not values:
+            return False
+        overwrite_idx: int | None = None
+        for i, v in enumerate(values):
+            is_ow, _ = _get_overwrite(v)
+            if is_ow:
+                if overwrite_idx is not None:
+                    msg = create_error_message(
+                        message="Can receive only one Overwrite value per super-step.",
+                        error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
+                    )
+                    raise InvalidUpdateError(msg)
+                overwrite_idx = i
+        if overwrite_idx is not None:
+            _, overwrite_value = _get_overwrite(values[overwrite_idx])
+            base = (
+                _copy.copy(overwrite_value)
+                if overwrite_value is not None
+                else self.typ()
+            )
+            remaining = [v for i, v in enumerate(values) if i != overwrite_idx]
+            self.value = self.reducer(base, remaining) if remaining else base
+            return True
+        base = self.typ() if self.value is MISSING else self.value
+        self.value = self.reducer(base, list(values))
+        return True
+
+    def get(self) -> Any:
+        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 stored representation: always `MISSING`.
+
+        Snapshot decisions live in `create_checkpoint` (which has the channel
+        version) and write `_DeltaSnapshot(ch.get())` directly into
+        `channel_values`. For non-snapshot steps the channel does not appear
+        in `channel_values`; reconstruction walks ancestor writes via the
+        saver's `get_delta_channel_history`.
+        """
+        return MISSING
@@ -1,8 +1,9 @@
 from __future__ import annotations

 from collections.abc import Sequence
+from dataclasses import dataclass
 from enum import Enum
-from typing import Any
+from typing import Any, Literal
 from warnings import warn

 # EmptyChannelError is re-exported from langgraph.channels.base
@@ -15,11 +16,14 @@ from langgraph.warnings import LangGraphDeprecatedSinceV10
 __all__ = (
    "EmptyChannelError",
    "ErrorCode",
+    "GraphDrained",
    "GraphRecursionError",
    "InvalidUpdateError",
    "GraphBubbleUp",
    "GraphInterrupt",
+    "NodeError",
    "NodeInterrupt",
+    "NodeTimeoutError",
    "ParentCommand",
    "EmptyInputError",
    "TaskNotFound",
@@ -42,6 +46,23 @@ def create_error_message(*, message: str, error_code: ErrorCode) -> str:
    )


+class GraphBubbleUp(Exception):
+    pass
+
+
+class GraphDrained(GraphBubbleUp):
+    """Raised when a graph run exits early due to a drain request.
+
+    This indicates the graph stopped cooperatively at a superstep boundary
+    because `RunControl.request_drain()` was called (e.g., in response to
+    SIGTERM). The checkpoint is saved and the run can be resumed later.
+    """
+
+    def __init__(self, reason: str = "shutdown") -> None:
+        self.reason = reason
+        super().__init__(f"Graph drained: {reason}")
+
+
 class GraphRecursionError(RecursionError):
    """Raised when the graph has exhausted the maximum number of steps.

@@ -77,10 +98,6 @@ class InvalidUpdateError(Exception):
    pass


-class GraphBubbleUp(Exception):
-    pass
-
-
 class GraphInterrupt(GraphBubbleUp):
    """Raised when a subgraph is interrupted, suppressed by the root graph.
    Never raised directly, or surfaced to the user."""
@@ -125,3 +142,77 @@ class TaskNotFound(Exception):
    """Raised when the executor is unable to find a task (for distributed mode)."""

    pass
+
+
+@dataclass(frozen=True, slots=True)
+class NodeError:
+    """Failure context passed to a node-level error handler.
+
+    Inject by adding a parameter typed `NodeError` to a handler registered via
+    `StateGraph.add_node(..., error_handler=...)`:
+
+    ```python
+    def handler(state: State, error: NodeError) -> Command:
+        return Command(update={"status": f"recovered from {error.node}: {error.error}"})
+    ```
+    """
+
+    node: str
+    """Name of the node whose execution failed."""
+
+    error: BaseException
+    """Exception raised by the failed node."""
+
+
+class NodeTimeoutError(Exception):
+    """Raised when a node invocation exceeds one of its configured timeouts.
+
+    Does **not** inherit from the built-in `TimeoutError` (a subclass of
+    `OSError`) so that the default `RetryPolicy` treats it as retryable.
+
+    Both `idle_timeout` and `run_timeout` reflect the configured policy at the
+    time of the failure (each is `None` if not configured). `kind` and
+    `timeout` identify which one fired.
+    """
+
+    node: str
+    timeout: float
+    run_timeout: float | None
+    idle_timeout: float | None
+    elapsed: float
+    kind: Literal["idle", "run"]
+
+    def __init__(
+        self,
+        node: str,
+        elapsed: float,
+        *,
+        kind: Literal["idle", "run"],
+        idle_timeout: float | None = None,
+        run_timeout: float | None = None,
+    ) -> None:
+        if kind == "idle":
+            if idle_timeout is None:
+                raise ValueError("idle_timeout is required when kind='idle'")
+            message = (
+                f"Node '{node}' exceeded its idle timeout of "
+                f"{idle_timeout:.3f}s without making progress "
+                f"(elapsed: {elapsed:.3f}s)."
+            )
+            self.timeout = idle_timeout
+        elif kind == "run":
+            if run_timeout is None:
+                raise ValueError("run_timeout is required when kind='run'")
+            message = (
+                f"Node '{node}' exceeded its run timeout of "
+                f"{run_timeout:.3f}s (elapsed: {elapsed:.3f}s)."
+            )
+            self.timeout = run_timeout
+        else:
+            raise ValueError("kind must be 'idle' or 'run'")
+        super().__init__(message)
+        self.node = node
+        self.elapsed = elapsed
+        self.kind = kind
+        self.idle_timeout = idle_timeout
+        self.run_timeout = run_timeout
@@ -5,6 +5,7 @@ import inspect
 import warnings
 from collections.abc import Awaitable, Callable, Sequence
 from dataclasses import dataclass
+from datetime import timedelta
 from typing import (
    Any,
    Generic,
@@ -22,6 +23,11 @@ from typing_extensions import Unpack

 from langgraph._internal import _serde
 from langgraph._internal._constants import CACHE_NS_WRITES, PREVIOUS
+from langgraph._internal._runnable import is_async_callable
+from langgraph._internal._timeout import (
+    coerce_timeout_policy,
+    sync_timeout_unsupported,
+)
 from langgraph._internal._typing import MISSING, DeprecatedKwargs
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
@@ -31,13 +37,19 @@ from langgraph.pregel._call import (
    P,
    SyncAsyncFuture,
    T,
-    call,
+    _call_with_options,
    get_runnable_for_entrypoint,
    identifier,
 )
 from langgraph.pregel._read import PregelNode
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
-from langgraph.types import _DC_KWARGS, CachePolicy, RetryPolicy, StreamMode
+from langgraph.types import (
+    _DC_KWARGS,
+    CachePolicy,
+    RetryPolicy,
+    StreamMode,
+    TimeoutPolicy,
+)
 from langgraph.typing import ContextT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10

@@ -51,6 +63,7 @@ class _TaskFunction(Generic[P, T]):
        *,
        retry_policy: Sequence[RetryPolicy],
        cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+        timeout: TimeoutPolicy | None = None,
        name: str | None = None,
    ) -> None:
        if name is not None:
@@ -67,15 +80,17 @@ class _TaskFunction(Generic[P, T]):
        self.func = func
        self.retry_policy = retry_policy
        self.cache_policy = cache_policy
+        self.timeout = timeout
        functools.update_wrapper(self, func)

    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> SyncAsyncFuture[T]:
-        return call(
+        return _call_with_options(
            self.func,
+            args,
+            kwargs,
            retry_policy=self.retry_policy,
            cache_policy=self.cache_policy,
-            *args,
-            **kwargs,
+            timeout=self.timeout,
        )

    def clear_cache(self, cache: BaseCache) -> None:
@@ -98,6 +113,7 @@ def task(
    name: str | None = None,
    retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
    cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+    timeout: float | timedelta | TimeoutPolicy | None = None,
    **kwargs: Unpack[DeprecatedKwargs],
 ) -> Callable[
    [Callable[P, Awaitable[T]] | Callable[P, T]],
@@ -119,6 +135,7 @@ def task(
    name: str | None = None,
    retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
    cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+    timeout: float | timedelta | TimeoutPolicy | None = None,
    **kwargs: Unpack[DeprecatedKwargs],
 ) -> (
    Callable[[Callable[P, Awaitable[T]] | Callable[P, T]], _TaskFunction[P, T]]
@@ -142,6 +159,14 @@ def task(
        name: An optional name for the task. If not provided, the function name will be used.
        retry_policy: An optional retry policy (or list of policies) to use for the task in case of a failure.
        cache_policy: An optional cache policy to use for the task. This allows caching of the task results.
+        timeout: Timeout for each task attempt. A number or `timedelta` is a hard
+            wall-clock cap and is not refreshed. Use `TimeoutPolicy` to configure
+            both a wall-clock `run_timeout` and an `idle_timeout` refreshed by
+            progress signals. For long-running work that doesn't naturally emit
+            progress, call `runtime.heartbeat()` from inside the task. When the
+            timeout fires, `NodeTimeoutError` is raised and the retry policy (if
+            any) decides whether to retry. Supported only for async tasks; sync
+            tasks cannot be safely cancelled in-process.

    Returns:
        A callable function when used as a decorator.
@@ -196,6 +221,7 @@ def task(
        )
        if retry_policy is None:
            retry_policy = retry  # type: ignore[assignment]
+    timeout_policy = coerce_timeout_policy(timeout)

    retry_policies: Sequence[RetryPolicy] = (
        ()
@@ -208,8 +234,15 @@ def task(
    def decorator(
        func: Callable[P, Awaitable[T]] | Callable[P, T],
    ) -> Callable[P, SyncAsyncFuture[T]]:
+        if timeout_policy is not None and not is_async_callable(func):
+            name_ = name or getattr(func, "__name__", func.__class__.__name__)
+            raise sync_timeout_unsupported(str(name_), kind="Task")
        return _TaskFunction(
-            func, retry_policy=retry_policies, cache_policy=cache_policy, name=name
+            func,
+            retry_policy=retry_policies,
+            cache_policy=cache_policy,
+            timeout=timeout_policy,
+            name=name,
        )

    if __func_or_none__ is not None:
@@ -268,6 +301,15 @@ class entrypoint(Generic[ContextT]):
            passed to the workflow.
        cache_policy: A cache policy to use for caching the results of the workflow.
        retry_policy: A retry policy (or list of policies) to use for the workflow in case of a failure.
+        timeout: Timeout for each workflow attempt. A number or `timedelta` is a
+            hard wall-clock cap and is not refreshed. Use `TimeoutPolicy` to
+            configure both a wall-clock `run_timeout` and an `idle_timeout`
+            refreshed by progress signals. For long-running work that doesn't
+            naturally emit progress, call `runtime.heartbeat()` from inside the
+            workflow. When the timeout fires, `NodeTimeoutError` is raised and
+            the retry policy (if any) decides whether to retry. Supported only
+            for async workflows; sync workflows cannot be safely cancelled
+            in-process.

    !!! warning "`config_schema` Deprecated"
        The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0.
@@ -400,6 +442,7 @@ class entrypoint(Generic[ContextT]):
        context_schema: type[ContextT] | None = None,
        cache_policy: CachePolicy | None = None,
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
        **kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
        """Initialize the entrypoint decorator."""
@@ -426,6 +469,7 @@ class entrypoint(Generic[ContextT]):
        self.cache = cache
        self.cache_policy = cache_policy
        self.retry_policy = retry_policy
+        self.timeout = coerce_timeout_policy(timeout)
        self.context_schema = context_schema

    @dataclass(**_DC_KWARGS)
@@ -535,6 +579,7 @@ class entrypoint(Generic[ContextT]):
                    bound=bound,
                    triggers=[START],
                    channels=START,
+                    timeout=self.timeout,
                    writers=[
                        ChannelWrite(
                            [
@@ -9,7 +9,7 @@ from langgraph.store.base import BaseStore

 from langgraph._internal._typing import EMPTY_SEQ
 from langgraph.runtime import Runtime
-from langgraph.types import CachePolicy, RetryPolicy, StreamWriter
+from langgraph.types import CachePolicy, RetryPolicy, StreamWriter, TimeoutPolicy
 from langgraph.typing import ContextT, NodeInputT, NodeInputT_contra


@@ -88,5 +88,8 @@ class StateNodeSpec(Generic[NodeInputT, ContextT]):
    input_schema: type[NodeInputT]
    retry_policy: RetryPolicy | Sequence[RetryPolicy] | None
    cache_policy: CachePolicy | None
+    is_error_handler: bool = False
+    error_handler_node: str | None = None
    ends: tuple[str, ...] | dict[str, str] | None = EMPTY_SEQ
    defer: bool = False
+    timeout: TimeoutPolicy | None = None
@@ -244,6 +244,71 @@ def add_messages(
    return merged


+def _messages_delta_reducer(
+    state: list[AnyMessage], writes: list[list[AnyMessage]]
+) -> list[AnyMessage]:
+    """**Experimental.** Batch reducer for use with `DeltaChannel`.
+
+    Processes all writes in one pass — dedup by ID, `RemoveMessage`
+    tombstoning — without calling `add_messages`.
+
+    This reducer is batching-invariant, as required by `DeltaChannel`:
+    `reducer(reducer(state, xs), ys) == reducer(state, xs + ys)`.
+
+    Raw dict / string / tuple inputs are coerced to typed `BaseMessage`
+    objects so that HTTP-driven graphs work without a separate coercion
+    step. This is not full `add_messages` parity — `REMOVE_ALL_MESSAGES`,
+    unknown-id `RemoveMessage` errors, missing-id UUID assignment, and
+    `BaseMessageChunk` conversion are not handled here.
+
+    Example::
+
+        from typing import Annotated
+        from langgraph.channels.delta import DeltaChannel
+        from langgraph.graph.message import _messages_delta_reducer
+
+        class State(TypedDict):
+            messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+    """
+
+    # Each write is either a list of message-likes or a single message-like
+    # (BaseMessage / dict / str / tuple). Only lists flatten; everything
+    # else is one message.
+    flat: list[Any] = []
+    for w in writes:
+        if isinstance(w, list):
+            flat.extend(w)
+        else:
+            flat.append(w)
+    # Steady state: the reducer's own output is already typed, so skip
+    # `convert_to_messages` on state when the first element is a BaseMessage.
+    # Only raw input (initial dicts, deserialized blobs) hits the slow path.
+    if state and isinstance(state[0], BaseMessage):
+        state_msgs = state
+    else:
+        state_msgs = cast("list[AnyMessage]", convert_to_messages(state))
+    msgs = cast("list[AnyMessage]", convert_to_messages(flat))
+
+    index: dict[str, int] = {
+        m.id: i for i, m in enumerate(state_msgs) if m.id is not None
+    }
+    result: list[AnyMessage | None] = list(state_msgs)
+    for msg in msgs:
+        mid = msg.id
+        if mid is None:
+            result.append(msg)
+        elif isinstance(msg, RemoveMessage):
+            if mid in index:
+                result[index[mid]] = None
+                del index[mid]
+        elif mid in index:
+            result[index[mid]] = msg
+        else:
+            index[mid] = len(result)
+            result.append(msg)
+    return [m for m in result if m is not None]
+
+
@deprecated(
    "MessageGraph is deprecated in langgraph 1.0.0, to be removed in 2.0.0. Please use StateGraph with a `messages` key instead.",
    category=None,
@@ -7,6 +7,7 @@ import warnings
 from collections import defaultdict
 from collections.abc import Awaitable, Callable, Hashable, Sequence
 from dataclasses import is_dataclass
+from datetime import timedelta
 from functools import partial
 from inspect import isclass, isfunction, ismethod, signature
 from types import FunctionType
@@ -45,9 +46,11 @@ from langgraph._internal._fields import (
 )
 from langgraph._internal._pydantic import create_model
 from langgraph._internal._runnable import coerce_to_runnable
+from langgraph._internal._timeout import coerce_timeout_policy
 from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -81,6 +84,7 @@ from langgraph.types import (
    Command,
    RetryPolicy,
    Send,
+    TimeoutPolicy,
    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
@@ -299,7 +303,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        input_schema: None = None,
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
        cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
        destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
        **kwargs: Unpack[DeprecatedKwargs],
    ) -> Self:
        """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
@@ -366,7 +372,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        input_schema: type[NodeInputT],
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
        cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
        destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
        **kwargs: Unpack[DeprecatedKwargs],
    ) -> Self:
        """Add a new node to the `StateGraph` where input schema is specified.
@@ -438,7 +446,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        input_schema: None = None,
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
        cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
        destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
        **kwargs: Unpack[DeprecatedKwargs],
    ) -> Self:
        """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
@@ -505,7 +515,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        input_schema: type[NodeInputT],
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
        cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
        destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
        **kwargs: Unpack[DeprecatedKwargs],
    ) -> Self:
        """Add a new node to the `StateGraph`, input schema is specified.
@@ -579,7 +591,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        input_schema: type[NodeInputT] | None = None,
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
        cache_policy: CachePolicy | None = None,
+        error_handler: StateNode[Any, ContextT] | None = None,
        destinations: dict[str, str] | tuple[str, ...] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
        **kwargs: Unpack[DeprecatedKwargs],
    ) -> Self:
        """Add a new node to the `StateGraph`.
@@ -598,6 +612,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):

                If a sequence is provided, the first matching policy will be applied.
            cache_policy: The cache policy for the node.
+            error_handler: Optional node-level error handler callable for this node.
            destinations: Destinations that indicate where a node can route to.

                Useful for edgeless graphs with nodes that return `Command` objects.
@@ -609,6 +624,14 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                !!! warning

                    This is only used for graph rendering and doesn't have any effect on the graph execution.
+            timeout: Timeout for each node attempt. A number or `timedelta` is
+                a hard wall-clock cap and is not refreshed. Use `TimeoutPolicy`
+                to configure both a wall-clock `run_timeout` and an
+                `idle_timeout` refreshed by progress signals. When exceeded, a
+                [`NodeTimeoutError`][langgraph.errors.NodeTimeoutError] is raised
+                and the retry policy (if any) decides whether to retry. Timeouts
+                are supported only for async nodes; sync nodes cannot be safely
+                cancelled in-process.

        Example:
            ```python
@@ -662,6 +685,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            )
            if input_schema is None:
                input_schema = cast(type[NodeInputT] | None, input_)
+        timeout = coerce_timeout_policy(timeout)

        if not isinstance(node, str):
            action = node
@@ -748,6 +772,25 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        if destinations is not None:
            ends = destinations

+        resolved_input_schema: type[Any] = (
+            input_schema or inferred_input_schema or self.state_schema
+        )
+        handler_node_name: str | None = None
+        if error_handler is not None:
+            handler_node_name = f"__error_handler__{node}"
+            if handler_node_name in self.nodes:
+                raise ValueError(
+                    f"Auto-generated error handler node `{handler_node_name}` already exists."
+                )
+            self.nodes[handler_node_name] = StateNodeSpec[Any, ContextT](
+                coerce_to_runnable(error_handler, name=handler_node_name, trace=False),  # type: ignore[arg-type]
+                metadata=None,
+                input_schema=resolved_input_schema,
+                retry_policy=None,
+                cache_policy=None,
+                is_error_handler=True,
+            )
+
        if input_schema is not None:
            self.nodes[node] = StateNodeSpec[NodeInputT, ContextT](
                coerce_to_runnable(action, name=node, trace=False),  # type: ignore[arg-type]
@@ -755,8 +798,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                input_schema=input_schema,
                retry_policy=retry_policy,
                cache_policy=cache_policy,
+                error_handler_node=handler_node_name,
                ends=ends,
                defer=defer,
+                timeout=timeout,
            )
        elif inferred_input_schema is not None:
            self.nodes[node] = StateNodeSpec(
@@ -765,8 +810,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                input_schema=inferred_input_schema,
                retry_policy=retry_policy,
                cache_policy=cache_policy,
+                error_handler_node=handler_node_name,
                ends=ends,
                defer=defer,
+                timeout=timeout,
            )
        else:
            self.nodes[node] = StateNodeSpec[StateT, ContextT](
@@ -775,8 +822,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                input_schema=self.state_schema,
                retry_policy=retry_policy,
                cache_policy=cache_policy,
+                error_handler_node=handler_node_name,
                ends=ends,
                defer=defer,
+                timeout=timeout,
            )

        input_schema = input_schema or inferred_input_schema
@@ -1031,7 +1080,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            for node in interrupt:
                if node not in self.nodes:
                    raise ValueError(f"Interrupt node `{node}` not found")
-
        self.compiled = True
        return self

@@ -1045,6 +1093,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        interrupt_after: All | list[str] | None = None,
        debug: bool = False,
        name: str | None = None,
+        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
    ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
        """Compiles the `StateGraph` into a `CompiledStateGraph` object.

@@ -1077,11 +1126,19 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            interrupt_after: An optional list of node names to interrupt after.
            debug: A flag indicating whether to enable debug mode.
            name: The name to use for the compiled graph.
+            transformers: Optional sequence of `StreamTransformer` classes or
+                configured factories. Classes and factories are instantiated
+                per run whenever `stream_events(version="v3")` / `astream_events(version="v3")` is called and are
+                propagated to subgraph scopes. Custom factories should follow
+                the standard `StreamTransformer` constructor shape by
+                accepting `scope` as their first argument. Appended after the
+                built-in stream transformers.

        Returns:
            CompiledStateGraph: The compiled `StateGraph`.
        """
        checkpointer = ensure_valid_checkpointer(checkpointer)
+
        serde_allowlist: set[tuple[str, ...]] | None = None
        if _serde.STRICT_MSGPACK_ENABLED:
            schema_types: list[type[Any]] = [
@@ -1136,6 +1193,11 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                key for key, val in self.channels.items() if not is_managed_value(val)
            ]
        )
+        node_error_handler_map = {
+            node_name: spec.error_handler_node
+            for node_name, spec in self.nodes.items()
+            if spec.error_handler_node is not None
+        }

        compiled = CompiledStateGraph[StateT, ContextT, InputT, OutputT](
            builder=self,
@@ -1158,7 +1220,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            debug=debug,
            store=store,
            cache=cache,
+            node_error_handler_map=node_error_handler_map,
            name=name or "LangGraph",
+            stream_transformers=transformers,
        )
        compiled._serde_allowlist = serde_allowlist

@@ -1331,7 +1395,10 @@ class CompiledStateGraph(
                metadata=node.metadata,
                retry_policy=node.retry_policy,
                cache_policy=node.cache_policy,
+                is_error_handler=node.is_error_handler,
+                error_handler_node=node.error_handler_node,
                bound=node.runnable,  # type: ignore[arg-type]
+                timeout=node.timeout,
            )
        else:
            raise RuntimeError
@@ -1667,6 +1734,20 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
        # Search through all annotated medata to find channel annotations
        for item in meta:
            if isinstance(item, BaseChannel):
+                if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
+                    origin = typ.__origin__
+                    # Unwrap parameterized Required[X]/NotRequired[X] to X
+                    # (e.g. Annotated[NotRequired[dict[...]], ...]).
+                    if hasattr(origin, "__origin__") and origin.__origin__ in (
+                        Required,
+                        NotRequired,
+                    ):
+                        origin = origin.__args__[0]
+                    item = item.__class__(
+                        item.reducer,
+                        origin,
+                        snapshot_frequency=item.snapshot_frequency,
+                    )
                return item
            elif isclass(item) and issubclass(item, BaseChannel):
                # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]
@@ -39,6 +39,7 @@ from langgraph._internal._constants import (
    CONFIG_KEY_CHECKPOINT_MAP,
    CONFIG_KEY_CHECKPOINT_NS,
    CONFIG_KEY_CHECKPOINTER,
+    CONFIG_KEY_NODE_ERROR,
    CONFIG_KEY_READ,
    CONFIG_KEY_RESUME_MAP,
    CONFIG_KEY_RUNTIME,
@@ -47,6 +48,7 @@ from langgraph._internal._constants import (
    CONFIG_KEY_TASK_ID,
    CONFIG_KEY_THREAD_ID,
    ERROR,
+    ERROR_SOURCE_NODE,
    INTERRUPT,
    NO_WRITES,
    NS_END,
@@ -66,6 +68,7 @@ from langgraph.channels.base import BaseChannel
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.constants import TAG_HIDDEN
+from langgraph.errors import NodeError
 from langgraph.managed.base import ManagedValueMapping
 from langgraph.pregel._call import get_runnable_for_task, identifier
 from langgraph.pregel._io import read_channels
@@ -80,6 +83,7 @@ from langgraph.types import (
    PregelTask,
    RetryPolicy,
    Send,
+    TimeoutPolicy,
 )

 GetNextVersion = Callable[[V | None, None], V]
@@ -114,13 +118,21 @@ class PregelTaskWrites(NamedTuple):


 class Call:
-    __slots__ = ("func", "input", "retry_policy", "cache_policy", "callbacks")
+    __slots__ = (
+        "func",
+        "input",
+        "retry_policy",
+        "cache_policy",
+        "callbacks",
+        "timeout",
+    )

    func: Callable
    input: tuple[tuple[Any, ...], dict[str, Any]]
    retry_policy: Sequence[RetryPolicy] | None
    cache_policy: CachePolicy | None
    callbacks: Callbacks
+    timeout: TimeoutPolicy | None

    def __init__(
        self,
@@ -130,12 +142,14 @@ class Call:
        retry_policy: Sequence[RetryPolicy] | None,
        cache_policy: CachePolicy | None,
        callbacks: Callbacks,
+        timeout: TimeoutPolicy | None = None,
    ) -> None:
        self.func = func
        self.input = input
        self.retry_policy = retry_policy
        self.cache_policy = cache_policy
        self.callbacks = callbacks
+        self.timeout = timeout


 def should_interrupt(
@@ -281,7 +295,15 @@ def apply_writes(
    pending_writes_by_channel: dict[str, list[Any]] = defaultdict(list)
    for task in tasks:
        for chan, val in task.writes:
-            if chan in (NO_WRITES, PUSH, RESUME, INTERRUPT, RETURN, ERROR):
+            if chan in (
+                NO_WRITES,
+                PUSH,
+                RESUME,
+                INTERRUPT,
+                RETURN,
+                ERROR,
+                ERROR_SOURCE_NODE,
+            ):
                pass
            elif chan in channels:
                pending_writes_by_channel[chan].append(val)
@@ -733,11 +755,48 @@ def prepare_single_task(
                        task_path[:3],
                        writers=proc.flat_writers,
                        subgraphs=proc.subgraphs,
+                        timeout=proc.timeout,
                    )
            else:
                return PregelTask(task_id, name, task_path[:3])


+def _coerce_pending_error(value: Any) -> BaseException:
+    if isinstance(value, BaseException):
+        return value
+    return Exception(str(value))
+
+
+def _read_errors_from_pending_writes(
+    pending_writes: list[PendingWrite],
+) -> list[BaseException]:
+    errors: list[BaseException] = []
+    for _, channel, value in pending_writes:
+        if channel == ERROR:
+            errors.append(_coerce_pending_error(value))
+    return errors
+
+
+def _read_error_for_task_id_from_pending_writes(
+    pending_writes: list[PendingWrite], task_id: str
+) -> BaseException | None:
+    for pending_task_id, channel, value in reversed(pending_writes):
+        if pending_task_id == task_id and channel == ERROR:
+            return _coerce_pending_error(value)
+    return None
+
+
+def _read_error_source_node_from_pending_writes(
+    pending_writes: list[PendingWrite], task_id: str
+) -> str | None:
+    for pending_task_id, channel, value in reversed(pending_writes):
+        if pending_task_id == task_id and channel == ERROR_SOURCE_NODE:
+            if isinstance(value, str):
+                return value
+            return str(value)
+    return None
+
+
 def prepare_push_task_functional(
    task_path: tuple[str, tuple, int, str, Call],
    # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
@@ -870,6 +929,7 @@ def prepare_push_task_functional(
            cache_key,
            task_id,
            in_progress_task_path,
+            timeout=call.timeout,
        )
    else:
        return PregelTask(task_id, name, in_progress_task_path)
@@ -1041,11 +1101,153 @@ def prepare_push_task_send(
            translated_task_path,
            writers=proc.flat_writers,
            subgraphs=proc.subgraphs,
+            timeout=packet.timeout if packet.timeout is not None else proc.timeout,
        )
    else:
        return PregelTask(task_id, packet.node, translated_task_path)


+def prepare_node_error_handler_task(
+    failed_task: PregelExecutableTask,
+    *,
+    handler_node_name: str,
+    failed_error: BaseException,
+    checkpoint: Checkpoint,
+    pending_writes: list[PendingWrite],
+    processes: Mapping[str, PregelNode],
+    channels: Mapping[str, BaseChannel],
+    managed: ManagedValueMapping,
+    config: RunnableConfig,
+    step: int,
+    stop: int,
+    store: BaseStore | None = None,
+    checkpointer: BaseCheckpointSaver | None = None,
+    manager: None | ParentRunManager | AsyncParentRunManager = None,
+    cache_policy: CachePolicy | None = None,
+    retry_policy: Sequence[RetryPolicy] = (),
+) -> PregelExecutableTask | None:
+    """Prepare an immediate node-level error handler task for a failed task."""
+    if handler_node_name not in processes:
+        return None
+    proc = processes[handler_node_name]
+    proc_node = proc.node
+    if proc_node is None:
+        return None
+
+    checkpoint_id_bytes = binascii.unhexlify(checkpoint["id"].replace("-", ""))
+    task_id_func = _xxhash_str if checkpoint["v"] > 1 else _uuid5_str
+    configurable = config.get(CONF, {})
+    parent_ns = configurable.get(CONFIG_KEY_CHECKPOINT_NS, "")
+    checkpoint_ns = (
+        f"{parent_ns}{NS_SEP}{handler_node_name}" if parent_ns else handler_node_name
+    )
+    task_id = task_id_func(
+        checkpoint_id_bytes,
+        checkpoint_ns,
+        str(step),
+        handler_node_name,
+        PUSH,
+        "node_error_handler",
+        failed_task.id,
+    )
+    task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+    translated_task_path = (*failed_task.path[:3], "node_error_handler", False)
+    metadata = {
+        "langgraph_step": step,
+        "langgraph_node": handler_node_name,
+        "langgraph_triggers": PUSH_TRIGGER,
+        "langgraph_path": translated_task_path,
+        "langgraph_checkpoint_ns": task_checkpoint_ns,
+    }
+    if proc.metadata:
+        metadata.update(proc.metadata)
+    writes: deque[tuple[str, Any]] = deque()
+
+    effective_retry_policy = proc.retry_policy or retry_policy
+    effective_cache_policy = proc.cache_policy or cache_policy
+    if effective_cache_policy:
+        args_key = effective_cache_policy.key_func(failed_task.input)
+        cache_key = CacheKey(
+            (
+                CACHE_NS_WRITES,
+                (identifier(proc) or "__dynamic__"),
+                handler_node_name,
+            ),
+            xxh3_128_hexdigest(
+                args_key.encode() if isinstance(args_key, str) else args_key
+            ),
+            effective_cache_policy.ttl,
+        )
+    else:
+        cache_key = None
+
+    scratchpad = _scratchpad(
+        config[CONF].get(CONFIG_KEY_SCRATCHPAD),
+        pending_writes,
+        task_id,
+        xxh3_128_hexdigest(task_checkpoint_ns.encode()),
+        config[CONF].get(CONFIG_KEY_RESUME_MAP),
+        step,
+        stop,
+    )
+    runtime = cast(Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME))
+    runtime = runtime.override(
+        store=store, previous=checkpoint["channel_values"].get(PREVIOUS, None)
+    )
+    additional_config: RunnableConfig = {
+        "metadata": metadata,
+        "tags": proc.tags,
+    }
+    return PregelExecutableTask(
+        handler_node_name,
+        failed_task.input,
+        proc_node,
+        writes,
+        patch_config(
+            merge_configs(config, additional_config),
+            run_name=handler_node_name,
+            callbacks=manager.get_child(f"graph:step:{step}") if manager else None,
+            configurable={
+                CONFIG_KEY_TASK_ID: task_id,
+                CONFIG_KEY_SEND: writes.extend,
+                CONFIG_KEY_READ: partial(
+                    local_read,
+                    scratchpad,
+                    channels,
+                    managed,
+                    PregelTaskWrites(
+                        translated_task_path,
+                        handler_node_name,
+                        writes,
+                        PUSH_TRIGGER,
+                    ),
+                ),
+                CONFIG_KEY_CHECKPOINTER: (
+                    checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
+                ),
+                CONFIG_KEY_CHECKPOINT_MAP: {
+                    **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
+                    parent_ns: checkpoint["id"],
+                },
+                CONFIG_KEY_CHECKPOINT_ID: None,
+                CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
+                CONFIG_KEY_SCRATCHPAD: scratchpad,
+                CONFIG_KEY_RUNTIME: runtime,
+                CONFIG_KEY_NODE_ERROR: NodeError(
+                    node=failed_task.name, error=failed_error
+                ),
+            },
+        ),
+        PUSH_TRIGGER,
+        effective_retry_policy,
+        cache_key,
+        task_id,
+        translated_task_path,
+        writers=proc.flat_writers,
+        subgraphs=proc.subgraphs,
+    )
+
+
 def checkpoint_null_version(
    checkpoint: Checkpoint,
 ) -> V | None:
@@ -1255,4 +1457,4 @@ def sanitize_untracked_values_in_send(
        for k, v in packet.arg.items()
        if not isinstance(channels.get(k), UntrackedValue)
    }
-    return Send(node=packet.node, arg=sanitized_arg)
+    return Send(node=packet.node, arg=sanitized_arg, timeout=packet.timeout)
@@ -8,6 +8,7 @@ import inspect
 import sys
 import types
 from collections.abc import Awaitable, Callable, Generator, Sequence
+from datetime import timedelta
 from typing import Any, Generic, TypeVar, cast

 from langchain_core.runnables import Runnable
@@ -20,9 +21,13 @@ from langgraph._internal._runnable import (
    is_async_callable,
    run_in_executor,
 )
+from langgraph._internal._timeout import (
+    coerce_timeout_policy,
+    sync_timeout_unsupported,
+)
 from langgraph.config import get_config
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
-from langgraph.types import CachePolicy, RetryPolicy
+from langgraph.types import CachePolicy, RetryPolicy, TimeoutPolicy

 ##
 # Utilities borrowed from cloudpickle.
@@ -255,8 +260,31 @@ def call(
    *args: Any,
    retry_policy: Sequence[RetryPolicy] | None = None,
    cache_policy: CachePolicy | None = None,
+    timeout: float | timedelta | TimeoutPolicy | None = None,
    **kwargs: Any,
 ) -> SyncAsyncFuture[T]:
+    return _call_with_options(
+        func,
+        args,
+        kwargs,
+        retry_policy=retry_policy,
+        cache_policy=cache_policy,
+        timeout=coerce_timeout_policy(timeout),
+    )
+
+
+def _call_with_options(
+    func: Callable[P, Awaitable[T]] | Callable[P, T],
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+    *,
+    retry_policy: Sequence[RetryPolicy] | None = None,
+    cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
+) -> SyncAsyncFuture[T]:
+    if timeout is not None and not is_async_callable(func):
+        name = getattr(func, "__name__", func.__class__.__name__)
+        raise sync_timeout_unsupported(name, kind="Task")
    config = get_config()
    impl = config[CONF][CONFIG_KEY_CALL]
    fut = impl(
@@ -265,5 +293,6 @@ def call(
        retry_policy=retry_policy,
        cache_policy=cache_policy,
        callbacks=config["callbacks"],
+        timeout=timeout,
    )
    return fut
@@ -1,17 +1,27 @@
 from __future__ import annotations

-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from datetime import datetime, timezone
+from typing import Any, cast

-from langgraph.checkpoint.base import Checkpoint
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    BaseCheckpointSaver,
+    Checkpoint,
+    CheckpointMetadata,
+)
 from langgraph.checkpoint.base.id import uuid6
+from langgraph.checkpoint.serde.types import _DeltaSnapshot

 from langgraph._internal._typing import MISSING
 from langgraph.channels.base import BaseChannel
+from langgraph.channels.delta import DeltaChannel
 from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec

 LATEST_VERSION = 4

+GetNextVersion = Callable[[Any, None], Any]
+

 def empty_checkpoint() -> Checkpoint:
    return Checkpoint(
@@ -24,6 +34,30 @@ def empty_checkpoint() -> Checkpoint:
    )


+def _should_snapshot_delta(
+    name: str,
+    ch: DeltaChannel,
+    updates_since_snapshot: Mapping[str, int],
+    *,
+    force: bool,
+) -> bool:
+    """Decide whether `ch` should write a `_DeltaSnapshot` this step.
+
+    Triggers:
+      * `force` — always snapshot (used by `durability="exit"`).
+      * Update-count: this channel has accumulated at least
+        `snapshot_frequency` updates since its last snapshot. The count
+        is supplied by the caller via `updates_since_snapshot[name]` and
+        is reset to `0` whenever a snapshot fires.
+
+    Version-format-independent: works for `int`, `float`, and `str`
+    versioning schemes alike.
+    """
+    if force:
+        return True
+    return updates_since_snapshot.get(name, 0) >= ch.snapshot_frequency
+
+
 def create_checkpoint(
    checkpoint: Checkpoint,
    channels: Mapping[str, BaseChannel] | None,
@@ -31,35 +65,108 @@ def create_checkpoint(
    *,
    id: str | None = None,
    updated_channels: set[str] | None = None,
+    get_next_version: GetNextVersion | None = None,
+    force_delta_snapshot: bool = False,
+    updates_since_snapshot: Mapping[str, int] | None = None,
+    new_updates_since_snapshot: dict[str, int] | None = None,
 ) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
+    """Create a checkpoint for the given channels.
+
+    For each `DeltaChannel`, a `_DeltaSnapshot(value)` blob is written into
+    `channel_values[k]` when this channel has accumulated at least
+    `snapshot_frequency` updates since its last snapshot (counter supplied
+    via `updates_since_snapshot`). Otherwise the channel is omitted from
+    `channel_values`; its `channel_versions` entry still bumps so that the
+    saver tracks the channel and the ancestor walk can replay writes.
+
+    Snapshots are eager: even if the channel had no write this step, a
+    version bump is forced (via `get_next_version`) so `put()` includes
+    the channel in `new_versions` and stores the blob.
+
+    `force_delta_snapshot` ignores the cadence and always snapshots —
+    used by `durability="exit"` where intermediate writes are not stored
+    as ancestor `checkpoint_writes`.
+
+    If `new_updates_since_snapshot` is provided, the function resets the
+    counter to `0` for any channel that snapshotted this step. Counters
+    for channels that did not snapshot are left untouched (the caller is
+    responsible for incrementing them based on `updated_channels`).
+    """
    ts = datetime.now(timezone.utc).isoformat()
+    counts = updates_since_snapshot or {}
    if channels is None:
        values = checkpoint["channel_values"]
+        channel_versions = checkpoint["channel_versions"]
    else:
        values = {}
+        channel_versions = dict(checkpoint["channel_versions"])
        for k in channels:
-            if k not in checkpoint["channel_versions"]:
+            if k not in channel_versions:
                continue
-            v = channels[k].checkpoint()
-            if v is not MISSING:
-                values[k] = v
+            ch = channels[k]
+            if (
+                isinstance(ch, DeltaChannel)
+                and ch.is_available()
+                and _should_snapshot_delta(
+                    k,
+                    ch,
+                    counts,
+                    force=force_delta_snapshot,
+                )
+            ):
+                # Eager snapshot: bump version if not already written this step
+                # so put() includes this channel in new_versions and stores blob.
+                if get_next_version is not None and (
+                    updated_channels is None or k not in updated_channels
+                ):
+                    channel_versions[k] = get_next_version(channel_versions[k], None)
+                values[k] = _DeltaSnapshot(ch.get())
+                if new_updates_since_snapshot is not None:
+                    new_updates_since_snapshot[k] = 0
+            else:
+                v = ch.checkpoint()
+                if v is not MISSING:
+                    values[k] = v
    return Checkpoint(
        v=LATEST_VERSION,
        ts=ts,
        id=id or str(uuid6(clock_seq=step)),
        channel_values=values,
-        channel_versions=checkpoint["channel_versions"],
+        channel_versions=channel_versions,
        versions_seen=checkpoint["versions_seen"],
        updated_channels=None if updated_channels is None else sorted(updated_channels),
    )


+def _needs_replay(spec: BaseChannel, stored: object) -> bool:
+    """True if `spec` is a `DeltaChannel` and no value is stored at this
+    checkpoint, requiring an ancestor walk to reconstruct.
+
+    `_DeltaSnapshot` blobs and plain values (migration) resolve directly via
+    `from_checkpoint` — only absence (`MISSING`) triggers replay.
+    """
+    if not isinstance(spec, DeltaChannel):
+        return False
+    return stored is MISSING
+
+
 def channels_from_checkpoint(
    specs: Mapping[str, BaseChannel | ManagedValueSpec],
    checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
 ) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Get channels from a checkpoint."""
+    """Hydrate channels from a checkpoint.
+
+    For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
+    is sufficient. `DeltaChannel` is the exception: when the channel is
+    absent from `channel_values`, an ancestor walk via
+    `saver.get_delta_channel_history` is required to find the nearest seed
+    (`_DeltaSnapshot` blob or pre-migration plain value) and accumulate
+    the writes between it and the target. All delta channels needing
+    replay are batched into a single saver call.
+    """
    channel_specs: dict[str, BaseChannel] = {}
    managed_specs: dict[str, ManagedValueSpec] = {}
    for k, v in specs.items():
@@ -67,13 +174,73 @@ 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,
-    )
+
+    delta_channels: list[str] = [
+        k
+        for k, spec in channel_specs.items()
+        if _needs_replay(spec, checkpoint["channel_values"].get(k, MISSING))
+    ]
+    histories: Mapping[str, Any] = {}
+    if delta_channels and saver is not None and config is not None:
+        histories = saver.get_delta_channel_history(
+            config=config, channels=delta_channels
+        )
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        if k in histories:
+            delta_spec = cast(DeltaChannel, spec)
+            history = histories[k]
+            replay_ch = delta_spec.from_checkpoint(history.get("seed", MISSING))
+            replay_ch.replay_writes(history["writes"])
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
+        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
+
+    delta_channels: list[str] = [
+        k
+        for k, spec in channel_specs.items()
+        if _needs_replay(spec, checkpoint["channel_values"].get(k, MISSING))
+    ]
+    histories: Mapping[str, Any] = {}
+    if delta_channels and saver is not None and config is not None:
+        histories = await saver.aget_delta_channel_history(
+            config=config, channels=delta_channels
+        )
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        if k in histories:
+            delta_spec = cast(DeltaChannel, spec)
+            history = histories[k]
+            replay_ch = delta_spec.from_checkpoint(history.get("seed", MISSING))
+            replay_ch.replay_writes(history["writes"])
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
+        channels[k] = ch
+    return channels, managed_specs


 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -86,3 +253,17 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
        versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
        updated_channels=checkpoint.get("updated_channels", None),
    )
+
+
+def read_delta_updates_since_snapshot(
+    metadata: CheckpointMetadata | None,
+) -> dict[str, int]:
+    """Read the per-channel update counter from checkpoint metadata.
+
+    Returns an empty dict for missing/None metadata; the dict is
+    `total=False` on `CheckpointMetadata`, so absence means "no prior
+    delta-channel activity tracked."
+    """
+    if not metadata:
+        return {}
+    return dict(metadata.get("delta_updates_since_snapshot", {}) or {})
@@ -45,11 +45,13 @@ from langgraph._internal._constants import (
    CONFIG_KEY_REPLAY_STATE,
    CONFIG_KEY_RESUME_MAP,
    CONFIG_KEY_RESUMING,
+    CONFIG_KEY_RUNTIME,
    CONFIG_KEY_SCRATCHPAD,
    CONFIG_KEY_STREAM,
    CONFIG_KEY_TASK_ID,
    CONFIG_KEY_THREAD_ID,
    ERROR,
+    ERROR_SOURCE_NODE,
    INPUT,
    INTERRUPT,
    NS_END,
@@ -68,6 +70,7 @@ from langgraph.callbacks import (
    GraphResumeEvent,
 )
 from langgraph.channels.base import BaseChannel
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.constants import TAG_HIDDEN
 from langgraph.errors import (
@@ -86,12 +89,14 @@ from langgraph.pregel._algo import (
    checkpoint_null_version,
    increment,
    prepare_next_tasks,
+    prepare_node_error_handler_task,
    prepare_single_task,
    sanitize_untracked_values_in_send,
    should_interrupt,
    task_path_str,
 )
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
    channels_from_checkpoint,
    copy_checkpoint,
    create_checkpoint,
@@ -117,6 +122,7 @@ from langgraph.pregel.debug import (
    map_debug_tasks,
 )
 from langgraph.pregel.protocol import StreamChunk, StreamProtocol
+from langgraph.runtime import RunControl, Runtime
 from langgraph.types import (
    All,
    CachePolicy,
@@ -188,6 +194,8 @@ class PregelLoop:
    _migrate_checkpoint: Callable[[Checkpoint], None] | None
    submit: Submit
    channels: Mapping[str, BaseChannel]
+    # Only set on AsyncPregelLoop; sync loops keep this as None.
+    _delta_write_futs: list[Any] | None = None
    managed: ManagedValueMapping
    checkpoint: Checkpoint
    checkpoint_id_saved: str
@@ -202,10 +210,12 @@ class PregelLoop:
        "input",
        "pending",
        "done",
+        "draining",
        "interrupt_before",
        "interrupt_after",
        "out_of_steps",
    ]
+    control: RunControl | None
    tasks: dict[str, PregelExecutableTask]
    output: None | dict[str, Any] | Any = None
    updated_channels: set[str] | None = None
@@ -313,6 +323,8 @@ class PregelLoop:
            else ()
        )
        self.prev_checkpoint_config = None
+        runtime = self.config[CONF].get(CONFIG_KEY_RUNTIME)
+        self.control = runtime.control if isinstance(runtime, Runtime) else None

    def _push_graph_lifecycle_event(
        self,
@@ -320,11 +332,16 @@ class PregelLoop:
        *,
        interrupts: tuple[Interrupt, ...] = (),
    ) -> None:
+        # drain status never reaches lifecycle events: tick() returns False
+        # before pushing, and interrupts are raised through GraphInterrupt
+        if self.status == "draining":
+            raise RuntimeError("Draining status cannot emit lifecycle events")
+        status = self.status
        if kind == "resume":
            self._graph_lifecycle_events.append(
                GraphResumeEvent(
                    run_id=None,
-                    status=self.status,
+                    status=status,
                    checkpoint_id=self.checkpoint["id"],
                    checkpoint_ns=self.checkpoint_ns,
                )
@@ -333,7 +350,7 @@ class PregelLoop:
            self._graph_lifecycle_events.append(
                GraphInterruptEvent(
                    run_id=None,
-                    status=self.status,
+                    status=status,
                    checkpoint_id=self.checkpoint["id"],
                    checkpoint_ns=self.checkpoint_ns,
                    interrupts=interrupts,
@@ -406,7 +423,7 @@ class PregelLoop:
                    task = self.tasks.get(task_id)
                else:
                    task = None
-                self.submit(
+                fut = self.submit(
                    self.checkpointer_put_writes,
                    config,
                    writes_to_save,
@@ -414,12 +431,16 @@ class PregelLoop:
                    task_path_str(task.path) if task else "",
                )
            else:
-                self.submit(
+                fut = self.submit(
                    self.checkpointer_put_writes,
                    config,
                    writes_to_save,
                    task_id,
                )
+            if self._delta_write_futs is not None and any(
+                isinstance(self.specs.get(c), DeltaChannel) for c, _ in writes_to_save
+            ):
+                self._delta_write_futs.append(fut)
        # output writes
        if hasattr(self, "tasks"):
            self.output_writes(task_id, writes)
@@ -503,6 +524,16 @@ class PregelLoop:
            # return the new task, to be started if not run before
            return pushed

+    def schedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        raise NotImplementedError
+
+    async def aschedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        raise NotImplementedError
+
    def tick(self) -> bool:
        """Execute a single iteration of the Pregel loop.

@@ -561,6 +592,10 @@ class PregelLoop:
            self.status = "done"
            return False

+        if self.control is not None and self.control.drain_requested:
+            self.status = "draining"
+            return False
+
        # if there are pending writes from a previous loop, apply them
        if not self.is_replaying and self.checkpoint_pending_writes:
            self._match_writes(self.tasks)
@@ -627,7 +662,7 @@ class PregelLoop:

    def _match_writes(self, tasks: Mapping[str, PregelExecutableTask]) -> None:
        for tid, k, v in self.checkpoint_pending_writes:
-            if k in (ERROR, INTERRUPT, RESUME):
+            if k in (ERROR, ERROR_SOURCE_NODE, INTERRUPT, RESUME):
                continue
            if task := tasks.get(tid):
                task.writes.append((k, v))
@@ -875,6 +910,22 @@ class PregelLoop:
        if exiting and self.checkpoint["id"] == self.checkpoint_id_saved:
            # checkpoint already saved
            return
+        # Carry per-delta-channel update bookkeeping forward across
+        # supersteps. Capture from the OLD metadata before potentially
+        # replacing it with a fresh dict that wouldn't contain it. Then
+        # increment for any delta channel updated this step (so the count
+        # reflects "supersteps that wrote to this channel since last
+        # snapshot"). create_checkpoint will reset entries to 0 for any
+        # channel that fires a snapshot this step.
+        prev_counts = dict(
+            self.checkpoint_metadata.get("delta_updates_since_snapshot", {}) or {}
+        )
+        new_counts = dict(prev_counts)
+        if self.updated_channels:
+            for ch_name in self.updated_channels:
+                ch_obj = self.channels.get(ch_name)
+                if isinstance(ch_obj, DeltaChannel):
+                    new_counts[ch_name] = new_counts.get(ch_name, 0) + 1
        if not exiting:
            metadata["step"] = self.step
            metadata["parents"] = self.config[CONF].get(CONFIG_KEY_CHECKPOINT_MAP, {})
@@ -890,7 +941,17 @@ class PregelLoop:
            self.step,
            id=self.checkpoint["id"] if exiting else None,
            updated_channels=self.updated_channels,
+            get_next_version=self.checkpointer_get_next_version
+            if do_checkpoint
+            else None,
+            force_delta_snapshot=exiting and self.durability == "exit",
+            updates_since_snapshot=new_counts,
+            new_updates_since_snapshot=new_counts,
        )
+        if new_counts:
+            self.checkpoint_metadata["delta_updates_since_snapshot"] = new_counts
+        elif "delta_updates_since_snapshot" in self.checkpoint_metadata:
+            del self.checkpoint_metadata["delta_updates_since_snapshot"]
        # sanitize TASK channel in the checkpoint before saving (durability=="exit")
        if TASKS in self.checkpoint["channel_values"] and any(
            isinstance(channel, UntrackedValue) for channel in self.channels.values()
@@ -1200,6 +1261,45 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
                self.output_writes(task.id, task.writes, cached=True)
        return pushed

+    def schedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        handler_node = self.nodes[failed_task.name].error_handler_node
+        if not handler_node:
+            return None
+        writes = list(failed_task.writes)
+        writes.append((ERROR_SOURCE_NODE, failed_task.name))
+        self.put_writes(
+            failed_task.id,
+            writes,
+        )
+        handler_task = prepare_node_error_handler_task(
+            failed_task,
+            handler_node_name=handler_node,
+            failed_error=error,
+            checkpoint=self.checkpoint,
+            pending_writes=self.checkpoint_pending_writes,
+            processes=self.nodes,
+            channels=self.channels,
+            managed=self.managed,
+            config=failed_task.config,
+            step=self.step,
+            stop=self.stop,
+            store=self.store,
+            checkpointer=self.checkpointer,
+            manager=self.manager,
+            retry_policy=self.retry_policy,
+            cache_policy=self.cache_policy,
+        )
+        if handler_task is None:
+            return None
+        self.tasks[handler_task.id] = handler_task
+        if not self.is_replaying:
+            self._match_writes({handler_task.id: handler_task})
+        for task in self.match_cached_writes():
+            self.output_writes(task.id, task.writes, cached=True)
+        return handler_task
+
    def put_writes(self, task_id: str, writes: WritesT) -> None:
        """Put writes for a task, to be read by the next tick."""
        super().put_writes(task_id, writes)
@@ -1273,7 +1373,10 @@ 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"
@@ -1368,6 +1471,11 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
        metadata: CheckpointMetadata,
        new_versions: ChannelVersions,
    ) -> RunnableConfig:
+        # Drain DeltaChannel write futures before committing the checkpoint so
+        # ancestor walks never see a checkpoint without its backing writes.
+        if self._delta_write_futs:
+            futs, self._delta_write_futs = self._delta_write_futs, []
+            await asyncio.gather(*futs)
        try:
            if prev is not None:
                await prev
@@ -1399,6 +1507,45 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
                self.output_writes(task.id, task.writes, cached=True)
        return pushed

+    async def aschedule_error_handler(
+        self, failed_task: PregelExecutableTask, error: BaseException
+    ) -> PregelExecutableTask | None:
+        handler_node = self.nodes[failed_task.name].error_handler_node
+        if not handler_node:
+            return None
+        writes = list(failed_task.writes)
+        writes.append((ERROR_SOURCE_NODE, failed_task.name))
+        self.put_writes(
+            failed_task.id,
+            writes,
+        )
+        handler_task = prepare_node_error_handler_task(
+            failed_task,
+            handler_node_name=handler_node,
+            failed_error=error,
+            checkpoint=self.checkpoint,
+            pending_writes=self.checkpoint_pending_writes,
+            processes=self.nodes,
+            channels=self.channels,
+            managed=self.managed,
+            config=failed_task.config,
+            step=self.step,
+            stop=self.stop,
+            store=self.store,
+            checkpointer=self.checkpointer,
+            manager=self.manager,
+            retry_policy=self.retry_policy,
+            cache_policy=self.cache_policy,
+        )
+        if handler_task is None:
+            return None
+        self.tasks[handler_task.id] = handler_task
+        if not self.is_replaying:
+            self._match_writes({handler_task.id: handler_task})
+        for task in await self.amatch_cached_writes():
+            self.output_writes(task.id, task.writes, cached=True)
+        return handler_task
+
    def put_writes(self, task_id: str, writes: WritesT) -> None:
        """Put writes for a task, to be read by the next tick."""
        super().put_writes(task_id, writes)
@@ -1473,11 +1620,15 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
            if saved.pending_writes is not None
            else []
        )
+        self._delta_write_futs = []
        self.submit = await self.stack.enter_async_context(
            AsyncBackgroundExecutor(self.config)
        )
-        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"
@@ -24,6 +24,11 @@ try:
 except ImportError:
    _StreamingCallbackHandler = object  # type: ignore

+try:
+    from langchain_core.tracers._streaming import _V2StreamingCallbackHandler
+except ImportError:
+    _V2StreamingCallbackHandler = object  # type: ignore
+
 T = TypeVar("T")
 Meta = tuple[tuple[str, ...], dict[str, Any]]

@@ -248,3 +253,126 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
        **kwargs: Any,
    ) -> Any:
        self.metadata.pop(run_id, None)
+
+
+class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler):
+    """v2 variant of `StreamMessagesHandler`.
+
+    Declaring `_V2StreamingCallbackHandler` as a base flips
+    `BaseChatModel.invoke` to route through `_stream_chat_model_events`
+    (firing `on_stream_event`) instead of `_stream` (firing
+    `on_llm_new_token`). Inherits `on_stream_event` from the parent,
+    which forwards protocol events onto the messages stream channel.
+
+    Pregel attaches this class instead of the v1 handler only when
+    `StreamingHandler` opts in via the internal
+    `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
+    `graph.stream(stream_mode="messages")` callers keep the v1
+    AIMessageChunk shape.
+    """
+
+    def on_llm_new_token(
+        self,
+        token: str,
+        *,
+        chunk: ChatGenerationChunk | None = None,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Intentional no-op — v1 chunks are not used on v2-flagged runs.
+
+        The v2 marker already steers `invoke` to the event generator, so
+        `on_llm_new_token` should not fire under normal routing. This
+        override stays a pass-through (no call to `super()`) to make
+        the intent explicit and to guard against any caller (e.g. a
+        node that calls `model.stream()` directly, which still fires
+        the v1 callback) leaking AIMessageChunks onto a v2-flagged
+        messages stream.
+        """
+        # Intentionally empty: v2 handler does not forward v1 chunks.
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        super().__init__(stream, subgraphs, parent_ns=parent_ns)
+        self._streamed_run_ids: set[UUID] = set()
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        if meta := self.metadata.get(run_id):
+            if response.generations and response.generations[0]:
+                gen = response.generations[0][0]
+                if isinstance(gen, ChatGeneration):
+                    if run_id in self._streamed_run_ids:
+                        if gen.message.id is None:
+                            gen.message.id = str(uuid4())
+                        self.seen.add(gen.message.id)
+                    else:
+                        self._emit(meta, gen.message, dedupe=True)
+        self._streamed_run_ids.discard(run_id)
+        self.metadata.pop(run_id, None)
+
+    def on_llm_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._streamed_run_ids.discard(run_id)
+        super().on_llm_error(
+            error,
+            run_id=run_id,
+            parent_run_id=parent_run_id,
+            **kwargs,
+        )
+
+    def on_stream_event(
+        self,
+        event: dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Forward a protocol event from `stream_events(version="v3")` as a messages stream part.
+
+        Fires once per `MessagesData` event (`message-start`, per-block
+        `content-block-*`, `message-finish`). The transformer layer
+        correlates events back to a single `ChatModelStream` via
+        `metadata["run_id"]` — attached here so the v1
+        `stream_mode="messages"` output (which emits
+        `(AIMessageChunk, metadata)` via `on_llm_new_token`) keeps its
+        original metadata shape.
+
+        Lives on the v2 handler rather than the v1 base: content-block
+        events are a v2-only concept, and forwarding them only when the
+        v2 handler is attached keeps the message channel's shape
+        predictable for v1 callers.
+        """
+        if meta := self.metadata.get(run_id):
+            # Record message_id on message-start so on_chain_end's
+            # dedupe skips the finalized AIMessage the node returns
+            # (otherwise the messages projection double-counts: once
+            # from streaming, once from the chain output).
+            if event.get("event") == "message-start":
+                self._streamed_run_ids.add(run_id)
+                msg_id = event.get("message_id")
+                if msg_id:
+                    self.seen.add(msg_id)
+            v2_meta = {**meta[1], "run_id": str(run_id)}
+            self.stream((meta[0], "messages", (event, v2_meta)))
@@ -1,6 +1,7 @@
 from __future__ import annotations

 from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
+from datetime import timedelta
 from functools import cached_property
 from typing import (
    Any,
@@ -11,10 +12,11 @@ from langchain_core.runnables import Runnable, RunnableConfig
 from langgraph._internal._config import merge_configs
 from langgraph._internal._constants import CONF, CONFIG_KEY_READ
 from langgraph._internal._runnable import RunnableCallable, RunnableSeq
+from langgraph._internal._timeout import coerce_timeout_policy
 from langgraph.pregel._utils import find_subgraph_pregel
 from langgraph.pregel._write import ChannelWrite
 from langgraph.pregel.protocol import PregelProtocol
-from langgraph.types import CachePolicy, RetryPolicy
+from langgraph.types import CachePolicy, RetryPolicy, TimeoutPolicy

 READ_TYPE = Callable[[str | Sequence[str], bool], Any | dict[str, Any]]
 INPUT_CACHE_KEY_TYPE = tuple[Callable[..., Any], tuple[str, ...]]
@@ -123,12 +125,25 @@ class PregelNode:
    cache_policy: CachePolicy | None
    """The cache policy to use when invoking the node."""

+    timeout: TimeoutPolicy | None
+    """Timeout policy for a single invocation.
+
+    If exceeded, `NodeTimeoutError` is raised and the retry policy (if any)
+    decides whether to retry. Supported only for async nodes.
+    """
+
    tags: Sequence[str] | None
    """Tags to attach to the node for tracing."""

    metadata: Mapping[str, Any] | None
    """Metadata to attach to the node for tracing."""

+    is_error_handler: bool
+    """Whether this node is registered as an error handler node."""
+
+    error_handler_node: str | None
+    """Optional handler node name for failures from this node."""
+
    subgraphs: Sequence[PregelProtocol]
    """Subgraphs used by the node."""

@@ -144,7 +159,10 @@ class PregelNode:
        bound: Runnable[Any, Any] | None = None,
        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
        cache_policy: CachePolicy | None = None,
+        is_error_handler: bool = False,
+        error_handler_node: str | None = None,
        subgraphs: Sequence[PregelProtocol] | None = None,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
    ) -> None:
        self.channels = channels
        self.triggers = list(triggers)
@@ -156,8 +174,11 @@ class PregelNode:
            self.retry_policy = (retry_policy,)
        else:
            self.retry_policy = retry_policy
+        self.timeout = coerce_timeout_policy(timeout)
        self.tags = tags
        self.metadata = metadata
+        self.is_error_handler = is_error_handler
+        self.error_handler_node = error_handler_node
        if subgraphs is not None:
            self.subgraphs = subgraphs
        elif self.bound is not DEFAULT_BOUND:
@@ -4,32 +4,487 @@ import asyncio
 import logging
 import random
 import sys
+import threading
 import time
+import weakref
 from collections.abc import Awaitable, Callable, Sequence
-from dataclasses import replace
-from typing import Any
+from contextlib import suppress
+from dataclasses import dataclass, replace
+from datetime import datetime, timedelta, timezone
+from typing import Any, Literal, NamedTuple

+from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.runnables import RunnableConfig

-from langgraph._internal._config import patch_configurable, recast_checkpoint_ns
+from langgraph._internal._config import (
+    merge_configs,
+    patch_configurable,
+    recast_checkpoint_ns,
+)
 from langgraph._internal._constants import (
    CONF,
+    CONFIG_KEY_CALL,
    CONFIG_KEY_CHECKPOINT_ID,
    CONFIG_KEY_CHECKPOINT_NS,
    CONFIG_KEY_RESUMING,
    CONFIG_KEY_RUNTIME,
+    CONFIG_KEY_SEND,
+    CONFIG_KEY_STREAM,
    CONFIG_KEY_TASK_ID,
    CONFIG_KEY_THREAD_ID,
+    CONFIG_KEY_TIMED_ATTEMPT_OBSERVER,
    NS_SEP,
 )
-from langgraph.errors import GraphBubbleUp, ParentCommand
+from langgraph._internal._runnable import create_task_in_config_context
+from langgraph._internal._timeout import sync_timeout_unsupported
+from langgraph.errors import GraphBubbleUp, NodeTimeoutError, ParentCommand
+from langgraph.pregel.protocol import StreamProtocol
 from langgraph.runtime import ExecutionInfo, Runtime
-from langgraph.types import Command, PregelExecutableTask, RetryPolicy
+from langgraph.types import Command, PregelExecutableTask, RetryPolicy, TimeoutPolicy

 logger = logging.getLogger(__name__)
 SUPPORTS_EXC_NOTES = sys.version_info >= (3, 11)


+def _timeout_secs(value: float | timedelta) -> float:
+    return value.total_seconds() if isinstance(value, timedelta) else value
+
+
+@dataclass(frozen=True, slots=True)
+class _ResolvedTimeout:
+    run_timeout_secs: float | None
+    idle_timeout_secs: float | None
+    refresh_on: Literal["auto", "heartbeat"] | None
+
+
+def _resolve_timeout(timeout: TimeoutPolicy) -> _ResolvedTimeout:
+    idle_timeout_secs = (
+        _timeout_secs(timeout.idle_timeout)
+        if timeout.idle_timeout is not None
+        else None
+    )
+    return _ResolvedTimeout(
+        run_timeout_secs=(
+            _timeout_secs(timeout.run_timeout)
+            if timeout.run_timeout is not None
+            else None
+        ),
+        idle_timeout_secs=idle_timeout_secs,
+        refresh_on=timeout.refresh_on if idle_timeout_secs is not None else None,
+    )
+
+
+class _AttemptContext(NamedTuple):
+    """Immutable per-attempt metadata shared across start/progress/finish events.
+
+    Built once at attempt start and referenced (not copied) by every emitted
+    `_AttemptEvent`, so per-event allocation is just the small event wrapper.
+
+    Intentionally underscore-prefixed: this and `_AttemptEvent` are part of an
+    internal observer contract consumed by langgraph-server. Do not move to
+    `langgraph.types` — server imports them by this path.
+    """
+
+    task_id: str
+    task_name: str
+    attempt: int
+    run_id: str | None
+    thread_id: str | None
+    checkpoint_ns: str | None
+    started_at: datetime
+    run_timeout_secs: float | None
+    idle_timeout_secs: float | None
+    refresh_on: Literal["auto", "heartbeat"] | None
+
+
+@dataclass(frozen=True, slots=True)
+class _AttemptEvent:
+    """One lifecycle event for a timed attempt.
+
+    Holds a reference to the shared `_AttemptContext` and the event-specific
+    fields. The observer must treat this and `context` as read-only — they
+    are reused across all events for the same attempt.
+    """
+
+    context: _AttemptContext
+    event: Literal["start", "progress", "finish"]
+    progress_at: datetime | None = None
+    finished_at: datetime | None = None
+    status: Literal["success", "error"] | None = None
+    error_type: str | None = None
+    error_message: str | None = None
+
+
+class _TimedAttemptScope:
+    """Guarded-config window for timed attempts.
+
+    The wrapped config marks writes, stream events, runtime stream writer calls,
+    child task scheduling, and any LangChain callback event emitted under the
+    node's run as observable progress when `refresh_on="auto"`.
+    `runtime.heartbeat()` exposes a manual progress signal for work that doesn't
+    otherwise emit any of these, and is the only progress signal when
+    `refresh_on="heartbeat"`.
+    Guarded writes are serialized with `close()` so cancelled background tasks
+    cannot persist writes past the timeout boundary. Stream/custom output is
+    best-effort: it is dropped after close is observed, but callbacks run outside
+    the lock because they may contain arbitrary user/runtime code.
+    """
+
+    __slots__ = (
+        "__weakref__",
+        "_active",
+        "_last_progress",
+        "_last_progress_emit",
+        "_lock",
+        "_on_progress",
+        "_progress_min_interval",
+        "_refresh_on",
+    )
+
+    def __init__(
+        self,
+        on_progress: Callable[[], None] | None = None,
+        progress_min_interval: float = 0.0,
+        refresh_on: Literal["auto", "heartbeat"] | None = None,
+    ) -> None:
+        self._active = True
+        self._last_progress = time.monotonic()
+        self._lock = threading.Lock()
+        self._on_progress = on_progress
+        self._progress_min_interval = progress_min_interval
+        self._refresh_on = refresh_on
+        # `-inf` so the first touch always passes the rate-limit gate.
+        self._last_progress_emit: float = float("-inf")
+
+    def wrap_config(self, config: RunnableConfig) -> RunnableConfig:
+        configurable = config.get(CONF, {})
+        patch: dict[str, Any] = {}
+        if (send := configurable.get(CONFIG_KEY_SEND)) is not None:
+            patch[CONFIG_KEY_SEND] = self._guard_send(send)
+        if (stream := configurable.get(CONFIG_KEY_STREAM)) is not None:
+            patch[CONFIG_KEY_STREAM] = self._guard_stream(stream)
+        if (call := configurable.get(CONFIG_KEY_CALL)) is not None:
+            patch[CONFIG_KEY_CALL] = self._guard_call(call)
+        if isinstance(runtime := configurable.get(CONFIG_KEY_RUNTIME), Runtime):
+            if self._refresh_on is not None:
+                patch[CONFIG_KEY_RUNTIME] = runtime.override(
+                    stream_writer=self._guard_stream_writer(runtime.stream_writer),
+                    heartbeat=self.touch,
+                )
+            else:
+                patch[CONFIG_KEY_RUNTIME] = runtime.override(
+                    stream_writer=self._guard_stream_writer(runtime.stream_writer)
+                )
+        new_config = patch_configurable(config, patch) if patch else config
+        if self._refresh_on == "auto":
+            return merge_configs(
+                new_config, {"callbacks": [_IdleProgressCallbackHandler(self)]}
+            )
+        return new_config
+
+    def touch(self) -> None:
+        # Avoid locking this hot progress path. We accept a small race window in
+        # timestamp ordering because idle_timeout is expected to be coarse compared
+        # with scheduler/thread timing.
+        now = time.monotonic()
+        self._last_progress = now
+        if self._on_progress is None:
+            return
+        # Best-effort rate limit: a benign race may emit a duplicate progress
+        # event under heavy concurrency, which observers must already tolerate
+        # (callbacks fire from arbitrary threads).
+        if now - self._last_progress_emit < self._progress_min_interval:
+            return
+        self._last_progress_emit = now
+        self._on_progress()
+
+    def close(self) -> None:
+        with self._lock:
+            self._active = False
+
+    async def wait_for_idle_timeout(self, idle_timeout_s: float) -> None:
+        while True:
+            with self._lock:
+                if not self._active:
+                    return
+                remaining = self._last_progress + idle_timeout_s - time.monotonic()
+            if remaining <= 0:
+                raise asyncio.TimeoutError
+            await asyncio.sleep(remaining)
+
+    def _guard_send(
+        self, send: Callable[[Sequence[tuple[str, Any]]], None]
+    ) -> Callable[[Sequence[tuple[str, Any]]], None]:
+        def guarded_send(writes: Sequence[tuple[str, Any]]) -> None:
+            with self._lock:
+                if self._active:
+                    if writes and self._refresh_on == "auto":
+                        self._last_progress = time.monotonic()
+                    send(writes)
+
+        return guarded_send
+
+    def _guard_stream(self, stream: StreamProtocol) -> StreamProtocol:
+        # No lock: stream callbacks fire from the event loop only, so the
+        # active-check + write happen atomically between awaits.
+        def guarded_stream(chunk: tuple[tuple[str, ...], str, Any]) -> None:
+            if not self._active:
+                return
+            if self._refresh_on == "auto":
+                self._last_progress = time.monotonic()
+            stream(chunk)
+
+        return StreamProtocol(guarded_stream, stream.modes)
+
+    def _guard_call(self, call: Callable[..., Any]) -> Callable[..., Any]:
+        # No lock: child-task scheduling happens from the event loop only.
+        def guarded_call(*args: Any, **kwargs: Any) -> Any:
+            if not self._active:
+                raise asyncio.CancelledError
+            if self._refresh_on == "auto":
+                self._last_progress = time.monotonic()
+            return call(*args, **kwargs)
+
+        return guarded_call
+
+    def _guard_stream_writer(
+        self, stream_writer: Callable[[Any], None]
+    ) -> Callable[[Any], None]:
+        def guarded_stream_writer(chunk: Any) -> None:
+            with self._lock:
+                if not self._active:
+                    return
+                if self._refresh_on == "auto":
+                    self._last_progress = time.monotonic()
+            stream_writer(chunk)
+
+        return guarded_stream_writer
+
+
+class _IdleProgressCallbackHandler(BaseCallbackHandler):
+    """Resets the idle timeout clock on any LangChain callback event.
+
+    Inherits via `config["callbacks"]`, so it sees only events emitted by
+    runs descended from the node's attempt — sibling nodes do not bleed
+    through. Holds the scope by weakref so a child manager that outlives
+    the attempt cannot keep the scope alive.
+    """
+
+    # Run inline so progress is recorded in callback emission order;
+    # thread-pool dispatch would introduce extra reordering.
+    run_inline = True
+
+    def __init__(self, scope: _TimedAttemptScope) -> None:
+        self._scope_ref = weakref.ref(scope)
+
+    def _touch(self, *args: Any, **kwargs: Any) -> None:
+        if (scope := self._scope_ref()) is not None:
+            scope.touch()
+
+    on_llm_start = _touch
+    on_chat_model_start = _touch
+    on_llm_new_token = _touch
+    on_llm_end = _touch
+    on_llm_error = _touch
+    on_chain_start = _touch
+    on_chain_end = _touch
+    on_chain_error = _touch
+    on_tool_start = _touch
+    on_tool_end = _touch
+    on_tool_error = _touch
+    on_retriever_start = _touch
+    on_retriever_end = _touch
+    on_retriever_error = _touch
+    on_agent_action = _touch
+    on_agent_finish = _touch
+    on_text = _touch
+    on_retry = _touch
+    on_custom_event = _touch
+
+
+def _drain_cancelled(task: asyncio.Task[Any]) -> None:
+    # Mark the abandoned task's exception as retrieved so asyncio doesn't log it.
+    with suppress(asyncio.CancelledError):
+        task.exception()
+
+
+def _start_timed_attempt(
+    task: PregelExecutableTask, config: RunnableConfig, timeout: _ResolvedTimeout
+) -> _AttemptContext | None:
+    configurable = config.get(CONF, {})
+    callback = configurable.get(CONFIG_KEY_TIMED_ATTEMPT_OBSERVER)
+    if callback is None:
+        return None
+    runtime = configurable.get(CONFIG_KEY_RUNTIME)
+    execution_info = runtime.execution_info if isinstance(runtime, Runtime) else None
+    context = _AttemptContext(
+        task_id=task.id,
+        task_name=task.name,
+        attempt=execution_info.node_attempt if execution_info is not None else 1,
+        run_id=execution_info.run_id if execution_info is not None else None,
+        thread_id=execution_info.thread_id if execution_info is not None else None,
+        checkpoint_ns=(
+            execution_info.checkpoint_ns if execution_info is not None else None
+        ),
+        started_at=datetime.now(timezone.utc),
+        run_timeout_secs=timeout.run_timeout_secs,
+        idle_timeout_secs=timeout.idle_timeout_secs,
+        refresh_on=timeout.refresh_on,
+    )
+    _dispatch_observer(callback, _AttemptEvent(context=context, event="start"))
+    return context
+
+
+def _finish_timed_attempt(
+    config: RunnableConfig,
+    context: _AttemptContext | None,
+    error: BaseException | None = None,
+) -> None:
+    if context is None:
+        return
+    callback = config.get(CONF, {}).get(CONFIG_KEY_TIMED_ATTEMPT_OBSERVER)
+    if callback is None:
+        return
+    _dispatch_observer(
+        callback,
+        _AttemptEvent(
+            context=context,
+            event="finish",
+            finished_at=datetime.now(timezone.utc),
+            status="error" if error is not None else "success",
+            error_type=type(error).__name__ if error is not None else None,
+            error_message=str(error) if error is not None else None,
+        ),
+    )
+
+
+def _emit_progress(
+    callback: Callable[[_AttemptEvent], None],
+    context: _AttemptContext,
+) -> None:
+    _dispatch_observer(
+        callback,
+        _AttemptEvent(
+            context=context,
+            event="progress",
+            progress_at=datetime.now(timezone.utc),
+        ),
+    )
+
+
+def _dispatch_observer(
+    callback: Callable[[_AttemptEvent], None],
+    event: _AttemptEvent,
+) -> None:
+    try:
+        callback(event)
+    except Exception:
+        logger.warning("Timed attempt observer failed", exc_info=True)
+
+
+async def _run_timeout_watchdog(run_timeout_s: float) -> None:
+    await asyncio.sleep(run_timeout_s)
+    raise asyncio.TimeoutError
+
+
+async def _arun_with_timeout(
+    task: PregelExecutableTask,
+    config: RunnableConfig,
+    timeout: _ResolvedTimeout,
+    attempt_ctx: _AttemptContext | None,
+    *,
+    stream: bool,
+) -> Any:
+    run_timeout_s = timeout.run_timeout_secs
+    idle_timeout_s = timeout.idle_timeout_secs
+    on_progress: Callable[[], None] | None = None
+    if attempt_ctx is not None:
+        callback = config.get(CONF, {}).get(CONFIG_KEY_TIMED_ATTEMPT_OBSERVER)
+        if callback is not None and idle_timeout_s is not None:
+            on_progress = lambda: _emit_progress(callback, attempt_ctx)  # noqa: E731
+    scope = _TimedAttemptScope(
+        on_progress=on_progress,
+        # Cap progress emission at ~4 events per idle window so token-rate
+        # callbacks don't flood the observer.
+        progress_min_interval=idle_timeout_s / 4 if idle_timeout_s is not None else 0.0,
+        refresh_on=timeout.refresh_on,
+    )
+    scoped_config = scope.wrap_config(config)
+    start = time.monotonic()
+    if stream:
+        # Yielded chunks count as progress only under `refresh_on="auto"`.
+        # `refresh_on="heartbeat"` is the strict mode where only explicit
+        # `runtime.heartbeat()` calls reset the idle clock.
+        async def run() -> Any:
+            async for _ in task.proc.astream(task.input, scoped_config):
+                if timeout.refresh_on == "auto":
+                    scope.touch()
+
+    else:
+
+        async def run() -> Any:
+            return await task.proc.ainvoke(task.input, scoped_config)
+
+    bg = create_task_in_config_context(run, scoped_config)
+    watchdogs: dict[asyncio.Task[None], Literal["idle", "run"]] = {}
+    if idle_timeout_s is not None:
+        watchdogs[asyncio.create_task(scope.wait_for_idle_timeout(idle_timeout_s))] = (
+            "idle"
+        )
+    if run_timeout_s is not None:
+        watchdogs[asyncio.create_task(_run_timeout_watchdog(run_timeout_s))] = "run"
+    try:
+        done, _ = await asyncio.wait(
+            {bg, *watchdogs}, return_when=asyncio.FIRST_COMPLETED
+        )
+        if bg in done:
+            # Task completed in time.
+            for watchdog in watchdogs:
+                watchdog.cancel()
+            # FIRST_COMPLETED can return both; a watchdog may have
+            # already raised TimeoutError before we cancelled it.
+            for watchdog in watchdogs:
+                with suppress(asyncio.CancelledError, asyncio.TimeoutError):
+                    await watchdog
+            return await bg
+        # bg was not in `done`, so every member of `done` is one of our
+        # watchdogs. Only a watchdog's TimeoutError converts to
+        # NodeTimeoutError; any TimeoutError raised by the proc itself
+        # propagates unchanged.
+        for watchdog in done:
+            kind = watchdogs[watchdog]
+            try:
+                await watchdog
+            except asyncio.TimeoutError as exc:
+                elapsed = time.monotonic() - start
+                scope.close()
+                task.writes.clear()
+                bg.cancel()
+                bg.add_done_callback(_drain_cancelled)
+                raise NodeTimeoutError(
+                    task.name,
+                    elapsed,
+                    kind=kind,
+                    idle_timeout=idle_timeout_s,
+                    run_timeout=run_timeout_s,
+                ) from exc
+            raise RuntimeError(
+                f"{kind} timeout watchdog completed without raising TimeoutError"
+            )
+        raise RuntimeError("timeout wait completed without task or watchdog")
+    except asyncio.CancelledError:
+        scope.close()
+        bg.cancel()
+        for watchdog in watchdogs:
+            watchdog.cancel()
+        bg.add_done_callback(_drain_cancelled)
+        raise
+    finally:
+        scope.close()
+        for watchdog in watchdogs:
+            watchdog.cancel()
+
+
 def _ensure_execution_info(
    runtime: Runtime, config: RunnableConfig, task: PregelExecutableTask
 ) -> Runtime:
@@ -90,6 +545,10 @@ def run_with_retry(
 ) -> None:
    """Run a task with retries."""
    retry_policy = task.retry_policy or retry_policy
+    if task.timeout is not None:
+        # `validate_timeout_supported` catches sync nodes at compile time;
+        # this is a runtime safety net for paths that may bypass that validation.
+        raise sync_timeout_unsupported(task.name)
    attempts = 0
    node_first_attempt_time = time.time()
    config = task.config
@@ -195,6 +654,9 @@ async def arun_with_retry(
 ) -> None:
    """Run a task asynchronously with retries."""
    retry_policy = task.retry_policy or retry_policy
+    resolved_timeout = (
+        _resolve_timeout(task.timeout) if task.timeout is not None else None
+    )
    attempts = 0
    node_first_attempt_time = time.time()
    config = task.config
@@ -229,35 +691,53 @@ async def arun_with_retry(
                    )
                },
            )
+        attempt_ctx = (
+            _start_timed_attempt(task, config, resolved_timeout)
+            if resolved_timeout is not None
+            else None
+        )
        try:
-            # clear any writes from previous attempts
            task.writes.clear()
-            # run the task
+            if resolved_timeout is None:
+                if stream:
+                    async for _ in task.proc.astream(task.input, config):
+                        pass
+                    break
+                return await task.proc.ainvoke(task.input, config)
+            result = await _arun_with_timeout(
+                task, config, resolved_timeout, attempt_ctx, stream=stream
+            )
+            _finish_timed_attempt(config, attempt_ctx)
            if stream:
-                async for _ in task.proc.astream(task.input, config):
-                    pass
                # if successful, end
                break
-            else:
-                return await task.proc.ainvoke(task.input, config)
+            return result
        except ParentCommand as exc:
            ns: str = config[CONF][CONFIG_KEY_CHECKPOINT_NS]
            cmd = exc.args[0]
            # strip task_ids from namespace for comparison (ns format: "node1|node2:task_id")
            if cmd.graph in (ns, recast_checkpoint_ns(ns), task.name):
-                # this command is for the current graph, handle it
-                for w in task.writers:
-                    w.invoke(cmd, config)
+                try:
+                    # this command is for the current graph, handle it
+                    for w in task.writers:
+                        w.invoke(cmd, config)
+                except Exception as writer_exc:
+                    _finish_timed_attempt(config, attempt_ctx, writer_exc)
+                    raise
+                _finish_timed_attempt(config, attempt_ctx)
                break
            elif cmd.graph == Command.PARENT:
                # this command is for the parent graph, assign it to the parent.
                exc.args = (replace(cmd, graph=_checkpoint_ns_for_parent_command(ns)),)
-            # bubble up
+            _finish_timed_attempt(config, attempt_ctx)
+            # bubble up the exception to the parent graph
            raise
        except GraphBubbleUp:
            # if interrupted, end
+            _finish_timed_attempt(config, attempt_ctx)
            raise
        except Exception as exc:
+            _finish_timed_attempt(config, attempt_ctx, exc)
            if SUPPORTS_EXC_NOTES:
                exc.add_note(f"During task with name '{task.name}' and id '{task.id}'")
            if not retry_policy:
@@ -10,8 +10,10 @@ from collections.abc import (
    AsyncIterator,
    Awaitable,
    Callable,
+    Collection,
    Iterable,
    Iterator,
+    Mapping,
    Sequence,
 )
 from functools import partial
@@ -46,6 +48,7 @@ from langgraph.types import (
    CachePolicy,
    PregelExecutableTask,
    RetryPolicy,
+    TimeoutPolicy,
 )

 F = TypeVar("F", concurrent.futures.Future, asyncio.Future)
@@ -71,6 +74,10 @@ SKIP_RERAISE_SET: weakref.WeakSet[concurrent.futures.Future | asyncio.Future] =
 class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
    event: E
    callback: weakref.ref[Callable[[PregelExecutableTask, BaseException | None], None]]
+    # Stop condition is injected by PregelRunner instead of hard-coded here.
+    # This lets the runner treat graph-error-handled exceptions as non-fatal
+    # so `on_done` does not trigger an early stop for those futures.
+    should_stop: Callable[[set[F]], bool]
    counter: int
    done: set[F]
    lock: threading.Lock
@@ -81,6 +88,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
        callback: weakref.ref[
            Callable[[PregelExecutableTask, BaseException | None], None]
        ],
+        should_stop: Callable[[set[F]], bool],
        future_type: type[F],
        # used for generic typing, newer py supports FutureDict[...](...)
    ) -> None:
@@ -88,6 +96,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
        self.lock = threading.Lock()
        self.event = event
        self.callback = callback
+        self.should_stop = should_stop
        self.counter = 0
        self.done: set[F] = set()

@@ -108,6 +117,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
        task: PregelExecutableTask,
        fut: F,
    ) -> None:
+        # Called automatically by future.add_done_callback registered in __setitem__.
        try:
            if cb := self.callback():
                cb(task, _exception(fut))
@@ -115,7 +125,9 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
            with self.lock:
                self.done.add(fut)
                self.counter -= 1
-                if self.counter == 0 or _should_stop_others(self.done):
+                # Wake waiter when all tracked futures are done, or when runner-level
+                # stop condition is met (for example, a non-handled fatal exception).
+                if self.counter == 0 or self.should_stop(self.done):
                    self.event.set()


@@ -131,11 +143,34 @@ class PregelRunner:
        put_writes: weakref.ref[Callable[[str, Sequence[tuple[str, Any]]], None]],
        use_astream: bool = False,
        node_finished: Callable[[str], None] | None = None,
+        node_error_handler_map: Mapping[str, str] | None = None,
+        schedule_error_handler: Callable[
+            [PregelExecutableTask, BaseException], PregelExecutableTask | None
+        ]
+        | None = None,
+        aschedule_error_handler: Callable[
+            [PregelExecutableTask, BaseException],
+            Awaitable[PregelExecutableTask | None],
+        ]
+        | None = None,
    ) -> None:
        self.submit = submit
        self.put_writes = put_writes
        self.use_astream = use_astream
        self.node_finished = node_finished
+        self.node_error_handler_map = dict(node_error_handler_map or {})
+        self.error_handler_nodes = set(self.node_error_handler_map.values())
+        self.schedule_error_handler = schedule_error_handler
+        self.aschedule_error_handler = aschedule_error_handler
+        # Exception object ids that are already routed to graph-level error handler.
+        # These ids are consulted by stop/panic checks to avoid re-raising handled
+        # exceptions via the normal fatal path in the same run.
+        self._handled_exception_ids: set[int] = set()
+
+    def _should_route_to_error_handler(self, task: PregelExecutableTask) -> bool:
+        if task.name in self.error_handler_nodes:
+            return False
+        return task.name in self.node_error_handler_map

    def tick(
        self,
@@ -154,6 +189,9 @@ class PregelRunner:
        futures = FuturesDict(
            callback=weakref.WeakMethod(self.commit),
            event=threading.Event(),
+            should_stop=partial(
+                _should_stop_others, handled_exception_ids=self._handled_exception_ids
+            ),
            future_type=concurrent.futures.Future,
        )
        # give control back to the caller
@@ -163,6 +201,7 @@ class PregelRunner:
            return
        elif len(tasks) == 1 and timeout is None and get_waiter is None:
            t = tasks[0]
+            scheduled_error_handler = False
            try:
                run_with_retry(
                    t,
@@ -181,12 +220,23 @@ class PregelRunner:
                self.commit(t, None)
            except Exception as exc:
                self.commit(t, exc)
+                if (
+                    not isinstance(exc, GraphBubbleUp)
+                    and self._should_route_to_error_handler(t)
+                    and self.schedule_error_handler is not None
+                ):
+                    self._handled_exception_ids.add(id(exc))
+                    if handler_task := self.schedule_error_handler(t, exc):
+                        tasks = (handler_task,)
+                        scheduled_error_handler = True
+                        # Continue to the regular scheduling path for handler execution.
                if reraise and futures:
-                    # will be re-raised after futures are done
-                    fut: concurrent.futures.Future = concurrent.futures.Future()
-                    fut.set_exception(exc)
-                    futures.done.add(fut)
-                elif reraise:
+                    if id(exc) not in self._handled_exception_ids:
+                        # will be re-raised after futures are done
+                        fut: concurrent.futures.Future = concurrent.futures.Future()
+                        fut.set_exception(exc)
+                        futures.done.add(fut)
+                elif reraise and id(exc) not in self._handled_exception_ids:
                    if tb := exc.__traceback__:
                        while tb.tb_next is not None and any(
                            tb.tb_frame.f_code.co_filename.endswith(name)
@@ -195,10 +245,12 @@ class PregelRunner:
                            tb = tb.tb_next
                        exc.__traceback__ = tb
                    raise
-            if not futures:  # maybe `t` scheduled another task
+            if not futures and not scheduled_error_handler:
+                # maybe `t` scheduled another task
                return
            else:
-                tasks = ()  # don't reschedule this task
+                if not scheduled_error_handler:
+                    tasks = ()  # don't reschedule this task
        # add waiter task if requested
        if get_waiter is not None:
            futures[get_waiter()] = None
@@ -225,6 +277,7 @@ class PregelRunner:
        # each task is independent from all other concurrent tasks
        # yield updates/debug output as each task finishes
        end_time = timeout + time.monotonic() if timeout else None
+        handled_futures: set[concurrent.futures.Future[Any]] = set()
        while len(futures) > (1 if get_waiter is not None else 0):
            done, inflight = concurrent.futures.wait(
                futures,
@@ -233,17 +286,49 @@ class PregelRunner:
            )
            if not done:
                break  # timed out
+            done_for_stop: set[concurrent.futures.Future[Any]] = set()
            for fut in done:
                task = futures.pop(fut)
                if task is None:
                    # waiter task finished, schedule another
                    if inflight and get_waiter is not None:
                        futures[get_waiter()] = None
+                elif (
+                    (task_exc := _exception(fut))
+                    and self._should_route_to_error_handler(task)
+                    and not isinstance(task_exc, GraphBubbleUp)
+                ):
+                    self._handled_exception_ids.add(id(task_exc))
+                    SKIP_RERAISE_SET.add(fut)
+                    handled_futures.add(fut)
+                    if self.schedule_error_handler is not None:
+                        if handler_task := self.schedule_error_handler(task, task_exc):
+                            handler_fut = self.submit()(  # type: ignore[misc]
+                                run_with_retry,
+                                handler_task,
+                                retry_policy,
+                                configurable={
+                                    CONFIG_KEY_CALL: partial(
+                                        _call,
+                                        weakref.ref(handler_task),
+                                        retry_policy=retry_policy,
+                                        futures=weakref.ref(futures),
+                                        schedule_task=schedule_task,
+                                        submit=self.submit,
+                                    ),
+                                },
+                                __reraise_on_exit__=reraise,
+                            )
+                            futures[handler_fut] = handler_task
+                else:
+                    done_for_stop.add(fut)
            else:
                # remove references to loop vars
                del fut, task
            # maybe stop other tasks
-            if _should_stop_others(done):
+            if _should_stop_others(
+                done_for_stop, handled_exception_ids=self._handled_exception_ids
+            ):
                break
            # give control back to the caller
            yield
@@ -258,6 +343,8 @@ class PregelRunner:
            _panic_or_proceed(
                futures.done.union(f for f, t in futures.items() if t is not None),
                panic=reraise,
+                handled_exception_ids=self._handled_exception_ids,
+                handled_futures=handled_futures,
            )
        except Exception as exc:
            if tb := exc.__traceback__:
@@ -291,6 +378,9 @@ class PregelRunner:
        futures = FuturesDict(
            callback=weakref.WeakMethod(self.commit),
            event=asyncio.Event(),
+            should_stop=partial(
+                _should_stop_others, handled_exception_ids=self._handled_exception_ids
+            ),
            future_type=asyncio.Future,
        )
        # give control back to the caller
@@ -300,6 +390,7 @@ class PregelRunner:
            return
        elif len(tasks) == 1 and get_waiter is None and timeout is None:
            t = tasks[0]
+            scheduled_error_handler = False
            try:
                await arun_with_retry(
                    t,
@@ -321,12 +412,22 @@ class PregelRunner:
                self.commit(t, None)
            except Exception as exc:
                self.commit(t, exc)
+                if (
+                    not isinstance(exc, GraphBubbleUp)
+                    and self._should_route_to_error_handler(t)
+                    and self.aschedule_error_handler is not None
+                ):
+                    self._handled_exception_ids.add(id(exc))
+                    if handler_task := await self.aschedule_error_handler(t, exc):
+                        tasks = (handler_task,)
+                        scheduled_error_handler = True
                if reraise and futures:
-                    # will be re-raised after futures are done
-                    fut: asyncio.Future = loop.create_future()
-                    fut.set_exception(exc)
-                    futures.done.add(fut)
-                elif reraise:
+                    if id(exc) not in self._handled_exception_ids:
+                        # will be re-raised after futures are done
+                        fut: asyncio.Future = loop.create_future()
+                        fut.set_exception(exc)
+                        futures.done.add(fut)
+                elif reraise and id(exc) not in self._handled_exception_ids:
                    if tb := exc.__traceback__:
                        while tb.tb_next is not None and any(
                            tb.tb_frame.f_code.co_filename.endswith(name)
@@ -335,10 +436,12 @@ class PregelRunner:
                            tb = tb.tb_next
                        exc.__traceback__ = tb
                    raise
-            if not futures:  # maybe `t` scheduled another task
+            if not futures and not scheduled_error_handler:
+                # maybe `t` scheduled another task
                return
            else:
-                tasks = ()  # don't reschedule this task
+                if not scheduled_error_handler:
+                    tasks = ()  # don't reschedule this task
        # add waiter task if requested
        if get_waiter is not None:
            futures[get_waiter()] = None
@@ -373,6 +476,7 @@ class PregelRunner:
        # each task is independent from all other concurrent tasks
        # yield updates/debug output as each task finishes
        end_time = timeout + loop.time() if timeout else None
+        handled_futures: set[asyncio.Future[Any]] = set()
        while len(futures) > (1 if get_waiter is not None else 0):
            done, inflight = await asyncio.wait(
                futures,
@@ -381,17 +485,59 @@ class PregelRunner:
            )
            if not done:
                break  # timed out
+            done_for_stop: set[asyncio.Future[Any]] = set()
            for fut in done:
                task = futures.pop(fut)
                if task is None:
                    # waiter task finished, schedule another
                    if inflight and get_waiter is not None:
                        futures[get_waiter()] = None
+                elif (
+                    (task_exc := _exception(fut))
+                    and self._should_route_to_error_handler(task)
+                    and not isinstance(task_exc, GraphBubbleUp)
+                ):
+                    self._handled_exception_ids.add(id(task_exc))
+                    SKIP_RERAISE_SET.add(fut)
+                    handled_futures.add(fut)
+                    if self.aschedule_error_handler is not None:
+                        if handler_task := await self.aschedule_error_handler(
+                            task, task_exc
+                        ):
+                            handler_fut = cast(
+                                asyncio.Future,
+                                self.submit()(  # type: ignore[misc]
+                                    arun_with_retry,
+                                    handler_task,
+                                    retry_policy,
+                                    stream=self.use_astream,
+                                    configurable={
+                                        CONFIG_KEY_CALL: partial(
+                                            _acall,
+                                            weakref.ref(handler_task),
+                                            retry_policy=retry_policy,
+                                            stream=self.use_astream,
+                                            futures=weakref.ref(futures),
+                                            schedule_task=schedule_task,
+                                            submit=self.submit,
+                                            loop=loop,
+                                        ),
+                                    },
+                                    __name__=handler_task.name,
+                                    __cancel_on_exit__=True,
+                                    __reraise_on_exit__=reraise,
+                                ),
+                            )
+                            futures[handler_fut] = handler_task
+                else:
+                    done_for_stop.add(fut)
            else:
                # remove references to loop vars
                del fut, task
            # maybe stop other tasks
-            if _should_stop_others(done):
+            if _should_stop_others(
+                done_for_stop, handled_exception_ids=self._handled_exception_ids
+            ):
                break
            # give control back to the caller
            yield
@@ -411,6 +557,8 @@ class PregelRunner:
                futures.done.union(f for f, t in futures.items() if t is not None),
                timeout_exc_cls=asyncio.TimeoutError,
                panic=reraise,
+                handled_exception_ids=self._handled_exception_ids,
+                handled_futures=handled_futures,
            )
        except Exception as exc:
            if tb := exc.__traceback__:
@@ -446,6 +594,11 @@ class PregelRunner:
            else:
                # save error to checkpointer
                task.writes.append((ERROR, exception))
+                if self._should_route_to_error_handler(task) and not isinstance(
+                    exception, GraphBubbleUp
+                ):
+                    # Mark early in commit path; loop-side routing may happen later.
+                    self._handled_exception_ids.add(id(exception))
                self.put_writes()(task.id, task.writes)  # type: ignore[misc]
        else:
            if self.node_finished and (
@@ -461,6 +614,8 @@ class PregelRunner:

 def _should_stop_others(
    done: set[F],
+    *,
+    handled_exception_ids: set[int] | None = None,
 ) -> bool:
    """Check if any task failed, if so, cancel all other tasks.
    GraphInterrupts are not considered failures."""
@@ -468,7 +623,11 @@ def _should_stop_others(
        if fut.cancelled():
            continue
        elif exc := fut.exception():
-            if not isinstance(exc, GraphBubbleUp) and fut not in SKIP_RERAISE_SET:
+            if (
+                id(exc) not in (handled_exception_ids or set())
+                and not isinstance(exc, GraphBubbleUp)
+                and fut not in SKIP_RERAISE_SET
+            ):
                return True

    return False
@@ -492,6 +651,9 @@ def _panic_or_proceed(
    *,
    timeout_exc_cls: type[Exception] = TimeoutError,
    panic: bool = True,
+    handled_exception_ids: set[int] | None = None,
+    handled_futures: Collection[concurrent.futures.Future[Any] | asyncio.Future[Any]]
+    | None = None,
 ) -> None:
    """Cancel remaining tasks if any failed, re-raise exception if panic is True."""
    done: set[concurrent.futures.Future[Any] | asyncio.Future[Any]] = set()
@@ -508,6 +670,10 @@ def _panic_or_proceed(
        # if any task failed
        fut = done.pop()
        if exc := _exception(fut):
+            if fut in (handled_futures or set()):
+                continue
+            if id(exc) in (handled_exception_ids or set()):
+                continue
            # cancel all pending tasks
            while inflight:
                inflight.pop().cancel()
@@ -537,6 +703,7 @@ def _call(
    *,
    retry_policy: Sequence[RetryPolicy] | None = None,
    cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
    callbacks: Callbacks = None,
    futures: weakref.ref[FuturesDict],
    schedule_task: Callable[
@@ -560,6 +727,7 @@ def _call(
            retry_policy=retry_policy,
            cache_policy=cache_policy,
            callbacks=callbacks,
+            timeout=timeout,
        ),
    ):
        if fut := next(
@@ -624,6 +792,7 @@ def _acall(
    *,
    retry_policy: Sequence[RetryPolicy] | None = None,
    cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
    callbacks: Callbacks = None,
    # injected dependencies
    futures: weakref.ref[FuturesDict],
@@ -657,6 +826,7 @@ def _acall(
            input,
            retry_policy=retry_policy,
            cache_policy=cache_policy,
+            timeout=timeout,
            callbacks=callbacks,
            futures=futures,
            schedule_task=schedule_task,
@@ -678,6 +848,7 @@ async def _acall_impl(
    *,
    retry_policy: Sequence[RetryPolicy] | None = None,
    cache_policy: CachePolicy | None = None,
+    timeout: TimeoutPolicy | None = None,
    callbacks: Callbacks = None,
    # injected dependencies
    futures: weakref.ref[FuturesDict[asyncio.Future, asyncio.Event]],
@@ -703,6 +874,7 @@ async def _acall_impl(
                retry_policy=retry_policy,
                cache_policy=cache_policy,
                callbacks=callbacks,
+                timeout=timeout,
            ),
        ):
            if fut := next(
@@ -0,0 +1,268 @@
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Callable, Iterator
+from contextvars import ContextVar, Token
+from typing import Any, TypeVar, cast
+from uuid import UUID
+
+from langchain_core.callbacks import BaseCallbackHandler
+
+from langgraph._internal._constants import NS_SEP
+from langgraph.constants import TAG_NOSTREAM
+from langgraph.pregel.protocol import StreamChunk
+
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = object  # type: ignore[assignment,misc]
+
+
+T = TypeVar("T")
+
+ToolCallWriter = Callable[[Any], None]
+"""A closure bound to a single tool call that emits `tool-output-delta` events."""
+
+_tool_call_writer: ContextVar[ToolCallWriter | None] = ContextVar(
+    "langgraph_tool_call_writer", default=None
+)
+"""ContextVar holding the writer for the currently-executing tool call.
+
+Set by `StreamToolCallHandler.on_tool_start` and reset on end/error.
+Read by `ToolRuntime.emit_output_delta` (in `langgraph.prebuilt`).
+"""
+
+
+class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
+    """Callback handler that emits tool-call lifecycle events on the stream.
+
+    Fires on LangChain's `on_tool_*` callbacks and pushes to the `tools`
+    stream mode. Emits `tool-started` / `tool-output-delta` /
+    `tool-finished` / `tool-error` payloads keyed by `tool_call_id`.
+
+    While a tool is executing, this handler sets `_tool_call_writer` to a
+    closure bound to that call's namespace and `tool_call_id`.
+    `ToolRuntime.emit_output_delta` reads that ContextVar so tool bodies
+    can stream partial output without threading the writer through their
+    own signature.
+
+    Attached by `Pregel.stream` / `astream` when `"tools"` is in
+    `stream_modes`. `run_inline = True` keeps event ordering
+    deterministic.
+    """
+
+    run_inline = True
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        """Configure the handler to stream tool-call events.
+
+        Args:
+            stream: Callable that accepts a `StreamChunk` tuple
+                `(namespace, mode, payload)` and enqueues it.
+            subgraphs: Whether to emit events from tools called inside
+                nested subgraphs. When False, only tools at the
+                handler's own scope (`parent_ns`) emit.
+            parent_ns: Namespace where the handler was attached.
+                Mirrors the `StreamMessagesHandler` escape hatch:
+                tools whose containing namespace equals `parent_ns`
+                still emit even with `subgraphs=False`, so a node that
+                explicitly streams a subgraph with `stream_mode="tools"`
+                sees its own tools.
+        """
+        self.stream = stream
+        self.subgraphs = subgraphs
+        self.parent_ns = parent_ns
+        # run_id → (namespace, tool_call_id, ContextVar token)
+        # `on_tool_end` does not receive `tool_call_id` in kwargs, so
+        # we correlate by `run_id` which is present on every callback.
+        self._run_to_call: dict[
+            UUID, tuple[tuple[str, ...], str, Token[ToolCallWriter | None]]
+        ] = {}
+
+    def _ns_for_emit(
+        self,
+        metadata: dict[str, Any] | None,
+        tags: list[str] | None,
+    ) -> tuple[str, ...] | None:
+        """Resolve the namespace this tool call should emit at, or `None` to skip.
+
+        Mirrors `StreamMessagesHandler.on_chat_model_start`'s namespace
+        derivation: parses `langgraph_checkpoint_ns` (which ends with
+        the `node_name:task_id` of the calling node), drops that
+        trailing segment, and returns the containing subgraph's own
+        namespace. Returns `None` when the call should be silently
+        suppressed:
+
+        - `metadata` is missing — handler is attached to a context
+          without Pregel routing info.
+        - `TAG_NOSTREAM` is in `tags` — caller explicitly opted out.
+        - Tool runs in a subgraph (`len(ns) > 0`) and the handler was
+          attached with `subgraphs=False` and a different `parent_ns`
+          than the call's containing subgraph.
+        """
+        if not metadata:
+            return None
+        if tags and TAG_NOSTREAM in tags:
+            return None
+        nskey = metadata.get("langgraph_checkpoint_ns")
+        if not nskey:
+            ns: tuple[str, ...] = ()
+        else:
+            ns = tuple(cast(str, nskey).split(NS_SEP))[:-1]
+        if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
+            return None
+        return ns
+
+    def _start(
+        self,
+        serialized: dict[str, Any] | None,
+        input_str: str,
+        *,
+        run_id: UUID,
+        metadata: dict[str, Any] | None,
+        tags: list[str] | None,
+        inputs: dict[str, Any] | None,
+        kwargs: dict[str, Any],
+    ) -> None:
+        ns = self._ns_for_emit(metadata, tags)
+        if ns is None:
+            return
+        tool_call_id = cast("str | None", kwargs.get("tool_call_id")) or str(run_id)
+        tool_name = (
+            (serialized or {}).get("name")
+            or cast("str | None", kwargs.get("name"))
+            or ""
+        )
+
+        def writer(delta: Any) -> None:
+            self.stream(
+                (
+                    ns,
+                    "tools",
+                    {
+                        "event": "tool-output-delta",
+                        "tool_call_id": tool_call_id,
+                        "delta": delta,
+                    },
+                )
+            )
+
+        token = _tool_call_writer.set(writer)
+        self._run_to_call[run_id] = (ns, tool_call_id, token)
+
+        payload: dict[str, Any] = {
+            "event": "tool-started",
+            "tool_call_id": tool_call_id,
+            "tool_name": tool_name,
+        }
+        if inputs is not None:
+            payload["input"] = inputs
+        self.stream((ns, "tools", payload))
+
+    def _end(self, output: Any, *, run_id: UUID) -> None:
+        info = self._run_to_call.pop(run_id, None)
+        if info is None:
+            return
+        ns, tool_call_id, token = info
+        self._reset_writer(token)
+        self.stream(
+            (
+                ns,
+                "tools",
+                {
+                    "event": "tool-finished",
+                    "tool_call_id": tool_call_id,
+                    "output": output,
+                },
+            )
+        )
+
+    def _error(self, error: BaseException, *, run_id: UUID) -> None:
+        info = self._run_to_call.pop(run_id, None)
+        if info is None:
+            return
+        ns, tool_call_id, token = info
+        self._reset_writer(token)
+        self.stream(
+            (
+                ns,
+                "tools",
+                {
+                    "event": "tool-error",
+                    "tool_call_id": tool_call_id,
+                    "message": str(error),
+                },
+            )
+        )
+
+    def tap_output_aiter(
+        self, run_id: UUID, output: AsyncIterator[T]
+    ) -> AsyncIterator[T]:
+        """Pass-through — required by the `_StreamingCallbackHandler` protocol."""
+        return output
+
+    def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
+        """Pass-through — sync counterpart to `tap_output_aiter`."""
+        return output
+
+    @staticmethod
+    def _reset_writer(token: Token[ToolCallWriter | None]) -> None:
+        # Token is invalid if `on_tool_end` runs in a different context
+        # than `on_tool_start` (e.g. langchain may hand off to a thread
+        # worker without copying the context). Swallow that case; the
+        # ContextVar lifetime is bounded by the enclosing task anyway.
+        try:
+            _tool_call_writer.reset(token)
+        except ValueError:
+            pass
+
+    # ------------------------------------------------------------------
+    # Sync callbacks
+    # ------------------------------------------------------------------
+
+    def on_tool_start(
+        self,
+        serialized: dict[str, Any],
+        input_str: str,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        metadata: dict[str, Any] | None = None,
+        inputs: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._start(
+            serialized,
+            input_str,
+            run_id=run_id,
+            metadata=metadata,
+            tags=tags,
+            inputs=inputs,
+            kwargs=kwargs,
+        )
+
+    def on_tool_end(
+        self,
+        output: Any,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._end(output, run_id=run_id)
+
+    def on_tool_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._error(error, run_id=run_id)
@@ -4,16 +4,27 @@ import ast
 import inspect
 import re
 import textwrap
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
+from functools import partial
 from typing import Any

-from langchain_core.runnables import Runnable, RunnableLambda, RunnableSequence
+from langchain_core.runnables import (
+    Runnable,
+    RunnableLambda,
+    RunnableParallel,
+    RunnableSequence,
+)
+from langchain_core.runnables.base import RunnableBindingBase
+from langchain_core.runnables.config import run_in_executor
 from langgraph.checkpoint.base import ChannelVersions
 from typing_extensions import override

 from langgraph._internal._runnable import RunnableCallable, RunnableSeq
+from langgraph._internal._timeout import sync_timeout_unsupported
 from langgraph.pregel.protocol import PregelProtocol

+_SEQUENCE_TYPES = (RunnableSeq, RunnableSequence)
+

 def get_new_channel_versions(
    previous_versions: ChannelVersions, current_versions: ChannelVersions
@@ -64,6 +75,68 @@ def find_subgraph_pregel(candidate: Runnable) -> PregelProtocol | None:
    return None


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

@@ -1,5 +1,6 @@
 from __future__ import annotations

+from collections.abc import Callable
 from dataclasses import dataclass, field, replace
 from typing import Any, Generic, cast

@@ -15,6 +16,7 @@ from langgraph.typing import ContextT
 __all__ = (
    "BaseUser",
    "ExecutionInfo",
+    "RunControl",
    "Runtime",
    "ServerInfo",
    "get_runtime",
@@ -74,16 +76,49 @@ class ServerInfo:
    """


+class RunControl:
+    """Run-scoped control surface for cooperative draining.
+
+    Intended for a single graph run. Create a fresh `RunControl` per run;
+    reusing a control after `request_drain()` leaves it drained.
+
+    Safe to call from any thread: the drain request is represented by a
+    single attribute write, so no lock is needed for this signal.
+    If more mutable state is added here, add synchronization.
+    """
+
+    __slots__ = ("_drain_reason",)
+
+    def __init__(self) -> None:
+        self._drain_reason: str | None = None
+
+    def request_drain(self, reason: str = "shutdown") -> None:
+        self._drain_reason = reason
+
+    @property
+    def drain_requested(self) -> bool:
+        return self._drain_reason is not None
+
+    @property
+    def drain_reason(self) -> str | None:
+        return self._drain_reason
+
+
 def _no_op_stream_writer(_: Any) -> None: ...


+def _no_op_heartbeat() -> None: ...
+
+
 class _RuntimeOverrides(TypedDict, Generic[ContextT], total=False):
    context: ContextT
    store: BaseStore | None
    stream_writer: StreamWriter
+    heartbeat: Callable[[], None]
    previous: Any
    execution_info: ExecutionInfo
    server_info: ServerInfo | None
+    control: RunControl | None


@dataclass(**_DC_KWARGS)
@@ -162,7 +197,7 @@ class Runtime(Generic[ContextT]):

    context: ContextT = field(default=None)  # type: ignore[assignment]
    """Static context for the graph run, like `user_id`, `db_conn`, etc.
-    
+
    Can also be thought of as 'run dependencies'."""

    store: BaseStore | None = field(default=None)
@@ -171,9 +206,19 @@ class Runtime(Generic[ContextT]):
    stream_writer: StreamWriter = field(default=_no_op_stream_writer)
    """Function that writes to the custom stream."""

+    heartbeat: Callable[[], None] = field(default=_no_op_heartbeat)
+    """Record progress for the current node's `idle_timeout`.
+
+    Call this from inside long-running work that does not naturally emit
+    writes, stream chunks, child tasks, or LangChain callback events, to
+    prevent the node from being treated as idle. It is also the only
+    progress signal honored under `TimeoutPolicy(refresh_on="heartbeat")`.
+    Outside an idle-timed attempt this is a no-op.
+    """
+
    previous: Any = field(default=None)
    """The previous return value for the given thread.
-    
+
    Only available with the functional API when a checkpointer is provided.
    """

@@ -185,6 +230,13 @@ class Runtime(Generic[ContextT]):
    server_info: ServerInfo | None = field(default=None)
    """Metadata injected by LangGraph Server. None when running open-source LangGraph without LangSmith deployments."""

+    control: RunControl | None = field(default=None)
+    """Run-scoped control plane for cooperative draining.
+
+    Populated automatically during graph runs. None outside an active
+    graph runtime.
+    """
+
    def merge(self, other: Runtime[ContextT]) -> Runtime[ContextT]:
        """Merge two runtimes together.

@@ -196,9 +248,13 @@ class Runtime(Generic[ContextT]):
            stream_writer=other.stream_writer
            if other.stream_writer is not _no_op_stream_writer
            else self.stream_writer,
+            heartbeat=other.heartbeat
+            if other.heartbeat is not _no_op_heartbeat
+            else self.heartbeat,
            previous=self.previous if other.previous is None else other.previous,
            execution_info=other.execution_info or self.execution_info,
            server_info=other.server_info or self.server_info,
+            control=other.control or self.control,
        )

    def override(
@@ -217,13 +273,23 @@ class Runtime(Generic[ContextT]):
            execution_info=self.execution_info.patch(**overrides),
        )

+    @property
+    def drain_requested(self) -> bool:
+        return self.control.drain_requested if self.control is not None else False
+
+    @property
+    def drain_reason(self) -> str | None:
+        return self.control.drain_reason if self.control is not None else None
+

 DEFAULT_RUNTIME = Runtime(
    context=None,
    store=None,
    stream_writer=_no_op_stream_writer,
+    heartbeat=_no_op_heartbeat,
    previous=None,
    execution_info=None,
+    control=None,
 )


@@ -0,0 +1,45 @@
+"""Streaming infrastructure for LangGraph.
+
+Compile a graph with `transformers=[...]` and call `graph.stream_events(version="v3")` /
+`graph.astream_events(version="v3")` to drive a transformer pipeline that projects the
+graph's raw events into ergonomic per-channel streams.
+"""
+
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    AsyncSubgraphRunStream,
+    GraphRunStream,
+    SubgraphRunStream,
+)
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    CheckpointsTransformer,
+    CustomTransformer,
+    DebugTransformer,
+    LifecyclePayload,
+    LifecycleTransformer,
+    SubgraphStatus,
+    SubgraphTransformer,
+    TasksTransformer,
+    UpdatesTransformer,
+)
+
+__all__ = [
+    "AsyncGraphRunStream",
+    "AsyncSubgraphRunStream",
+    "CheckpointsTransformer",
+    "CustomTransformer",
+    "DebugTransformer",
+    "GraphRunStream",
+    "LifecyclePayload",
+    "LifecycleTransformer",
+    "ProtocolEvent",
+    "StreamChannel",
+    "StreamTransformer",
+    "SubgraphRunStream",
+    "SubgraphStatus",
+    "SubgraphTransformer",
+    "TasksTransformer",
+    "UpdatesTransformer",
+]
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+import time
+from typing import Any, cast
+
+from langgraph.stream._types import ProtocolEvent, _ProtocolEventParams
+from langgraph.types import StreamPart
+
+
+def convert_to_protocol_event(part: StreamPart) -> ProtocolEvent:
+    """Convert a v2 StreamPart to a ProtocolEvent.
+
+    Args:
+        part: A stream part with keys `type`, `ns`, `data`, and
+            optionally `interrupts` (present on values events).
+
+    Returns:
+        The equivalent ProtocolEvent.
+    """
+    part_dict = cast(dict[str, Any], part)
+    params: _ProtocolEventParams = {
+        "namespace": list(part_dict["ns"]),
+        "timestamp": int(time.time() * 1000),
+        "data": part_dict["data"],
+    }
+    if "interrupts" in part_dict:
+        params["interrupts"] = part_dict["interrupts"]
+    return {
+        "type": "event",
+        "method": part_dict["type"],
+        "params": params,
+    }
@@ -0,0 +1,498 @@
+from __future__ import annotations
+
+import asyncio
+import time
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langgraph.stream._types import (
+    ProtocolEvent,
+    StreamTransformer,
+    transformer_requires_async,
+)
+from langgraph.stream.stream_channel import StreamChannel
+
+TransformerFactory = Callable[["tuple[str, ...]"], StreamTransformer]
+"""Factory that builds a scoped transformer for a mux.
+
+Called once per `StreamMux` with the mux's scope (typically `()` for
+the root). Standard transformer classes accept a single positional
+scope argument, so the class itself is a valid factory. User
+transformers can close over their config:
+`lambda scope: MyTransformer(scope, foo=...)`.
+"""
+
+
+class StreamMux:
+    """Central event dispatcher for the streaming infrastructure.
+
+    Owns the main event log and routes events through a transformer
+    pipeline. StreamChannels with a name discovered in transformer
+    projections are auto-wired so that every `push()` also injects a
+    `ProtocolEvent` into the main log. StreamChannels without a name
+    are local-only.
+
+    Pass `is_async=True` when the mux will be consumed via async
+    iteration (`handler.astream()`). All StreamChannel instances
+    discovered during registration are automatically bound to the
+    matching mode.
+
+    Attributes:
+        extensions: Merged projection dict across all registered
+            transformers. Treat as read-only — mutations won't be
+            reflected back in individual transformers' state.
+        native_keys: Projection keys contributed by transformers with
+            `_native = True`.
+    """
+
+    def __init__(
+        self,
+        transformers: list[StreamTransformer] | None = None,
+        *,
+        is_async: bool = False,
+        factories: list[TransformerFactory] | None = None,
+        scope: tuple[str, ...] = (),
+        _assign_seq: bool = True,
+    ) -> None:
+        """Initialize the mux and register transformers in order.
+
+        Callers pass either `transformers` (pre-built instances) or
+        `factories` (callables producing fresh instances per mux). Each
+        transformer's `init()` is called, projections are merged into
+        `extensions`, `_native` keys are recorded in `native_keys`, and
+        any StreamChannel instances are bound and (if named) wired.
+
+        Args:
+            transformers: Already-built transformer instances. Registered
+                only on this mux — they are NOT cloned into child
+                mini-muxes built by `_make_child`. Use `factories` for
+                transformers that should propagate to nested scopes.
+            is_async: True for async dispatch (`apush` / `aclose` /
+                `afail`), False for the sync path.
+            factories: One-argument callables `(scope) -> StreamTransformer`.
+                Called once with this mux's `scope` here, and cloned
+                again per child scope by `_make_child` so each
+                sub-mux gets fresh instances.
+            scope: The namespace the mux operates within. The root mux
+                is `()`.
+            _assign_seq: Internal flag for child muxes. Root muxes assign
+                monotonic `seq` numbers when appending to their main event
+                log; child muxes share forwarded event objects and must not
+                mutate their envelopes.
+
+        Raises:
+            RuntimeError: If any transformer requires an async run but
+                the mux is in sync mode.
+            TypeError: If a transformer's `init()` doesn't return a dict.
+            ValueError: If transformers' projection keys collide.
+        """
+        self.is_async = is_async
+        self.scope: tuple[str, ...] = scope
+        self._assign_seq = _assign_seq
+        self._events: StreamChannel[ProtocolEvent] = StreamChannel()
+        self._events._bind(is_async=is_async)
+        self._events._bind_mux(self)
+        self._transformers: list[StreamTransformer] = []
+        self._channels: list[StreamChannel[Any]] = []
+        self._seq = 0
+        self._push_seq = 0
+
+        self.extensions: dict[str, Any] = {}
+        self.native_keys: set[str] = set()
+        self._projection_owners: dict[str, str] = {}
+        self._transformer_by_key: dict[str, StreamTransformer] = {}
+
+        # Stored only when constructed from factories — used by
+        # `_make_child` to clone the transformer pipeline at a deeper
+        # scope. Pre-built transformers can't be cloned, so a mux
+        # built with `transformers=` rejects child construction.
+        self._factories: list[TransformerFactory] | None = (
+            list(factories) if factories is not None else None
+        )
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+        # Factories run first (they propagate to child mini-muxes
+        # via `_make_child`), then any pre-built `transformers=`
+        # instances are registered as root-only — they aren't cloned
+        # for child scopes.
+        if factories is not None:
+            for factory in factories:
+                self._register(factory(scope))
+        for transformer in transformers or ():
+            self._register(transformer)
+
+    def transformer_by_key(self, key: str) -> StreamTransformer | None:
+        """Return the transformer that contributed `key` to the projection."""
+        return self._transformer_by_key.get(key)
+
+    def _next_push_seq(self) -> int:
+        self._push_seq += 1
+        return self._push_seq
+
+    # ------------------------------------------------------------------
+    # Pump wiring + mini-mux nesting
+    # ------------------------------------------------------------------
+
+    def bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback onto every projection in this mux.
+
+        Records the pump on the mux so child mini-muxes built by
+        `_make_child` can inherit it. Propagates to:
+        - the main event log (`self._events`)
+        - every projection StreamChannel in `extensions`
+        - any registered transformer that exposes `_bind_pump` (e.g.
+          `MessagesTransformer` so `ChatModelStream` instances drive the
+          shared pump from their cursors)
+        """
+        self._pump_fn = fn
+        self._events._request_more = fn
+        for ch in self._channels:
+            ch._request_more = fn
+        for transformer in self._transformers:
+            bind = getattr(transformer, "_bind_pump", None)
+            if bind is not None:
+                bind(fn)
+
+    def bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Async counterpart to `bind_pump`."""
+        self._apump_fn = fn
+        self._events._arequest_more = fn
+        for ch in self._channels:
+            ch._arequest_more = fn
+        for transformer in self._transformers:
+            abind = getattr(transformer, "_bind_apump", None)
+            if abind is not None:
+                abind(fn)
+
+    def _make_child(self, scope: tuple[str, ...]) -> StreamMux:
+        """Build a mini-mux with the same factories scoped to `scope`.
+
+        Used by `SubgraphTransformer` to attach a fresh transformer
+        pipeline to each discovered subgraph handle. The child mux
+        inherits the current pump bindings (so cursors on its
+        projection logs drive the root pump), carries the same factory
+        list forward to any grandchild subgraphs, and does not assign
+        `seq` numbers so forwarded events can be shared without
+        mutating their envelope.
+
+        Raises:
+            RuntimeError: If the mux was not constructed with
+                `factories=`. Mini-muxes require factories so each scope
+                gets its own fresh transformer instances.
+        """
+        if self._factories is None:
+            raise RuntimeError(
+                "StreamMux._make_child requires the mux to be constructed "
+                "with `factories=`; pre-built transformers can't be "
+                "cloned to a new scope."
+            )
+        child = StreamMux(
+            factories=self._factories,
+            is_async=self.is_async,
+            scope=scope,
+            _assign_seq=False,
+        )
+        if self._pump_fn is not None:
+            child.bind_pump(self._pump_fn)
+        if self._apump_fn is not None:
+            child.bind_apump(self._apump_fn)
+        return child
+
+    def _register(self, transformer: StreamTransformer) -> None:
+        """Register a single transformer.
+
+        Calls `transformer.init()`, stores the transformer for event
+        processing, binds any StreamChannel instances in the projection,
+        and merges the projection into `extensions`.
+        """
+        if transformer_requires_async(transformer) and not self.is_async:
+            raise RuntimeError(
+                f"{type(transformer).__name__} requires an async run — "
+                "it overrides aprocess/afinalize/afail or sets "
+                "requires_async=True. Use astream(), not stream()."
+            )
+        projection = transformer.init()
+        if not isinstance(projection, dict):
+            raise TypeError(
+                f"StreamTransformer.init() must return a dict, "
+                f"got {type(projection).__name__}"
+            )
+        conflicts = set(projection) & set(self.extensions)
+        if conflicts:
+            attributions = ", ".join(
+                f"{key!r} (owned by {self._projection_owners[key]})"
+                for key in sorted(conflicts)
+            )
+            raise ValueError(
+                f"Transformer {type(transformer).__name__} returned "
+                f"projection keys that conflict with already-registered "
+                f"keys: {attributions}"
+            )
+        is_native = bool(getattr(transformer, "_native", False))
+        self._transformers.append(transformer)
+        self._bind_and_wire(projection, native=is_native)
+        self.extensions.update(projection)
+        owner_name = type(transformer).__name__
+        for key in projection:
+            self._projection_owners[key] = owner_name
+            self._transformer_by_key[key] = transformer
+        if is_native:
+            self.native_keys.update(projection.keys())
+        transformer._on_register(self)
+
+    def push(self, event: ProtocolEvent) -> None:
+        """Route an event through all transformers, then append to the main log.
+
+        Each transformer's `process()` is called in registration order.
+        If any transformer returns False, the event is suppressed from
+        the main log, but transformers that already saw it keep their
+        side effects.
+
+        On the root mux, `seq` is assigned right before an event enters
+        the main log, not before the transformer pipeline runs. This
+        ensures that events auto-forwarded from StreamChannels during
+        `process()` get earlier seq numbers than the original event,
+        preserving monotonic ordering in the root log. Child muxes do
+        not assign `seq`, so subgraph forwarding can share event objects
+        without mutating their envelopes.
+
+        Args:
+            event: The protocol event to dispatch.
+        """
+        keep = True
+        for transformer in self._transformers:
+            if not transformer.process(event):
+                keep = False
+        if keep:
+            if self._assign_seq:
+                self._seq += 1
+                event["seq"] = self._seq
+            self._events.push(event)
+
+    def close(self) -> None:
+        """Finalize all transformers, close all projections and the main log.
+
+        StreamChannels discovered in transformer projections are
+        auto-closed after `finalize()` runs — transformers don't need
+        to close them manually. If any transformer's `finalize()` raises,
+        the remaining transformers, projections, and the main log are
+        still closed; the first error is re-raised after cleanup
+        completes.
+
+        Raises:
+            BaseException: The first error raised by a transformer's
+                `finalize()`, re-raised after cleanup finishes.
+        """
+        first_error: BaseException | None = None
+        for transformer in self._transformers:
+            try:
+                transformer.finalize()
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        for ch in self._channels:
+            if not ch._closed:
+                ch.close()
+        self._events.close()
+        if first_error is not None:
+            raise first_error
+
+    def fail(self, err: BaseException) -> None:
+        """Fail all transformers, projections, and the main log.
+
+        StreamChannels discovered in transformer projections are
+        auto-failed — transformers don't need to fail them manually.
+        If any transformer's `fail()` raises, the remaining
+        transformers, projections, and the main log are still failed.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        for transformer in self._transformers:
+            try:
+                transformer.fail(err)
+            except BaseException:
+                pass
+        for ch in self._channels:
+            if not ch._closed:
+                ch.fail(err)
+        self._events.fail(err)
+
+    # ------------------------------------------------------------------
+    # Async dispatch
+    # ------------------------------------------------------------------
+
+    async def apush(self, event: ProtocolEvent) -> None:
+        """Dispatch an event on the async lane.
+
+        Awaits each transformer's `aprocess` in registration order
+        before appending to the main log. A slow `aprocess` serializes
+        the pipeline by design — that's the guarantee that lets a later
+        transformer (or a synchronous consumer) see the result of the
+        async work. For decoupled work, use `schedule()` from inside
+        `process` / `aprocess` instead.
+
+        The main log append is a non-blocking `push` — matching v1's
+        `put_nowait` shape. The root mux assigns `seq`; child muxes do
+        not, so forwarded subgraph events can be shared without copying.
+        Memory is bounded by caller pace via the caller-driven pump; see
+        `StreamChannel` for the full tradeoff story.
+
+        Args:
+            event: The protocol event to dispatch.
+        """
+        keep = True
+        for transformer in self._transformers:
+            if not await transformer.aprocess(event):
+                keep = False
+        if keep:
+            if self._assign_seq:
+                self._seq += 1
+                event["seq"] = self._seq
+            self._events.push(event)
+
+    async def aclose(self) -> None:
+        """Finalize on the async lane.
+
+        Awaits every task started via `StreamTransformer.schedule()`
+        across all transformers, then calls `afinalize()` on each,
+        then auto-closes channels and the main event log.
+
+        If any scheduled task raised under `on_error="raise"`, or any
+        transformer's `afinalize` raises, the exception propagates.
+        The caller (the pump) handles it by routing into `afail`.
+
+        Raises:
+            BaseException: The first scheduled-task or `afinalize`
+                error, re-raised after cleanup.
+        """
+        pending = self._collect_scheduled_tasks()
+        if pending:
+            results = await asyncio.gather(*pending, return_exceptions=True)
+            first_err = next(
+                (
+                    r
+                    for r in results
+                    if isinstance(r, BaseException)
+                    and not isinstance(r, asyncio.CancelledError)
+                ),
+                None,
+            )
+            if first_err is not None:
+                raise first_err
+
+        first_error: BaseException | None = None
+        for transformer in self._transformers:
+            try:
+                await transformer.afinalize()
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        for ch in self._channels:
+            if not ch._closed:
+                ch.close()
+        self._events.close()
+        if first_error is not None:
+            raise first_error
+
+    async def afail(self, err: BaseException) -> None:
+        """Fail on the async lane.
+
+        Cancels every scheduled task across all transformers, awaits
+        them to completion, then runs each transformer's `afail` hook
+        and auto-fails channels and the main event log.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        pending = self._collect_scheduled_tasks()
+        for task in pending:
+            task.cancel()
+        if pending:
+            await asyncio.gather(*pending, return_exceptions=True)
+
+        for transformer in self._transformers:
+            try:
+                await transformer.afail(err)
+            except BaseException:
+                pass
+        for ch in self._channels:
+            if not ch._closed:
+                ch.fail(err)
+        if not self._events._closed:
+            self._events.fail(err)
+
+    def _collect_scheduled_tasks(self) -> list[asyncio.Task[Any]]:
+        """Return a snapshot of in-flight tasks scheduled via transformers."""
+        return [
+            task
+            for transformer in self._transformers
+            for task in getattr(transformer, "_stream_scheduled_tasks", ())
+            if not task.done()
+        ]
+
+    # ------------------------------------------------------------------
+    # Binding and StreamChannel auto-wiring
+    # ------------------------------------------------------------------
+
+    def _bind_and_wire(
+        self, projection: dict[str, Any], *, native: bool = False
+    ) -> None:
+        """Bind and optionally wire StreamChannel instances in a projection.
+
+        All StreamChannels are bound and tracked. Channels with a name
+        are additionally wired for protocol auto-forwarding.
+
+        Args:
+            projection: The projection dict returned by a transformer's
+                `init()`.
+            native: True when the owning transformer is `_native`.
+                Named channels owned by a native transformer use the
+                channel name directly as the protocol method;
+                user-defined channels are prefixed with `custom:`.
+        """
+        for value in projection.values():
+            if isinstance(value, StreamChannel):
+                value._bind(is_async=self.is_async)
+                value._bind_mux(self)
+                self._channels.append(value)
+                if value.name is not None:
+                    method = value.name if native else f"custom:{value.name}"
+
+                    def _make_forward(method_name: str) -> Callable[[Any], None]:
+                        def _forward(item: Any) -> None:
+                            self._forward(method_name, item)
+
+                        return _forward
+
+                    value._wire(_make_forward(method))
+
+    def _forward(self, method: str, item: Any) -> None:
+        """Inject a ProtocolEvent for a StreamChannel push.
+
+        Forwarded events bypass the transformer pipeline to avoid
+        infinite recursion (a transformer that pushes to a channel
+        during `process()` would re-trigger itself). These events are
+        visible in this mux's main event log but are not passed through
+        transformers' `process()` methods. Only the root mux assigns
+        `seq` to forwarded channel events.
+
+        Args:
+            method: The full protocol method (already with or without
+                the `custom:` prefix; resolved by `_bind_and_wire`).
+            item: The payload pushed onto the channel.
+        """
+        event: ProtocolEvent = {
+            "type": "event",
+            "method": method,
+            "params": {
+                "namespace": [],
+                "timestamp": int(time.time() * 1000),
+                "data": item,
+            },
+        }
+        if self._assign_seq:
+            self._seq += 1
+            event["seq"] = self._seq
+        self._events.push(event)
@@ -0,0 +1,313 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Coroutine
+from typing import Any, ClassVar, Literal
+
+from typing_extensions import NotRequired, TypedDict
+
+_logger = logging.getLogger(__name__)
+
+
+class _ProtocolEventParams(TypedDict):
+    """Parameters for a protocol event.
+
+    `timestamp` is wall-clock milliseconds since the epoch and can go
+    backwards across NTP adjustments — use `ProtocolEvent.seq` for
+    ordering.
+    """
+
+    namespace: list[str]
+    timestamp: int
+    data: Any
+    interrupts: NotRequired[tuple[Any, ...]]
+
+
+class ProtocolEvent(TypedDict):
+    """A protocol event emitted by the streaming infrastructure.
+
+    Wraps a raw stream part (values, messages, custom, etc.) in a uniform
+    envelope with a monotonic sequence number assigned by the root StreamMux.
+    Consumers that need a total order across root events should use `seq`, not
+    `params.timestamp` (which is wall-clock and not monotonic).
+    """
+
+    type: Literal["event"]
+    eventId: NotRequired[str]
+    seq: NotRequired[int]
+    method: str  # StreamMode value: "values", "messages", "custom", etc.
+    params: _ProtocolEventParams
+
+
+class StreamTransformer(ABC):
+    """Extension point for custom stream projections.
+
+    Transformers observe protocol events flowing through the StreamMux and
+    build typed derived projections (StreamChannels, promises, etc.).
+
+    Set `_native = True` on a transformer to have its projection keys
+    exposed as direct attributes on the run stream (in addition to
+    appearing in `run.extensions`).
+
+    Subclasses must implement `init` and override at least one of
+    `process` / `aprocess`. The `finalize` / `afinalize` and `fail` /
+    `afail` hooks are optional — the default implementations are no-ops.
+    StreamChannel instances in the projection dict are auto-closed /
+    auto-failed by the mux, so most transformers don't need `finalize`
+    or `fail` at all.
+
+    Transformers that need async work pick the async lane by:
+
+    1. Overriding `aprocess` (and optionally `afinalize` / `afail`), or
+    2. Calling `self.schedule(coro)` from inside a sync `process`, or
+    3. Setting `requires_async = True` explicitly.
+
+    The mux detects these cases at registration and raises if they're
+    used under sync `stream()` — they only work under `astream()`.
+
+    Use `aprocess` when the pump must wait for async work before the
+    next transformer sees the event (e.g. PII redaction that mutates
+    `event` in place). Use `schedule()` for decoupled async work whose
+    result lands on an independent projection (e.g. async moderation
+    scoring, cost lookup, external tracing).
+
+    Attributes:
+        scope: Namespace the transformer operates within — `()` for the
+            root mux. Set at construction from the mux's scope (each
+            factory is called as `factory(scope)`).
+        requires_async: Explicit opt-in for transformers that need a
+            running event loop but don't override any async method (for
+            example, transformers that call `schedule()` from a sync
+            `process`). The mux also auto-detects the async lane when
+            `aprocess`, `afinalize`, or `afail` is overridden.
+        supports_sync: Set True only for transformers that override
+            async-lane hooks while still fully supporting the sync lane.
+            Such transformers may be registered under `stream()`.
+        required_stream_modes: Stream modes the graph must emit for
+            this transformer to have anything to process. Computed as
+            the union across all registered transformers to determine
+            which modes a `stream_events(version="v3")` run requests from the graph.
+            Empty tuple means the transformer consumes only synthetic
+            events (or is purely passive).
+    """
+
+    requires_async: ClassVar[bool] = False
+    supports_sync: ClassVar[bool] = False
+    required_stream_modes: ClassVar[tuple[str, ...]] = ()
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        """Initialize the transformer with its mux's scope.
+
+        Args:
+            scope: The namespace tuple the owning mux is scoped to.
+                `()` for the root. Factories receive this at
+                construction time (`factory(scope)` in `StreamMux`).
+        """
+        self.scope: tuple[str, ...] = scope
+
+    @abstractmethod
+    def init(self) -> dict[str, Any]:
+        """Return the projection dict.
+
+        Keys become entries in `run.extensions`. If the transformer has
+        `_native = True`, keys are also set as direct attributes on the
+        run stream.
+
+        StreamChannel instances in the return value are automatically
+        wired by the StreamMux for protocol event auto-forwarding.
+        """
+        ...
+
+    def _on_register(self, mux: Any) -> None:
+        """Called by `StreamMux._register` after this transformer is wired in.
+
+        Default is a no-op. Override to capture a reference to the
+        owning mux — needed for transformers that build mini-muxes
+        via `mux._make_child(...)` (e.g. `SubgraphTransformer`).
+        """
+
+    def process(self, event: ProtocolEvent) -> bool:
+        """Handle an event on the sync lane.
+
+        Called for every event before it is appended to the main event
+        log. Subclasses must override either `process` or `aprocess`.
+        The default raises so a missing override fails loudly rather
+        than silently passing every event through.
+
+        Args:
+            event: The protocol event to observe.
+
+        Returns:
+            True to keep the event in the main log, False to suppress it.
+        """
+        raise NotImplementedError(
+            f"{type(self).__name__} must override process() or aprocess()"
+        )
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        """Handle an event on the async lane.
+
+        The mux awaits this before dispatching to the next transformer,
+        so a slow `aprocess` serializes the pipeline. Use it only when
+        a later transformer — or a consumer reading the event
+        synchronously — must see the result of the async work (e.g.
+        PII redaction that mutates `event` in place).
+
+        The default delegates to `process`, so purely-sync transformers
+        run unchanged under `astream()`.
+
+        Args:
+            event: The protocol event to observe.
+
+        Returns:
+            True to keep the event in the main log, False to suppress it.
+        """
+        return self.process(event)
+
+    def finalize(self) -> None:
+        """Called when the run ends normally (sync lane).
+
+        Override to close StreamChannels, resolve promises, or perform
+        other teardown. StreamChannel instances in the projection dict
+        are auto-closed by the mux.
+        """
+
+    async def afinalize(self) -> None:
+        """Called when the run ends normally (async lane).
+
+        By the time this runs, the mux has already awaited every task
+        started via `schedule()`, so StreamChannels can be closed here
+        without a last-task-wins race.
+
+        The default delegates to `finalize`.
+        """
+        self.finalize()
+
+    def fail(self, err: BaseException) -> None:
+        """Called when the run ends with an error (sync lane).
+
+        Override to fail StreamChannels, reject promises, or perform
+        other teardown. StreamChannel instances in the projection dict
+        are auto-failed by the mux.
+
+        Args:
+            err: The exception that ended the run.
+        """
+
+    async def afail(self, err: BaseException) -> None:
+        """Called when the run ends with an error (async lane).
+
+        The mux cancels and awaits every task started via `schedule()`
+        before calling this, so cleanup doesn't race with in-flight work.
+
+        The default delegates to `fail`.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        self.fail(err)
+
+    # ------------------------------------------------------------------
+    # Scheduled async work
+    # ------------------------------------------------------------------
+
+    def schedule(
+        self,
+        coro: Coroutine[Any, Any, Any],
+        *,
+        on_error: Literal["log", "raise"] = "log",
+    ) -> asyncio.Task[Any]:
+        """Schedule a coroutine tied to this transformer's lifecycle.
+
+        The mux holds the task reference, awaits all scheduled tasks
+        during `aclose()` before calling `afinalize()`, and cancels
+        them on `afail()`. Authors don't need to track tasks or
+        implement the last-task-closes-the-log dance.
+
+        Requires a running event loop — call only under `astream()`.
+        Set `requires_async = True` on the class so registration under
+        sync `stream()` fails fast with a clear message.
+
+        Args:
+            coro: The coroutine to run. Its lifecycle is owned by the
+                mux from this point on.
+            on_error: `"log"` (default) catches and logs any exception
+                the coroutine raises, so a single failure doesn't tear
+                down the run. `"raise"` lets the exception propagate
+                when the mux joins pendings, converting the close path
+                into the fail path.
+
+        Returns:
+            The asyncio Task. Authors rarely need to await it directly
+            — consumers read results from whatever projection the
+            coroutine pushes into.
+
+        Raises:
+            RuntimeError: If called without a running event loop (i.e.
+                under sync `stream()` rather than `astream()`).
+        """
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            raise RuntimeError(
+                f"{type(self).__name__}.schedule() requires a running "
+                "event loop; this transformer must run under astream(), "
+                "not stream(). Set requires_async=True on the class so "
+                "this fails at registration rather than at first event."
+            ) from None
+
+        wrapped = self._wrap_scheduled(coro) if on_error == "log" else coro
+        task = asyncio.create_task(wrapped)
+        tasks = self._scheduled_task_set()
+        tasks.add(task)
+        task.add_done_callback(tasks.discard)
+        return task
+
+    @staticmethod
+    async def _wrap_scheduled(coro: Coroutine[Any, Any, Any]) -> Any:
+        try:
+            return await coro
+        except asyncio.CancelledError:
+            raise
+        except BaseException:
+            _logger.exception("Scheduled StreamTransformer task failed")
+
+    def _scheduled_task_set(self) -> set[asyncio.Task[Any]]:
+        """Return the lazily-allocated task set.
+
+        Avoids requiring subclasses to call `super().__init__()`.
+        """
+        tasks: set[asyncio.Task[Any]] | None = getattr(
+            self, "_stream_scheduled_tasks", None
+        )
+        if tasks is None:
+            tasks = set()
+            self._stream_scheduled_tasks = tasks
+        return tasks
+
+
+def transformer_requires_async(transformer: StreamTransformer) -> bool:
+    """Return True if the transformer needs a running event loop.
+
+    A transformer requires async if it explicitly opts in
+    (`requires_async = True`) or overrides any of the async-lane methods
+    (`aprocess`, `afinalize`, `afail`) without also declaring that it
+    supports the sync lane.
+
+    Args:
+        transformer: The transformer to inspect.
+
+    Returns:
+        True if the transformer cannot run under sync `stream()`.
+    """
+    if transformer.requires_async:
+        return True
+    if transformer.supports_sync:
+        return False
+    cls = type(transformer)
+    for name in ("aprocess", "afinalize", "afail"):
+        if getattr(cls, name) is not getattr(StreamTransformer, name):
+            return True
+    return False
@@ -0,0 +1,608 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping
+from types import MappingProxyType, TracebackType
+from typing import TYPE_CHECKING, Any
+
+from langchain_core._api import beta
+
+from langgraph.stream._convert import convert_to_protocol_event
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+
+if TYPE_CHECKING:
+    from langgraph.stream.transformers import SubgraphStatus
+
+
+def _drive_until_done(pump: Callable[[], bool]) -> None:
+    """Call the sync pump until it returns False."""
+    while pump():
+        pass
+
+
+async def _adrive_until_done(pump: Callable[[], Awaitable[bool]]) -> None:
+    """Call the async pump until it returns False."""
+    while await pump():
+        pass
+
+
+@beta(message="The v3 streaming protocol on Pregel is experimental.")
+class GraphRunStream:
+    """Sync run stream with caller-driven pumping.
+
+    The caller's iteration on any projection (`values`, `messages`,
+    raw events, or `output`) drives the graph forward. No background
+    thread is used — the caller's `for` loop is the pump.
+
+    Projections are single-consumer — iterating `run.values` twice
+    raises. Use `projection.tee(n)` if you genuinely need fan-out.
+
+    All transformer projections live in `extensions`. Native transformer
+    projections (those with `_native = True`) are also set as direct
+    attributes on this instance (e.g. `run.values`, `run.messages`).
+
+    !!! warning
+
+        Returned by `Pregel.stream_events(version="v3")`, which is
+        experimental and may change.
+    """
+
+    def __init__(
+        self,
+        graph_iter: Iterator[Any] | None,
+        mux: StreamMux,
+        *,
+        wire_pump: bool = True,
+    ) -> None:
+        """Initialize the run stream.
+
+        Args:
+            graph_iter: Pull-based iterator over the graph's stream,
+                or `None` for nested run streams whose pump is driven
+                by an outer run (e.g. `SubgraphRunStream`).
+            mux: The StreamMux owning projections and the main log.
+            wire_pump: When True (default), bind `_pump_next` as the
+                mux's pump callable. Subclasses that inherit a parent
+                pump via `StreamMux._make_child` should pass False to
+                preserve the parent binding.
+        """
+        self._graph_iter = graph_iter
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        self._exhausted = False
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        self._scope_list: list[str] = list(mux.scope)
+        for key in mux.native_keys:
+            setattr(self, key, mux.extensions[key])
+        if wire_pump:
+            self._wire_request_more(mux)
+
+    def _wire_request_more(self, mux: StreamMux) -> None:
+        """Wire the sync pull callback through the mux.
+
+        Routing through `mux.bind_pump` (rather than walking
+        projections directly here) lets child mini-muxes built by
+        `mux._make_child(...)` inherit the same pump callable, so
+        cursors on a subgraph handle's projections drive the root
+        pump just like cursors on `run.values` do.
+        """
+        mux.bind_pump(self._pump_next)
+
+    def _observe_event(self, event: ProtocolEvent) -> None:
+        """Track values-event state for output/interrupted/interrupts."""
+        if event["method"] != "values":
+            return
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+
+    def _pump_next(self) -> bool:
+        """Pull one event from the graph and push it through the mux.
+
+        Returns:
+            True if an event was pulled, False if the graph is exhausted
+            or has raised. Always False when constructed with
+            `graph_iter=None` (the run is driven by an outer pump).
+        """
+        if self._exhausted or self._graph_iter is None:
+            return False
+        try:
+            part = next(self._graph_iter)
+            event = convert_to_protocol_event(part)
+            self._observe_event(event)
+            self._mux.push(event)
+            return True
+        except StopIteration:
+            self._mux.close()
+            self._exhausted = True
+            return False
+        except Exception as e:
+            self._mux.fail(e)
+            self._exhausted = True
+            return False
+
+    def abort(self) -> None:
+        """Stop the run early.
+
+        Closes the mux and marks the stream exhausted. The graph
+        iterator is dropped; any in-flight nodes see the closure on
+        their next yield point. Idempotent.
+        """
+        if self._exhausted:
+            return
+        self._exhausted = True
+        try:
+            self._mux.close()
+        except Exception:
+            pass
+
+    def __enter__(self) -> GraphRunStream:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        self.abort()
+
+    @property
+    def output(self) -> dict[str, Any] | None:
+        """Drive the run to completion and return the final state."""
+        _drive_until_done(self._pump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._latest
+
+    @property
+    def interrupted(self) -> bool:
+        """Drive the run to completion, then return whether it was
+        interrupted.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        _drive_until_done(self._pump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupted
+
+    @property
+    def interrupts(self) -> list[Any]:
+        """Drive the run to completion, then return interrupt payloads.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        _drive_until_done(self._pump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupts
+
+    def __iter__(self) -> Iterator[ProtocolEvent]:
+        """Subscribe to the main event log and iterate protocol events."""
+        return iter(self._mux._events)
+
+    def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
+        """Iterate multiple projections in arrival order, yielding ``(name, item)``.
+
+        Items are ordered by a monotonic push stamp assigned when each
+        transformer pushes into its `StreamChannel`. This gives strict
+        arrival ordering across projections, unlike round-robin.
+
+        Args:
+            *names: Projection keys to interleave. Must match keys in
+                ``extensions``.
+
+        Yields:
+            ``(name, item)`` tuples in arrival order across the named
+            projections.
+
+        Each named channel is locked for the duration of iteration and
+        released when the generator completes, is closed, or raises.
+        Channels cannot be subscribed concurrently — use `.tee(n)` if
+        you need fan-out.
+
+        Raises:
+            KeyError: If a name doesn't match a registered projection.
+
+        Example:
+            ```python
+            for name, item in run.interleave("messages", "values"):
+                if name == "messages":
+                    print("msg:", item)
+                else:
+                    print("val:", item)
+            ```
+        """
+        from langgraph.stream.stream_channel import StreamChannel
+
+        channels: dict[str, StreamChannel[Any]] = {}
+        try:
+            for name in names:
+                ch = self.extensions[name]
+                if not isinstance(ch, StreamChannel):
+                    raise TypeError(
+                        f"interleave() requires StreamChannel projections, "
+                        f"got {type(ch).__name__} for {name!r}"
+                    )
+                if ch._is_async is None:
+                    raise TypeError(
+                        f"StreamChannel {name!r} has not been bound yet. "
+                        "Register the transformer with a StreamMux first."
+                    )
+                if ch._is_async:
+                    raise TypeError(
+                        f"StreamChannel {name!r} is bound to async mode — "
+                        "sync interleave() cannot consume async channels."
+                    )
+                if ch._subscribed:
+                    raise RuntimeError(
+                        f"StreamChannel {name!r} already has a subscriber; "
+                        "use .tee(n) for fan-out."
+                    )
+                ch._subscribed = True
+                channels[name] = ch
+
+            done: set[str] = set()
+
+            while len(done) < len(channels):
+                best: tuple[int, str] | None = None
+                for name, ch in channels.items():
+                    if name in done:
+                        continue
+                    if ch._closed and not ch._items:
+                        if ch._error is not None:
+                            raise ch._error
+                        done.add(name)
+                        continue
+                    if ch._items:
+                        stamp = ch._items[0][0]
+                        if best is None or stamp < best[0]:
+                            best = (stamp, name)
+
+                if best is not None:
+                    _stamp, item = channels[best[1]]._items.popleft()
+                    yield (best[1], item)
+                else:
+                    pump = self._mux._pump_fn
+                    if pump is None or not pump():
+                        before = len(done)
+                        for name, ch in channels.items():
+                            if name not in done and not ch._items:
+                                if ch._closed:
+                                    if ch._error is not None:
+                                        raise ch._error
+                                    done.add(name)
+                        if len(done) == before:
+                            break
+        finally:
+            for ch in channels.values():
+                ch._subscribed = False
+
+
+@beta(message="The v3 streaming protocol on Pregel is experimental.")
+class AsyncGraphRunStream:
+    """Async run stream with caller-driven pumping.
+
+    Async iteration on any projection drives the graph forward — there
+    is no background task. Concurrent consumers share a single-flight
+    pump via an `asyncio.Lock`, so each awaiting cursor contributes one
+    event per acquisition. Backpressure comes from the logs: when a
+    subscribed log's buffer reaches `maxlen`, `apush` awaits the
+    subscriber to drain, which holds back the pump and paces the graph.
+
+    Projections are single-consumer — a second `aiter(run.values)`
+    raises. Use `projection.tee(n)` for fan-out.
+
+    Use as an async context manager to guarantee clean shutdown on
+    early exit:
+
+    ```python
+    async with await handler.astream(input) as run:
+        async for msg in run.messages:
+            ...
+    ```
+
+    !!! warning
+
+        Awaited from `Pregel.astream_events(version="v3")`, which is
+        experimental and may change.
+    """
+
+    def __init__(
+        self,
+        graph_aiter: AsyncIterator[Any] | None,
+        mux: StreamMux,
+        *,
+        wire_pump: bool = True,
+    ) -> None:
+        """Initialize the async run stream.
+
+        Args:
+            graph_aiter: Async iterator over the graph's stream, or
+                `None` for nested run streams whose pump is driven by
+                an outer run (e.g. `AsyncSubgraphRunStream`).
+            mux: The StreamMux owning projections and the main log.
+            wire_pump: When True (default), bind `_apump_next` as the
+                mux's async pump callable. Subclasses that inherit a
+                parent pump via `StreamMux._make_child` should pass
+                False to preserve the parent binding.
+        """
+        self._graph_aiter = graph_aiter
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        self._exhausted = False
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        self._scope_list: list[str] = list(mux.scope)
+        self._pump_cond = asyncio.Condition()
+        self._pumping = False
+        for key in mux.native_keys:
+            setattr(self, key, mux.extensions[key])
+        if wire_pump:
+            self._wire_arequest_more(mux)
+
+    def _observe_event(self, event: ProtocolEvent) -> None:
+        """Track values-event state for output/interrupted/interrupts."""
+        if event["method"] != "values":
+            return
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+
+    def _wire_arequest_more(self, mux: StreamMux) -> None:
+        """Wire the async pull callback through the mux.
+
+        Mirrors `_wire_request_more`: routing through
+        `mux.bind_apump` lets child mini-muxes inherit the pump
+        callable so cursors on subgraph handles drive the root
+        pump.
+        """
+        mux.bind_apump(self._apump_next)
+
+    async def _apump_next(self) -> bool:
+        """Drive one pump step, or wait for the active pumper to drive one.
+
+        "Take-a-number" semantics: at most one task at a time calls
+        `graph_aiter.__anext__()` (asyncio iterators can't be advanced
+        concurrently). Other callers wait on a Condition that the
+        active pumper notifies after each step. This lets a "passive"
+        consumer — one whose projection's buffer is being filled by the
+        active pumper's push — wake up as soon as its data lands,
+        instead of queueing on the pump and only observing its data one
+        graph event late.
+
+        `except Exception` is intentional — `CancelledError` and other
+        `BaseException` subclasses propagate, matching asyncio's
+        cancellation contract.
+
+        Returns:
+            True if a pump step completed (by this task or another),
+            False if the graph is exhausted.
+        """
+        async with self._pump_cond:
+            if self._exhausted or self._graph_aiter is None:
+                return False
+            if self._pumping:
+                # Another task is pumping; wait for its progress signal.
+                await self._pump_cond.wait()
+                return not self._exhausted
+            self._pumping = True
+
+        try:
+            try:
+                part = await self._graph_aiter.__anext__()
+                event = convert_to_protocol_event(part)
+                self._observe_event(event)
+                await self._mux.apush(event)
+                return True
+            except StopAsyncIteration:
+                self._exhausted = True
+                await self._mux.aclose()
+                return False
+            except Exception as e:
+                self._exhausted = True
+                await self._mux.afail(e)
+                return False
+        finally:
+            async with self._pump_cond:
+                self._pumping = False
+                self._pump_cond.notify_all()
+
+    async def abort(self) -> None:
+        """Stop the run early.
+
+        Marks the stream exhausted, wakes any pump-waiters, and closes
+        the mux. Any `apush` blocked on backpressure wakes and returns
+        without appending. Idempotent.
+        """
+        async with self._pump_cond:
+            if self._exhausted:
+                return
+            self._exhausted = True
+            self._pump_cond.notify_all()
+        try:
+            await self._mux.aclose()
+        except Exception:
+            pass
+
+    async def __aenter__(self) -> AsyncGraphRunStream:
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        await self.abort()
+
+    async def output(self) -> dict[str, Any] | None:
+        """Drive the run to completion and return the final state.
+
+        Methods (not properties) on the async lane so `run.output`
+        without `await` raises at type-check time instead of silently
+        yielding a coroutine object.
+
+        Example:
+            ```python
+            output = await run.output()
+            ```
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._latest
+
+    async def interrupted(self) -> bool:
+        """Drive the run to completion and return whether it was
+        interrupted.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupted
+
+    async def interrupts(self) -> list[Any]:
+        """Drive the run to completion and return interrupt payloads.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._mux._events._error) is not None:
+            raise err
+        return self._interrupts
+
+    def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
+        """Subscribe to the main event log and iterate protocol events."""
+        return self._mux._events.__aiter__()
+
+
+class _SubgraphRunStreamMixin:
+    """Subgraph metadata + parent-pump delegation shared by both lanes.
+
+    Inherits from `GraphRunStream` (or `AsyncGraphRunStream`) with
+    `graph_iter=None` + `wire_pump=False` — the mini-mux is driven
+    by the parent's pump (inherited via `StreamMux._make_child`), and
+    the handle never pulls upstream itself. Pump-driving methods
+    delegate to the parent pump so `handle.output` and friends drive
+    the root run.
+
+    Subclasses set the parent pump function captured at construction
+    (`_parent_pump_fn` / `_parent_apump_fn`) and override
+    `_pump_next` / `_apump_next` to delegate to it.
+
+    Status is updated in place by `SubgraphTransformer`. Iterate
+    `run.subgraphs` to receive handles as subgraphs spawn, then
+    drill into projections inside the loop body **before** the next
+    pump cycle — same lazy-subscribe constraint as root projections.
+    """
+
+    path: tuple[str, ...]
+    graph_name: str | None
+    trigger_call_id: str | None
+    status: SubgraphStatus
+    error: str | None
+    _seen_terminal: bool
+
+
+class SubgraphRunStream(GraphRunStream, _SubgraphRunStreamMixin):
+    """Sync handle for a discovered subgraph (extends `GraphRunStream`)."""
+
+    def __init__(
+        self,
+        mux: StreamMux,
+        *,
+        path: tuple[str, ...],
+        graph_name: str | None = None,
+        trigger_call_id: str | None = None,
+    ) -> None:
+        # Capture the parent-inherited pump before super().__init__
+        # touches anything; we delegate to it from `_pump_next`.
+        self._parent_pump_fn: Callable[[], bool] | None = mux._pump_fn
+        super().__init__(
+            graph_iter=None,
+            mux=mux,
+            wire_pump=False,
+        )
+        self.path = path
+        self.graph_name = graph_name
+        self.trigger_call_id = trigger_call_id
+        self.status = "started"
+        self.error = None
+        self._seen_terminal = False
+
+    def _pump_next(self) -> bool:
+        """Delegate to the parent's pump.
+
+        Cursors on this handle's projections call here when their
+        buffers empty. Driving the parent fans events into our
+        mini-mux, transparently advancing the whole run.
+        """
+        if (
+            self._exhausted
+            or self._seen_terminal
+            or self._mux._events._closed
+            or self._parent_pump_fn is None
+        ):
+            return False
+        return self._parent_pump_fn()
+
+
+class AsyncSubgraphRunStream(AsyncGraphRunStream, _SubgraphRunStreamMixin):
+    """Async handle for a discovered subgraph (extends `AsyncGraphRunStream`)."""
+
+    def __init__(
+        self,
+        mux: StreamMux,
+        *,
+        path: tuple[str, ...],
+        graph_name: str | None = None,
+        trigger_call_id: str | None = None,
+    ) -> None:
+        self._parent_apump_fn: Callable[[], Awaitable[bool]] | None = mux._apump_fn
+        super().__init__(
+            graph_aiter=None,
+            mux=mux,
+            wire_pump=False,
+        )
+        self.path = path
+        self.graph_name = graph_name
+        self.trigger_call_id = trigger_call_id
+        self.status = "started"
+        self.error = None
+        self._seen_terminal = False
+
+    async def _apump_next(self) -> bool:
+        """Delegate to the parent's async pump."""
+        if (
+            self._exhausted
+            or self._seen_terminal
+            or self._mux._events._closed
+            or self._parent_apump_fn is None
+        ):
+            return False
+        return await self._parent_apump_fn()
@@ -0,0 +1,341 @@
+from __future__ import annotations
+
+import asyncio
+from collections import deque
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
+from typing import TYPE_CHECKING, Generic, TypeVar
+
+if TYPE_CHECKING:
+    from langgraph.stream._mux import StreamMux
+
+T = TypeVar("T")
+
+
+class StreamChannel(Generic[T]):
+    """Single-consumer drainable queue for streaming events, with optional
+    protocol auto-forwarding.
+
+    When constructed with a `name`, the StreamMux auto-wires every
+    `push()` to also inject a `ProtocolEvent` into the main event stream
+    using the channel's name as the method. When constructed without a
+    name, the channel is local-only — items are only visible to
+    in-process consumers that iterate the channel directly.
+
+    Items are popped off the front as the consumer advances — there is
+    no retention beyond what's currently queued. A channel accepts
+    exactly one subscriber; a second `__iter__` / `__aiter__` call
+    raises. Use `tee(n)` / `atee(n)` for fan-out.
+
+    Starts unbound — neither `__iter__` nor `__aiter__` is available
+    until the StreamMux calls `_bind(is_async)`. After binding, only
+    the matching iteration protocol works; the other raises `TypeError`.
+
+    Pump wiring (set by the run stream, not by `_bind`):
+        - `_request_more`: sync pump callable, returns True if a new
+          event was produced.
+        - `_arequest_more`: async pump coroutine factory, same contract.
+
+    Memory is bounded by caller pace: both sync and async use caller-
+    driven pumps, so each cursor advance produces at most one event.
+
+    Lazy-subscribe: `push` appends to the local buffer only when a
+    subscriber has registered. Auto-forward via `_wire_fn` always fires
+    regardless of subscription state.
+
+    Lifecycle (`close` / `fail`) is managed by the mux — transformers
+    don't need to close their channels manually.
+    """
+
+    def __init__(self, name: str | None = None, *, maxlen: int | None = None) -> None:
+        """Initialize the channel.
+
+        Args:
+            name: Optional protocol channel name. When set, the
+                StreamMux wires every `push()` to also inject a
+                `ProtocolEvent` into the main event stream. Surfaced
+                on the wire as `custom:<name>` for user-defined
+                transformers, or as `<name>` for channels owned by a
+                native transformer (`_native = True`). When `None`,
+                the channel is local-only.
+            maxlen: Accepted for forward compatibility; currently
+                unused. The caller-driven pump bounds memory naturally
+                for single-consumer use.
+
+        Raises:
+            ValueError: If `maxlen` is not a positive integer or `None`.
+        """
+        if maxlen is not None and maxlen <= 0:
+            raise ValueError("StreamChannel maxlen must be a positive int or None")
+        self.name = name
+        self._items: deque[tuple[int, T]] = deque()
+        self._maxlen: int | None = maxlen
+        self._closed = False
+        self._error: BaseException | None = None
+
+        self._is_async: bool | None = None
+
+        self._subscribed = False
+
+        self._request_more: Callable[[], bool] | None = None
+        self._arequest_more: Callable[[], Awaitable[bool]] | None = None
+
+        self._wire_fn: Callable[[T], None] | None = None
+        self._mux: StreamMux | None = None
+
+    # ------------------------------------------------------------------
+    # Binding
+    # ------------------------------------------------------------------
+
+    def _bind_mux(self, mux: StreamMux) -> None:
+        self._mux = mux
+
+    def _bind(self, *, is_async: bool) -> None:
+        """Bind this channel to sync or async mode.
+
+        Called by the StreamMux after transformer registration. Must be
+        called exactly once before any iteration.
+
+        Args:
+            is_async: True to enable async iteration, False for sync.
+
+        Raises:
+            RuntimeError: If the channel has already been bound.
+        """
+        if self._is_async is not None:
+            raise RuntimeError("StreamChannel is already bound")
+        self._is_async = is_async
+
+    # ------------------------------------------------------------------
+    # Mux wiring (not called by transformers directly)
+    # ------------------------------------------------------------------
+
+    def _wire(self, fn: Callable[[T], None]) -> None:
+        """Install the auto-forward callback (called by StreamMux)."""
+        self._wire_fn = fn
+
+    # ------------------------------------------------------------------
+    # Producer API
+    # ------------------------------------------------------------------
+
+    def push(self, item: T) -> None:
+        """Append an item. Auto-forwards if wired.
+
+        The local buffer append is a no-op when no subscriber is
+        registered, but auto-forwarding always fires so wired events
+        reach the main event log regardless of subscription state.
+
+        Items are stored as `(stamp, item)` tuples where stamp is a
+        monotonic counter from the owning mux. Stamps are stripped by
+        the default cursors; raw stamped tuples are visible on `_items`.
+
+        Raises:
+            RuntimeError: If the channel is closed (and subscribed).
+        """
+        if self._subscribed:
+            if self._closed:
+                raise RuntimeError("Cannot push to a closed StreamChannel")
+            stamp = self._mux._next_push_seq() if self._mux is not None else 0
+            self._items.append((stamp, item))
+        if self._wire_fn is not None:
+            self._wire_fn(item)
+
+    def close(self) -> None:
+        """Mark the channel as complete."""
+        self._closed = True
+
+    def fail(self, err: BaseException) -> None:
+        """Mark the channel as errored.
+
+        Args:
+            err: The exception to surface to the subscriber.
+        """
+        self._error = err
+        self._closed = True
+
+    # ------------------------------------------------------------------
+    # Sync iteration (caller-driven pump)
+    # ------------------------------------------------------------------
+
+    def __iter__(self) -> Iterator[T]:
+        """Subscribe and return a sync cursor. Can be called only once.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to async mode.
+            RuntimeError: If the channel already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "StreamChannel has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if self._is_async:
+            raise TypeError(
+                "This StreamChannel is bound to async mode — use 'async for' instead."
+            )
+        if self._subscribed:
+            raise RuntimeError(
+                "StreamChannel already has a subscriber; use .tee(n) for fan-out."
+            )
+        self._subscribed = True
+        return self._sync_cursor()
+
+    def _sync_cursor(self) -> Iterator[T]:
+        while True:
+            if self._items:
+                _stamp, item = self._items.popleft()
+                yield item
+            elif self._closed:
+                if self._error is not None:
+                    raise self._error
+                return
+            elif self._request_more is not None:
+                if not self._request_more():
+                    if not self._items and not self._closed:
+                        return
+            else:
+                return
+
+    # ------------------------------------------------------------------
+    # Async iteration (caller-driven pump)
+    # ------------------------------------------------------------------
+
+    def __aiter__(self) -> AsyncIterator[T]:
+        """Subscribe and return an async cursor. Can be called only once.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to sync mode.
+            RuntimeError: If the channel already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "StreamChannel has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if not self._is_async:
+            raise TypeError(
+                "This StreamChannel is bound to sync mode — use 'for' instead."
+            )
+        if self._subscribed:
+            raise RuntimeError(
+                "StreamChannel already has a subscriber; use .atee(n) for fan-out."
+            )
+        self._subscribed = True
+        return self._async_cursor()
+
+    async def _async_cursor(self) -> AsyncIterator[T]:
+        while True:
+            if self._items:
+                _stamp, item = self._items.popleft()
+                yield item
+            elif self._closed:
+                if self._error is not None:
+                    raise self._error
+                return
+            elif self._arequest_more is not None:
+                if not await self._arequest_more():
+                    if not self._items and not self._closed:
+                        return
+            else:
+                return
+
+    # ------------------------------------------------------------------
+    # Fan-out via tee
+    # ------------------------------------------------------------------
+
+    def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
+        """Subscribe and return `n` independent sync iterators.
+
+        Each branch has its own buffer; items pulled from the
+        underlying cursor are copied into every branch. Branches are
+        naturally bounded by caller pace since the sync pump is
+        caller-driven.
+
+        Args:
+            n: Number of branches to create. Must be >= 1.
+
+        Returns:
+            A tuple of `n` iterators over the same underlying stream.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to async mode.
+            RuntimeError: If the channel already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("tee() requires n >= 1")
+        source = self.__iter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+
+        def branch(i: int) -> Iterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                elif exhausted[0]:
+                    return
+                else:
+                    try:
+                        item = next(source)
+                    except StopIteration:
+                        exhausted[0] = True
+                        return
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))
+
+    def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
+        """Subscribe and return `n` independent async iterators.
+
+        Caller-driven fan-out: each branch's `__anext__` either pops
+        from its own buffer or, under a shared `asyncio.Lock`, pulls
+        one item from the underlying cursor and distributes it to
+        every branch's buffer.
+
+        Args:
+            n: Number of branches to create. Must be >= 1.
+
+        Returns:
+            A tuple of `n` async iterators over the same underlying
+            stream.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to sync mode.
+            RuntimeError: If the channel already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("atee() requires n >= 1")
+        source = self.__aiter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+        error: list[BaseException | None] = [None]
+        lock = asyncio.Lock()
+
+        async def branch(i: int) -> AsyncIterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                    continue
+                if exhausted[0]:
+                    if error[0] is not None:
+                        raise error[0]
+                    return
+                async with lock:
+                    if buf or exhausted[0]:
+                        continue
+                    try:
+                        item = await source.__anext__()
+                    except StopAsyncIteration:
+                        exhausted[0] = True
+                        continue
+                    except Exception as e:
+                        error[0] = e
+                        exhausted[0] = True
+                        continue
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))
@@ -0,0 +1,928 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any, Literal, cast
+
+from langchain_core.language_models._compat_bridge import message_to_events
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessageChunk, BaseMessage
+from langchain_protocol.protocol import MessagesData
+from typing_extensions import NotRequired, TypedDict
+
+from langgraph.errors import GraphDrained, GraphInterrupt
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import AsyncSubgraphRunStream, SubgraphRunStream
+from langgraph.stream.stream_channel import StreamChannel
+
+if TYPE_CHECKING:
+    from collections.abc import Awaitable, Callable
+
+    from langgraph.stream._mux import StreamMux
+
+_logger = logging.getLogger(__name__)
+
+
+class ValuesTransformer(StreamTransformer):
+    """Capture values events as a drainable stream of state snapshots.
+
+    Provides the `run.values` projection. `run.output`,
+    `run.interrupted` and `run.interrupts` are tracked directly
+    by the run stream and do not depend on this transformer.
+
+    Native transformer — projection keys are exposed as direct
+    attributes on the run stream (e.g. `run.values`).
+
+    Only values events at the run's own level are captured; snapshots
+    from deeper subgraphs are left in the main event log but excluded
+    from the projection. "Own level" is defined by `scope`, which
+    `stream_events(version="v3")` / `astream_events(version="v3")` populate from the caller's
+    checkpoint namespace so that a nested `stream_events(version="v3")` call still
+    sees its own root snapshots.
+    """
+
+    _native = True
+    required_stream_modes = ("values",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        # Cached as a list once for cheap equality with the protocol
+        # event's `namespace` field, which is `list[str]`.
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"values": self._log}
+
+    @property
+    def error(self) -> BaseException | None:
+        """The error that ended the run, or `None` if it succeeded.
+
+        Set by the mux when it auto-fails the projection log.
+        """
+        return self._log._error
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "values":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+        self._log.push(params["data"])
+        return True
+
+
+class CustomTransformer(StreamTransformer):
+    """Capture custom events as a drainable stream of arbitrary payloads.
+
+    Nodes emit custom data via `get_stream_writer()`. This transformer
+    surfaces those events on `run.custom` as a `StreamChannel[Any]`,
+    preserving payloads in arrival order.
+
+    Only events at the run's own scope are captured; custom data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.custom` projection.
+
+    Native transformer — `run.custom` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("custom",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[Any] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"custom": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "custom":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class UpdatesTransformer(StreamTransformer):
+    """Capture updates events as a drainable stream of node outputs.
+
+    Surfaces `stream_mode="updates"` data on `run.updates` as a
+    `StreamChannel[dict[str, Any]]`. Each item is a dict mapping a node
+    (or task) name to the update it returned after a step.
+
+    Only events at the run's own scope are captured; updates from deeper
+    subgraphs are available on the respective subgraph handle's
+    `.updates` projection.
+
+    Native transformer — `run.updates` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("updates",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"updates": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "updates":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class MessagesTransformer(StreamTransformer):
+    """Capture messages events as ChatModelStream objects.
+
+    The messages projection yields one `ChatModelStream` (or
+    `AsyncChatModelStream`) per LLM call. Consumers iterate
+    `run.messages` to get stream handles, then use each handle's typed
+    projections (`.text`, `.reasoning`, `.tool_calls`, `.usage`,
+    `.output`) for per-message content.
+
+    Two input shapes are handled (via `params["data"] = (payload,
+    metadata)` from `StreamMessagesHandler`):
+
+    1. Protocol event (dict with `"event"` key) — emitted by
+       `stream_events(version="v3")` / `astream_events(version="v3")` via the `on_stream_event`
+       callback. Routed to an existing `ChatModelStream` by
+       `metadata["run_id"]`. A `message-start` event creates a new
+       stream; `message-finish` closes it.
+    2. Whole `AIMessage` — emitted from `on_chain_end` when a node
+       returns a finalized message. Replayed as a synthetic protocol
+       event lifecycle via `message_to_events`, then the
+       already-complete stream is pushed to the log.
+
+    V1 `AIMessageChunk` tuples (from `on_llm_new_token`) are not
+    streamed into this projection: chat models that want to populate
+    `run.messages` with content-block streaming must use
+    `stream_events(version="v3")` / `astream_events(version="v3")`. Models called via the legacy
+    `stream()` method still surface their final `AIMessage` via
+    `on_chain_end` when a node returns it as state.
+
+    Only events at the run's own level are projected; tokens from
+    deeper subgraphs are left in the main event log but excluded from
+    `.messages`. "Own level" is defined by `scope`, which
+    `stream_events(version="v3")` / `astream_events(version="v3")` populate from the caller's checkpoint
+    namespace so that a `stream_events(version="v3")` call inside a node still sees its
+    own root chat model streams on `.messages`. Consumers that need
+    subgraph tokens should iterate the raw event stream or register a
+    custom transformer.
+
+    Native transformer — the `messages` projection is exposed as a
+    direct attribute on the run stream.
+    """
+
+    _native = True
+    required_stream_modes = ("messages",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[ChatModelStream] = StreamChannel()
+        # Correlate protocol events back to a ChatModelStream by run_id
+        # (attached to the event's metadata by StreamMessagesHandler).
+        self._by_run: dict[str, ChatModelStream] = {}
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+        # Cached as a list once for cheap equality with the protocol
+        # event's `namespace` field, which is `list[str]`.
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"messages": self._log}
+
+    def _bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback. Called by GraphRunStream._wire_request_more."""
+        self._pump_fn = fn
+
+    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Wire the async pull callback.
+
+        Called by `AsyncGraphRunStream._wire_arequest_more` so each
+        `AsyncChatModelStream` this transformer creates can drive the
+        shared graph pump from its projection cursors.
+        """
+        self._apump_fn = fn
+
+    def _make_stream(
+        self,
+        *,
+        namespace: list[str],
+        node: str | None,
+        message_id: str | None,
+    ) -> ChatModelStream:
+        """Create a ChatModelStream (sync) or AsyncChatModelStream (async).
+
+        Wires whichever pump is bound. Prefers the async pump so nested
+        iteration under `AsyncGraphRunStream` drives the graph forward
+        without a background task. The unwired fallback (no pump bound)
+        is used by unit tests that dispatch events manually.
+        """
+        if self._apump_fn is not None:
+            astream = AsyncChatModelStream(
+                namespace=namespace,
+                node=node,
+                message_id=message_id,
+            )
+            astream.set_arequest_more(self._apump_fn)
+            return astream
+        if self._pump_fn is not None:
+            stream: ChatModelStream = ChatModelStream(
+                namespace=namespace,
+                node=node,
+                message_id=message_id,
+            )
+            stream.set_request_more(self._pump_fn)
+            return stream
+        return AsyncChatModelStream(
+            namespace=namespace,
+            node=node,
+            message_id=message_id,
+        )
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "messages":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+
+        payload, metadata = params["data"]
+        node: str | None = metadata.get("langgraph_node")
+        run_id = str(metadata.get("run_id", "")) if metadata else ""
+
+        if isinstance(payload, dict) and "event" in payload:
+            self._route_protocol_event(
+                cast("MessagesData", payload), run_id=run_id, node=node
+            )
+        elif isinstance(payload, BaseMessage) and not isinstance(
+            payload, AIMessageChunk
+        ):
+            self._route_whole_message(payload, node=node)
+        # Legacy AIMessageChunk tuples (from on_llm_new_token) are ignored;
+        # v1 streaming callers must switch to stream_events(version="v3") to populate this
+        # projection.
+
+        return True
+
+    def _route_protocol_event(
+        self,
+        event: MessagesData,
+        *,
+        run_id: str,
+        node: str | None,
+    ) -> None:
+        event_type = event.get("event")
+        if event_type == "message-start":
+            message_id = event.get("message_id")
+            stream = self._make_stream(
+                namespace=[],
+                node=node,
+                message_id=str(message_id) if message_id is not None else None,
+            )
+            self._by_run[run_id] = stream
+            self._log.push(stream)
+            stream.dispatch(event)
+        elif run_id in self._by_run:
+            stream = self._by_run[run_id]
+            stream.dispatch(event)
+            if event_type == "message-finish":
+                del self._by_run[run_id]
+
+    def _route_whole_message(self, message: BaseMessage, *, node: str | None) -> None:
+        stream = self._make_stream(namespace=[], node=node, message_id=message.id)
+        for evt in message_to_events(message, message_id=message.id):
+            stream.dispatch(evt)
+        self._log.push(stream)
+
+    def finalize(self) -> None:
+        """Clear any routing state — streams close themselves via `message-finish`."""
+        self._by_run.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Propagate run error to any streams still open when the graph fails."""
+        for stream in list(self._by_run.values()):
+            stream.fail(err)
+        self._by_run.clear()
+
+
+SubgraphStatus = Literal["started", "completed", "failed", "interrupted", "drained"]
+
+
+def _parse_ns_segment(segment: str) -> tuple[str, str | None]:
+    """Split a namespace segment into `(graph_name, trigger_call_id)`.
+
+    Segments are formatted `node_name:task_id` by `prepare_next_tasks`.
+    Returns `(segment, None)` if no `:` is present.
+    """
+    name, sep, task_id = segment.partition(":")
+    return name, task_id if sep else None
+
+
+class LifecyclePayload(TypedDict, total=False):
+    """Payload of a lifecycle event surfaced on the `lifecycle` channel.
+
+    Auto-forwarded as `lifecycle` protocol events (no `custom:` prefix
+    because `LifecycleTransformer` is a native transformer) so remote
+    SDK clients receive the same data in-process consumers see via
+    `run.lifecycle`.
+    """
+
+    event: SubgraphStatus
+    namespace: list[str]
+    graph_name: NotRequired[str]
+    trigger_call_id: NotRequired[str]
+    error: NotRequired[str]
+
+
+class _TasksLifecycleBase(StreamTransformer):
+    """Shared bookkeeping for `tasks`-event-driven lifecycle inference.
+
+    Both `LifecycleTransformer` (wire-serializable channel) and
+    `SubgraphTransformer` (in-process navigation handles) discover
+    subgraphs by watching the same `tasks` stream — `started` on the
+    first event at a tracked namespace, terminal status when the
+    parent's `TaskResultPayload` arrives. Centralizing the dispatch
+    + open-set bookkeeping here keeps the inference rules from
+    drifting between the two surfaces.
+
+    Subclasses provide three template-method hooks:
+
+    - `_should_track(ns)` — scope filter (e.g. multi-depth vs
+      direct-children-only).
+    - `_on_started(ns, graph_name, trigger_call_id)` — first sighting
+      action (push payload / build handle / etc.). Called once per
+      discovered namespace.
+    - `_on_terminal(ns, status, error)` — terminal action (push
+      terminal payload / mark handle status). Called once per
+      tracked namespace at result time, or via `finalize` / `fail`
+      sweeps if no parent result arrived.
+
+    Tasks events are suppressed from the main event log (`process`
+    returns False) — they're folded into whichever projection the
+    subclass populates; consumers iterating the raw protocol stream
+    see the higher-level view.
+    """
+
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._seen: set[tuple[str, ...]] = set()
+        # Maps tracked namespace -> task_id of the parent task whose
+        # `TaskResultPayload` will close it.
+        self._open: dict[tuple[str, ...], str] = {}
+
+    # --- Template-method hooks (subclass overrides) ---
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        """Scope filter — return True iff `ns` is in this transformer's region."""
+        raise NotImplementedError
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        """Fired once per discovered namespace (first observed task event)."""
+        raise NotImplementedError
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        """Fired once per tracked namespace when its parent's result arrives,
+        or via finalize/fail safety-net sweeps.
+        """
+        raise NotImplementedError
+
+    # --- Dispatch + bookkeeping (shared) ---
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "tasks":
+            return True
+        ns = tuple(event["params"]["namespace"])
+        data = event["params"]["data"]
+        if "result" in data:
+            self._handle_task_result(ns, data)
+        else:
+            self._handle_task_start(ns)
+        # Tasks events are folded into the synthesized projections;
+        # suppress from the main event log so iterators don't double-see
+        # the same information in two shapes.
+        return False
+
+    def _handle_task_start(self, ns: tuple[str, ...]) -> None:
+        if not self._should_track(ns) or ns in self._seen:
+            return
+        self._seen.add(ns)
+        graph_name, trigger_call_id = _parse_ns_segment(ns[-1])
+        self._on_started(ns, graph_name or None, trigger_call_id)
+        if trigger_call_id is not None:
+            self._open[ns] = trigger_call_id
+
+    def _pop_terminal_transitions(
+        self, ns: tuple[str, ...], data: dict[str, Any]
+    ) -> list[tuple[tuple[str, ...], SubgraphStatus, str | None]]:
+        """Return and remove tracked children closed by this task result."""
+        result_id = data.get("id")
+        if not result_id:
+            return []
+        transitions: list[tuple[tuple[str, ...], SubgraphStatus, str | None]] = []
+        for child_ns, parent_task_id in list(self._open.items()):
+            if child_ns[:-1] != ns or parent_task_id != result_id:
+                continue
+            status, error = _terminal_from_result(data)
+            transitions.append((child_ns, status, error))
+            del self._open[child_ns]
+        return transitions
+
+    def _handle_task_result(self, ns: tuple[str, ...], data: dict[str, Any]) -> None:
+        for child_ns, status, error in self._pop_terminal_transitions(ns, data):
+            self._on_terminal(child_ns, status, error)
+
+    def finalize(self) -> None:
+        """Emit `completed` for any tracked namespace still open at run end."""
+        for ns in list(self._open):
+            self._on_terminal(ns, "completed", None)
+        self._open.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Emit terminal status for any tracked namespace still open."""
+        status, error_str = _status_from_exception(err)
+        for ns in list(self._open):
+            self._on_terminal(ns, status, error_str)
+        self._open.clear()
+
+
+def _status_from_exception(err: BaseException) -> tuple[SubgraphStatus, str | None]:
+    """Map a run exception to a subgraph terminal status and error string."""
+    if isinstance(err, GraphDrained):
+        return "drained", None
+    if isinstance(err, GraphInterrupt):
+        return "interrupted", None
+    return "failed", str(err)
+
+
+def _terminal_from_result(
+    payload: dict[str, Any],
+) -> tuple[SubgraphStatus, str | None]:
+    """Map a `TaskResultPayload` to a `(status, error)` pair.
+
+    Order matters: a result with both `error` and `interrupts` prefers
+    the interrupt classification, since `GraphInterrupt` manifests as
+    a populated `interrupts` list, not as `error`.
+    """
+    if payload.get("interrupts"):
+        return "interrupted", None
+    error = payload.get("error")
+    if error:
+        return "failed", str(error)
+    return "completed", None
+
+
+class LifecycleTransformer(_TasksLifecycleBase):
+    """Surface subgraph lifecycle as `lifecycle` protocol events.
+
+    Pushes `LifecyclePayload` to a `StreamChannel` named `lifecycle`.
+    The channel is auto-forwarded by the mux so payloads land in the
+    main event log under `method = "lifecycle"` (native transformer —
+    no `custom:` prefix) — visible to remote SDK clients over the
+    wire and to in-process consumers via `run.lifecycle`.
+
+    Tracks subgraphs at every depth strictly below the transformer's
+    scope, so a graph → subgraph → subgraph chain produces lifecycle
+    events for both nested levels in a flat stream.
+
+    Native transformer — projection key `lifecycle` is exposed as
+    `run.lifecycle`.
+    """
+
+    _native = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._channel: StreamChannel[LifecyclePayload] = StreamChannel("lifecycle")
+
+    def init(self) -> dict[str, Any]:
+        return {"lifecycle": self._channel}
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        depth = len(self.scope)
+        return len(ns) > depth and ns[:depth] == self.scope
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        if trigger_call_id is None:
+            # Without a task id we can't correlate a parent-result
+            # event back to this namespace — skip the started payload
+            # and rely on finalize/fail to close.
+            return
+        payload: LifecyclePayload = {"event": "started", "namespace": list(ns)}
+        if graph_name:
+            payload["graph_name"] = graph_name
+        payload["trigger_call_id"] = trigger_call_id
+        self._channel.push(payload)
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        payload: LifecyclePayload = {"event": status, "namespace": list(ns)}
+        if error is not None:
+            payload["error"] = error
+        self._channel.push(payload)
+
+
+class SubgraphTransformer(_TasksLifecycleBase):
+    """Discover subgraph invocations as in-process navigation handles.
+
+    Per discovered direct-child subgraph, builds a `SubgraphRunStream`
+    (or `AsyncSubgraphRunStream`) wrapping a child mini-mux scoped to
+    the subgraph's namespace. Consumers iterate `run.subgraphs` to
+    receive handles, then drill into `handle.values` / `handle.messages`
+    / `handle.subgraphs` (recursive grandchildren) / `handle.lifecycle`.
+
+    Each mini-mux owns its own scope and uses its own
+    `SubgraphTransformer` to discover its direct children, so
+    grandchildren live on the child handle — never on the root's
+    `subgraphs` log. Forwarding events into the matching child mini-mux
+    is what keeps the child's projections populated.
+
+    Native transformer — `subgraphs` is exposed as `run.subgraphs`.
+    """
+
+    _native = True
+    supports_sync = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[SubgraphRunStream | AsyncSubgraphRunStream] = (
+            StreamChannel()
+        )
+        self._handles: dict[
+            tuple[str, ...], SubgraphRunStream | AsyncSubgraphRunStream
+        ] = {}
+        self._mux: StreamMux | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"subgraphs": self._log}
+
+    def _on_register(self, mux: Any) -> None:
+        self._mux = mux
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        # Direct children only — grandchildren are picked up by the
+        # child mini-mux's own SubgraphTransformer.
+        depth = len(self.scope)
+        return len(ns) == depth + 1 and ns[:depth] == self.scope
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        if self._mux is None:
+            return
+        try:
+            child_mux = self._mux._make_child(ns)
+        except RuntimeError:
+            return
+        handle_cls = AsyncSubgraphRunStream if child_mux.is_async else SubgraphRunStream
+        handle = handle_cls(
+            mux=child_mux,
+            path=ns,
+            graph_name=graph_name,
+            trigger_call_id=trigger_call_id,
+        )
+        self._handles[ns] = handle
+        self._log.push(handle)
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        handle = self._handles.get(ns)
+        if handle is None or not self._mark_terminal(handle, status, error):
+            return
+        self._close_or_fail_handle(handle, status, error)
+
+    async def _aon_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        handle = self._handles.get(ns)
+        if handle is None or not self._mark_terminal(handle, status, error):
+            return
+        await self._aclose_or_fail_handle(handle, status, error)
+
+    def _mark_terminal(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> bool:
+        """Mark a handle terminal once. Returns True on first transition."""
+        if handle._seen_terminal:
+            return False
+        handle.status = status
+        if error is not None and handle.error is None:
+            handle.error = error
+        handle._seen_terminal = True
+        return True
+
+    def _close_or_fail_handle(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        if handle._mux is None or handle._mux._events._closed:
+            return
+        if status == "failed":
+            handle._mux.fail(RuntimeError(error or "Subgraph failed"))
+        else:
+            handle._mux.close()
+
+    async def _aclose_or_fail_handle(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        if handle._mux is None or handle._mux._events._closed:
+            return
+        if status == "failed":
+            await handle._mux.afail(RuntimeError(error or "Subgraph failed"))
+        else:
+            await handle._mux.aclose()
+
+    def _handle_for_event(
+        self, event: ProtocolEvent
+    ) -> SubgraphRunStream | AsyncSubgraphRunStream | None:
+        ns = tuple(event["params"]["namespace"])
+        depth = len(self.scope)
+        if len(ns) < depth + 1:
+            return None
+        handle = self._handles.get(ns[: depth + 1])
+        if handle is None or handle._mux is None or handle._mux._events._closed:
+            return None
+        return handle
+
+    def process(self, event: ProtocolEvent) -> bool:
+        # Run tasks bookkeeping first so a `started` handle exists
+        # by the time we forward the event to the child mini-mux.
+        keep = super().process(event)
+        handle = self._handle_for_event(event)
+        if handle is not None:
+            handle._observe_event(event)
+            handle._mux.push(event)
+        return keep
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        # Async counterpart: repeats the tasks bookkeeping here so
+        # child mini-muxes receive events through their async lane.
+        if event["method"] == "tasks":
+            ns = tuple(event["params"]["namespace"])
+            data = event["params"]["data"]
+            if "result" in data:
+                for child_ns, status, error in self._pop_terminal_transitions(ns, data):
+                    await self._aon_terminal(child_ns, status, error)
+            else:
+                self._handle_task_start(ns)
+            keep = False
+        else:
+            keep = True
+        handle = self._handle_for_event(event)
+        if handle is not None:
+            handle._observe_event(event)
+            await handle._mux.apush(event)
+        return keep
+
+    def _complete_open_handles(self) -> BaseException | None:
+        first_error: BaseException | None = None
+        for ns in list(self._open):
+            try:
+                self._on_terminal(ns, "completed", None)
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        self._open.clear()
+        for handle in self._handles.values():
+            if self._mark_terminal(handle, "completed", None):
+                try:
+                    self._close_or_fail_handle(handle, "completed", None)
+                except BaseException as e:
+                    if first_error is None:
+                        first_error = e
+        return first_error
+
+    async def _acomplete_open_handles(self) -> BaseException | None:
+        first_error: BaseException | None = None
+        for ns in list(self._open):
+            try:
+                await self._aon_terminal(ns, "completed", None)
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        self._open.clear()
+        for handle in self._handles.values():
+            if self._mark_terminal(handle, "completed", None):
+                try:
+                    await self._aclose_or_fail_handle(handle, "completed", None)
+                except BaseException as e:
+                    if first_error is None:
+                        first_error = e
+        return first_error
+
+    def finalize(self) -> None:
+        first_error = self._complete_open_handles()
+        if first_error is not None:
+            raise first_error
+
+    async def afinalize(self) -> None:
+        first_error = await self._acomplete_open_handles()
+        if first_error is not None:
+            raise first_error
+
+    def fail(self, err: BaseException) -> None:
+        status, error_str = _status_from_exception(err)
+        self._open.clear()
+        for handle in self._handles.values():
+            self._mark_terminal(handle, status, error_str)
+            if handle._mux is not None and not handle._mux._events._closed:
+                try:
+                    handle._mux.fail(err)
+                except Exception:
+                    _logger.warning(
+                        "Error failing subgraph mini-mux at %s; "
+                        "subscribers may not see the terminal error.",
+                        handle.path,
+                        exc_info=True,
+                    )
+
+    async def afail(self, err: BaseException) -> None:
+        status, error_str = _status_from_exception(err)
+        self._open.clear()
+        for handle in self._handles.values():
+            self._mark_terminal(handle, status, error_str)
+            if handle._mux is not None and not handle._mux._events._closed:
+                try:
+                    await handle._mux.afail(err)
+                except Exception:
+                    _logger.warning(
+                        "Error failing subgraph mini-mux at %s; "
+                        "subscribers may not see the terminal error.",
+                        handle.path,
+                        exc_info=True,
+                    )
+
+
+class CheckpointsTransformer(StreamTransformer):
+    """Capture checkpoint events as a drainable stream.
+
+    Surfaces `stream_mode="checkpoints"` data on `run.checkpoints` as
+    a `StreamChannel[dict[str, Any]]`. Each item is in the same format
+    as returned by `get_state()`.
+
+    Checkpoint events are only emitted when a checkpointer is configured
+    on the graph. When no checkpointer is present, the projection exists
+    but receives no events.
+
+    Only events at the run's own scope are captured; checkpoint data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.checkpoints` projection.
+
+    Native transformer — `run.checkpoints` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("checkpoints",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"checkpoints": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "checkpoints":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class DebugTransformer(StreamTransformer):
+    """Capture debug events as a drainable stream.
+
+    Surfaces `stream_mode="debug"` data on `run.debug` as a
+    `StreamChannel[dict[str, Any]]`. Each item is a debug event with
+    step-level detail (checkpoint snapshots, task payloads, and
+    task results wrapped with step number and timestamp).
+
+    Only events at the run's own scope are captured; debug data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.debug` projection.
+
+    Native transformer — `run.debug` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("debug",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"debug": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "debug":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
+
+
+class TasksTransformer(StreamTransformer):
+    """Capture raw task events as a drainable stream.
+
+    Surfaces `stream_mode="tasks"` data on `run.tasks` as a
+    `StreamChannel[dict[str, Any]]`. Each item is a task payload
+    (start or result).
+
+    `LifecycleTransformer` and `SubgraphTransformer` also consume
+    `tasks` events for subgraph discovery and lifecycle tracking.
+    This transformer captures the raw payloads independently for
+    consumers who need task-level detail.
+
+    Only events at the run's own scope are captured; task data from
+    deeper subgraphs is available on the respective subgraph handle's
+    `.tasks` projection.
+
+    Native transformer — `run.tasks` is a direct attribute.
+    """
+
+    _native = True
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"tasks": self._log}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "tasks":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._log.push(params["data"])
+        return True
@@ -4,6 +4,7 @@ import sys
 from collections import deque
 from collections.abc import Callable, Hashable, Sequence
 from dataclasses import asdict, dataclass
+from datetime import timedelta
 from typing import (
    TYPE_CHECKING,
    Any,
@@ -67,6 +68,7 @@ __all__ = (
    "CheckpointPayload",
    "DebugPayload",
    "RetryPolicy",
+    "TimeoutPolicy",
    "CachePolicy",
    "Interrupt",
    "StateUpdate",
@@ -423,6 +425,83 @@ class RetryPolicy(NamedTuple):
    """List of exception classes that should trigger a retry, or a callable that returns `True` for exceptions that should trigger a retry."""


+def _coerce_timeout_seconds(
+    value: float | timedelta | None, *, field: str
+) -> float | None:
+    if value is None:
+        return None
+    seconds = value.total_seconds() if isinstance(value, timedelta) else float(value)
+    if seconds <= 0:
+        raise ValueError(f"{field} must be greater than 0")
+    return seconds
+
+
+@dataclass(**_DC_KWARGS)
+class TimeoutPolicy:
+    """Configuration for timing out node attempts.
+
+    !!! note "Cooperative cancellation"
+
+        Timeouts rely on asyncio cancellation. If your node uses synchronous
+        time.sleep() or other CPU-bound work that blocks the GIL, the timeout will not
+        be fired until after the event loop has been released.
+
+    !!! note "Inline callback dispatch"
+
+        Under `refresh_on="auto"`, an internal handler refreshes the timeout on any
+        callback event that occurs in the execution of the node or its nested descendants.
+    """
+
+    run_timeout: float | timedelta | None = None
+    """Hard wall-clock cap (in seconds) for a single node attempt.
+
+    This timeout is never refreshed by progress signals or `runtime.heartbeat()`.
+    """
+
+    idle_timeout: float | timedelta | None = None
+    """Maximum time (in seconds) a single node attempt may go without observable progress."""
+
+    refresh_on: Literal["auto", "heartbeat"] = "auto"
+    """Which signals refresh `idle_timeout`.
+
+    `"auto"` refreshes on standard graph progress signals and explicit heartbeats.
+    `"heartbeat"` refreshes only on explicit `runtime.heartbeat()` calls.
+    """
+
+    @classmethod
+    def coerce(
+        cls, value: float | timedelta | TimeoutPolicy | None
+    ) -> TimeoutPolicy | None:
+        """Normalize a timeout value to positive-second policy fields."""
+        if value is None:
+            return None
+        if isinstance(value, TimeoutPolicy):
+            # Fast path: a policy already produced by coerce() has float
+            # timeouts and a validated refresh_on, so we can return it as-is.
+            # `frozen=True` makes this safe to share.
+            rt, it = value.run_timeout, value.idle_timeout
+            if (
+                value.refresh_on in ("auto", "heartbeat")
+                and (rt is None or (type(rt) is float and rt > 0))
+                and (it is None or (type(it) is float and it > 0))
+                and (rt is not None or it is not None)
+            ):
+                return value
+        else:
+            value = cls(run_timeout=value)
+        if value.refresh_on not in ("auto", "heartbeat"):
+            raise ValueError("refresh_on must be 'auto' or 'heartbeat'")
+        run_timeout = _coerce_timeout_seconds(value.run_timeout, field="run_timeout")
+        idle_timeout = _coerce_timeout_seconds(value.idle_timeout, field="idle_timeout")
+        if run_timeout is None and idle_timeout is None:
+            return None
+        return cls(
+            run_timeout=run_timeout,
+            idle_timeout=idle_timeout,
+            refresh_on=value.refresh_on,
+        )
+
+
 KeyFuncT = TypeVar("KeyFuncT", bound=Callable[..., str | bytes])


@@ -548,6 +627,7 @@ class PregelExecutableTask:
    path: tuple[str | int | tuple, ...]
    writers: Sequence[Runnable] = ()
    subgraphs: Sequence[PregelProtocol] = ()
+    timeout: TimeoutPolicy | None = None


 class StateSnapshot(NamedTuple):
@@ -587,6 +667,8 @@ class Send:
    Attributes:
        node (str): The name of the target node to send the message to.
        arg (Any): The state or message to send to the target node.
+        timeout (TimeoutPolicy | None): Optional timeout policy for this specific
+            pushed task. If omitted, the target node's timeout policy is used.

    !!! example

@@ -616,33 +698,47 @@ class Send:
        ```
    """

-    __slots__ = ("node", "arg")
+    __slots__ = ("node", "arg", "timeout")

    node: str
    arg: Any
+    timeout: TimeoutPolicy | None

-    def __init__(self, /, node: str, arg: Any) -> None:
+    def __init__(
+        self,
+        /,
+        node: str,
+        arg: Any,
+        *,
+        timeout: float | timedelta | TimeoutPolicy | None = None,
+    ) -> None:
        """
        Initialize a new instance of the `Send` class.

        Args:
            node: The name of the target node to send the message to.
            arg: The state or message to send to the target node.
+            timeout: Optional timeout policy for this specific pushed task. A
+                number or `timedelta` is treated as a hard `run_timeout`.
        """
        self.node = node
        self.arg = arg
+        self.timeout = TimeoutPolicy.coerce(timeout)

    def __hash__(self) -> int:
-        return hash((self.node, self.arg))
+        return hash((self.node, self.arg, self.timeout))

    def __repr__(self) -> str:
-        return f"Send(node={self.node!r}, arg={self.arg!r})"
+        if self.timeout is None:
+            return f"Send(node={self.node!r}, arg={self.arg!r})"
+        return f"Send(node={self.node!r}, arg={self.arg!r}, timeout={self.timeout!r})"

    def __eq__(self, value: object) -> bool:
        return (
            isinstance(value, Send)
            and self.node == value.node
            and self.arg == value.arg
+            and self.timeout == value.timeout
        )


@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph"
-version = "1.1.9"
+version = "1.2.0a7"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -24,10 +24,10 @@ classifiers = [
    'Programming Language :: Python :: 3.13',
 ]
 dependencies = [
-    "langchain-core>=1.3.0,<2",
-    "langgraph-checkpoint>=2.1.0,<5.0.0",
+    "langchain-core>=1.4.0a2,<2",
+    "langgraph-checkpoint>=4.1.0a4,<5.0.0",
    "langgraph-sdk>=0.3.0,<0.4.0",
-    "langgraph-prebuilt>=1.0.9,<1.1.0",
+    "langgraph-prebuilt>=1.1.0a2,<1.2.0",
    "xxhash>=3.5.0",
    "pydantic>=2.7.4",
 ]
@@ -38,7 +38,7 @@ Homepage = "https://docs.langchain.com/oss/python/langgraph/overview"
 Documentation = "https://reference.langchain.com/python/langgraph/"
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/langgraph"
 Changelog = "https://github.com/langchain-ai/langgraph/releases"
-Twitter = "https://x.com/LangChain"
+Twitter = "https://x.com/langchain_oss"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"

@@ -1,18 +1,33 @@
 import operator
 from collections.abc import Sequence
+from typing import Annotated

 import pytest
+from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
+from typing_extensions import NotRequired, TypedDict

 from langgraph._internal._typing import MISSING
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import _messages_delta_reducer
+from langgraph.graph.state import _get_channel
+from langgraph.types import Overwrite

 pytestmark = pytest.mark.anyio


+# ---------------------------------------------------------------------------
+# Core channel primitives
+# ---------------------------------------------------------------------------
+
+
 def test_last_value() -> None:
    channel = LastValue(int).from_checkpoint(MISSING)
    assert channel.ValueType is int
@@ -95,25 +110,563 @@ def test_untracked_value() -> None:
    assert channel.ValueType is dict
    assert channel.UpdateType is dict

-    # UntrackedValue should start empty
    with pytest.raises(EmptyChannelError):
        channel.get()

-    # Should be able to update with a value
    test_data = {"session": "test", "temp": "dir"}
    channel.update([test_data])
    assert channel.get() == test_data

-    # Update with new value
    new_data = {"session": "updated", "temp": "newdir"}
    channel.update([new_data])
    assert channel.get() == new_data

-    # On checkpoint, UntrackedValue should return MISSING
    checkpoint = channel.checkpoint()
    assert checkpoint is MISSING

-    # Creating from checkpoint with MISSING should start empty
    new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
    with pytest.raises(EmptyChannelError):
        new_channel.get()
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — message reducer
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_basic_two_steps() -> None:
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
+
+    ch.update([HumanMessage(content="hi", id="h1")])
+    d1 = ch.checkpoint()
+    assert d1 is MISSING
+
+    ch.update([AIMessage(content="hello", id="a1")])
+    d2 = ch.checkpoint()
+    assert d2 is MISSING
+
+    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."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    ch = spec.from_checkpoint(MISSING)
+    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:
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    old_value = [HumanMessage(content="old", id="h1")]
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == old_value
+
+
+def test_delta_channel_overwrite() -> None:
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
+    ch.update([HumanMessage(content="old", id="h1")])
+
+    ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
+    d = ch.checkpoint()
+    assert d is MISSING
+    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."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    ch = spec.from_checkpoint(MISSING)
+
+    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"),
+    ]
+
+    ch.update([RemoveMessage(id="a1")])
+    assert ch.get() == [HumanMessage(content="hi", id="h1")]
+
+    ch2 = spec.from_checkpoint(MISSING)
+    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."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    ch = spec.from_checkpoint(MISSING)
+
+    ch.update([HumanMessage(content="original", id="h1")])
+    ch.update([HumanMessage(content="updated", id="h1")])
+    assert ch.get() == [HumanMessage(content="updated", id="h1")]
+
+    ch2 = spec.from_checkpoint(MISSING)
+    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_dict_coercion() -> None:
+    """_messages_delta_reducer coerces dict writes to BaseMessage objects.
+
+    HTTP-driven input always arrives as JSON dicts.  The reducer must coerce
+    them (same contract as add_messages) so graphs work without a separate
+    coercion step.
+    """
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
+
+    # dict input — simulates what arrives from the HTTP API
+    ch.update([{"role": "human", "content": "hello", "id": "h1"}])
+    assert len(ch.get()) == 1
+    assert isinstance(ch.get()[0], HumanMessage)
+    assert ch.get()[0].content == "hello"
+    assert ch.get()[0].id == "h1"
+
+    # update by ID via dict
+    ch.update([{"role": "ai", "content": "world", "id": "h1"}])
+    assert len(ch.get()) == 1
+    assert ch.get()[0].content == "world"
+
+    # remove via RemoveMessage instance (same contract as add_messages)
+    ch.update([RemoveMessage(id="h1")])
+    assert ch.get() == []
+
+
+def test_messages_delta_reducer_coerces_state() -> None:
+    """State (left side) is coerced when raw — supports raw initial input
+    and deserialized blobs. The steady-state path (state already typed)
+    short-circuits and skips coercion.
+    """
+    state = [{"role": "human", "content": "hello", "id": "h1"}]
+    writes = [[{"role": "ai", "content": "world", "id": "h1"}]]
+    result = _messages_delta_reducer(state, writes)  # type: ignore[arg-type]
+    assert len(result) == 1
+    assert isinstance(result[0], AIMessage)
+    assert result[0].content == "world"
+    assert result[0].id == "h1"
+
+
+def test_messages_delta_reducer_tuple_write_is_one_message() -> None:
+    """A top-level tuple write is one message-like, not a sequence to flatten.
+
+    `("user", "hi")` is a valid `MessageLikeRepresentation`; flattening it
+    would produce two HumanMessages ("user", "hi") instead of one.
+    """
+    result = _messages_delta_reducer([], [("user", "hi")])  # type: ignore[arg-type]
+    assert len(result) == 1
+    assert isinstance(result[0], HumanMessage)
+    assert result[0].content == "hi"
+
+
+def test_delta_channel_checkpoint_returns_missing() -> None:
+    """checkpoint() always returns MISSING regardless of state.
+
+    Pregel writes `_DeltaSnapshot(ch.get())` directly into `channel_values`
+    on snapshot steps; the channel itself never participates in snapshot
+    serialization, so its `checkpoint()` is always the absence sentinel.
+    """
+    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
+    assert ch.checkpoint() is MISSING
+
+    ch.update([HumanMessage(content="hi", id="h1")])
+    assert ch.checkpoint() is MISSING
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — snapshot frequency
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_snapshot_version_based() -> None:
+    """Snapshots fire when a channel accumulates `snapshot_frequency` updates.
+
+    Under the version-delta cadence, every time the channel's
+    `current_version - last_snapshot_version >= snapshot_frequency` a
+    `_DeltaSnapshot` blob is written. Bounds the ancestor walk to at most
+    `snapshot_frequency` steps on any read for that channel.
+    """
+
+    class State(TypedDict):
+        messages: Annotated[
+            list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=5)
+        ]
+        other: str
+
+    def node_a(state: State) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
+
+    def node_b(state: State) -> dict:
+        return {"other": "y"}
+
+    g = StateGraph(State)
+    g.add_node("a", node_a)
+    g.add_node("b", node_b)
+    g.add_edge(START, "a")
+    g.add_edge("a", "b")
+    saver = InMemorySaver()
+    graph = g.compile(checkpointer=saver)
+
+    config = {"configurable": {"thread_id": "t1"}}
+    for i in range(6):
+        graph.invoke(
+            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "other": ""},
+            config,
+        )
+
+    msg_blob_values = [
+        saver.serde.loads_typed((type_tag, blob))
+        for k, (type_tag, blob) in saver.blobs.items()
+        if k[2] == "messages" and type_tag == "msgpack" and blob
+    ]
+    snapshots = [v for v in msg_blob_values if isinstance(v, _DeltaSnapshot)]
+    assert snapshots, "expected at least one _DeltaSnapshot blob for messages"
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 12  # 6 human + 6 AI
+
+
+# TODO(delta-channel-cadence): the previous "snapshot fires even when channel
+# was not written" test asserted eager step-based snapshotting; under the new
+# version-delta cadence (`should_snapshot` triggers on per-channel update
+# count, not superstep count), no snapshot fires for an unwritten channel.
+# Replace with a test that exercises the version-delta trigger plus the
+# durability="exit" force-snapshot branch — see
+# `docs/superpowers/specs/2026-05-04-delta-channel-batched-reads-design.md`
+# section "Snapshot cadence".
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — end-to-end (InMemorySaver)
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_inmemory_saver_assembles_writes() -> None:
+    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer, list)]
+
+    n = {"v": 0}
+
+    def respond(state: State) -> dict:
+        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)
+
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "messages" not in saved.checkpoint["channel_values"]
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — dict reducer
+# ---------------------------------------------------------------------------
+
+
+def _delta_channel_with_type(op, typ):
+    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
+    return _get_channel("_test", Annotated[typ, DeltaChannel(op)])
+
+
+def test_delta_channel_dict_reducer_fresh_channel() -> None:
+    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    assert ch.is_available()
+    assert ch.get() == {}
+
+
+def test_delta_channel_dict_reducer_basic_updates() -> None:
+    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+
+    ch.update([{"a": 1}])
+    d1 = ch.checkpoint()
+    assert d1 is MISSING
+
+    ch.update([{"b": 2}])
+    d2 = ch.checkpoint()
+    assert d2 is MISSING
+
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
+    """replay_writes on a fresh channel replays through a dict merge reducer."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(MISSING)
+    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."""
+
+    def merge_files(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            for k, v in w.items():
+                if v is None:
+                    result.pop(k, None)
+                else:
+                    result[k] = v
+        return result
+
+    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
+    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
+    ch.update([{"file1.py": None, "file3.py": "content3"}])
+    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+    spec = _delta_channel_with_type(merge_files, dict)
+    ch2 = spec.from_checkpoint(MISSING)
+    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."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    ch.update([{"a": 1}])
+    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."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(MISSING)
+    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]`."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    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."""
+
+    def merge_files(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            for k, v in w.items():
+                if v is None:
+                    result.pop(k, None)
+                else:
+                    result[k] = v
+        return result
+
+    class State(TypedDict):
+        files: Annotated[dict[str, str], DeltaChannel(merge_files)]
+
+    turn = {"v": 0}
+
+    def write_file(state: State) -> dict:
+        turn["v"] += 1
+        n = turn["v"]
+        return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
+
+    builder = StateGraph(State)
+    builder.add_node("write_file", write_file)
+    builder.add_edge(START, "write_file")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "fs"}}
+
+    for _ in range(3):
+        graph.invoke({"files": {}}, config)
+
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "files" not in saved.checkpoint["channel_values"]
+    state = graph.get_state(config)
+    assert state.values["files"] == {
+        "/doc_1.txt": "content for turn 1",
+        "/doc_2.txt": "content for turn 2",
+        "/doc_3.txt": "content for turn 3",
+    }
+
+    def delete_file(state: State) -> dict:
+        return {"files": {"/doc_1.txt": None}}
+
+    builder2 = StateGraph(State)
+    builder2.add_node("write_file", write_file)
+    builder2.add_node("delete_file", delete_file)
+    builder2.add_edge(START, "write_file")
+    builder2.add_edge("write_file", "delete_file")
+    turn["v"] = 0
+    saver2 = InMemorySaver()
+    graph2 = builder2.compile(checkpointer=saver2)
+    config2 = {"configurable": {"thread_id": "fs2"}}
+    graph2.invoke({"files": {}}, config2)
+    state2 = graph2.get_state(config2)
+    assert state2.values["files"] == {}
+
+
+def test_delta_channel_dict_reducer_backwards_compat() -> None:
+    """A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
+
+    def merge_dicts(state: dict, writes: list) -> dict:
+        result = dict(state)
+        for w in writes:
+            result.update(w)
+        return result
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    old_value = {"a": 1, "b": 2}
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+# ---------------------------------------------------------------------------
+# DeltaChannel — seed / pre-delta migration
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_from_checkpoint_honors_seed() -> None:
+    """A non-sentinel value to from_checkpoint is used as the pre-delta seed.
+
+    Guards the pre-delta migration path: when the saver's ancestor walk hits
+    a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
+    the post-migration state correctly rather than replaying from empty.
+    """
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    seed = [HumanMessage(content="pre-delta", id="p1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes(
+        [
+            ("t0", "messages", AIMessage(content="delta-1", id="d1")),
+            ("t1", "messages", HumanMessage(content="delta-2", id="d2")),
+        ]
+    )
+    msgs = ch.get()
+    assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
+
+
+def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
+    """Reconstruction at a pre-delta ancestor with no newer deltas returns
+    just the seed — the saver's terminator fired immediately."""
+    spec = DeltaChannel(_messages_delta_reducer, list)
+    seed = [HumanMessage(content="only-snap", id="s1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes([])
+    assert ch.get() == seed
+
+
+def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
+    """`seed=None` must start replay from None, not from an empty channel.
+
+    The `MISSING` absence sentinel means 'no seed'; passing `None`
+    explicitly should feed None to the reducer as the left operand.
+    """
+
+    def replace(state, writes):
+        return writes[-1] if writes else state
+
+    spec = DeltaChannel(replace, list)
+    ch = spec.from_checkpoint(None)
+    ch.replay_writes([("t0", "x", "after")])
+    assert ch.get() == "after"
@@ -0,0 +1,321 @@
+"""Benchmark: DeltaChannel — multi-channel reads, mixed snapshot frequencies.
+
+Run directly:  python tests/test_delta_channel_benchmark.py
+Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
+
+Sweeps `(K delta channels, snapshot_frequency strategy, turn count)` and
+reports per-scenario read latency, write latency, storage, and peak Python
+heap usage during `get_state`.
+
+Scenarios cover the dimensions where this branch's optimizations matter:
+
+  * K-channel batching   — varying K (number of `DeltaChannel`s the graph
+                           reads on hydrate) shows the effect of merging
+                           per-channel reads into a single saver call.
+  * Mixed frequencies    — channels with very different snapshot cadences
+                           in one graph exercise the per-channel chain
+                           bound in stage-2.
+  * Turn count           — chain depth shows how paged stage-1 holds up.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import gc
+import os
+import sys
+import time
+import tracemalloc
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import HumanMessage
+from langgraph.checkpoint.memory import MemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.delta import DeltaChannel
+from langgraph.graph import END, StateGraph
+from langgraph.graph.message import _messages_delta_reducer
+
+try:
+    from langgraph.checkpoint.postgres import PostgresSaver
+
+    _POSTGRES_AVAILABLE = True
+    _POSTGRES_URI = os.environ.get(
+        "LANGGRAPH_BENCH_POSTGRES_URI",
+        "postgres://postgres@localhost:5432/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."
+)
+
+_TOPICS = [
+    "distributed tracing",
+    "eventual consistency",
+    "schema migration",
+    "backpressure handling",
+    "idempotency guarantees",
+]
+_CONCERNS = ["concurrency model", "retry semantics", "ordering guarantees"]
+_COMPONENTS = ["persistence", "ingestion", "routing"]
+
+
+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)],
+    )
+
+
+# ---------------------------------------------------------------------------
+# State / graph factory: K DeltaChannel fields with per-channel freqs
+# ---------------------------------------------------------------------------
+
+
+def _make_state_cls(freqs: list[int]) -> type:
+    """Build a TypedDict with one DeltaChannel per entry in `freqs`."""
+    fields: dict[str, Any] = {}
+    for i, freq in enumerate(freqs):
+        ch = DeltaChannel(_messages_delta_reducer, snapshot_frequency=freq)
+        fields[f"ch{i}"] = Annotated[list, ch]
+    return TypedDict(  # type: ignore[return-value]
+        "_BenchState_" + "-".join(str(f) for f in freqs),
+        fields,
+    )
+
+
+def _make_graph(state_cls: type, K: int, checkpointer: Any = None) -> Any:
+    """Graph: one node, writes a fresh message into every channel each turn."""
+
+    def fanout(state: Any) -> dict[str, Any]:
+        i = max(len(state.get(f"ch{j}", [])) for j in range(K))
+        # Each channel gets its own copy with a unique id so the reducer
+        # does meaningful per-channel state accumulation.
+        return {
+            f"ch{j}": [HumanMessage(content=_human_content(i), id=f"c{j}_{i}")]
+            for j in range(K)
+        }
+
+    g = StateGraph(state_cls)
+    g.add_node("fanout", fanout)
+    g.set_entry_point("fanout")
+    g.add_edge("fanout", END)
+    return g.compile(checkpointer=checkpointer or MemorySaver())
+
+
+# ---------------------------------------------------------------------------
+# Storage / memory measurement
+# ---------------------------------------------------------------------------
+
+
+def _inmemory_blob_bytes(saver: MemorySaver) -> int:
+    return sum(
+        len(blob) for (_, _, _, _), (_, blob) in saver.blobs.items() if blob is not None
+    )
+
+
+def _postgres_storage_bytes(saver: Any, thread_id: str) -> int:
+    """Total bytes across checkpoints / checkpoint_blobs / checkpoint_writes
+    rows for this `thread_id`. Uses `pg_column_size` for an in-row payload
+    estimate; faster than full-table size and scoped to the thread."""
+    sql = """
+    SELECT COALESCE(SUM(pg_column_size(c.*)), 0)
+         + COALESCE((SELECT SUM(pg_column_size(b.*)) FROM checkpoint_blobs b
+                     WHERE b.thread_id = %s), 0)
+         + COALESCE((SELECT SUM(pg_column_size(w.*)) FROM checkpoint_writes w
+                     WHERE w.thread_id = %s), 0)
+         AS total
+    FROM checkpoints c
+    WHERE c.thread_id = %s
+    """
+    with saver._cursor() as cur:
+        cur.execute(sql, (thread_id, thread_id, thread_id))
+        row = cur.fetchone()
+    if row is None:
+        return 0
+    if isinstance(row, dict):
+        return int(row.get("total") or 0)
+    return int(row[0] or 0)
+
+
+def _run_scenario(
+    freqs: list[int],
+    n_turns: int,
+    checkpointer: Any,
+    thread_id: str,
+) -> dict[str, float | int]:
+    """Drive `n_turns` invocations of a K-channel graph, measure
+    write/read/storage/peak-memory."""
+    K = len(freqs)
+    state_cls = _make_state_cls(freqs)
+    graph = _make_graph(state_cls, K, checkpointer)
+    config = {"configurable": {"thread_id": thread_id}}
+
+    # Write phase
+    t0 = time.perf_counter()
+    for i in range(n_turns):
+        graph.invoke({}, config)
+    write_elapsed = time.perf_counter() - t0
+
+    # Read phase + tracemalloc peak across get_state calls
+    gc.collect()
+    tracemalloc.start()
+    t1 = time.perf_counter()
+    for _ in range(5):
+        graph.get_state(config)
+    read_elapsed = (time.perf_counter() - t1) / 5
+    _, peak_bytes = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+
+    # Storage
+    if isinstance(graph.checkpointer, MemorySaver):
+        storage = _inmemory_blob_bytes(graph.checkpointer)
+    elif _POSTGRES_AVAILABLE and isinstance(graph.checkpointer, PostgresSaver):
+        storage = _postgres_storage_bytes(graph.checkpointer, thread_id)
+    else:
+        storage = -1
+
+    return {
+        "K": K,
+        "turns": n_turns,
+        "write_total_s": write_elapsed,
+        "write_per_invoke_ms": (write_elapsed / n_turns) * 1000,
+        "read_avg_ms": read_elapsed * 1000,
+        "storage_bytes": storage,
+        "peak_mem_bytes": peak_bytes,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Postgres helpers
+# ---------------------------------------------------------------------------
+
+
+@contextlib.contextmanager
+def _pg_saver(thread_id: str):
+    with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
+        saver.setup()
+        with saver._cursor() as cur:
+            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
+                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
+        yield saver
+        with saver._cursor() as cur:
+            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
+                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
+
+
+def _checkpointers() -> list[tuple[str, Any]]:
+    result: list[tuple[str, Any]] = [("InMemory", None)]
+    if _POSTGRES_AVAILABLE:
+        try:
+            import psycopg
+
+            psycopg.connect(_POSTGRES_URI).close()
+            result.append(("Postgres", "postgres"))
+        except Exception:
+            pass
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Scenarios
+# ---------------------------------------------------------------------------
+
+SCENARIOS: list[tuple[str, list[int]]] = [
+    ("K=1, freq=50", [50]),
+    ("K=3, freq=50 uniform", [50, 50, 50]),
+    ("K=3, freq=mixed", [50, 200, 1000]),
+    ("K=8, freq=50 uniform", [50] * 8),
+    ("K=8, freq=mixed", [25, 50, 100, 200, 500, 1000, 1000, 1000]),
+]
+
+TURN_COUNTS = [100, 500]
+
+
+def _fmt_bytes(n: int) -> str:
+    if n < 0:
+        return "n/a"
+    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 _print_scenario_table(cp_label: str, rows: list[dict]) -> None:
+    print(f"\n  [{cp_label}]")
+    header = (
+        f"  {'scenario':<28}{'turns':>8}{'write_ms':>11}"
+        f"{'read_ms':>10}{'storage':>12}{'peak_mem':>12}"
+    )
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for row in rows:
+        print(
+            f"  {row['scenario']:<28}"
+            f"{row['turns']:>8}"
+            f"{row['write_per_invoke_ms']:>10.1f}"
+            f"{row['read_avg_ms']:>10.2f}"
+            f"{_fmt_bytes(row['storage_bytes']):>12}"
+            f"{_fmt_bytes(row['peak_mem_bytes']):>12}"
+        )
+
+
+def run_benchmark() -> list[dict]:
+    """Run the full sweep and return all measurement rows."""
+    all_rows: list[dict] = []
+    for cp_label, cp_hint in _checkpointers():
+        rows: list[dict] = []
+        for scenario_label, freqs in SCENARIOS:
+            for turns in TURN_COUNTS:
+                thread_id = f"bench-{scenario_label.replace(' ', '_')}-{turns}".lower()
+                if cp_hint is None:
+                    saver_ctx: Any = contextlib.nullcontext(None)
+                else:
+                    saver_ctx = _pg_saver(thread_id)
+                with saver_ctx as saver:
+                    measured = _run_scenario(freqs, turns, saver, thread_id)
+                measured["scenario"] = scenario_label
+                measured["saver"] = cp_label
+                rows.append(measured)
+                all_rows.append(measured)
+        _print_scenario_table(cp_label, rows)
+    return all_rows
+
+
+# ---------------------------------------------------------------------------
+# 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:
+    """Manual benchmark — see module docstring."""
+    with capsys.disabled():
+        run_benchmark()
+
+
+# ---------------------------------------------------------------------------
+# Script entry point
+# ---------------------------------------------------------------------------
+
+
+if __name__ == "__main__":
+    print("DeltaChannel benchmark — multi-channel reads, mixed frequencies")
+    print("=" * 78)
+    run_benchmark()
+    sys.exit(0)
@@ -0,0 +1,618 @@
+"""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 public `get_delta_channel_history(config,
+channels)` walks the parent chain; when it encounters an ancestor whose
+`channel_values[channel]` is a real value, it populates that channel's
+`seed` in the returned `DeltaChannelHistory`. If the walk reaches the root
+without finding a stored value, the `seed` key is omitted (TypedDict
+absence indicates "start empty"). `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_delta_channel_history` must produce the same
+   result as the optimized path.
+5. **Channel-type isolation across threads**: two threads on the same
+   checkpointer under the delta-channel graph — one freshly-started,
+   one migrated from pre-migration state — don't cross-contaminate.
+   The parent-chain walk is scoped to the thread.
+
+TODO: add postgres variants in the existing `libs/checkpoint-postgres`
+test files (different fixture setup; not this file).
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import _messages_delta_reducer, add_messages
+
+pytestmark = pytest.mark.anyio
+
+
+# ---------------------------------------------------------------------------
+# Graph factories
+#
+# A minimal reducer (`operator.add` on lists of str) with a noop node keeps
+# state change localized to the HumanMessage-like payload passed through
+# `invoke`. That isolates the pre/post-migration parity assertions to
+# channel-hydration semantics.
+# ---------------------------------------------------------------------------
+
+
+def _noop(_state: Any) -> dict:
+    return {}
+
+
+def _list_concat(state: list, writes: list) -> list:
+    result = list(state)
+    for w in writes:
+        result.extend(w if isinstance(w, list) else [w])
+    return result
+
+
+def _binop_graph(checkpointer: Any) -> Any:
+    class BinopState(TypedDict):
+        items: Annotated[list, BinaryOperatorAggregate(list, operator.add)]
+
+    return (
+        StateGraph(BinopState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_graph(checkpointer: Any) -> Any:
+    class DeltaState(TypedDict):
+        items: Annotated[list, DeltaChannel(_list_concat)]
+
+    return (
+        StateGraph(DeltaState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _drive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        graph.invoke({"items": [f"{tag}{i}"]}, config)
+
+
+async def _adrive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        await graph.ainvoke({"items": [f"{tag}{i}"]}, config)
+
+
+def _settled_boundaries(history: list) -> list[tuple[dict, list]]:
+    """Return `[(config, items), ...]` for every checkpoint in `history`
+    whose `next == ('__start__',)` — the stable boundaries between invokes.
+    """
+    return [
+        (s.config, list(s.values.get("items", [])))
+        for s in history
+        if s.next == ("__start__",)
+    ]
+
+
+# ---------------------------------------------------------------------------
+# 1. Basic migration (sync + async)
+# ---------------------------------------------------------------------------
+
+
+def test_basic_migration_preserves_pre_migration_state() -> None:
+    """Build state under `BinaryOperatorAggregate`, migrate to
+    `DeltaChannel` on the same checkpointer, and verify that every
+    settled pre-migration super-step boundary round-trips exactly.
+
+    Settled boundaries (`next=('__start__',)`) are the stable hydration
+    targets for the migration path: writes that produced the NEXT
+    super-step are kept as `pending_writes` on the ancestor, so walking
+    from a descendant finds the ancestor's blob as the seed and
+    reconstructs the correct state.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-sync"}}
+
+    # Pre-migration: accumulate items across 3 invokes.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert len(pre_boundaries) >= 2, "expected multiple settled boundaries"
+
+    # Migrate: swap the annotation on the same checkpointer.
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = delta.get_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"snapshot mismatch at {cfg['configurable']['checkpoint_id']}: "
+            f"expected {items}, got {snap.values.get('items', [])}"
+        )
+
+
+async def test_basic_migration_preserves_pre_migration_state_async() -> None:
+    """Async variant of the basic migration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    pre_history = [s async for s in binop.aget_state_history(config)]
+    pre_boundaries = _settled_boundaries(pre_history)
+    assert len(pre_boundaries) >= 2
+
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = await delta.aget_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"async snapshot mismatch at {cfg['configurable']['checkpoint_id']}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 2. Time travel into a pre-migration checkpoint after migration
+# ---------------------------------------------------------------------------
+
+
+def test_time_travel_into_pre_migration_checkpoint() -> None:
+    """After migration, `graph.get_state(pre_migration_config)` at a
+    settled ancestor returns the state as stored at that point."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "time-travel"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert pre_boundaries, "no settled ancestors to time-travel to"
+
+    delta = _delta_graph(checkpointer)
+
+    # Pick the oldest non-empty boundary — a long distance to walk back.
+    non_empty = [(cfg, items) for cfg, items in pre_boundaries if items]
+    assert non_empty, "expected at least one non-empty boundary"
+    target_cfg, expected_items = non_empty[-1]
+
+    snap = delta.get_state(target_cfg)
+    assert list(snap.values.get("items", [])) == expected_items
+
+
+# ---------------------------------------------------------------------------
+# 3. Continuing a migrated thread: deltas fold onto pre-migration seed
+# ---------------------------------------------------------------------------
+
+
+def test_continuing_migrated_thread_folds_deltas_on_seed() -> None:
+    """Resume a pre-migration settled ancestor via `invoke(None, cfg)`
+    under the delta-channel graph. Since the pre-migration checkpoint
+    has an existing `pending_writes` entry (the input for the NEXT
+    super-step), re-running from that ancestor reproduces the same
+    post-ancestor state as the original binop run.
+
+    This proves the seed-terminator + write-replay pipeline works
+    end-to-end across the migration boundary.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "continue"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Pick the oldest settled boundary with non-empty state.
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    target_cfg, seed_items = next(
+        (cfg, items) for cfg, items in reversed(pre_boundaries) if items
+    )
+    assert seed_items, "need a non-empty seed boundary"
+
+    # Migrate and resume from the pre-migration ancestor. `invoke(None,
+    # cfg)` replays the pending writes staged at `cfg` under the new
+    # channel; the reducer folds those deltas onto the seed.
+    delta = _delta_graph(checkpointer)
+    result = delta.invoke(None, target_cfg)
+
+    # The resumed state must include the pre-migration seed items in order.
+    result_items = list(result.get("items", []))
+    for idx, prefix_item in enumerate(seed_items):
+        assert result_items[idx] == prefix_item, (
+            f"pre-migration seed item at {idx} not preserved: "
+            f"got {result_items[: idx + 1]}, expected {seed_items}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 4. Base-saver fallback path
+# ---------------------------------------------------------------------------
+
+
+class _ThirdPartyStyleSaver(InMemorySaver):
+    """Simulates a third-party saver that inherits the reference
+    `get_delta_channel_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_delta_channel_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1].get_delta_channel_history  # type: ignore[attr-defined]
+    )
+    aget_delta_channel_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1].aget_delta_channel_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_delta_channel_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 its delta channel absent from `channel_values`,
+    or stored as a `_DeltaSnapshot` on a snapshot step), `update_state`
+    writes a concrete value into 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 where the delta channel is absent from `channel_values`
+    # (no snapshot fired this step).
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # `update_state` writes a concrete value into a new checkpoint's blob
+    # via the reducer against the hydrated prior state.
+    delta.update_state(config, {"items": ["x", "y"]})
+
+    snap = delta.get_state(config)
+    updated_items = list(snap.values.get("items", []))
+    # Must include the "x","y" update; without the hydration fix, the
+    # update_state-written blob would be skipped in favor of an ancestor
+    # walk, and the update values would disappear.
+    assert "x" in updated_items and "y" in updated_items, (
+        f"update_state values missing from snapshot: {updated_items}"
+    )
+    # The "x","y" items should be folded onto the prior accumulated state,
+    # not stand alone. This verifies the update-written blob is used
+    # directly by `get_state` (no ancestor walk past it).
+    assert len(updated_items) >= 4, (
+        f"update_state snapshot should preserve pre-update state, got {updated_items}"
+    )
+    assert updated_items[-2:] == ["x", "y"], (
+        f"update_state deltas should be at the tail, got {updated_items}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 8. Fork from an `update_state` checkpoint: a new run branched off the
+# update_state-produced checkpoint must see that checkpoint's concrete
+# `channel_values` as its base, with new deltas folded on top.
+# ---------------------------------------------------------------------------
+
+
+def test_fork_from_update_state_checkpoint() -> None:
+    """Branching a new run from the checkpoint produced by `update_state`
+    must use that checkpoint's concrete blob as the base. Additional
+    deltas from the forked run fold onto it through the reducer."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "fork"}}
+
+    # Pre-migration build-up, then migrate and add one post-migration step.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # Apply `update_state` and capture the returned config (references
+    # the new checkpoint produced by the update).
+    update_cfg = delta.update_state(config, {"items": ["x", "y"]})
+
+    update_snap = delta.get_state(update_cfg)
+    base_items = list(update_snap.values.get("items", []))
+    assert "x" in base_items and "y" in base_items, (
+        f"update_state values missing from snapshot: {base_items}"
+    )
+    assert base_items[-2:] == ["x", "y"], (
+        f"sanity: update_state deltas should be at the tail, got {base_items}"
+    )
+
+    # Fork: invoke from the update_state checkpoint with a new delta.
+    forked = delta.invoke({"items": ["fork0"]}, update_cfg)
+    forked_items = list(forked.get("items", []))
+    # The fork must see the update_state-written blob as its base (not
+    # walk past it), and the new delta must fold on top of it.
+    assert forked_items[: len(base_items)] == base_items, (
+        f"fork lost update_state base: base={base_items}, forked={forked_items}"
+    )
+    assert forked_items[-1] == "fork0", f"fork delta not appended: {forked_items}"
+
+
+# ---------------------------------------------------------------------------
+# 9. Migration from `add_messages` → `DeltaChannel(_messages_delta_reducer)`
+#
+# `add_messages` is the primary real-world use case: it creates a
+# BinaryOperatorAggregate with dedup-by-ID and RemoveMessage semantics.
+# After swapping the annotation to DeltaChannel, pre-migration blobs
+# (plain lists of Message objects) must be used directly as the seed.
+# ---------------------------------------------------------------------------
+
+
+def _add_messages_graph(checkpointer: Any) -> Any:
+    class MessagesState(TypedDict):
+        messages: Annotated[list, add_messages]
+
+    return (
+        StateGraph(MessagesState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_messages_graph(checkpointer: Any) -> Any:
+    class DeltaMessagesState(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    return (
+        StateGraph(DeltaMessagesState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def test_add_messages_to_delta_migration_preserves_message_history() -> None:
+    """Migration from `add_messages` to `DeltaChannel(_messages_delta_reducer)`
+    preserves message ordering and IDs at both the tip and settled ancestor
+    boundaries.
+
+    The pre-migration blob is a plain list of Message objects; DeltaChannel
+    must use it directly as the seed without walking ancestors past it.
+    """
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "add-messages-migration"}}
+
+    pre_graph = _add_messages_graph(checkpointer)
+    pre_graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
+    pre_graph.invoke({"messages": [AIMessage(content="hi", id="a1")]}, config)
+    pre_graph.invoke({"messages": [HumanMessage(content="thanks", id="h2")]}, config)
+
+    pre_tip = pre_graph.get_state(config)
+    assert [m.id for m in pre_tip.values["messages"]] == ["h1", "a1", "h2"]
+
+    delta_graph = _delta_messages_graph(checkpointer)
+
+    # Tip: latest checkpoint has a full list blob — must use it directly.
+    snap = delta_graph.get_state(config)
+    assert [m.id for m in snap.values["messages"]] == ["h1", "a1", "h2"], (
+        f"tip hydration mismatch: got {[m.id for m in snap.values['messages']]}"
+    )
+
+    # Settled ancestor boundaries must also match.
+    pre_settled = [
+        [m.id for m in s.values.get("messages", [])]
+        for s in pre_graph.get_state_history(config)
+        if s.next == ("__start__",)
+    ]
+    delta_settled = [
+        [m.id for m in s.values.get("messages", [])]
+        for s in delta_graph.get_state_history(config)
+        if s.next == ("__start__",)
+    ]
+    assert delta_settled == pre_settled, (
+        f"settled boundary mismatch after migration: "
+        f"pre={pre_settled}, delta={delta_settled}"
+    )
+
+
+async def test_add_messages_to_delta_migration_preserves_message_history_async() -> (
+    None
+):
+    """Async variant of the add_messages migration test."""
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "add-messages-migration-async"}}
+
+    pre_graph = _add_messages_graph(checkpointer)
+    await pre_graph.ainvoke(
+        {"messages": [HumanMessage(content="hello", id="h1")]}, config
+    )
+    await pre_graph.ainvoke({"messages": [AIMessage(content="hi", id="a1")]}, config)
+
+    delta_graph = _delta_messages_graph(checkpointer)
+    snap = await delta_graph.aget_state(config)
+    assert [m.id for m in snap.values["messages"]] == ["h1", "a1"], (
+        f"async tip hydration mismatch: got {[m.id for m in snap.values['messages']]}"
+    )
@@ -0,0 +1,359 @@
+"""Tests for arrival-ordered interleave and push stamps."""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.stream import StreamChannel, StreamTransformer
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+from langgraph.stream.run_stream import GraphRunStream
+from langgraph.stream.transformers import ValuesTransformer
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+class _TwoChannelTransformer(StreamTransformer):
+    """Transformer that exposes two named channels for testing interleave."""
+
+    _native = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._alpha: StreamChannel[str] = StreamChannel("alpha")
+        self._beta: StreamChannel[str] = StreamChannel("beta")
+
+    def init(self) -> dict[str, Any]:
+        return {"alpha": self._alpha, "beta": self._beta}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class SimpleState(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _build_simple_graph():
+    def node_a(state: SimpleState) -> dict:
+        return {"value": state["value"] + "A", "items": ["a"]}
+
+    def node_b(state: SimpleState) -> dict:
+        return {"value": state["value"] + "B", "items": ["b"]}
+
+    builder = StateGraph(SimpleState)
+    builder.add_node("node_a", node_a)
+    builder.add_node("node_b", node_b)
+    builder.add_edge(START, "node_a")
+    builder.add_edge("node_a", "node_b")
+    builder.add_edge("node_b", END)
+    return builder.compile()
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: push stamps on StreamChannel
+# ---------------------------------------------------------------------------
+
+
+class TestPushStamps:
+    def test_stamps_are_monotonic_across_channels(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        beta = mux.extensions["beta"]
+
+        alpha._subscribed = True
+        beta._subscribed = True
+
+        alpha.push("a1")
+        beta.push("b1")
+        alpha.push("a2")
+        beta.push("b2")
+
+        all_stamped = list(alpha._items) + list(beta._items)
+        stamps = [s for s, _ in all_stamped]
+        assert len(set(stamps)) == 4
+        items_by_arrival = [item for _, item in sorted(all_stamped)]
+        assert items_by_arrival == ["a1", "b1", "a2", "b2"]
+
+    def test_regular_iter_strips_stamps(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        it = iter(alpha)
+        alpha.push("a1")
+        alpha.push("a2")
+        alpha.close()
+        items = list(it)
+        assert items == ["a1", "a2"]
+        assert all(isinstance(item, str) for item in items)
+
+    def test_events_channel_gets_real_stamps(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+
+        alpha._subscribed = True
+        alpha.push("a1")
+
+        mux._events._subscribed = True
+        mux._events.push({"method": "test", "data": "x"})
+
+        alpha.push("a2")
+
+        all_stamps = [s for s, _ in alpha._items] + [s for s, _ in mux._events._items]
+        assert len(set(all_stamps)) == len(all_stamps), "all stamps should be unique"
+        assert all(s > 0 for s in all_stamps), "no stamp should be zero"
+
+    def test_channel_without_mux_gets_zero_stamp(self) -> None:
+        ch: StreamChannel[str] = StreamChannel()
+        ch._bind(is_async=False)
+        ch._subscribed = True
+        ch.push("x")
+        assert list(ch._items) == [(0, "x")]
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: interleave arrival order
+# ---------------------------------------------------------------------------
+
+
+class TestInterleaveArrivalOrder:
+    def test_arrival_order_not_round_robin(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        beta = mux.extensions["beta"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        # interleave() subscribes channels directly and reads _items
+        # for stamp-ordered iteration. We simulate the pump by wiring
+        # a custom callback that pushes items in a known order.
+        push_script = [
+            ("alpha", "a1"),
+            ("alpha", "a2"),
+            ("beta", "b1"),
+            ("alpha", "a3"),
+            ("beta", "b2"),
+        ]
+        push_iter = iter(push_script)
+        channels = {"alpha": alpha, "beta": beta}
+
+        def fake_pump() -> bool:
+            try:
+                name, item = next(push_iter)
+                channels[name].push(item)
+                return True
+            except StopIteration:
+                mux.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha", "beta"))
+        names = [name for name, _ in result]
+        items = [item for _, item in result]
+
+        assert items == ["a1", "a2", "b1", "a3", "b2"]
+        assert names == ["alpha", "alpha", "beta", "alpha", "beta"]
+
+    def test_single_projection(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        push_script = [("alpha", "a1"), ("alpha", "a2")]
+        push_iter = iter(push_script)
+
+        def fake_pump() -> bool:
+            try:
+                _, item = next(push_iter)
+                alpha.push(item)
+                return True
+            except StopIteration:
+                mux.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha"))
+        assert result == [("alpha", "a1"), ("alpha", "a2")]
+
+    def test_empty_projection(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        push_script = [("alpha", "a1"), ("alpha", "a2")]
+        push_iter = iter(push_script)
+        channels = {"alpha": alpha}
+
+        def fake_pump() -> bool:
+            try:
+                name, item = next(push_iter)
+                channels[name].push(item)
+                return True
+            except StopIteration:
+                mux.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha", "beta"))
+        assert result == [("alpha", "a1"), ("alpha", "a2")]
+
+    def test_unknown_projection_raises(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        run = GraphRunStream(None, mux, wire_pump=False)
+        mux.close()
+        with pytest.raises((KeyError, AttributeError)):
+            list(run.interleave("alpha", "does_not_exist"))
+
+    def test_all_empty(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        def fake_pump() -> bool:
+            mux.close()
+            return False
+
+        mux.bind_pump(fake_pump)
+
+        result = list(run.interleave("alpha", "beta"))
+        assert result == []
+
+    def test_error_propagation(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        beta = mux.extensions["beta"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        err = RuntimeError("boom")
+
+        push_script = [
+            ("alpha", "a1"),
+            ("beta", "b1"),
+        ]
+        push_iter = iter(push_script)
+        channels = {"alpha": alpha, "beta": beta}
+
+        def fake_pump() -> bool:
+            try:
+                name, item = next(push_iter)
+                channels[name].push(item)
+                return True
+            except StopIteration:
+                alpha.fail(err)
+                beta.close()
+                return False
+
+        mux.bind_pump(fake_pump)
+
+        collected = []
+        with pytest.raises(RuntimeError, match="boom"):
+            for pair in run.interleave("alpha", "beta"):
+                collected.append(pair)
+
+        assert ("alpha", "a1") in collected
+        assert ("beta", "b1") in collected
+
+
+# ---------------------------------------------------------------------------
+# Integration test: interleave with stream_events(version="v3")
+# ---------------------------------------------------------------------------
+
+
+class TestInterleaveIntegration:
+    def test_interleave_values_and_messages(self) -> None:
+        run = _build_simple_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        tagged = list(run.interleave("values", "messages"))
+        names = [name for name, _ in tagged]
+        assert set(names).issubset({"values", "messages"})
+        assert names.count("values") >= 1
+
+    def test_interleave_rejects_already_subscribed(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        run = GraphRunStream(None, mux, wire_pump=False)
+
+        # Subscribe alpha via iter first
+        _ = iter(alpha)
+        mux.close()
+
+        with pytest.raises(RuntimeError, match="already has a subscriber"):
+            list(run.interleave("alpha"))
+
+    def test_interleave_releases_projections_on_completion(self) -> None:
+        run = _build_simple_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        list(run.interleave("values", "messages"))
+        # Subscriptions should be released after the generator completes,
+        # so the channels can be re-iterated (they'll be empty / closed).
+        assert run.extensions["values"]._subscribed is False
+        assert run.extensions["messages"]._subscribed is False
+
+    def test_interleave_releases_projections_on_early_break(self) -> None:
+        run = _build_simple_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        gen = run.interleave("values", "messages")
+        next(gen)
+        gen.close()
+        assert run.extensions["values"]._subscribed is False
+        assert run.extensions["messages"]._subscribed is False
+
+    def test_interleave_releases_projections_on_validation_failure(self) -> None:
+        mux = StreamMux(
+            factories=[ValuesTransformer, _TwoChannelTransformer],
+            is_async=False,
+        )
+        alpha = mux.extensions["alpha"]
+        # Pre-subscribe alpha so that interleave will fail validation when
+        # it gets to the second name. The first (already-validated) channel
+        # should still be released.
+        run = GraphRunStream(None, mux, wire_pump=False)
+        mux.close()
+        alpha._subscribed = True
+
+        with pytest.raises(RuntimeError, match="already has a subscriber"):
+            list(run.interleave("values", "alpha"))
+
+        assert mux.extensions["values"]._subscribed is False
@@ -16,7 +16,7 @@ from typing import Annotated, Any, Literal, get_type_hints

 import pytest
 from langchain_core.language_models import GenericFakeChatModel
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage
 from langchain_core.runnables import (
    RunnableConfig,
    RunnableLambda,
@@ -41,6 +41,7 @@ from typing_extensions import NotRequired, TypedDict

 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
@@ -49,7 +50,7 @@ from langgraph.config import get_stream_writer
 from langgraph.errors import GraphRecursionError, InvalidUpdateError, ParentCommand
 from langgraph.func import entrypoint, task
 from langgraph.graph import END, START, StateGraph
-from langgraph.graph.message import MessagesState, add_messages
+from langgraph.graph.message import MessagesState, _messages_delta_reducer, add_messages
 from langgraph.pregel import (
    NodeBuilder,
    Pregel,
@@ -120,6 +121,29 @@ def test_graph_validation() -> None:
        graph.invoke({"hello": "there"})


+def test_request_drain_allows_inflight_call_scheduling(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    from langgraph.runtime import RunControl
+
+    @task
+    def child(x: int) -> int:
+        return x + 1
+
+    control = RunControl()
+
+    @entrypoint(checkpointer=sync_checkpointer)
+    def graph(x: int) -> int:
+        control.request_drain()
+        fut = child(x)
+        return fut.result()
+
+    config = {"configurable": {"thread_id": "drain-call-sync"}}
+
+    assert graph.invoke(1, config=config, control=control) == 2
+    assert control.drain_requested
+
+
 def test_invalid_checkpointer_type() -> None:
    class State(TypedDict):
        foo: str
@@ -9400,3 +9424,263 @@ 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."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    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."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    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."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    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."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    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"
+
+
+async def test_delta_channel_durability_exit_stores_snapshot() -> None:
+    """DeltaChannel must reload from a durability='exit' checkpoint."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    def respond(state: State) -> dict:
+        return {"messages": [AIMessage(content="reply", id="ai1")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    graph = builder.compile(checkpointer=InMemorySaver())
+    config = {"configurable": {"thread_id": "delta-exit-test"}}
+
+    result = graph.invoke(
+        {"messages": [HumanMessage(content="hello", id="h1")]},
+        config,
+        durability="exit",
+    )
+    assert [m.content for m in result["messages"]] == ["hello", "reply"]
+
+    state = graph.get_state(config)
+    assert [m.content for m in state.values["messages"]] == ["hello", "reply"]
+
+
+async def test_delta_channel_async_write_ordering() -> None:
+    """In async mode, DeltaChannel write futures are awaited before the checkpoint
+    is committed, so aput_writes always precedes aput for delta-channel
+    checkpoints (those where the delta channel had a versioned write but
+    is absent from `channel_values`, i.e. no snapshot fired this step)."""
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    def respond(state: State) -> dict:
+        i = len(state["messages"])
+        return {"messages": [AIMessage(content=f"r{i}", id=f"ai{i}")]}
+
+    order: list[str] = []
+    original_aput_writes = InMemorySaver.aput_writes
+    original_aput = InMemorySaver.aput
+
+    async def tracked_aput_writes(self, config, writes, task_id, task_path=""):
+        result = await original_aput_writes(self, config, writes, task_id, task_path)
+        order.append("aput_writes")
+        return result
+
+    async def tracked_aput(self, config, checkpoint, metadata, new_versions):
+        # A "delta" checkpoint here = `messages` versioned but absent from
+        # `channel_values` (no snapshot fired). When a snapshot does fire,
+        # `channel_values["messages"]` is a `_DeltaSnapshot` — also a delta
+        # checkpoint shape, since the writes still have to be persisted
+        # before the parent checkpoint commits.
+        channel_values = checkpoint.get("channel_values", {})
+        is_delta_step = (
+            "messages" in checkpoint.get("channel_versions", {})
+            and "messages" not in channel_values
+        )
+        order.append("aput_delta" if is_delta_step else "aput_other")
+        return await original_aput(self, config, checkpoint, metadata, new_versions)
+
+    InMemorySaver.aput_writes = tracked_aput_writes
+    InMemorySaver.aput = tracked_aput
+    try:
+        builder = StateGraph(State)
+        builder.add_node("respond", respond)
+        builder.add_edge(START, "respond")
+        graph = builder.compile(checkpointer=InMemorySaver())
+        config = {"configurable": {"thread_id": "async-ordering-test"}}
+
+        for i in range(3):
+            await graph.ainvoke(
+                {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config
+            )
+
+        # Every aput_delta must be preceded by at least one aput_writes
+        for i, event in enumerate(order):
+            if event == "aput_delta":
+                preceding = order[:i]
+                assert "aput_writes" in preceding, (
+                    f"aput_delta at {i} had no preceding aput_writes: {order}"
+                )
+                last_write_idx = max(
+                    j for j, e in enumerate(order[:i]) if e == "aput_writes"
+                )
+                assert last_write_idx < i, (
+                    f"aput_writes at {last_write_idx} should precede aput_delta at {i}: {order}"
+                )
+    finally:
+        InMemorySaver.aput_writes = original_aput_writes
+        InMemorySaver.aput = original_aput
+
+    state = await graph.aget_state(config)
+    assert len(state.values["messages"]) == 6  # 3 human + 3 AI
@@ -16,6 +16,7 @@ from typing import (
    Literal,
    Optional,
 )
+from unittest.mock import patch
 from uuid import UUID

 import pytest
@@ -48,6 +49,7 @@ from langgraph.channels.topic import Topic
 from langgraph.errors import (
    GraphRecursionError,
    InvalidUpdateError,
+    NodeError,
    ParentCommand,
 )
 from langgraph.func import entrypoint, task
@@ -215,6 +217,30 @@ async def test_checkpoint_errors() -> None:
            pass


+@NEEDS_CONTEXTVARS
+async def test_request_drain_allows_inflight_acall_scheduling(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    from langgraph.runtime import RunControl
+
+    @task
+    async def child(x: int) -> int:
+        return x + 1
+
+    control = RunControl()
+
+    @entrypoint(checkpointer=async_checkpointer)
+    async def graph(x: int) -> int:
+        control.request_drain()
+        fut = child(x)
+        return await fut
+
+    config = {"configurable": {"thread_id": "drain-call-async"}}
+
+    assert await graph.ainvoke(1, config=config, control=control) == 2
+    assert control.drain_requested
+
+
 async def test_py_async_with_cancel_behavior() -> None:
    """This test confirms that in all versions of Python we support, __aexit__
    is not cancelled when the coroutine containing the async with block is cancelled."""
@@ -6101,6 +6127,36 @@ async def test_parent_command(
    )


+async def test_delta_channel_durability_exit_stores_snapshot_async() -> None:
+    """DeltaChannel must reload from an async durability='exit' checkpoint."""
+    from langchain_core.messages import AIMessage
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import _messages_delta_reducer
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    async def respond(state: State) -> dict:
+        return {"messages": [AIMessage(content="reply", id="ai1")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    graph = builder.compile(checkpointer=InMemorySaver())
+    config = {"configurable": {"thread_id": "delta-exit-async-test"}}
+
+    result = await graph.ainvoke(
+        {"messages": [HumanMessage(content="hello", id="h1")]},
+        config,
+        durability="exit",
+    )
+    assert [m.content for m in result["messages"]] == ["hello", "reply"]
+
+    state = await graph.aget_state(config)
+    assert [m.content for m in state.values["messages"]] == ["hello", "reply"]
+
+
@NEEDS_CONTEXTVARS
 async def test_interrupt_subgraph(async_checkpointer: BaseCheckpointSaver) -> None:
    class State(TypedDict):
@@ -9709,3 +9765,126 @@ async def test_fork_does_not_apply_pending_writes(

    # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
    assert result == {"value": 121}
+
+
+async def test_graph_error_handler_async_runtime_info() -> None:
+    class State(TypedDict):
+        foo: str
+
+    attempts = 0
+    captured: dict[str, object] = {}
+
+    async def always_failing_node(state: State) -> State:
+        nonlocal attempts
+        attempts += 1
+        raise ValueError("Always fails async")
+
+    async def err_handler_node(state: State, error: NodeError) -> State:
+        captured["from_node_name"] = error.node
+        captured["from_node_error"] = error.error
+        return {"foo": "handled_async"}
+
+    graph = (
+        StateGraph(State)
+        .add_node(
+            "always_failing",
+            always_failing_node,
+            retry_policy=RetryPolicy(
+                max_attempts=2,
+                initial_interval=0.01,
+                jitter=False,
+                retry_on=ValueError,
+            ),
+            error_handler=err_handler_node,
+        )
+        .add_edge(START, "always_failing")
+        .compile()
+    )
+
+    with patch("asyncio.sleep"):
+        result = await graph.ainvoke({"foo": ""})
+
+    assert attempts == 2
+    assert result["foo"] == "handled_async"
+    assert captured["from_node_name"] == "always_failing"
+    assert isinstance(captured["from_node_error"], BaseException)
+
+
+@NEEDS_CONTEXTVARS
+async def test_graph_error_handler_does_not_swallow_interrupt_concurrent() -> None:
+    """When a graph error handler is configured and a node calls interrupt()
+    concurrently with other nodes, the interrupt must still be raised — not
+    silently swallowed."""
+
+    class State(TypedDict):
+        foo: str
+
+    async def node_a(state: State) -> State:
+        val = interrupt("need human input")
+        return {"foo": f"a_{val}"}
+
+    async def node_b(state: State) -> State:
+        return {}
+
+    async def err_handler(state: State) -> State:
+        return {"foo": "handled"}
+
+    checkpointer = InMemorySaver()
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a, error_handler=err_handler)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge(START, "node_b")
+        .compile(checkpointer=checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "test-interrupt-concurrent-async"}}
+
+    await graph.ainvoke({"foo": ""}, config)
+
+    state = await graph.aget_state(config)
+    assert len(state.tasks) > 0
+
+    interrupts = [t for t in state.tasks if hasattr(t, "interrupts") and t.interrupts]
+    assert len(interrupts) > 0, (
+        "GraphInterrupt was swallowed — interrupt() in node_a "
+        "should have paused execution"
+    )
+
+
+async def test_node_error_handler_handles_subgraph_internal_failure_async() -> None:
+    class SubState(TypedDict):
+        foo: str
+
+    class ParentState(TypedDict):
+        foo: str
+
+    captured: dict[str, object] = {}
+
+    async def sub_fail_node(state: SubState) -> SubState:
+        raise ValueError("async subgraph boom")
+
+    async def parent_handler(state: ParentState, error: NodeError) -> ParentState:
+        captured["from_node_name"] = error.node
+        captured["from_node_error"] = error.error
+        return {"foo": "handled_async_subgraph"}
+
+    subgraph = (
+        StateGraph(SubState)
+        .add_node("sub_fail_node", sub_fail_node)
+        .add_edge(START, "sub_fail_node")
+        .compile()
+    )
+
+    parent_graph = (
+        StateGraph(ParentState)
+        .add_node("subgraph_node", subgraph, error_handler=parent_handler)
+        .add_edge(START, "subgraph_node")
+        .compile()
+    )
+
+    result = await parent_graph.ainvoke({"foo": ""})
+    assert result["foo"] == "handled_async_subgraph"
+    assert captured["from_node_name"] == "subgraph_node"
+    assert isinstance(captured["from_node_error"], BaseException)
@@ -1,3 +1,6 @@
+import asyncio
+import threading
+import time
 from dataclasses import dataclass
 from typing import Any

@@ -6,8 +9,15 @@ from langgraph.checkpoint.memory import MemorySaver
 from pydantic import BaseModel, ValidationError
 from typing_extensions import TypedDict

+from langgraph.errors import GraphDrained
 from langgraph.graph import END, START, StateGraph
-from langgraph.runtime import ExecutionInfo, Runtime, ServerInfo, get_runtime
+from langgraph.runtime import (
+    ExecutionInfo,
+    RunControl,
+    Runtime,
+    ServerInfo,
+    get_runtime,
+)


 def test_injected_runtime() -> None:
@@ -79,6 +89,183 @@ def test_merge_runtime() -> None:
    assert runtime1.merge(runtime3).context.api_key == "abc"  # type: ignore


+def test_merge_runtime_preserves_run_control() -> None:
+    control = RunControl()
+    runtime1 = Runtime(control=control)
+    runtime2 = Runtime(context=None)
+
+    assert runtime1.merge(runtime2).control is control
+
+
+def test_run_control_request_drain_stops_future_steps() -> None:
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    control = RunControl()
+
+    def first_node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"first": "done"}
+
+    def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    with pytest.raises(GraphDrained, match="shutdown"):
+        graph.compile().invoke({}, control=control)
+
+
+@pytest.mark.anyio
+async def test_run_control_request_drain_stops_future_steps_async() -> None:
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    control = RunControl()
+
+    async def first_node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"first": "done"}
+
+    async def second_node(state: State) -> dict[str, str]:
+        return {"second": "should-not-run"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    with pytest.raises(GraphDrained, match="shutdown"):
+        await graph.compile().ainvoke({}, control=control)
+
+
+def test_drain_requested_in_terminal_step_finishes_normally() -> None:
+    class State(TypedDict, total=False):
+        value: str
+
+    control = RunControl()
+
+    def node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"value": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.add_edge(START, "node")
+    graph.add_edge("node", END)
+
+    assert graph.compile().invoke({}, control=control) == {"value": "done"}
+    assert control.drain_requested
+
+
+def test_drain_with_exit_durability_persists_resume_checkpoint() -> None:
+    class State(TypedDict, total=False):
+        first: str
+        second: str
+
+    control = RunControl()
+
+    def first_node(state: State) -> dict[str, str]:
+        control.request_drain("sigterm")
+        return {"first": "done"}
+
+    def second_node(state: State) -> dict[str, str]:
+        return {"second": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("first", first_node)
+    graph.add_node("second", second_node)
+    graph.add_edge(START, "first")
+    graph.add_edge("first", "second")
+    graph.add_edge("second", END)
+
+    compiled = graph.compile(checkpointer=MemorySaver())
+    config = {"configurable": {"thread_id": "drain-exit"}}
+
+    with pytest.raises(GraphDrained, match="sigterm"):
+        compiled.invoke({}, config, durability="exit", control=control)
+
+    assert compiled.invoke(None, config, durability="exit") == {
+        "first": "done",
+        "second": "done",
+    }
+
+
+def test_drain_from_subgraph_can_resume_parent() -> None:
+    class State(TypedDict, total=False):
+        child_first: str
+        child_second: str
+        parent_second: str
+
+    control = RunControl()
+
+    def child_first(state: State) -> dict[str, str]:
+        control.request_drain("sigterm")
+        return {"child_first": "done"}
+
+    def child_second(state: State) -> dict[str, str]:
+        return {"child_second": "done"}
+
+    child_builder = StateGraph(State)
+    child_builder.add_node("child_first", child_first)
+    child_builder.add_node("child_second", child_second)
+    child_builder.add_edge(START, "child_first")
+    child_builder.add_edge("child_first", "child_second")
+    child_builder.add_edge("child_second", END)
+    child_graph = child_builder.compile(checkpointer=True)
+
+    def parent_second(state: State) -> dict[str, str]:
+        return {"parent_second": "done"}
+
+    parent_builder = StateGraph(State)
+    parent_builder.add_node("child", child_graph)
+    parent_builder.add_node("parent_second", parent_second)
+    parent_builder.add_edge(START, "child")
+    parent_builder.add_edge("child", "parent_second")
+    parent_builder.add_edge("parent_second", END)
+
+    compiled = parent_builder.compile(checkpointer=MemorySaver())
+    config = {"configurable": {"thread_id": "drain-subgraph"}}
+
+    with pytest.raises(GraphDrained, match="sigterm"):
+        compiled.invoke({}, config, control=control)
+
+    assert compiled.invoke(None, config) == {
+        "child_first": "done",
+        "child_second": "done",
+        "parent_second": "done",
+    }
+
+
+@pytest.mark.anyio
+async def test_drain_requested_in_terminal_step_finishes_normally_async() -> None:
+    class State(TypedDict, total=False):
+        value: str
+
+    control = RunControl()
+
+    async def node(state: State) -> dict[str, str]:
+        control.request_drain()
+        return {"value": "done"}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.add_edge(START, "node")
+    graph.add_edge("node", END)
+
+    assert await graph.compile().ainvoke({}, control=control) == {"value": "done"}
+    assert control.drain_requested
+
+
 def test_runtime_propogated_to_subgraph() -> None:
    @dataclass
    class Context:
@@ -392,6 +579,334 @@ def test_context_coercion_pydantic_validation_errors() -> None:
        )


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


@@ -0,0 +1,707 @@
+"""Tests for CustomTransformer, UpdatesTransformer, CheckpointsTransformer, DebugTransformer, TasksTransformer.
+
+These transformers capture raw protocol events for their respective stream
+modes and expose them as native projections on the run stream (run.custom,
+run.updates, run.checkpoints, run.debug, run.tasks). Tests dispatch synthetic
+protocol events through a StreamMux to isolate transformer logic; the final
+group exercises real graphs through stream_events(version="v3").
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from typing import Annotated, Any
+
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    CheckpointsTransformer,
+    CustomTransformer,
+    DebugTransformer,
+    LifecycleTransformer,
+    TasksTransformer,
+    UpdatesTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+def _custom_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "custom",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _checkpoints_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "checkpoints",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _debug_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "debug",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _tasks_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _updates_event(namespace: list[str], data: Any) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "updates",
+        "params": {"namespace": namespace, "timestamp": TS, "data": data},
+    }
+
+
+def _arm(mux: StreamMux, transformer: Any) -> None:
+    """Force projection logs to accept pushes (skip lazy-subscribe gate)."""
+    mux._events._subscribed = True
+    transformer._log._subscribed = True
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _drain(transformer: Any) -> list[Any]:
+    return _unstamped(transformer._log._items)
+
+
+# ---------------------------------------------------------------------------
+# CustomTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_custom_captures_root_scope_events() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], {"status": "processing"}))
+    mux.push(_custom_event([], {"status": "done"}))
+
+    items = _drain(t)
+    assert items == [{"status": "processing"}, {"status": "done"}]
+
+
+def test_custom_ignores_subgraph_scope_events() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event(["subgraph:abc"], {"from": "child"}))
+
+    assert _drain(t) == []
+
+
+def test_custom_scoped_transformer_captures_own_scope() -> None:
+    t = CustomTransformer(scope=("agent:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], {"from": "root"}))
+    mux.push(_custom_event(["agent:abc"], {"from": "self"}))
+    mux.push(_custom_event(["agent:abc", "deep:def"], {"from": "child"}))
+
+    items = _drain(t)
+    assert items == [{"from": "self"}]
+
+
+def test_custom_preserves_any_payload_type() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], "string_payload"))
+    mux.push(_custom_event([], 42))
+    mux.push(_custom_event([], [1, 2, 3]))
+
+    assert _drain(t) == ["string_payload", 42, [1, 2, 3]]
+
+
+def test_custom_does_not_suppress_from_main_log() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_custom_event([], "data"))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "custom" in methods
+
+
+def test_custom_ignores_other_methods() -> None:
+    t = CustomTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(
+        {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": [], "timestamp": TS, "data": {}},
+        }
+    )
+    assert _drain(t) == []
+
+
+def test_custom_required_stream_modes() -> None:
+    assert CustomTransformer.required_stream_modes == ("custom",)
+
+
+def test_custom_is_native() -> None:
+    assert getattr(CustomTransformer, "_native", False) is True
+
+
+def test_custom_init_returns_correct_key() -> None:
+    t = CustomTransformer()
+    projection = t.init()
+    assert "custom" in projection
+    assert isinstance(projection["custom"], StreamChannel)
+
+
+# ---------------------------------------------------------------------------
+# CheckpointsTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_checkpoints_captures_root_scope_events() -> None:
+    t = CheckpointsTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    checkpoint_data = {"values": {"x": 1}, "next": ["node_b"]}
+    mux.push(_checkpoints_event([], checkpoint_data))
+
+    items = _drain(t)
+    assert items == [checkpoint_data]
+
+
+def test_checkpoints_ignores_subgraph_events() -> None:
+    t = CheckpointsTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_checkpoints_event(["child:abc"], {"values": {"x": 1}}))
+
+    assert _drain(t) == []
+
+
+def test_checkpoints_scoped_transformer() -> None:
+    t = CheckpointsTransformer(scope=("sub:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_checkpoints_event([], {"from": "root"}))
+    mux.push(_checkpoints_event(["sub:abc"], {"from": "self"}))
+
+    assert _drain(t) == [{"from": "self"}]
+
+
+def test_checkpoints_does_not_suppress_from_main_log() -> None:
+    t = CheckpointsTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_checkpoints_event([], {"values": {}}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "checkpoints" in methods
+
+
+def test_checkpoints_required_stream_modes() -> None:
+    assert CheckpointsTransformer.required_stream_modes == ("checkpoints",)
+
+
+def test_checkpoints_is_native() -> None:
+    assert getattr(CheckpointsTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# DebugTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_debug_captures_root_scope_events() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    debug_data = {
+        "step": 0,
+        "type": "checkpoint",
+        "timestamp": "2026-01-01T00:00:00Z",
+        "payload": {"values": {"x": 1}},
+    }
+    mux.push(_debug_event([], debug_data))
+
+    items = _drain(t)
+    assert items == [debug_data]
+
+
+def test_debug_ignores_subgraph_events() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_debug_event(["child:abc"], {"step": 0, "type": "task"}))
+
+    assert _drain(t) == []
+
+
+def test_debug_captures_multiple_event_types() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_debug_event([], {"step": 0, "type": "checkpoint", "payload": {}}))
+    mux.push(_debug_event([], {"step": 1, "type": "task", "payload": {}}))
+    mux.push(_debug_event([], {"step": 1, "type": "task_result", "payload": {}}))
+
+    items = _drain(t)
+    assert len(items) == 3
+    assert [d["type"] for d in items] == ["checkpoint", "task", "task_result"]
+
+
+def test_debug_does_not_suppress_from_main_log() -> None:
+    t = DebugTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_debug_event([], {"step": 0}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "debug" in methods
+
+
+def test_debug_required_stream_modes() -> None:
+    assert DebugTransformer.required_stream_modes == ("debug",)
+
+
+def test_debug_is_native() -> None:
+    assert getattr(DebugTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# TasksTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_tasks_captures_root_scope_events() -> None:
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    task_start = {"id": "t1", "name": "my_node", "input": None, "triggers": []}
+    mux.push(_tasks_event([], task_start))
+
+    items = _drain(t)
+    assert items == [task_start]
+
+
+def test_tasks_captures_start_and_result() -> None:
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    start = {"id": "t1", "name": "a", "input": None, "triggers": []}
+    result = {"id": "t1", "name": "a", "result": {"output": 42}, "error": None}
+    mux.push(_tasks_event([], start))
+    mux.push(_tasks_event([], result))
+
+    items = _drain(t)
+    assert items == [start, result]
+
+
+def test_tasks_ignores_subgraph_events() -> None:
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_tasks_event(["child:abc"], {"id": "t1", "name": "x"}))
+
+    assert _drain(t) == []
+
+
+def test_tasks_scoped_transformer() -> None:
+    t = TasksTransformer(scope=("agent:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_tasks_event([], {"id": "t1"}))
+    mux.push(_tasks_event(["agent:abc"], {"id": "t2"}))
+    mux.push(_tasks_event(["agent:abc", "deep:def"], {"id": "t3"}))
+
+    assert _drain(t) == [{"id": "t2"}]
+
+
+def test_tasks_does_not_suppress_from_main_log() -> None:
+    """TasksTransformer returns True — it doesn't suppress tasks events.
+
+    (LifecycleTransformer suppresses them, but that's independent.)
+    """
+    t = TasksTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_tasks_event([], {"id": "t1"}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "tasks" in methods
+
+
+def test_tasks_required_stream_modes() -> None:
+    assert TasksTransformer.required_stream_modes == ("tasks",)
+
+
+def test_tasks_is_native() -> None:
+    assert getattr(TasksTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# UpdatesTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_updates_captures_root_scope_events() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    update = {"my_node": {"value": "hello!"}}
+    mux.push(_updates_event([], update))
+
+    items = _drain(t)
+    assert items == [update]
+
+
+def test_updates_captures_multiple_steps() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event([], {"node_a": {"x": 1}}))
+    mux.push(_updates_event([], {"node_b": {"x": 2}}))
+
+    items = _drain(t)
+    assert items == [{"node_a": {"x": 1}}, {"node_b": {"x": 2}}]
+
+
+def test_updates_ignores_subgraph_events() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event(["child:abc"], {"inner_node": {"v": 1}}))
+
+    assert _drain(t) == []
+
+
+def test_updates_scoped_transformer() -> None:
+    t = UpdatesTransformer(scope=("agent:abc",))
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event([], {"from": "root"}))
+    mux.push(_updates_event(["agent:abc"], {"from": "self"}))
+
+    assert _drain(t) == [{"from": "self"}]
+
+
+def test_updates_does_not_suppress_from_main_log() -> None:
+    t = UpdatesTransformer()
+    mux = StreamMux([t], is_async=False)
+    _arm(mux, t)
+
+    mux.push(_updates_event([], {"n": {}}))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "updates" in methods
+
+
+def test_updates_required_stream_modes() -> None:
+    assert UpdatesTransformer.required_stream_modes == ("updates",)
+
+
+def test_updates_is_native() -> None:
+    assert getattr(UpdatesTransformer, "_native", False) is True
+
+
+# ---------------------------------------------------------------------------
+# Cross-transformer: unrelated events pass through
+# ---------------------------------------------------------------------------
+
+
+def test_unrelated_events_ignored_by_all() -> None:
+    """Non-matching method events don't land in any transformer's log."""
+    transformers = [
+        CustomTransformer(),
+        UpdatesTransformer(),
+        CheckpointsTransformer(),
+        DebugTransformer(),
+        TasksTransformer(),
+    ]
+    mux = StreamMux(transformers, is_async=False)
+    mux._events._subscribed = True
+    for t in transformers:
+        t._log._subscribed = True
+
+    mux.push(
+        {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
+        }
+    )
+
+    for t in transformers:
+        assert _unstamped(t._log._items) == []
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: real graphs through stream_events(version="v3")
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _my_node(state: _State) -> dict[str, Any]:
+    from langgraph.config import get_stream_writer
+
+    writer = get_stream_writer()
+    writer({"status": "working", "node": "my_node"})
+    return {"value": state["value"] + "!", "items": ["done"]}
+
+
+def _make_simple_graph() -> Any:
+    builder = StateGraph(_State, input_schema=_State)
+    builder.add_node("my_node", _my_node)
+    builder.add_edge(START, "my_node")
+    builder.add_edge("my_node", END)
+    return builder.compile()
+
+
+def test_stream_events_v3_custom_projection_opt_in() -> None:
+    """run.custom surfaces get_stream_writer() payloads when opted in."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "hello", "items": []}, version="v3", transformers=[CustomTransformer]
+    )
+
+    custom_events = list(run.custom)
+    assert len(custom_events) >= 1
+    assert any(e.get("status") == "working" for e in custom_events)
+
+
+def test_stream_events_v3_custom_and_values_coexist() -> None:
+    """Both run.custom and run.values work in the same run."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "hello", "items": []}, version="v3", transformers=[CustomTransformer]
+    )
+
+    custom_events = list(run.custom)
+    assert run.output is not None
+    assert run.output["value"] == "hello!"
+    assert len(custom_events) >= 1
+
+
+def test_stream_events_v3_tasks_projection_opt_in() -> None:
+    """run.tasks surfaces raw task events when opted in via transformers=."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []}, transformers=[TasksTransformer], version="v3"
+    )
+
+    tasks_events = list(run.tasks)
+    assert len(tasks_events) >= 1
+    names = [t.get("name") for t in tasks_events if "name" in t]
+    assert "my_node" in names
+
+
+def test_stream_events_v3_debug_projection_opt_in() -> None:
+    """run.debug surfaces debug events when opted in via transformers=."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []}, transformers=[DebugTransformer], version="v3"
+    )
+
+    debug_events = list(run.debug)
+    assert len(debug_events) >= 1
+    types = {d.get("type") for d in debug_events}
+    assert types & {"checkpoint", "task", "task_result"}
+
+
+def test_stream_events_v3_updates_projection_opt_in() -> None:
+    """run.updates surfaces node output dicts when opted in via transformers=."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []}, version="v3", transformers=[UpdatesTransformer]
+    )
+
+    updates = list(run.updates)
+    assert len(updates) >= 1
+    node_names = {k for u in updates for k in u if k != "__interrupt__"}
+    assert "my_node" in node_names
+
+
+def test_stream_events_v3_all_transformers_interleaved() -> None:
+    """All five transformers registered together, consumed via interleave."""
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        transformers=[
+            CustomTransformer,
+            UpdatesTransformer,
+            CheckpointsTransformer,
+            DebugTransformer,
+            TasksTransformer,
+        ],
+    )
+
+    collected: dict[str, list[Any]] = {
+        "custom": [],
+        "updates": [],
+        "debug": [],
+        "tasks": [],
+    }
+    for name, item in run.interleave("custom", "updates", "debug", "tasks"):
+        collected[name].append(item)
+
+    assert len(collected["custom"]) >= 1
+    assert len(collected["updates"]) >= 1
+    assert len(collected["tasks"]) >= 1
+    assert len(collected["debug"]) >= 1
+    types = {d.get("type") for d in collected["debug"]}
+    assert types & {"checkpoint", "task", "task_result"}
+    node_names = {k for u in collected["updates"] for k in u if k != "__interrupt__"}
+    assert "my_node" in node_names
+
+    assert run.output is not None
+    assert run.output["value"] == "x!"
+
+
+def test_stream_events_v3_all_transformers_with_checkpointer() -> None:
+    """All transformers with a checkpointer — run.checkpoints populated."""
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    builder = StateGraph(_State, input_schema=_State)
+    builder.add_node("my_node", _my_node)
+    builder.add_edge(START, "my_node")
+    builder.add_edge("my_node", END)
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        config={"configurable": {"thread_id": "test-all"}},
+        transformers=[
+            CustomTransformer,
+            UpdatesTransformer,
+            CheckpointsTransformer,
+            DebugTransformer,
+            TasksTransformer,
+        ],
+    )
+
+    collected: dict[str, list[Any]] = {
+        "custom": [],
+        "updates": [],
+        "checkpoints": [],
+        "debug": [],
+        "tasks": [],
+    }
+    for name, item in run.interleave(
+        "custom", "updates", "checkpoints", "debug", "tasks"
+    ):
+        collected[name].append(item)
+
+    assert len(collected["checkpoints"]) >= 1
+    assert len(collected["custom"]) >= 1
+
+
+def test_stream_events_v3_checkpoints_projection_opt_in() -> None:
+    """run.checkpoints surfaces checkpoint data when opted in with a checkpointer."""
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    builder = StateGraph(_State, input_schema=_State)
+    builder.add_node("my_node", _my_node)
+    builder.add_edge(START, "my_node")
+    builder.add_edge("my_node", END)
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        config={"configurable": {"thread_id": "test-ckpt-standalone"}},
+        transformers=[CheckpointsTransformer],
+    )
+
+    checkpoints = list(run.checkpoints)
+    assert len(checkpoints) >= 1
+
+
+# ---------------------------------------------------------------------------
+# TasksTransformer + LifecycleTransformer co-registration
+# ---------------------------------------------------------------------------
+
+
+def test_tasks_and_lifecycle_coregistration() -> None:
+    """When both are in the same StreamMux, LifecycleTransformer suppresses
+    tasks events from the main log (returns False) while TasksTransformer
+    still captures them into its own log.
+    """
+    lifecycle = LifecycleTransformer()
+    tasks = TasksTransformer()
+    mux = StreamMux([lifecycle, tasks], is_async=False)
+    mux._events._subscribed = True
+    tasks._log._subscribed = True
+    lifecycle._channel._subscribed = True
+
+    task_data = {"id": "t1", "name": "my_node", "input": None, "triggers": []}
+    mux.push(_tasks_event([], task_data))
+
+    assert _drain(tasks) == [task_data]
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "tasks" not in methods
+
+
+def test_tasks_and_lifecycle_coregistration_e2e() -> None:
+    """E2e: TasksTransformer captures task events even when LifecycleTransformer
+    is present and suppressing them from the main log.
+    """
+    graph = _make_simple_graph()
+    run = graph.stream_events(
+        {"value": "x", "items": []},
+        version="v3",
+        transformers=[TasksTransformer],
+    )
+
+    tasks_events = list(run.tasks)
+    assert len(tasks_events) >= 1
+    names = [t.get("name") for t in tasks_events if "name" in t]
+    assert "my_node" in names
@@ -19,9 +19,11 @@ from typing_extensions import TypedDict, assert_type

 from langgraph._internal._constants import INTERRUPT
 from langgraph.constants import END, START
+from langgraph.errors import GraphDrained
 from langgraph.func import entrypoint
 from langgraph.graph import StateGraph
 from langgraph.graph.message import MessagesState
+from langgraph.runtime import RunControl
 from langgraph.types import (
    CheckpointPayload,
    CheckpointStreamPart,
@@ -229,6 +231,32 @@ class TestV2Stream:
        for c in chunks:
            _assert_stream_part_shape(c)

+    def test_stream_events_v3_accepts_control_for_drain(self) -> None:
+        class DrainState(TypedDict, total=False):
+            value: str
+            skipped: str
+
+        control = RunControl()
+
+        def first_node(state: DrainState) -> dict[str, str]:
+            control.request_drain("sigterm")
+            return {"value": "done"}
+
+        def second_node(state: DrainState) -> dict[str, str]:
+            return {"skipped": "nope"}
+
+        builder = StateGraph(DrainState)
+        builder.add_node("first", first_node)
+        builder.add_node("second", second_node)
+        builder.add_edge(START, "first")
+        builder.add_edge("first", "second")
+        builder.add_edge("second", END)
+        graph = builder.compile()
+
+        run = graph.stream_events({}, control=control, version="v3")
+        with pytest.raises(GraphDrained, match="sigterm"):
+            list(run.values)
+
    def test_subgraphs_ns(self) -> None:
        outer = _make_subgraph()
        chunks = list(
@@ -1096,7 +1124,7 @@ class TestV2ValidationErrors:

    _INVALID_INPUT: dict[str, Any] = {"value": [1, 2, 3], "items": []}

-    def test_stream_v2_pydantic_validation_error(self) -> None:
+    def test_stream_events_v3_pydantic_validation_error(self) -> None:
        """Invalid input to stream with v2 + pydantic state raises ValidationError."""
        graph = _make_pydantic_graph()
        with pytest.raises(ValidationError):
@@ -0,0 +1,808 @@
+"""End-to-end tests exercising all stream_events(version="v3") projections together.
+
+Each test builds a realistic graph (subgraphs, LLM calls, custom writers,
+interrupts) and verifies that every projection — values, messages, lifecycle,
+subgraphs, raw events, output, interleave — produces correct, consistent
+results through a single stream_events(version="v3") / astream_events(version="v3") run.
+"""
+
+from __future__ import annotations
+
+import operator
+import sys
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessage
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import MessagesState, StateGraph
+from langgraph.stream import StreamChannel, StreamTransformer
+from langgraph.stream._types import ProtocolEvent
+from langgraph.types import StreamWriter, interrupt
+
+NEEDS_CONTEXTVARS = pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="Python 3.11+ is required for async contextvars support",
+)
+
+
+# ---------------------------------------------------------------------------
+# State and graph builders
+# ---------------------------------------------------------------------------
+
+
+class AgentState(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _make_nested_graph():
+    """Build a two-level graph with pure state transforms.
+
+    Structure:
+        outer:
+            router_node  (state transform)
+            inner_graph  (compiled subgraph)
+
+        inner_graph:
+            process_node  (state transform)
+    """
+
+    def process_node(state: AgentState) -> dict[str, Any]:
+        return {"value": state["value"] + "_processed", "items": ["processed"]}
+
+    inner_builder: StateGraph = StateGraph(AgentState, input_schema=AgentState)
+    inner_builder.add_node("process_node", process_node)
+    inner_builder.add_edge(START, "process_node")
+    inner_builder.add_edge("process_node", END)
+    inner_graph = inner_builder.compile()
+
+    def router_node(state: AgentState) -> dict[str, Any]:
+        return {"value": state["value"] + "_routed", "items": ["routed"]}
+
+    outer_builder: StateGraph = StateGraph(AgentState, input_schema=AgentState)
+    outer_builder.add_node("router", router_node)
+    outer_builder.add_node("inner", inner_graph)
+    outer_builder.add_edge(START, "router")
+    outer_builder.add_edge("router", "inner")
+    outer_builder.add_edge("inner", END)
+    return outer_builder.compile()
+
+
+def _make_messages_graph():
+    """Flat graph with an LLM call for messages projection testing."""
+    model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+    def call_model(state: MessagesState) -> dict[str, Any]:
+        return {"messages": model.invoke(state["messages"])}
+
+    return (
+        StateGraph(MessagesState)
+        .add_node("call_model", call_model)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", END)
+        .compile()
+    )
+
+
+def _make_messages_subgraph():
+    """Outer graph with a MessagesState subgraph that returns an AIMessage.
+
+    Uses the whole-message fallback path (node returns AIMessage directly)
+    to exercise messages through a subgraph boundary.
+    """
+
+    def return_message(state: MessagesState) -> dict[str, Any]:
+        return {"messages": AIMessage(content="from subgraph", id="sub-msg-1")}
+
+    inner = (
+        StateGraph(MessagesState)
+        .add_node("return_message", return_message)
+        .add_edge(START, "return_message")
+        .add_edge("return_message", END)
+        .compile()
+    )
+
+    class OuterState(TypedDict):
+        messages: Annotated[list[Any], operator.add]
+        done: bool
+
+    def pre_node(state: OuterState) -> dict[str, Any]:
+        return {"done": False}
+
+    return (
+        StateGraph(OuterState)
+        .add_node("pre", pre_node)
+        .add_node("inner", inner)
+        .add_edge(START, "pre")
+        .add_edge("pre", "inner")
+        .add_edge("inner", END)
+        .compile()
+    )
+
+
+def _make_custom_writer_graph():
+    """Graph where a node emits custom stream events via StreamWriter."""
+
+    def writer_node(state: AgentState, *, writer: StreamWriter) -> dict[str, Any]:
+        writer({"step": "start", "detail": "beginning work"})
+        writer({"step": "middle", "detail": "processing"})
+        writer({"step": "end", "detail": "done"})
+        return {"value": state["value"] + "_custom", "items": ["custom"]}
+
+    builder = StateGraph(AgentState)
+    builder.add_node("writer_node", writer_node)
+    builder.add_edge(START, "writer_node")
+    builder.add_edge("writer_node", END)
+    return builder.compile()
+
+
+def _make_interrupt_graph():
+    """Graph that interrupts after the first node."""
+
+    def step_one(state: AgentState) -> dict[str, Any]:
+        return {"value": state["value"] + "_step1", "items": ["step1"]}
+
+    def step_two(state: AgentState) -> dict[str, Any]:
+        answer = interrupt("need approval")
+        return {"value": state["value"] + f"_{answer}", "items": ["step2"]}
+
+    builder = StateGraph(AgentState)
+    builder.add_node("step_one", step_one)
+    builder.add_node("step_two", step_two)
+    builder.add_edge(START, "step_one")
+    builder.add_edge("step_one", "step_two")
+    builder.add_edge("step_two", END)
+    return builder.compile(checkpointer=InMemorySaver())
+
+
+def _make_error_subgraph():
+    """Graph with a subgraph that raises."""
+
+    def failing_node(state: AgentState) -> dict[str, Any]:
+        raise ValueError("subgraph explosion")
+
+    inner_builder = StateGraph(AgentState)
+    inner_builder.add_node("fail", failing_node)
+    inner_builder.add_edge(START, "fail")
+    inner_builder.add_edge("fail", END)
+    inner = inner_builder.compile()
+
+    outer_builder = StateGraph(AgentState)
+    outer_builder.add_node("inner", inner)
+    outer_builder.add_edge(START, "inner")
+    outer_builder.add_edge("inner", END)
+    return outer_builder.compile()
+
+
+class _CustomPassthroughTransformer(StreamTransformer):
+    required_stream_modes = ("custom",)
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _CounterTransformer(StreamTransformer):
+    """Custom transformer that counts values events via a StreamChannel."""
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._channel: StreamChannel[int] = StreamChannel("counter")
+        self._count = 0
+
+    def init(self) -> dict[str, Any]:
+        return {"counter": self._channel}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] == "values":
+            self._count += 1
+            self._channel.push(self._count)
+        return True
+
+
+# ---------------------------------------------------------------------------
+# Sync end-to-end: all projections on nested graph
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2ESync:
+    def test_all_projections_nested_graph(self) -> None:
+        """Run a nested graph through stream_events(version="v3") and verify values + lifecycle."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        values_snapshots: list[dict[str, Any]] = []
+        lifecycle_events: list[dict[str, Any]] = []
+        for name, item in run.interleave("values", "lifecycle"):
+            if name == "values":
+                values_snapshots.append(item)
+            elif name == "lifecycle":
+                lifecycle_events.append(item)
+
+        assert len(values_snapshots) >= 1
+        final = values_snapshots[-1]
+        assert "routed" in final["items"]
+        assert "processed" in final["items"]
+        assert "_routed" in final["value"]
+        assert "_processed" in final["value"]
+
+        assert len(lifecycle_events) >= 2
+        started = [e for e in lifecycle_events if e["event"] == "started"]
+        completed = [e for e in lifecycle_events if e["event"] == "completed"]
+        assert len(started) >= 1
+        assert len(completed) >= 1
+
+    def test_subgraph_handles_with_drill_down(self) -> None:
+        """Subgraph handles yield and support values drill-down."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        handles = []
+        for handle in run.subgraphs:
+            child_values = list(handle.values)
+            handles.append(
+                {
+                    "path": handle.path,
+                    "graph_name": handle.graph_name,
+                    "values_count": len(child_values),
+                }
+            )
+
+        assert len(handles) >= 1
+        assert handles[0]["values_count"] >= 1
+
+        output = run.output
+        assert output is not None
+        assert "_routed" in output["value"]
+        assert "_processed" in output["value"]
+
+    def test_raw_events_have_monotonic_seq(self) -> None:
+        """Raw protocol events have monotonically increasing seq numbers."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+        events = list(run)
+        assert len(events) > 0
+
+        seqs = [e["seq"] for e in events]
+        for i in range(1, len(seqs)):
+            assert seqs[i] > seqs[i - 1], f"seq not monotonic at {i}: {seqs}"
+
+        for event in events:
+            assert event["type"] == "event"
+            assert "method" in event
+            assert isinstance(event["params"]["timestamp"], int)
+
+    def test_output_matches_final_values_snapshot(self) -> None:
+        """output property returns the same state as the last values snapshot."""
+        run1 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        snapshots = list(run1.values)
+        final_via_values = snapshots[-1]
+
+        run2 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        final_via_output = run2.output
+
+        assert final_via_values == final_via_output
+
+    def test_context_manager_and_abort(self) -> None:
+        """Context manager calls abort, marking the stream exhausted."""
+        graph = _make_nested_graph()
+        with graph.stream_events({"value": "x", "items": []}, version="v3") as run:
+            first_val = next(iter(run.values))
+            assert isinstance(first_val, dict)
+        assert run._exhausted is True
+
+    def test_extensions_has_all_native_keys(self) -> None:
+        """Extensions dict exposes all native projection keys."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+        _ = run.output
+
+        assert "values" in run.extensions
+        assert "messages" in run.extensions
+        assert "lifecycle" in run.extensions
+        assert "subgraphs" in run.extensions
+        assert run.values is run.extensions["values"]
+        assert run.messages is run.extensions["messages"]
+        assert run.lifecycle is run.extensions["lifecycle"]
+        assert run.subgraphs is run.extensions["subgraphs"]
+
+
+# ---------------------------------------------------------------------------
+# Sync: messages projection
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2EMessages:
+    def test_messages_projection_from_invoke(self) -> None:
+        """Messages projection captures LLM calls via model.invoke() auto-routing."""
+        graph = _make_messages_graph()
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        streams = list(run.messages)
+
+        assert len(streams) >= 1
+        for stream in streams:
+            assert isinstance(stream, ChatModelStream)
+        assert streams[0].output.text == "hello world"
+
+    def test_messages_text_deltas(self) -> None:
+        """Text deltas from the messages projection concatenate correctly."""
+        model = GenericFakeChatModel(messages=iter(["streamed answer"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "go"}, version="v3")
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "streamed answer"
+
+    def test_messages_from_whole_ai_message(self) -> None:
+        """Node returning AIMessage directly produces a complete stream."""
+
+        def return_msg(state: MessagesState) -> dict[str, Any]:
+            return {"messages": AIMessage(content="hardcoded", id="msg-1")}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("return_msg", return_msg)
+            .add_edge(START, "return_msg")
+            .add_edge("return_msg", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert stream.output.text == "hardcoded"
+        assert stream.message_id == "msg-1"
+
+    def test_root_messages_only_shows_root_scope(self) -> None:
+        """Root messages projection doesn't surface subgraph-scoped messages."""
+        graph = _make_messages_subgraph()
+        run = graph.stream_events({"messages": ["hi"], "done": False}, version="v3")
+        root_streams = list(run.messages)
+        # The message is emitted inside the subgraph, so the root
+        # messages projection (scoped to root namespace) doesn't see it.
+        assert root_streams == []
+
+    def test_subgraph_handle_messages_drill_down(self) -> None:
+        """Drilling into subgraph handle's messages surfaces subgraph messages."""
+        graph = _make_messages_subgraph()
+        run = graph.stream_events({"messages": ["hi"], "done": False}, version="v3")
+
+        found_messages = False
+        for handle in run.subgraphs:
+            child_messages = list(handle.messages)
+            if child_messages:
+                found_messages = True
+                assert isinstance(child_messages[0], ChatModelStream)
+                assert child_messages[0].output.text == "from subgraph"
+        assert found_messages
+
+
+# ---------------------------------------------------------------------------
+# Sync: custom stream writer + custom transformer
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2ECustom:
+    def test_custom_events_with_passthrough_transformer(self) -> None:
+        """Custom StreamWriter events appear on the main log when a
+        transformer declares the custom mode."""
+        graph = _make_custom_writer_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CustomPassthroughTransformer],
+        )
+        events = list(run)
+        custom = [e for e in events if e["method"] == "custom"]
+        assert len(custom) == 3
+        steps = [e["params"]["data"]["step"] for e in custom]
+        assert steps == ["start", "middle", "end"]
+
+    def test_custom_events_suppressed_without_transformer(self) -> None:
+        """Without a custom-mode transformer, custom events don't flow."""
+        graph = _make_custom_writer_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+        events = list(run)
+        custom = [e for e in events if e["method"] == "custom"]
+        assert custom == []
+
+    def test_custom_transformer_with_stream_channel(self) -> None:
+        """A custom transformer with a StreamChannel produces extension data."""
+        graph = _make_nested_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+
+        assert "counter" in run.extensions
+        counter_iter = iter(run.extensions["counter"])
+        _ = run.output
+        counts = list(counter_iter)
+        assert len(counts) >= 1
+        assert all(isinstance(c, int) for c in counts)
+        assert counts == sorted(counts)
+
+    def test_custom_channel_events_on_main_log(self) -> None:
+        """StreamChannel auto-forward injects custom:<name> events into the main log."""
+        graph = _make_nested_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+        events = list(run)
+        counter_events = [e for e in events if e["method"] == "custom:counter"]
+        assert len(counter_events) >= 1
+        assert all(isinstance(e["params"]["data"], int) for e in counter_events)
+
+
+# ---------------------------------------------------------------------------
+# Sync: interrupt handling
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2EInterrupt:
+    def test_interrupt_sets_flags_and_surfaces_interrupts(self) -> None:
+        """Interrupted run has correct flags and interrupt payloads."""
+        graph = _make_interrupt_graph()
+        config: dict[str, Any] = {"configurable": {"thread_id": "int-1"}}
+        run = graph.stream_events({"value": "x", "items": []}, config, version="v3")
+
+        output = run.output
+        assert output is not None
+        assert run.interrupted is True
+        assert len(run.interrupts) > 0
+        assert output["items"] == ["step1"]
+        assert "_step1" in output["value"]
+
+    def test_interrupt_values_snapshot_has_partial_state(self) -> None:
+        """Values snapshots captured before the interrupt reflect partial state."""
+        graph = _make_interrupt_graph()
+        config: dict[str, Any] = {"configurable": {"thread_id": "int-2"}}
+        run = graph.stream_events({"value": "x", "items": []}, config, version="v3")
+
+        snapshots = list(run.values)
+        assert len(snapshots) >= 1
+        last = snapshots[-1]
+        assert "step1" in last["items"]
+
+
+# ---------------------------------------------------------------------------
+# Sync: error propagation
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2EErrors:
+    def test_subgraph_error_propagates_through_output(self) -> None:
+        """Error in a subgraph propagates through output."""
+        graph = _make_error_subgraph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        with pytest.raises(ValueError, match="subgraph explosion"):
+            _ = run.output
+
+    def test_subgraph_error_propagates_through_raw_events(self) -> None:
+        graph = _make_error_subgraph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        with pytest.raises(ValueError, match="subgraph explosion"):
+            list(run)
+
+    def test_error_subgraph_handle_status(self) -> None:
+        """Subgraph handle surfaces the error status."""
+        graph = _make_error_subgraph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        handle = next(iter(run.subgraphs))
+        with pytest.raises(RuntimeError, match="subgraph explosion"):
+            _ = handle.output
+        assert handle.status == "failed"
+        assert handle.error == "subgraph explosion"
+
+
+# ---------------------------------------------------------------------------
+# Async end-to-end
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+@NEEDS_CONTEXTVARS
+class TestStreamV2E2EAsync:
+    async def test_all_projections_async(self) -> None:
+        """Async run exercises values projection."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+
+        values_snapshots = [s async for s in run.values]
+        assert len(values_snapshots) >= 1
+        final = values_snapshots[-1]
+        assert "_routed" in final["value"]
+        assert "_processed" in final["value"]
+
+    async def test_async_output(self) -> None:
+        """Async output returns the final state."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        output = await run.output()
+        assert output is not None
+        assert output["value"] == "x_routed_processed"
+        assert "routed" in output["items"]
+        assert "processed" in output["items"]
+
+    async def test_async_raw_events(self) -> None:
+        """Async raw event iteration yields well-formed ProtocolEvents."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        events = [e async for e in run]
+        assert len(events) > 0
+        seqs = [e["seq"] for e in events]
+        for i in range(1, len(seqs)):
+            assert seqs[i] > seqs[i - 1]
+
+    async def test_async_messages_projection(self) -> None:
+        """Async messages projection captures LLM streams."""
+        model = GenericFakeChatModel(messages=iter(["async answer"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": await model.ainvoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+        streams = [s async for s in run.messages]
+        assert len(streams) >= 1
+        for s in streams:
+            assert isinstance(s, AsyncChatModelStream)
+        assert (await streams[0].output).text == "async answer"
+
+    async def test_async_interrupt(self) -> None:
+        """Async interrupted run has correct flags."""
+        graph = _make_interrupt_graph()
+        config: dict[str, Any] = {"configurable": {"thread_id": "async-int-1"}}
+        run = await graph.astream_events(
+            {"value": "x", "items": []}, config, version="v3"
+        )
+
+        output = await run.output()
+        assert output is not None
+        assert await run.interrupted() is True
+        assert len(await run.interrupts()) > 0
+
+    async def test_async_error_propagation(self) -> None:
+        """Async error from subgraph propagates through output."""
+        graph = _make_error_subgraph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        with pytest.raises(ValueError, match="subgraph explosion"):
+            await run.output()
+
+    async def test_async_context_manager(self) -> None:
+        """Async context manager calls abort on exit."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        async with run:
+            _ = await anext(aiter(run.values))
+        assert run._exhausted is True
+
+    async def test_async_extensions_present(self) -> None:
+        """Async run has all native extensions."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events({"value": "x", "items": []}, version="v3")
+        _ = await run.output()
+        assert "values" in run.extensions
+        assert "messages" in run.extensions
+        assert "lifecycle" in run.extensions
+        assert "subgraphs" in run.extensions
+
+    async def test_async_custom_transformer(self) -> None:
+        """Async custom transformer with StreamChannel works."""
+        graph = _make_nested_graph()
+        run = await graph.astream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+        assert "counter" in run.extensions
+        counter_cursor = aiter(run.extensions["counter"])
+        _ = await run.output()
+        counts = [c async for c in counter_cursor]
+        assert len(counts) >= 1
+        assert counts == sorted(counts)
+
+
+# ---------------------------------------------------------------------------
+# Sync: combined projections stress test
+# ---------------------------------------------------------------------------
+
+
+class TestStreamV2E2ECombined:
+    def test_interleave_all_native_projections(self) -> None:
+        """Interleave values + messages + lifecycle without deadlock."""
+        graph = _make_nested_graph()
+        run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+        seen_names: set[str] = set()
+        for name, _item in run.interleave("values", "messages", "lifecycle"):
+            seen_names.add(name)
+
+        assert "values" in seen_names
+        assert "lifecycle" in seen_names
+
+    def test_multiple_custom_transformers(self) -> None:
+        """Multiple custom transformers can coexist."""
+
+        class TagTransformer(StreamTransformer):
+            def __init__(self, scope: tuple[str, ...] = ()) -> None:
+                super().__init__(scope)
+                self._channel: StreamChannel[str] = StreamChannel("tags")
+
+            def init(self) -> dict[str, Any]:
+                return {"tags": self._channel}
+
+            def process(self, event: ProtocolEvent) -> bool:
+                if event["method"] == "values":
+                    self._channel.push(
+                        f"tag:{event['params']['data'].get('value', '')}"
+                    )
+                return True
+
+        graph = _make_nested_graph()
+        run = graph.stream_events(
+            {"value": "x", "items": []},
+            version="v3",
+            transformers=[_CounterTransformer, TagTransformer],
+        )
+
+        assert "counter" in run.extensions
+        assert "tags" in run.extensions
+
+        counter_iter = iter(run.extensions["counter"])
+        tags_iter = iter(run.extensions["tags"])
+        _ = run.output
+        counts = list(counter_iter)
+        tags = list(tags_iter)
+
+        assert len(counts) >= 1
+        assert len(tags) >= 1
+        assert all(t.startswith("tag:") for t in tags)
+
+    def test_two_sibling_subgraphs_both_discoverable(self) -> None:
+        """Two sequential subgraph invocations produce two handles."""
+
+        class _S(TypedDict):
+            items: Annotated[list[str], operator.add]
+
+        def _item(name: str):
+            def node(state: _S) -> dict[str, Any]:
+                return {"items": [name]}
+
+            return node
+
+        inner_a = (
+            StateGraph(_S)
+            .add_node("add_a", _item("a"))
+            .add_edge(START, "add_a")
+            .add_edge("add_a", END)
+            .compile()
+        )
+        inner_b = (
+            StateGraph(_S)
+            .add_node("add_b", _item("b"))
+            .add_edge(START, "add_b")
+            .add_edge("add_b", END)
+            .compile()
+        )
+
+        outer = (
+            StateGraph(_S)
+            .add_node("sub_a", inner_a)
+            .add_node("sub_b", inner_b)
+            .add_edge(START, "sub_a")
+            .add_edge("sub_a", "sub_b")
+            .add_edge("sub_b", END)
+            .compile()
+        )
+
+        run = outer.stream_events({"items": []}, version="v3")
+        handles = []
+        for handle in run.subgraphs:
+            list(handle.values)
+            handles.append(handle)
+
+        assert len(handles) == 2
+        names = [h.graph_name for h in handles]
+        assert "sub_a" in names
+        assert "sub_b" in names
+        assert all(h.status == "completed" for h in handles)
+
+        output = run.output
+        assert output is not None
+        assert set(output["items"]) == {"a", "b"}
+
+    def test_lifecycle_matches_subgraph_handles(self) -> None:
+        """Lifecycle events and subgraph handles agree on discovered subgraphs."""
+        run1 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        handle_paths: list[tuple[str, ...]] = []
+        for handle in run1.subgraphs:
+            list(handle.values)
+            handle_paths.append(handle.path)
+
+        run2 = _make_nested_graph().stream_events(
+            {"value": "x", "items": []}, version="v3"
+        )
+        lifecycle = list(run2.lifecycle)
+
+        started_ns = [
+            tuple(e["namespace"]) for e in lifecycle if e["event"] == "started"
+        ]
+        # Handle paths use format "graph_name:call_id", lifecycle namespaces
+        # use the same format. Both should have the same graph_name prefix.
+        handle_prefixes = {p[0].split(":")[0] for p in handle_paths}
+        lifecycle_prefixes = {ns[0].split(":")[0] for ns in started_ns}
+        assert handle_prefixes == lifecycle_prefixes
+
+    def test_values_plus_messages_plus_custom(self) -> None:
+        """Values, messages, and a custom transformer all produce data in one run."""
+        model = GenericFakeChatModel(messages=iter(["combined test"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events(
+            {"messages": "hi"},
+            version="v3",
+            transformers=[_CounterTransformer],
+        )
+
+        counter_iter = iter(run.extensions["counter"])
+        values_iter = iter(run.values)
+        messages_iter = iter(run.messages)
+
+        values = list(values_iter)
+        messages = list(messages_iter)
+        counts = list(counter_iter)
+
+        assert len(values) >= 1
+        assert len(messages) >= 1
+        assert len(counts) >= 1
+        assert messages[0].output.text == "combined test"
@@ -0,0 +1,108 @@
+"""Tests that ``(a)stream_events(version="v3")`` forwards extra kwargs to the
+underlying ``(a)stream`` call, and rejects the kwargs v3 owns internally.
+
+Background: prior to this change the v3 dispatcher silently dropped ``**kwargs``
+on the v3 branch while forwarding them on v1/v2, so callers passing e.g.
+``context=...`` saw their value disappear with no error. v3 now forwards
+caller kwargs to the inner ``(a)stream`` call but rejects ``stream_mode`` and
+``subgraphs`` since v3 owns them (``stream_mode`` is built from the
+transformer mux; ``subgraphs`` is forced True so nested namespaces flow
+through scoped muxes).
+"""
+
+from __future__ import annotations
+
+import sys
+from dataclasses import dataclass
+from typing import Any
+
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.runtime import Runtime
+
+NEEDS_CONTEXTVARS = pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="Python 3.11+ is required for async contextvars support",
+)
+
+
+@dataclass
+class _Ctx:
+    api_key: str
+
+
+class _State(TypedDict):
+    message: str
+
+
+def _build_context_reading_graph():
+    def read_context(state: _State, runtime: Runtime[_Ctx]) -> dict[str, Any]:
+        return {"message": f"api key: {runtime.context.api_key}"}
+
+    builder = StateGraph(state_schema=_State, context_schema=_Ctx)
+    builder.add_node("read_context", read_context)
+    builder.add_edge(START, "read_context")
+    builder.add_edge("read_context", END)
+    return builder.compile()
+
+
+class TestKwargForwardingSync:
+    def test_context_reaches_node(self) -> None:
+        run = _build_context_reading_graph().stream_events(
+            {"message": "hello"},
+            version="v3",
+            context=_Ctx(api_key="sk_sync"),
+        )
+        assert run.output == {"message": "api key: sk_sync"}
+
+    def test_rejects_stream_mode(self) -> None:
+        graph = _build_context_reading_graph()
+        with pytest.raises(TypeError, match="stream_mode"):
+            graph.stream_events(
+                {"message": "hello"},
+                version="v3",
+                stream_mode=["values"],
+            )
+
+    def test_rejects_subgraphs(self) -> None:
+        graph = _build_context_reading_graph()
+        with pytest.raises(TypeError, match="subgraphs"):
+            graph.stream_events(
+                {"message": "hello"},
+                version="v3",
+                subgraphs=False,
+            )
+
+
+@pytest.mark.anyio
+@NEEDS_CONTEXTVARS
+class TestKwargForwardingAsync:
+    async def test_context_reaches_node(self) -> None:
+        run = await _build_context_reading_graph().astream_events(
+            {"message": "hello"},
+            version="v3",
+            context=_Ctx(api_key="sk_async"),
+        )
+        output = await run.output()
+        assert output == {"message": "api key: sk_async"}
+
+    async def test_rejects_stream_mode(self) -> None:
+        graph = _build_context_reading_graph()
+        with pytest.raises(TypeError, match="stream_mode"):
+            await graph.astream_events(
+                {"message": "hello"},
+                version="v3",
+                stream_mode=["values"],
+            )
+
+    async def test_rejects_subgraphs(self) -> None:
+        graph = _build_context_reading_graph()
+        with pytest.raises(TypeError, match="subgraphs"):
+            await graph.astream_events(
+                {"message": "hello"},
+                version="v3",
+                subgraphs=False,
+            )
@@ -0,0 +1,406 @@
+"""Tests for LifecycleTransformer.
+
+Consumes the `tasks` stream mode and emits subgraph lifecycle payloads
+on the `lifecycle` channel for both in-process iteration via
+`run.lifecycle` and wire delivery via `custom:lifecycle` protocol
+events. Most tests dispatch synthetic protocol events through a
+`StreamMux` to keep the inference logic isolated; the end-of-file
+group exercises the path through real graphs (multi-depth
+discovery, nested `stream_events(version="v3")` calls with non-empty `parent_ns`).
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from typing import Annotated, Any
+
+from typing_extensions import TypedDict
+
+from langgraph._internal._constants import CONF, CONFIG_KEY_CHECKPOINT_NS
+from langgraph.constants import END, START
+from langgraph.errors import GraphInterrupt
+from langgraph.graph import StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.transformers import (
+    LifecyclePayload,
+    LifecycleTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+def _tasks_start(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+) -> dict[str, Any]:
+    """Build a `tasks` ProtocolEvent carrying a TaskPayload (start)."""
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "input": None,
+                "triggers": [],
+            },
+        },
+    }
+
+
+def _tasks_result(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+    error: str | None = None,
+    interrupts: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    """Build a `tasks` ProtocolEvent carrying a TaskResultPayload (finish)."""
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "error": error,
+                "interrupts": interrupts or [],
+                "result": {},
+            },
+        },
+    }
+
+
+def _arm(mux: StreamMux) -> None:
+    """Force projection channels to accept pushes (skip lazy-subscribe gate).
+
+    `StreamChannel.push` only appends to the local buffer when a
+    subscriber is attached. Tests that inspect `_items` directly need
+    the gate flipped before any event is dispatched.
+    """
+    mux._events._subscribed = True
+    for transformer in mux._transformers:
+        if isinstance(transformer, LifecycleTransformer):
+            transformer._channel._subscribed = True
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _drain_lifecycle(mux: StreamMux) -> list[LifecyclePayload]:
+    """Snapshot the lifecycle channel's buffer."""
+    transformer = mux.transformer_by_key("lifecycle")
+    assert isinstance(transformer, LifecycleTransformer)
+    return _unstamped(transformer._channel._items)
+
+
+def _build_lifecycle_mux(*, scope: tuple[str, ...] = ()) -> StreamMux:
+    mux = StreamMux([LifecycleTransformer(scope=scope)], is_async=False)
+    _arm(mux)
+    return mux
+
+
+# ---------------------------------------------------------------------------
+# LifecycleTransformer
+# ---------------------------------------------------------------------------
+
+
+def test_started_emitted_on_first_direct_child_task() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc123"], task_id="t1", name="tool"))
+
+    [payload] = _drain_lifecycle(mux)
+    assert payload["event"] == "started"
+    assert payload["namespace"] == ["agent:abc123"]
+    assert payload["graph_name"] == "agent"
+    assert payload["trigger_call_id"] == "abc123"
+
+
+def test_started_dedup_on_repeat_namespace() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="a"))
+    mux.push(_tasks_start(["agent:abc"], task_id="t2", name="b"))
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started"]
+
+
+def test_grandchild_namespace_discovered() -> None:
+    """Subgraphs at any depth below scope are tracked, not just direct children."""
+    mux = _build_lifecycle_mux()
+    # First-seen task at length-2 ns means a 2nd-level subgraph started.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t1", name="x"))
+
+    [payload] = _drain_lifecycle(mux)
+    assert payload["event"] == "started"
+    assert payload["namespace"] == ["agent:abc", "tool:def"]
+
+
+def test_nested_chain_emits_started_at_each_depth() -> None:
+    """A graph → subgraph → subgraph chain produces a started event per level."""
+    mux = _build_lifecycle_mux()
+    # Subgraph1 starts emitting tasks (events tagged with its own ns).
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    # Subgraph1 invokes subgraph2; subgraph2's first task event arrives.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["namespace"] for p in payloads] == [
+        ["agent:abc"],
+        ["agent:abc", "tool:def"],
+    ]
+    assert all(p["event"] == "started" for p in payloads)
+
+
+def test_nested_chain_emits_completed_at_each_depth() -> None:
+    """Each subgraph in a nested chain closes when its parent task result arrives."""
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    # Subgraph2's owning task (id=def, inside subgraph1) finishes.
+    mux.push(_tasks_result(["agent:abc"], task_id="def", name="tool"))
+    # Subgraph1's owning task (id=abc, at root) finishes.
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    payloads = _drain_lifecycle(mux)
+    events = [(p["event"], p["namespace"]) for p in payloads]
+    assert events == [
+        ("started", ["agent:abc"]),
+        ("started", ["agent:abc", "tool:def"]),
+        ("completed", ["agent:abc", "tool:def"]),
+        ("completed", ["agent:abc"]),
+    ]
+
+
+def test_completed_on_parent_task_result() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    events = [p["event"] for p in _drain_lifecycle(mux)]
+    assert events == ["started", "completed"]
+
+
+def test_failed_on_parent_task_result_with_error() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "failed"]
+    assert payloads[1]["error"] == "boom"
+
+
+def test_interrupted_on_parent_task_result_with_interrupts() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "interrupted"]
+
+
+def test_interrupt_takes_precedence_over_error() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            error="should-be-suppressed",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    last = _drain_lifecycle(mux)[-1]
+    assert last["event"] == "interrupted"
+    assert "error" not in last
+
+
+def test_finalize_completes_open_subgraphs() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.close()
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "completed"]
+
+
+def test_fail_emits_interrupted_for_graph_interrupt() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(GraphInterrupt())
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "interrupted"]
+    assert "error" not in payloads[1]
+
+
+def test_fail_emits_failed_for_other_exceptions() -> None:
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(RuntimeError("boom"))
+    payloads = _drain_lifecycle(mux)
+    assert [p["event"] for p in payloads] == ["started", "failed"]
+    assert payloads[1]["error"] == "boom"
+
+
+def test_unrelated_methods_pass_through() -> None:
+    """Non-`tasks` events are not consumed and don't emit lifecycle."""
+    mux = _build_lifecycle_mux()
+    mux.push(
+        {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": ["agent:abc"], "timestamp": TS, "data": {}},
+        }
+    )
+    assert _drain_lifecycle(mux) == []
+
+
+def test_scoped_transformer_filters_outside_scope_but_tracks_all_depths() -> None:
+    """Scope filters the prefix; subgraphs at any depth below scope are tracked."""
+    mux = _build_lifecycle_mux(scope=("agent:abc",))
+    # Root-level task — out of scope (no shared prefix).
+    mux.push(_tasks_start(["other:1"], task_id="t1", name="other"))
+    # Direct child of agent:abc — in scope.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="tool"))
+    # Grandchild of agent:abc — also in scope, tracked at its own depth.
+    mux.push(
+        _tasks_start(["agent:abc", "tool:def", "deep:ghi"], task_id="t3", name="deep")
+    )
+
+    payloads = _drain_lifecycle(mux)
+    assert [p["namespace"] for p in payloads] == [
+        ["agent:abc", "tool:def"],
+        ["agent:abc", "tool:def", "deep:ghi"],
+    ]
+
+
+def test_required_stream_modes_declared() -> None:
+    assert LifecycleTransformer.required_stream_modes == ("tasks",)
+
+
+def test_protocol_event_method_is_native() -> None:
+    """Native transformer — auto-forwarded events use `lifecycle`, not `custom:lifecycle`."""
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    methods = {evt["method"] for evt in _unstamped(mux._events._items)}
+    assert "lifecycle" in methods
+    assert "custom:lifecycle" not in methods
+
+
+def test_tasks_events_suppressed_from_main_log() -> None:
+    """Tasks events are folded into lifecycle and don't appear on the main log."""
+    mux = _build_lifecycle_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    methods = [evt["method"] for evt in _unstamped(mux._events._items)]
+    assert "tasks" not in methods
+    # Lifecycle events did make it through, though.
+    assert "lifecycle" in methods
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: real graphs through stream_events(version="v3")
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _passthrough(state: _State) -> dict[str, Any]:
+    return {"value": state["value"] + "!", "items": ["x"]}
+
+
+def _make_two_level_nested() -> Any:
+    """Build outer → middle → inner. Three Pregel instances, two nesting levels."""
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("inner_node", _passthrough)
+    inner_b.add_edge(START, "inner_node")
+    inner_b.add_edge("inner_node", END)
+    inner = inner_b.compile()
+
+    middle_b: StateGraph = StateGraph(_State, input_schema=_State)
+    middle_b.add_node("inner", inner)
+    middle_b.add_edge(START, "inner")
+    middle_b.add_edge("inner", END)
+    middle = middle_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("middle", middle)
+    outer_b.add_edge(START, "middle")
+    outer_b.add_edge("middle", END)
+    return outer_b.compile()
+
+
+def test_stream_events_v3_real_graph_emits_lifecycle_at_each_depth() -> None:
+    """Outer graph with two nested subgraphs surfaces lifecycle for both."""
+    graph = _make_two_level_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    # Iterating the projection drives the pump and drains synthesized
+    # lifecycle events at the same time.
+    payloads = list(run.lifecycle)
+    # Each subgraph instance produces a started + a terminal event. Two
+    # nested instances, so four payloads total in some interleaving.
+    by_event = {p["event"] for p in payloads}
+    assert "started" in by_event
+    assert "completed" in by_event
+    # Two distinct namespaces — direct child of root, and grandchild.
+    namespaces = {tuple(p["namespace"]) for p in payloads}
+    direct_children = {ns for ns in namespaces if len(ns) == 1}
+    grandchildren = {ns for ns in namespaces if len(ns) == 2}
+    assert direct_children, f"expected a level-1 lifecycle namespace, got {namespaces}"
+    assert grandchildren, f"expected a level-2 lifecycle namespace, got {namespaces}"
+    # Every direct-child namespace has a matching grandchild whose path extends it.
+    for parent in direct_children:
+        assert any(gc[: len(parent)] == parent for gc in grandchildren), (
+            f"grandchild does not extend parent {parent}: {grandchildren}"
+        )
+
+
+def test_stream_events_v3_with_nested_parent_ns_scopes_lifecycle() -> None:
+    """When `stream_events(version="v3")` is called with a non-empty checkpoint_ns in config,
+    `_resolve_parent_ns` returns that namespace and the registered
+    `LifecycleTransformer` is constructed with `scope=parent_ns`. This
+    exercises the path that exists today purely for nested-stream_events(version="v3")
+    callers; the test simulates such a caller by injecting a
+    checkpoint_ns into the config.
+    """
+    graph = _make_two_level_nested()
+    config = {CONF: {CONFIG_KEY_CHECKPOINT_NS: "outer:abc"}}
+    run = graph.stream_events({"value": "x", "items": []}, config=config, version="v3")
+
+    payloads = list(run.lifecycle)
+    # Every emitted lifecycle namespace must extend the caller's scope —
+    # nothing at root-level, nothing under a sibling prefix.
+    for p in payloads:
+        ns = tuple(p["namespace"])
+        assert ns[:1] == ("outer:abc",), (
+            f"namespace {ns} not within scoped prefix ('outer:abc',)"
+        )
@@ -0,0 +1,883 @@
+"""Tests for MessagesTransformer: protocol event routing, whole-message fallback,
+legacy v1 chunk filtering, and end-to-end via stream_events(version="v3") / astream_events(version="v3")."""
+
+from __future__ import annotations
+
+import time
+from typing import Any
+
+import pytest
+from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.runnables import RunnableConfig
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import MessagesState, StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.run_stream import GraphRunStream
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import MessagesTransformer, ValuesTransformer
+
+TS = int(time.time() * 1000)
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _proto_event(
+    event: dict[str, Any],
+    *,
+    run_id: str = "run-1",
+    node: str = "llm",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a protocol event dict (v2 path)."""
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (event, {"langgraph_node": node, "run_id": run_id}),
+        },
+    }
+
+
+def _v1_chunk(
+    text: str,
+    msg_id: str = "msg-1",
+    *,
+    finish: bool = False,
+    node: str = "llm",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a v1 AIMessageChunk tuple."""
+    rm: dict[str, Any] = {"finish_reason": "stop"} if finish else {}
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (
+                AIMessageChunk(content=text, id=msg_id, response_metadata=rm),
+                {"langgraph_node": node},
+            ),
+        },
+    }
+
+
+def _whole_msg(
+    text: str,
+    msg_id: str = "msg-10",
+    *,
+    node: str = "node",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a completed AIMessage."""
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (AIMessage(content=text, id=msg_id), {"langgraph_node": node}),
+        },
+    }
+
+
+def _make_sync_transformer() -> tuple[
+    MessagesTransformer, StreamChannel[ChatModelStream]
+]:
+    t = MessagesTransformer()
+    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    log._bind(is_async=False)
+    # Subscribe up front so pushes during process() are retained.
+    log._subscribed = True
+    t._bind_pump(lambda: False)
+    return t, log
+
+
+def _make_async_transformer() -> tuple[
+    MessagesTransformer, StreamChannel[ChatModelStream]
+]:
+    t = MessagesTransformer()
+    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    log._bind(is_async=True)
+    log._subscribed = True
+    return t, log
+
+
+def _lifecycle(
+    *, text: str = "hello world", message_id: str = "run-1"
+) -> list[dict[str, Any]]:
+    """Produce a valid protocol event lifecycle: start, delta, finish."""
+    half = len(text) // 2
+    first, second = text[:half], text[half:]
+    return [
+        {"event": "message-start", "role": "ai", "message_id": message_id},
+        {
+            "event": "content-block-start",
+            "index": 0,
+            "content_block": {"type": "text", "text": ""},
+        },
+        {
+            "event": "content-block-delta",
+            "index": 0,
+            "content_block": {"type": "text", "text": first},
+        },
+        {
+            "event": "content-block-delta",
+            "index": 0,
+            "content_block": {"type": "text", "text": second},
+        },
+        {
+            "event": "content-block-finish",
+            "index": 0,
+            "content_block": {"type": "text", "text": text},
+        },
+        {"event": "message-finish", "reason": "stop"},
+    ]
+
+
+def _simple_graph():
+    def call_model(state: MessagesState) -> dict[str, Any]:
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+        stream = model.stream_events(state["messages"], version="v3")
+        return {"messages": stream.output}
+
+    return (
+        StateGraph(MessagesState)
+        .add_node("call_model", call_model)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", END)
+        .compile()
+    )
+
+
+# ---------------------------------------------------------------------------
+# Protocol event routing
+# ---------------------------------------------------------------------------
+
+
+class TestProtocolEventRouting:
+    def test_message_start_creates_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+            )
+        )
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.message_id == "run-1"
+
+    def test_full_lifecycle_yields_done_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle(text="hello world"):
+            t.process(_proto_event(evt, run_id="run-1"))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.done
+        assert stream.output.text == "hello world"
+
+    def test_message_finish_cleans_up_routing(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle():
+            t.process(_proto_event(evt, run_id="run-1"))
+        assert t._by_run == {}
+
+    def test_events_without_prior_start_are_ignored(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {
+                    "event": "content-block-delta",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": "orphan"},
+                },
+                run_id="unknown",
+            )
+        )
+        log.close()
+        assert _unstamped(log._items) == []
+
+    def test_concurrent_streams_routed_by_run_id(self) -> None:
+        t, log = _make_sync_transformer()
+        life_a = _lifecycle(text="aaaa", message_id="run-a")
+        life_b = _lifecycle(text="bbbb", message_id="run-b")
+        for a, b in zip(life_a, life_b):
+            t.process(_proto_event(a, run_id="run-a"))
+            t.process(_proto_event(b, run_id="run-b"))
+        log.close()
+        streams = _unstamped(log._items)
+        assert len(streams) == 2
+        by_id = {s.message_id: s for s in streams}
+        assert by_id["run-a"].output.text == "aaaa"
+        assert by_id["run-b"].output.text == "bbbb"
+
+    def test_text_deltas_accumulated_on_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle(text="abcdef"):
+            t.process(_proto_event(evt))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert "".join(stream._text_proj._deltas) == "abcdef"
+
+    def test_stream_pushed_on_message_start_not_finish(self) -> None:
+        # Consumer can see the stream before message-finish arrives.
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+            )
+        )
+        assert len(log._items) == 1
+
+    def test_node_metadata_set_on_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+                node="my_llm",
+            )
+        )
+        (stream,) = _unstamped(log._items)
+        assert stream.node == "my_llm"
+
+
+# ---------------------------------------------------------------------------
+# Whole-message fallback
+# ---------------------------------------------------------------------------
+
+
+class TestWholeMessageFallback:
+    def test_whole_ai_message_produces_complete_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_whole_msg("the full answer"))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.done
+        assert stream.output.text == "the full answer"
+
+    def test_whole_message_has_full_lifecycle(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_whole_msg("full"))
+        log.close()
+        (stream,) = _unstamped(log._items)
+        assert [e["event"] for e in stream._events] == [
+            "message-start",
+            "content-block-start",
+            "content-block-delta",
+            "content-block-finish",
+            "message-finish",
+        ]
+
+
+# ---------------------------------------------------------------------------
+# Filtering
+# ---------------------------------------------------------------------------
+
+
+class TestFiltering:
+    def test_non_messages_events_pass_through(self) -> None:
+        t, _ = _make_sync_transformer()
+        assert (
+            t.process(
+                {
+                    "type": "event",
+                    "method": "values",
+                    "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
+                }
+            )
+            is True
+        )
+
+    def test_subgraph_namespace_dropped(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            {
+                "type": "event",
+                "method": "messages",
+                "params": {
+                    "namespace": ["subgraph"],
+                    "timestamp": TS,
+                    "data": (
+                        {"event": "message-start", "message_id": "run-x"},
+                        {"run_id": "run-x"},
+                    ),
+                },
+            }
+        )
+        log.close()
+        assert _unstamped(log._items) == []
+
+    def test_legacy_v1_chunks_ignored(self) -> None:
+        # v1 AIMessageChunk tuples (from on_llm_new_token) are not streamed
+        # into this projection; callers must migrate to stream_events(version="v3").
+        t, log = _make_sync_transformer()
+        t.process(_v1_chunk("hello"))
+        t.process(_v1_chunk(" world", finish=True))
+        log.close()
+        assert _unstamped(log._items) == []
+
+
+# ---------------------------------------------------------------------------
+# Lifecycle: fail / finalize
+# ---------------------------------------------------------------------------
+
+
+class TestLifecycle:
+    def test_fail_propagates_to_open_streams(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+            )
+        )
+        streams = _unstamped(log._items)
+        err = RuntimeError("graph died")
+        t.fail(err)
+        assert t._by_run == {}
+        assert streams[0]._error is err
+
+    def test_finalize_clears_routing_state(self) -> None:
+        t, _ = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+            )
+        )
+        assert "run-1" in t._by_run
+        t.finalize()
+        assert t._by_run == {}
+
+
+# ---------------------------------------------------------------------------
+# Async mode
+# ---------------------------------------------------------------------------
+
+
+class TestAsyncMode:
+    def test_async_mode_creates_async_stream(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="async stream"):
+            t.process(_proto_event(evt))
+        assert isinstance(_unstamped(log._items)[0], AsyncChatModelStream)
+
+    @pytest.mark.anyio
+    async def test_text_projection_yields_deltas(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="hello world"):
+            t.process(_proto_event(evt))
+        (stream,) = _unstamped(log._items)
+        assert isinstance(stream, AsyncChatModelStream)
+        assert "".join([d async for d in stream.text]) == "hello world"
+
+    @pytest.mark.anyio
+    async def test_output_awaitable(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="async"):
+            t.process(_proto_event(evt))
+        (stream,) = _unstamped(log._items)
+        assert (await stream.output).text == "async"
+
+
+# ---------------------------------------------------------------------------
+# GraphRunStream integration
+# ---------------------------------------------------------------------------
+
+
+class TestWireRequestMore:
+    def test_bind_pump_called_on_wire(self) -> None:
+        values_t = ValuesTransformer()
+        messages_t = MessagesTransformer()
+        mux = StreamMux([values_t, messages_t], is_async=False)
+
+        assert messages_t._pump_fn is None
+        run = GraphRunStream(iter([]), mux)
+        assert messages_t._pump_fn is not None
+        assert messages_t._pump_fn() is False
+        assert run._exhausted
+
+    def test_created_streams_have_request_more(self) -> None:
+        values_t = ValuesTransformer()
+        messages_t = MessagesTransformer()
+        mux = StreamMux([values_t, messages_t], is_async=False)
+        GraphRunStream(iter([]), mux)
+
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+        for evt in _lifecycle():
+            messages_t.process(_proto_event(evt))
+
+        (stream,) = _unstamped(log._items)
+        assert stream._request_more is messages_t._pump_fn
+
+
+# ---------------------------------------------------------------------------
+# End-to-end via StreamMux
+# ---------------------------------------------------------------------------
+
+
+class TestViaMux:
+    def _make_mux(
+        self,
+    ) -> tuple[MessagesTransformer, StreamMux, StreamChannel[ChatModelStream]]:
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=False)
+        t._bind_pump(lambda: False)
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+        return t, mux, log
+
+    def test_streaming_via_mux(self) -> None:
+        t, mux, log = self._make_mux()
+        for evt in _lifecycle(text="mux stream"):
+            mux.push(_proto_event(evt))
+        mux.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.output.text == "mux stream"
+
+    def test_whole_message_via_mux(self) -> None:
+        t, mux, log = self._make_mux()
+        mux.push(_whole_msg("result"))
+        mux.close()
+        (stream,) = _unstamped(log._items)
+        assert stream.output.text == "result"
+
+    @pytest.mark.anyio
+    async def test_async_streaming_via_mux(self) -> None:
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=True)
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+
+        for evt in _lifecycle(text="async mux"):
+            await mux.apush(_proto_event(evt))
+
+        (stream,) = _unstamped(log._items)
+        assert (await stream.output).text == "async mux"
+        await mux.aclose()
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: graph → stream_events(version="v3") → run.messages (node calls stream_events)
+# ---------------------------------------------------------------------------
+
+
+class TestEndToEnd:
+    """stream_events(version="v3") path: node calls model.stream_events() explicitly."""
+
+    def test_node_calling_stream_v2_populates_messages(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = model.stream_events(state["messages"], version="v3")
+            return {"messages": stream.output}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.text == "hello world"
+
+    def test_node_stream_v2_text_deltas_iterate(self) -> None:
+        """Consumer can iterate `.text` on the streamed message in real time."""
+        model = GenericFakeChatModel(messages=iter(["streamed answer"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = model.stream_events(state["messages"], version="v3")
+            return {"messages": stream.output}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "go"}, version="v3")
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "streamed answer"
+
+    def test_non_llm_message_returned_from_node(self) -> None:
+        """Whole-message fallback: node returns a finalized AIMessage directly."""
+
+        def return_message(state: MessagesState) -> dict[str, Any]:
+            return {"messages": AIMessage(content="hardcoded", id="msg-abc")}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("return_message", return_message)
+            .add_edge(START, "return_message")
+            .add_edge("return_message", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert stream.output.text == "hardcoded"
+
+    @pytest.mark.anyio
+    async def test_async_node_calling_astream_v2(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["async answer"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = await model.astream_events(state["messages"], version="v3")
+            return {"messages": await stream}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+        streams = [s async for s in run.messages]
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
+        assert (await streams[0].output).text == "async answer"
+
+    @pytest.mark.anyio
+    async def test_nested_async_iteration_yields_text_deltas(self) -> None:
+        """Inner stream.text drives the shared graph pump via the async pump binding."""
+        import asyncio
+
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = await model.astream_events(state["messages"], version="v3")
+            return {"messages": await stream}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+
+        async def consume() -> list[str]:
+            collected: list[str] = []
+            async for stream in run.messages:
+                async for delta in stream.text:
+                    collected.append(delta)
+            return collected
+
+        assert "".join(await asyncio.wait_for(consume(), timeout=2.0)) == "hello world"
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: graph → stream_events(version="v3") → run.messages (node calls invoke)
+# ---------------------------------------------------------------------------
+
+
+class TestEndToEndV2Invoke:
+    """Auto-routing path: stream_events(version="v3") injects CONFIG_KEY_STREAM_MESSAGES_V2,
+    causing BaseChatModel to drive the v2 protocol event generator even for
+    model.invoke()."""
+
+    def _graph(self, model):
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        return (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+    def test_invoke_populates_messages(self) -> None:
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["hello world"]))
+        ).stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.text == "hello world"
+
+    def test_invoke_emits_protocol_events(self) -> None:
+        """Iterating the stream yields the full v2 lifecycle, not v1 chunks."""
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["streamed answer"]))
+        ).stream_events({"messages": "go"}, version="v3")
+        (stream,) = list(run.messages)
+
+        events = list(stream)
+        event_types = [e.get("event") for e in events]
+        assert "message-start" in event_types
+        assert "content-block-start" in event_types
+        assert "content-block-delta" in event_types
+        assert "content-block-finish" in event_types
+        assert "message-finish" in event_types
+        # Sanity: every event is a dict carrying an "event" key — not an
+        # AIMessageChunk tuple from the v1 path.
+        for event in events:
+            assert isinstance(event, dict)
+            assert "event" in event
+        # Typed projection still assembles the final text.
+        assert stream.output.text == "streamed answer"
+
+    def test_invoke_text_deltas_iterate(self) -> None:
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["delta streaming works"]))
+        ).stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "delta streaming works"
+
+    def test_invoke_two_nodes_two_streams(self) -> None:
+        model_a = GenericFakeChatModel(messages=iter(["alpha"]))
+        model_b = GenericFakeChatModel(messages=iter(["beta"]))
+
+        def node_a(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model_a.invoke(state["messages"])}
+
+        def node_b(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model_b.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("node_a", node_a)
+            .add_node("node_b", node_b)
+            .add_edge(START, "node_a")
+            .add_edge("node_a", "node_b")
+            .add_edge("node_b", END)
+            .compile()
+        )
+
+        streams = list(graph.stream_events({"messages": "hi"}, version="v3").messages)
+        assert len(streams) == 2
+        assert {s.output.text for s in streams} == {"alpha", "beta"}
+
+    def test_invoke_plus_constructed_message_two_streams(self) -> None:
+        """Live-streamed node + constructed-message node → two ChatModelStreams."""
+        model = GenericFakeChatModel(messages=iter(["live stream"]))
+
+        def streaming_node(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        def constructed_node(state: MessagesState) -> dict[str, Any]:
+            return {"messages": [AIMessage(content="hardcoded", id="constructed-1")]}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("streaming_node", streaming_node)
+            .add_node("constructed_node", constructed_node)
+            .add_edge(START, "streaming_node")
+            .add_edge("streaming_node", "constructed_node")
+            .add_edge("constructed_node", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        streams = list(run.messages)
+        assert len(streams) == 2
+        assert streams[0].node == "streaming_node"
+        assert streams[0].output.text == "live stream"
+        assert streams[1].node == "constructed_node"
+        assert streams[1].output.text == "hardcoded"
+        assert streams[1].message_id == "constructed-1"
+
+    @pytest.mark.anyio
+    async def test_ainvoke_populates_messages(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["async invoke"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": await model.ainvoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_events({"messages": "hi"}, version="v3")
+        streams = [s async for s in run.messages]
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
+        assert (await streams[0].output).text == "async invoke"
+
+
+# ---------------------------------------------------------------------------
+# Regression: direct stream_mode="messages" must stay v1
+# ---------------------------------------------------------------------------
+
+
+class TestDirectMessagesModeStaysV1:
+    def test_direct_graph_stream_messages_yields_ai_message_chunks(self) -> None:
+        """graph.stream(stream_mode="messages") must not leak v2 event dicts —
+        the v2 flag is only injected by stream_events(version="v3") / astream_events(version="v3")."""
+        model = GenericFakeChatModel(messages=iter(["legacy path"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        parts = list(graph.stream({"messages": "hi"}, stream_mode="messages"))
+        assert parts, "expected stream_mode='messages' to emit tuples"
+        for payload, _metadata in parts:
+            assert isinstance(payload, AIMessageChunk)
+        assert (
+            "".join(p[0].content for p in parts if isinstance(p[0].content, str))
+            == "legacy path"
+        )
+
+    def test_nested_graph_stream_messages_stays_v1_under_outer_stream_events_v3(
+        self,
+    ) -> None:
+        """An outer `stream_events(version="v3")` run must not flip an inner direct
+        `stream_mode="messages"` call onto the v2 event protocol."""
+        model = GenericFakeChatModel(messages=iter(["nested legacy path"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        inner = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        class OuterState(TypedDict, total=False):
+            saw_only_chunks: bool
+            first_payload_type: str
+            text: str
+
+        def call_subgraph(state: OuterState, config: RunnableConfig) -> dict[str, Any]:
+            parts = list(
+                inner.stream(
+                    {"messages": "hi"},
+                    config,
+                    stream_mode="messages",
+                )
+            )
+            assert parts
+            payloads = [payload for payload, _metadata in parts]
+            return {
+                "saw_only_chunks": all(
+                    isinstance(payload, AIMessageChunk) for payload in payloads
+                ),
+                "first_payload_type": type(payloads[0]).__name__,
+                "text": "".join(
+                    payload.content
+                    for payload in payloads
+                    if isinstance(payload, AIMessageChunk)
+                    and isinstance(payload.content, str)
+                ),
+            }
+
+        outer = (
+            StateGraph(OuterState)
+            .add_node("call_subgraph", call_subgraph)
+            .add_edge(START, "call_subgraph")
+            .add_edge("call_subgraph", END)
+            .compile()
+        )
+
+        result = outer.stream_events({}, version="v3").output
+
+        assert result is not None
+        assert result["saw_only_chunks"] is True
+        assert result["first_payload_type"] == "AIMessageChunk"
+        assert result["text"] == "nested legacy path"
+
+
+# ---------------------------------------------------------------------------
+# StreamMessagesHandlerV2 unit
+# ---------------------------------------------------------------------------
+
+
+class TestStreamMessagesHandlerV2Unit:
+    def test_on_llm_new_token_is_noop(self) -> None:
+        """v2 handler must not emit v1 chunks even when on_llm_new_token fires."""
+        from uuid import uuid4
+
+        from langchain_core.outputs import ChatGenerationChunk
+
+        from langgraph.pregel._messages import StreamMessagesHandlerV2
+
+        emitted: list[Any] = []
+        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
+        run_id = uuid4()
+        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
+
+        handler.on_llm_new_token(
+            "hello",
+            chunk=ChatGenerationChunk(message=AIMessageChunk(content="hello")),
+            run_id=run_id,
+        )
+
+        assert emitted == []
+
+    def test_on_llm_end_dedupes_when_final_message_id_differs(self) -> None:
+        """A streamed v2 message should not be emitted again from the final
+        AIMessage fallback when its final id does not match `message-start`."""
+        from uuid import uuid4
+
+        from langchain_core.outputs import ChatGeneration, LLMResult
+
+        from langgraph.pregel._messages import StreamMessagesHandlerV2
+
+        emitted: list[Any] = []
+        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
+        run_id = uuid4()
+        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
+
+        handler.on_stream_event(
+            {"event": "message-start", "message_id": "stream-msg-1"},
+            run_id=run_id,
+        )
+        handler.on_llm_end(
+            LLMResult(
+                generations=[
+                    [
+                        ChatGeneration(
+                            message=AIMessage(content="hello", id="final-msg-1")
+                        )
+                    ]
+                ]
+            ),
+            run_id=run_id,
+        )
+
+        assert len(emitted) == 1
@@ -0,0 +1,856 @@
+"""Tests for SubgraphTransformer.
+
+Subscribes to `tasks` events and produces in-process `SubgraphRunStream`
+handles backed by mini-muxes (built via `StreamMux._make_child`). The
+synthetic-event tests isolate the inference / mini-mux wiring; the
+real-graph tests exercise the end-to-end navigation path through
+`stream_events(version="v3")`.
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from collections.abc import AsyncIterator
+from functools import partial
+from typing import Annotated, Any
+
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.errors import GraphInterrupt
+from langgraph.graph import StateGraph
+from langgraph.pregel.main import _normalize_stream_transformer_factories
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    AsyncSubgraphRunStream,
+    GraphRunStream,
+    SubgraphRunStream,
+)
+from langgraph.stream.transformers import (
+    LifecycleTransformer,
+    MessagesTransformer,
+    SubgraphTransformer,
+    ValuesTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _tasks_start(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "input": None,
+                "triggers": [],
+            },
+        },
+    }
+
+
+def _tasks_result(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+    error: str | None = None,
+    interrupts: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "error": error,
+                "interrupts": interrupts or [],
+                "result": {},
+            },
+        },
+    }
+
+
+def _native_factories() -> list[Any]:
+    """Mirror the factory list `Pregel.stream_events(version="v3")` registers."""
+    return [
+        ValuesTransformer,
+        MessagesTransformer,
+        LifecycleTransformer,
+        SubgraphTransformer,
+    ]
+
+
+def _stream_part(
+    method: str,
+    namespace: tuple[str, ...],
+    data: Any,
+) -> dict[str, Any]:
+    return {"type": method, "ns": namespace, "data": data}
+
+
+async def _astream_parts(*parts: dict[str, Any]) -> AsyncIterator[dict[str, Any]]:
+    for part in parts:
+        yield part
+
+
+def _arm(mux: StreamMux) -> None:
+    """Pre-subscribe every projection in the mux so synthetic pushes accumulate.
+
+    Real consumer code subscribes by iterating the projection; tests
+    inspect `_items` directly, so the lazy-subscribe gate has to be
+    flipped manually before any synthetic events are pushed.
+    """
+    mux._events._subscribed = True
+    for value in mux.extensions.values():
+        if hasattr(value, "_subscribed"):
+            value._subscribed = True
+
+
+def _arm_recursive(mux: StreamMux) -> None:
+    """Arm `mux` and every mini-mux currently held by SubgraphTransformer handles.
+
+    Mini-muxes are created during `mux.push(...)` when a new direct
+    child is discovered. Tests must call this after each push that
+    might have created a new mini-mux so subsequent pushes' projection
+    side effects accumulate (rather than dropping silently against an
+    unsubscribed log).
+    """
+    _arm(mux)
+    for handle in _subgraph_transformer(mux)._handles.values():
+        if handle._mux is not None:
+            _arm_recursive(handle._mux)
+
+
+def _build_root_mux(*, scope: tuple[str, ...] = ()) -> StreamMux:
+    mux = StreamMux(
+        factories=_native_factories(),
+        scope=scope,
+        is_async=False,
+    )
+    _arm(mux)
+    return mux
+
+
+def _subgraph_transformer(mux: StreamMux) -> SubgraphTransformer:
+    transformer = mux.transformer_by_key("subgraphs")
+    assert isinstance(transformer, SubgraphTransformer)
+    return transformer
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _drain_subgraphs(mux: StreamMux) -> list[SubgraphRunStream]:
+    return _unstamped(_subgraph_transformer(mux)._log._items)
+
+
+def _child_mux(handle: SubgraphRunStream | AsyncSubgraphRunStream) -> StreamMux:
+    assert handle._mux is not None
+    return handle._mux
+
+
+def _event_items(mux: StreamMux) -> list[ProtocolEvent]:
+    return _unstamped(mux._events._items)
+
+
+def _lifecycle_payloads(mux: StreamMux) -> list[dict[str, Any]]:
+    lifecycle_t = mux.transformer_by_key("lifecycle")
+    assert isinstance(lifecycle_t, LifecycleTransformer)
+    return _unstamped(lifecycle_t._channel._items)
+
+
+# ---------------------------------------------------------------------------
+# Synthetic-event tests
+# ---------------------------------------------------------------------------
+
+
+def test_handle_created_on_first_direct_child_task() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.path == ("agent:abc",)
+    assert handle.graph_name == "agent"
+    assert handle.trigger_call_id == "abc"
+    assert handle.status == "started"
+    _child_mux(handle)  # mini-mux backed
+
+
+def test_handle_status_completes_on_parent_result() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "completed"
+    assert handle.error is None
+
+
+def test_handle_status_failed_with_error() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "failed"
+    assert handle.error == "boom"
+
+
+def test_handle_status_interrupted() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "interrupted"
+
+
+def test_grandchild_discovered_via_child_mini_mux() -> None:
+    """Each mini-mux owns its own scope; grandchildren live on the child handle."""
+    mux = _build_root_mux()
+    # Direct child started — creates the mini-mux.
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    # Pre-subscribe the freshly-created mini-mux so subsequent
+    # forwarded events land on its projections (consumer would
+    # subscribe naturally by iterating handle.subgraphs, but the
+    # test inspects `_items` directly).
+    _arm_recursive(mux)
+    # Grandchild's first task event flows down into the child mini-mux.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    [child_handle] = _drain_subgraphs(mux)
+    assert child_handle.path == ("agent:abc",)
+    # The grandchild appears on the CHILD'S subgraphs projection.
+    grandchildren = _unstamped(child_handle.subgraphs._items)
+    assert len(grandchildren) == 1
+    assert grandchildren[0].path == ("agent:abc", "tool:def")
+
+
+def test_finalize_completes_open_handles() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.close()
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "completed"
+
+
+def test_fail_marks_open_handles_interrupted_for_graph_interrupt() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(GraphInterrupt())
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "interrupted"
+
+
+def test_fail_marks_open_handles_failed_for_other_errors() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(RuntimeError("boom"))
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "failed"
+    assert handle.error == "boom"
+
+
+def test_child_mux_requires_factories() -> None:
+    """A mux constructed only from `transformers=` can't clone factories."""
+    transformer = SubgraphTransformer()
+    mux = StreamMux(transformers=[transformer], is_async=False)
+    with pytest.raises(RuntimeError, match="factories"):
+        mux._make_child(("anything",))
+
+
+def test_subgraph_and_lifecycle_agree_on_terminal_status() -> None:
+    """Both transformers consume the same tasks signal — no drift."""
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    [handle] = _drain_subgraphs(mux)
+    payloads = _lifecycle_payloads(mux)
+    assert handle.status == "failed"
+    assert payloads[-1]["event"] == "failed"
+    assert handle.error == payloads[-1]["error"]
+
+
+def test_required_stream_modes_declared() -> None:
+    assert SubgraphTransformer.required_stream_modes == ("tasks",)
+
+
+def test_tasks_events_suppressed_from_main_log() -> None:
+    """Tasks events are folded into discovery and don't appear on the main log."""
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    methods = [evt["method"] for evt in _event_items(mux)]
+    assert "tasks" not in methods
+
+
+class _ChildEventObserver(StreamTransformer):
+    """Records child-scope event identity without mutating it."""
+
+    records: list[tuple[tuple[str, ...], int, int, bool]] = []
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if self.scope and event["method"] == "values":
+            self.records.append(
+                (
+                    self.scope,
+                    id(event),
+                    id(event["params"]["data"]),
+                    "seq" in event,
+                )
+            )
+        return True
+
+
+def test_child_forwarding_reuses_event_without_assigning_seq() -> None:
+    _ChildEventObserver.records = []
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildEventObserver,
+        ],
+        is_async=False,
+    )
+    _arm(mux)
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    data = {"x": 1}
+    event: ProtocolEvent = {
+        "type": "event",
+        "method": "values",
+        "params": {
+            "namespace": ["agent:abc"],
+            "timestamp": TS,
+            "data": data,
+        },
+    }
+    mux.push(event)
+
+    assert _ChildEventObserver.records == [(("agent:abc",), id(event), id(data), False)]
+    [root_event] = [evt for evt in _event_items(mux) if evt["method"] == "values"]
+    assert root_event is event
+    assert "seq" in root_event
+
+
+class _AsyncProbeTransformer(StreamTransformer):
+    """Async-only transformer used to verify mini-mux async dispatch."""
+
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self.seen: list[tuple[str, ...]] = []
+        self.finalized = False
+        self.failed: BaseException | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"async_probe": self}
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        self.seen.append(tuple(event["params"]["namespace"]))
+        return True
+
+    async def afinalize(self) -> None:
+        self.finalized = True
+
+    async def afail(self, err: BaseException) -> None:
+        self.failed = err
+
+
+@pytest.mark.anyio
+async def test_async_child_mini_mux_uses_async_lane() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _AsyncProbeTransformer,
+        ],
+        is_async=True,
+    )
+    await mux.apush(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    handle = _subgraph_transformer(mux)._handles[("agent:abc",)]
+    assert isinstance(handle, AsyncSubgraphRunStream)
+    probe = _child_mux(handle).transformer_by_key("async_probe")
+    assert isinstance(probe, _AsyncProbeTransformer)
+    assert probe.seen == [("agent:abc",)]
+
+    await mux.apush(_tasks_result([], task_id="abc", name="agent"))
+    assert probe.finalized is True
+
+
+@pytest.mark.anyio
+async def test_async_child_mini_mux_fail_uses_async_lane() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _AsyncProbeTransformer,
+        ],
+        is_async=True,
+    )
+    await mux.apush(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    handle = _subgraph_transformer(mux)._handles[("agent:abc",)]
+    probe = _child_mux(handle).transformer_by_key("async_probe")
+    assert isinstance(probe, _AsyncProbeTransformer)
+
+    err = RuntimeError("boom")
+    await mux.afail(err)
+    assert probe.failed is err
+
+
+class _StandardCtorTransformer(StreamTransformer):
+    """Transformer class that inherits the standard scoped constructor."""
+
+    def init(self) -> dict[str, Any]:
+        return {"standard_ctor": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ScopedTransformer(StreamTransformer):
+    """Transformer class that uses the inherited scoped construction."""
+
+    def init(self) -> dict[str, Any]:
+        return {"scoped": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ConfigurableFactoryTransformer(StreamTransformer):
+    """Transformer built by a configured per-scope factory."""
+
+    def __init__(self, scope: tuple[str, ...] = (), *, label: str) -> None:
+        super().__init__(scope)
+        self.label = label
+
+    def init(self) -> dict[str, Any]:
+        return {"configurable": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ChildExploder(StreamTransformer):
+    """Raise from child mini-muxes to verify errors propagate upstream."""
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if self.scope and event["method"] == "values":
+            raise RuntimeError("child boom")
+        return True
+
+
+class _ChildFinalizeExploder(StreamTransformer):
+    """Raise from child mini-mux finalization."""
+
+    supports_sync = True
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+    def finalize(self) -> None:
+        if self.scope:
+            raise RuntimeError("child finalize boom")
+
+    async def afinalize(self) -> None:
+        if self.scope:
+            raise RuntimeError("child afinalize boom")
+
+
+def test_normalize_transformer_factories_supports_scoped_classes() -> None:
+    factories = _normalize_stream_transformer_factories(
+        [_StandardCtorTransformer, _ScopedTransformer]
+    )
+
+    standard_ctor = factories[0](("child",))
+    scoped = factories[1](("child",))
+    assert isinstance(standard_ctor, _StandardCtorTransformer)
+    assert standard_ctor.scope == ("child",)
+    assert isinstance(scoped, _ScopedTransformer)
+    assert scoped.scope == ("child",)
+
+
+def test_normalize_transformer_factories_supports_configured_factories() -> None:
+    factories = _normalize_stream_transformer_factories(
+        [partial(_ConfigurableFactoryTransformer, label="configured")]
+    )
+
+    built = factories[0](("child",))
+    assert isinstance(built, _ConfigurableFactoryTransformer)
+    assert built.label == "configured"
+    assert built.scope == ("child",)
+
+
+def test_normalize_transformer_factories_rejects_instances() -> None:
+    with pytest.raises(TypeError, match="pre-built instance"):
+        _normalize_stream_transformer_factories([_StandardCtorTransformer()])
+
+
+def test_child_forwarding_errors_fail_sync_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildExploder,
+        ],
+        is_async=False,
+    )
+    run = GraphRunStream(
+        iter(
+            [
+                _stream_part(
+                    "tasks",
+                    ("agent:abc",),
+                    {
+                        "id": "t1",
+                        "name": "tool",
+                        "input": None,
+                        "triggers": [],
+                    },
+                ),
+                _stream_part("values", ("agent:abc",), {"x": 1}),
+            ]
+        ),
+        mux,
+    )
+
+    handle = next(iter(run.subgraphs))
+    assert handle.path == ("agent:abc",)
+    with pytest.raises(RuntimeError, match="child boom"):
+        _ = run.output
+    assert run._mux._events._error is not None
+
+
+@pytest.mark.anyio
+async def test_child_forwarding_errors_fail_async_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildExploder,
+        ],
+        is_async=True,
+    )
+    run = AsyncGraphRunStream(
+        _astream_parts(
+            _stream_part(
+                "tasks",
+                ("agent:abc",),
+                {
+                    "id": "t1",
+                    "name": "tool",
+                    "input": None,
+                    "triggers": [],
+                },
+            ),
+            _stream_part("values", ("agent:abc",), {"x": 1}),
+        ),
+        mux,
+    )
+
+    handle = await run.subgraphs.__aiter__().__anext__()
+    assert handle.path == ("agent:abc",)
+    with pytest.raises(RuntimeError, match="child boom"):
+        await run.output()
+    assert run._mux._events._error is not None
+
+
+def test_child_finalize_errors_propagate_to_sync_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildFinalizeExploder,
+        ],
+        is_async=False,
+    )
+    run = GraphRunStream(
+        iter(
+            [
+                _stream_part(
+                    "tasks",
+                    ("agent:abc",),
+                    {
+                        "id": "t1",
+                        "name": "tool",
+                        "input": None,
+                        "triggers": [],
+                    },
+                )
+            ]
+        ),
+        mux,
+    )
+
+    with pytest.raises(RuntimeError, match="child finalize boom"):
+        _ = run.output
+
+
+@pytest.mark.anyio
+async def test_child_finalize_errors_propagate_to_async_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildFinalizeExploder,
+        ],
+        is_async=True,
+    )
+    run = AsyncGraphRunStream(
+        _astream_parts(
+            _stream_part(
+                "tasks",
+                ("agent:abc",),
+                {
+                    "id": "t1",
+                    "name": "tool",
+                    "input": None,
+                    "triggers": [],
+                },
+            )
+        ),
+        mux,
+    )
+
+    with pytest.raises(RuntimeError, match="child afinalize boom"):
+        await run.output()
+
+
+# ---------------------------------------------------------------------------
+# End-to-end real-graph tests
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _passthrough(state: _State) -> dict[str, Any]:
+    return {"value": state["value"] + "!", "items": ["x"]}
+
+
+def _make_two_level_nested() -> Any:
+    """outer → middle → inner. Three Pregel instances, two nesting levels."""
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("inner_node", _passthrough)
+    inner_b.add_edge(START, "inner_node")
+    inner_b.add_edge("inner_node", END)
+    inner = inner_b.compile()
+
+    middle_b: StateGraph = StateGraph(_State, input_schema=_State)
+    middle_b.add_node("inner", inner)
+    middle_b.add_edge(START, "inner")
+    middle_b.add_edge("inner", END)
+    middle = middle_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("middle", middle)
+    outer_b.add_edge(START, "middle")
+    outer_b.add_edge("middle", END)
+    return outer_b.compile()
+
+
+def _item_node(item: str):
+    def node(state: _State) -> dict[str, Any]:
+        return {"items": [item]}
+
+    return node
+
+
+def _make_two_sibling_subgraphs() -> Any:
+    """outer → one → two, where both nodes are compiled subgraphs."""
+    one_b: StateGraph = StateGraph(_State, input_schema=_State)
+    one_b.add_node("add_one", _item_node("one"))
+    one_b.add_edge(START, "add_one")
+    one_b.add_edge("add_one", END)
+    one = one_b.compile()
+
+    two_b: StateGraph = StateGraph(_State, input_schema=_State)
+    two_b.add_node("add_two", _item_node("two"))
+    two_b.add_edge(START, "add_two")
+    two_b.add_edge("add_two", END)
+    two = two_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("one", one)
+    outer_b.add_node("two", two)
+    outer_b.add_edge(START, "one")
+    outer_b.add_edge("one", "two")
+    outer_b.add_edge("two", END)
+    return outer_b.compile()
+
+
+def _failing_node(state: _State) -> dict[str, Any]:
+    raise ValueError("child boom")
+
+
+def _make_failing_nested() -> Any:
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("fail", _failing_node)
+    inner_b.add_edge(START, "fail")
+    inner_b.add_edge("fail", END)
+    inner = inner_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("inner", inner)
+    outer_b.add_edge(START, "inner")
+    outer_b.add_edge("inner", END)
+    return outer_b.compile()
+
+
+def test_stream_events_v3_real_graph_yields_subgraph_handles() -> None:
+    """Iterating `run.subgraphs` yields handles for direct-child subgraphs."""
+    graph = _make_two_level_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    handle_paths: list[tuple[str, ...]] = []
+    final_status: dict[tuple[str, ...], str] = {}
+    for handle in run.subgraphs:
+        # Drill into the handle's projections inside the loop body so
+        # the mini-mux is subscribed before the next pump cycle.
+        list(handle.values)
+        handle_paths.append(handle.path)
+        final_status[handle.path] = handle.status
+
+    assert len(handle_paths) == 1
+    assert handle_paths[0][0].startswith("middle:")
+    assert final_status[handle_paths[0]] == "completed"
+
+
+def test_stream_events_v3_grandchild_visible_on_child_handle() -> None:
+    """Drilling into `handle.subgraphs` surfaces nested grandchildren."""
+    graph = _make_two_level_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    grandchild_paths: list[tuple[str, ...]] = []
+    middle_path: tuple[str, ...] | None = None
+    for middle_handle in run.subgraphs:
+        # Subscribe to grandchildren before the next pump cycle.
+        for inner_handle in middle_handle.subgraphs:
+            # Subscribe to inner.values so its mini-mux drains.
+            list(inner_handle.values)
+            grandchild_paths.append(inner_handle.path)
+        middle_path = middle_handle.path
+
+    assert middle_path is not None
+    assert len(grandchild_paths) == 1
+    inner_path = grandchild_paths[0]
+    assert inner_path[1].startswith("inner:")
+    assert inner_path[: len(middle_path)] == middle_path
+
+
+def test_subgraph_output_stops_at_own_terminal_without_draining_siblings() -> None:
+    """A handle's `output` must not pump past its terminal event.
+
+    If it over-pumps the root run, the second sibling handle is yielded
+    only after it has already completed, so subscribing to `values`
+    inside the loop body misses its events.
+    """
+    graph = _make_two_sibling_subgraphs()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    paths: list[tuple[str, ...]] = []
+    second_values: list[dict[str, Any]] = []
+    for handle in run.subgraphs:
+        paths.append(handle.path)
+        if handle.graph_name == "one":
+            assert handle.output is not None
+            assert handle.status == "completed"
+        elif handle.graph_name == "two":
+            second_values = list(handle.values)
+
+    assert [path[0].split(":", 1)[0] for path in paths] == ["one", "two"]
+    assert second_values
+    assert second_values[-1]["items"] == ["one", "two"]
+
+
+def test_aborted_subgraph_handle_does_not_fail_parent_forwarding() -> None:
+    graph = _make_two_sibling_subgraphs()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    seen: list[str | None] = []
+    for handle in run.subgraphs:
+        seen.append(handle.graph_name)
+        if handle.graph_name == "one":
+            # Subscribe before aborting to ensure forwarding into the
+            # closed mini-mux would have raised without the closed check.
+            iter(handle.values)
+            handle.abort()
+        elif handle.graph_name == "two":
+            assert list(handle.values)
+
+    assert seen == ["one", "two"]
+
+
+def test_failed_subgraph_output_raises_terminal_error() -> None:
+    graph = _make_failing_nested()
+    run = graph.stream_events({"value": "x", "items": []}, version="v3")
+
+    handle = next(iter(run.subgraphs))
+    with pytest.raises(RuntimeError, match="child boom"):
+        _ = handle.output
+    assert handle.status == "failed"
+    assert handle.error == "child boom"
@@ -0,0 +1,290 @@
+"""Tests for StreamToolCallHandler and ToolRuntime.emit_output_delta.
+
+These tests exercise the langgraph-core piece in isolation — the prebuilt
+`ToolCallTransformer` has its own test file. Here we feed real graphs
+through `Pregel.stream(stream_mode=["tools", ...])` and inspect the raw
+`(ns, mode, payload)` tuples on the `tools` channel.
+"""
+
+from __future__ import annotations
+
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode, ToolRuntime
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.pregel._tools import _tool_call_writer
+
+
+class _State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def _caller_sync(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
+    def caller(state: _State) -> dict:
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
+                )
+            ]
+        }
+
+    return caller
+
+
+def _caller_async(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
+    async def caller(state: _State) -> dict:
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
+                )
+            ]
+        }
+
+    return caller
+
+
+def _build_graph(caller, tools) -> Any:
+    sg = StateGraph(_State)
+    sg.add_node("caller", caller)
+    sg.add_node("tools", ToolNode(tools))
+    sg.add_edge(START, "caller")
+    sg.add_edge("caller", "tools")
+    sg.add_edge("tools", END)
+    return sg.compile()
+
+
+def _tool_events(stream) -> list[tuple[tuple[str, ...], dict]]:
+    """Collect `(ns, payload)` for every `tools`-mode chunk."""
+    out: list[tuple[tuple[str, ...], dict]] = []
+    for ns, mode, payload in stream:
+        if mode == "tools":
+            out.append((tuple(ns), payload))
+    return out
+
+
+class TestSyncGraphSyncTool:
+    def test_started_finished_cycle(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return f"echoed:{text}"
+
+        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        assert [p["event"] for _, p in events] == [
+            "tool-started",
+            "tool-finished",
+        ]
+        assert events[0][1]["tool_call_id"] == "tc1"
+        assert events[0][1]["tool_name"] == "echo"
+        assert events[0][1]["input"] == {"text": "hi"}
+        # ToolNode wraps the return in a ToolMessage.
+        assert events[1][1]["tool_call_id"] == "tc1"
+
+    def test_emit_output_delta_produces_delta_events(self) -> None:
+        @tool
+        def streaming_echo(text: str, runtime: ToolRuntime) -> str:
+            """stream chunks."""
+            for chunk in ("a", "b", "c"):
+                runtime.emit_output_delta(chunk)
+            return text
+
+        graph = _build_graph(
+            _caller_sync("streaming_echo", {"text": "x"}), [streaming_echo]
+        )
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        deltas = [p["delta"] for _, p in events if p["event"] == "tool-output-delta"]
+        assert deltas == ["a", "b", "c"]
+        # The deltas must be bracketed by started and finished.
+        ordered = [p["event"] for _, p in events]
+        assert ordered[0] == "tool-started"
+        assert ordered[-1] == "tool-finished"
+
+    def test_tool_error_event(self) -> None:
+        @tool
+        def boom() -> str:
+            """raises."""
+            raise ValueError("nope")
+
+        graph = _build_graph(_caller_sync("boom", {}), [boom])
+        events: list[tuple[tuple[str, ...], dict]] = []
+        with pytest.raises(ValueError, match="nope"):
+            for ns, mode, payload in graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            ):
+                if mode == "tools":
+                    events.append((tuple(ns), payload))
+
+        kinds = [p["event"] for _, p in events]
+        assert kinds == ["tool-started", "tool-error"]
+        assert events[1][1]["message"] == "nope"
+
+    def test_writer_unset_outside_tool(self) -> None:
+        # Outside any tool body the ContextVar that ToolRuntime reads
+        # is unset — emitting from there would be a no-op.
+        assert _tool_call_writer.get() is None
+
+    def test_no_events_without_tools_mode(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return text
+
+        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
+        # No "tools" in stream_mode — handler is not attached and zero
+        # `tools`-method events fire.
+        chunks = list(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["values"],
+                subgraphs=True,
+            )
+        )
+        assert all(
+            not (isinstance(c, tuple) and len(c) == 3 and c[1] == "tools")
+            for c in chunks
+        )
+
+
+class TestAsyncGraphAsyncTool:
+    @pytest.mark.anyio
+    async def test_async_tool_produces_events(self) -> None:
+        @tool
+        async def aecho(text: str, runtime: ToolRuntime) -> str:
+            """async echo."""
+            runtime.emit_output_delta(text)
+            return f"got:{text}"
+
+        graph = _build_graph(_caller_async("aecho", {"text": "hi"}), [aecho])
+        events: list[tuple[tuple[str, ...], dict]] = []
+        async for ns, mode, payload in graph.astream(
+            {"messages": []},
+            stream_mode=["tools"],
+            subgraphs=True,
+        ):
+            if mode == "tools":
+                events.append((tuple(ns), payload))
+
+        kinds = [p["event"] for _, p in events]
+        assert kinds == ["tool-started", "tool-output-delta", "tool-finished"]
+        assert events[1][1]["delta"] == "hi"
+
+
+class TestConcurrentToolCalls:
+    def test_parallel_tool_calls_do_not_bleed(self) -> None:
+        @tool
+        def streamer(marker: str, runtime: ToolRuntime) -> str:
+            """emits marker twice."""
+            runtime.emit_output_delta(f"{marker}-1")
+            runtime.emit_output_delta(f"{marker}-2")
+            return marker
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "streamer", "args": {"marker": "A"}, "id": "a"},
+                            {"name": "streamer", "args": {"marker": "B"}, "id": "b"},
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [streamer])
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        # Group deltas by tool_call_id.
+        by_id: dict[str, list[str]] = {}
+        for _, p in events:
+            if p["event"] == "tool-output-delta":
+                by_id.setdefault(p["tool_call_id"], []).append(p["delta"])
+        assert by_id["a"] == ["A-1", "A-2"]
+        assert by_id["b"] == ["B-1", "B-2"]
+
+
+class TestSubgraphNamespacePropagation:
+    def test_tool_inside_subgraph_emits_with_subgraph_ns(self) -> None:
+        @tool
+        def inner_tool(text: str) -> str:
+            """inner tool."""
+            return text
+
+        def sub_caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {
+                                "name": "inner_tool",
+                                "args": {"text": "x"},
+                                "id": "tc1",
+                            }
+                        ],
+                    )
+                ]
+            }
+
+        inner = StateGraph(_State)
+        inner.add_node("sub_caller", sub_caller)
+        inner.add_node("sub_tools", ToolNode([inner_tool]))
+        inner.add_edge(START, "sub_caller")
+        inner.add_edge("sub_caller", "sub_tools")
+        inner.add_edge("sub_tools", END)
+        inner_graph = inner.compile()
+
+        outer = StateGraph(_State)
+        outer.add_node("sub", inner_graph)
+        outer.add_edge(START, "sub")
+        outer.add_edge("sub", END)
+        graph = outer.compile()
+
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        # All `tools` events should carry a non-empty namespace rooted
+        # at the `sub` node.
+        assert events, "expected at least one tools event"
+        for ns, _ in events:
+            assert ns  # non-empty
+            assert ns[0].startswith("sub:")
@@ -1,5 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""

+from langgraph.prebuilt._tool_call_transformer import ToolCallTransformer
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import (
    InjectedState,
@@ -13,6 +14,7 @@ from langgraph.prebuilt.tool_validator import ValidationNode
 __all__ = [
    "create_react_agent",
    "ToolNode",
+    "ToolCallTransformer",
    "tools_condition",
    "ValidationNode",
    "InjectedState",
@@ -0,0 +1,117 @@
+"""In-process handle for a single tool call's streaming execution.
+
+Mirrors the shape of `ChatModelStream` from langchain-core but simpler —
+a tool has one output channel, no content-block multiplexing. Populated
+by `ToolCallTransformer` as `tool-started` / `tool-output-delta` /
+`tool-finished` / `tool-error` events flow in on the `tools` channel.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Iterator
+from typing import Any
+
+from langgraph.stream.stream_channel import StreamChannel
+
+
+class ToolCallStream:
+    """Scoped view of a single tool call's lifecycle.
+
+    Yielded on `run.tool_calls` once per `tool-started` event. Fields
+    are populated as events arrive:
+
+    - `tool_call_id`, `tool_name`, `input`: stable from the start event.
+    - `output_deltas`: a `StreamChannel` of delta chunks. Iterate (sync or
+      async) to consume partial output in arrival order.
+    - `output`: terminal payload from `tool-finished`, or `None` if the
+      call failed or is still in flight.
+    - `error`: terminal error string from `tool-error`, or `None` if the
+      call succeeded or is still in flight.
+    - `completed`: True once a terminal event (`tool-finished` or
+      `tool-error`) has been observed.
+
+    `ToolCallStream` is not meant to be constructed by end users — it's
+    produced by `ToolCallTransformer` as events flow through the mux.
+    """
+
+    def __init__(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        input: dict[str, Any] | None = None,
+    ) -> None:
+        """Initialize a fresh handle for a tool call.
+
+        Args:
+            tool_call_id: The `tool_call_id` from the AIMessage.
+            tool_name: The tool's name.
+            input: The tool's input arguments (as reported by
+                `on_tool_start`), or `None` if none were captured.
+        """
+        self.tool_call_id = tool_call_id
+        self.tool_name = tool_name
+        self.input = input
+        self._output_deltas: StreamChannel[Any] = StreamChannel()
+        self.output: Any = None
+        self.error: str | None = None
+        self.completed = False
+
+    @property
+    def output_deltas(self) -> StreamChannel[Any]:
+        """The channel of streamed `tool-output-delta` payloads.
+
+        Iterate (sync or async depending on how the run was started)
+        to consume partial output in arrival order. The log closes when
+        the tool finishes or errors.
+        """
+        return self._output_deltas
+
+    def _bind(self, *, is_async: bool) -> None:
+        """Bind the deltas log to sync or async iteration.
+
+        Called by `ToolCallTransformer` when constructing this handle so
+        the log matches the enclosing mux's mode.
+        """
+        self._output_deltas._bind(is_async=is_async)
+
+    def _push_delta(self, delta: Any) -> None:
+        self._output_deltas.push(delta)
+
+    def _finish(self, output: Any) -> None:
+        self.output = output
+        self.completed = True
+        self._output_deltas.close()
+
+    def _fail(self, message: str) -> None:
+        self.error = message
+        self.completed = True
+        self._output_deltas.close()
+
+    def __iter__(self) -> Iterator[Any]:
+        """Iterate delta chunks synchronously.
+
+        Equivalent to `iter(self.output_deltas)`. Raises `TypeError` if
+        the underlying log is bound to async mode.
+        """
+        return iter(self._output_deltas)
+
+    def __aiter__(self) -> AsyncIterator[Any]:
+        """Iterate delta chunks asynchronously.
+
+        Equivalent to `aiter(self.output_deltas)`. Raises `TypeError`
+        if the underlying log is bound to sync mode.
+        """
+        return self._output_deltas.__aiter__()
+
+    def __repr__(self) -> str:
+        status = (
+            "completed"
+            if self.completed and self.error is None
+            else "failed"
+            if self.completed
+            else "running"
+        )
+        return (
+            f"ToolCallStream(tool_call_id={self.tool_call_id!r}, "
+            f"tool_name={self.tool_name!r}, status={status})"
+        )
@@ -0,0 +1,135 @@
+"""Transformer that projects `tools` channel events into `ToolCallStream`s."""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.stream_channel import StreamChannel
+
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
+
+
+class ToolCallTransformer(StreamTransformer):
+    """Project `tools` channel events into `ToolCallStream` handles.
+
+    Each `tool-started` event spawns a `ToolCallStream`, pushed onto
+    `run.tool_calls`. Subsequent `tool-output-delta` events append to
+    that stream's deltas log; `tool-finished` and `tool-error` close it.
+
+    Native transformer — the `tool_calls` projection is exposed as a
+    direct attribute on the run stream.
+
+    A nameless `StreamChannel[ToolCallStream]` is used (no protocol
+    auto-forwarding) because the live handles are not serializable and
+    should not be injected into the main event log. Wire consumers
+    subscribe to the `tools` channel instead, where the raw protocol
+    events flow through untouched by this transformer (`process`
+    returns `True`).
+
+    Registered explicitly by users at compile time via
+    `builder.compile(transformers=[ToolCallTransformer])` — not a
+    default built-in, so the `tools` channel is user-opt-in.
+    """
+
+    _native = True
+    required_stream_modes = ("tools",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[ToolCallStream] = StreamChannel()
+        self._active: dict[str, ToolCallStream] = {}
+        self._is_async = False
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"tool_calls": self._log}
+
+    def _bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback onto this transformer.
+
+        Called by `StreamMux.bind_pump`. Stored so each new
+        `ToolCallStream` created by `process` can wire its deltas log
+        for pump-driven iteration.
+        """
+        self._pump_fn = fn
+        self._is_async = False
+
+    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Async counterpart to `_bind_pump`."""
+        self._apump_fn = fn
+        self._is_async = True
+
+    def _new_stream(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        tool_input: dict[str, Any] | None,
+    ) -> ToolCallStream:
+        stream = ToolCallStream(tool_call_id, tool_name, tool_input)
+        stream._bind(is_async=self._is_async)
+        if self._apump_fn is not None:
+            stream._output_deltas._arequest_more = self._apump_fn
+        if self._pump_fn is not None:
+            stream._output_deltas._request_more = self._pump_fn
+        return stream
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "tools":
+            return True
+
+        # Only project events emitted at this transformer's scope. Subgraph
+        # events still flow through the parent's mux (the parent's main
+        # event log keeps them) but they belong to the child mini-mux's
+        # `tool_calls` projection, not the parent's.
+        if tuple(event["params"]["namespace"]) != self.scope:
+            return True
+
+        data = event["params"]["data"]
+        tool_call_id = data.get("tool_call_id")
+        if tool_call_id is None:
+            return True
+        event_type = data.get("event")
+
+        stream: ToolCallStream | None
+        if event_type == "tool-started":
+            stream = self._new_stream(
+                tool_call_id,
+                data.get("tool_name", ""),
+                data.get("input"),
+            )
+            self._active[tool_call_id] = stream
+            self._log.push(stream)
+        elif event_type == "tool-output-delta":
+            stream = self._active.get(tool_call_id)
+            if stream is not None:
+                stream._push_delta(data.get("delta"))
+        elif event_type == "tool-finished":
+            stream = self._active.pop(tool_call_id, None)
+            if stream is not None:
+                stream._finish(data.get("output"))
+        elif event_type == "tool-error":
+            stream = self._active.pop(tool_call_id, None)
+            if stream is not None:
+                stream._fail(data.get("message", ""))
+
+        # Pass-through — wire consumers subscribe to the `tools` channel
+        # directly and reconstruct handles client-side.
+        return True
+
+    def finalize(self) -> None:
+        """Close any still-active tool streams left open at run end."""
+        for stream in self._active.values():
+            if not stream.completed:
+                stream._finish(None)
+        self._active.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Fail any still-active tool streams when the run errors."""
+        message = str(err)
+        for stream in self._active.values():
+            if not stream.completed:
+                stream._fail(message)
+        self._active.clear()
@@ -44,7 +44,7 @@ import inspect
 import json
 from collections.abc import Awaitable, Callable
 from copy import copy, deepcopy
-from dataclasses import dataclass, replace
+from dataclasses import dataclass, field, replace
 from types import UnionType
 from typing import (
    TYPE_CHECKING,
@@ -82,9 +82,11 @@ from langchain_core.tools.base import (
    _is_injected_arg_type,
    get_all_basemodel_annotations,
 )
+from langgraph._internal._constants import CONF, CONFIG_KEY_READ
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.pregel._tools import _tool_call_writer
 from langgraph.runtime import ExecutionInfo, ServerInfo  # noqa: TC002
 from langgraph.store.base import BaseStore  # noqa: TC002
 from langgraph.types import Command, Send, StreamWriter
@@ -800,7 +802,7 @@ class ToolNode(RunnableCallable):
        # Construct ToolRuntime instances at the top level for each tool call
        tool_runtimes = []
        for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input)
+            state = self._extract_state(input, cfg)
            tool_runtime = ToolRuntime(
                state=state,
                tool_call_id=call["id"],
@@ -835,7 +837,7 @@ class ToolNode(RunnableCallable):
        # Construct ToolRuntime instances at the top level for each tool call
        tool_runtimes = []
        for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input)
+            state = self._extract_state(input, cfg)
            tool_runtime = ToolRuntime(
                state=state,
                tool_call_id=call["id"],
@@ -1277,18 +1279,37 @@ class ToolNode(RunnableCallable):
        return None

    def _extract_state(
-        self, input: list[AnyMessage] | dict[str, Any] | BaseModel
+        self,
+        input: list[AnyMessage] | dict[str, Any] | BaseModel,
+        config: RunnableConfig,
    ) -> list[AnyMessage] | dict[str, Any] | BaseModel:
-        """Extract state from input, handling ToolCallWithContext if present.
+        """Extract state from input.

-        Args:
-            input: The input which may be raw state or ToolCallWithContext.
+        Three input shapes:

-        Returns:
-            The actual state to pass to wrap_tool_call wrappers.
+        - `ToolCallWithContext` dict — legacy Send payload carrying an inlined
+          state snapshot; return `input["state"]`.
+        - list of `ToolCall` dicts — new Send payload with no inlined state;
+          hydrate state from channels via `CONFIG_KEY_READ`.
+        - regular graph state (dict/list/BaseModel) — return `input` as-is.
        """
        if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
            return input["state"]
+        if (
+            isinstance(input, list)
+            and input
+            and isinstance(input[-1], dict)
+            and input[-1].get("type") == "tool_call"
+        ):
+            read = config.get(CONF, {}).get(CONFIG_KEY_READ)
+            if read is None:
+                return {}
+            # Pregel installs CONFIG_KEY_READ as
+            # `functools.partial(local_read, scratchpad, channels, managed, task)`.
+            # Match the previous inlined-state contract by reading channels only;
+            # managed values have their own injection path (`ToolRuntime.context`).
+            channels = read.args[1]
+            return cast("dict[str, Any]", read(list(channels), True))
        return input

    def _inject_tool_args(
@@ -1702,12 +1723,32 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
    context: ContextT
    config: RunnableConfig
    stream_writer: StreamWriter
-    tools: list[BaseTool]
    tool_call_id: str | None
    store: BaseStore | None
+    tools: list[BaseTool] = field(default_factory=list)
    execution_info: ExecutionInfo | None = None
    server_info: ServerInfo | None = None

+    def emit_output_delta(self, delta: Any) -> None:
+        """Stream a partial output chunk on the `tools` stream channel.
+
+        Reads the per-tool-call writer that `StreamToolCallHandler`
+        installs on a ContextVar at `on_tool_start` and forwards `delta`
+        through it. Silent no-op when the graph was not run with
+        `"tools"` in `stream_mode` (no writer is set), so tool authors
+        can leave `emit_output_delta` calls in place without gating
+        them on stream mode.
+
+        Args:
+            delta: Partial output chunk. Any JSON-serializable value;
+                surfaced as-is on the `tools` channel's
+                `tool-output-delta` payload under `"delta"`.
+        """
+        writer = _tool_call_writer.get()
+        if writer is None:
+            return
+        writer(delta)
+

 class InjectedState(InjectedToolArg):
    """Annotation for injecting graph state into tool arguments.
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-prebuilt"
-version = "1.0.11"
+version = "1.1.0a2"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -30,7 +30,7 @@ dependencies = [

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

@@ -1320,6 +1320,98 @@ async def test_state_extraction_with_tool_call_with_context_async() -> None:
    assert "tool_call" not in state_seen[0]


+def _config_with_channel_read(
+    channel_values: dict[str, object],
+    store: BaseStore | None = None,
+) -> RunnableConfig:
+    """Build a config that mimics `CONFIG_KEY_READ` as Pregel installs it.
+
+    Pregel always installs a `functools.partial(local_read, scratchpad,
+    channels, managed, task)`, and `ToolNode` introspects that partial to
+    learn channel names. The stub matches the shape: partial whose second and
+    third positional args are `channels` and `managed` mappings.
+    """
+    import functools
+
+    channels_stub = {k: None for k in channel_values}
+    managed_stub: dict[str, object] = {}
+
+    # Shape matches pregel's real partial:
+    # functools.partial(local_read, scratchpad, channels, managed, task)
+    def _read(scratchpad, channels, managed, task, select, fresh):  # noqa: ARG001
+        if isinstance(select, str):
+            return channel_values[select]
+        return {k: channel_values[k] for k in select if k in channel_values}
+
+    read = functools.partial(_read, None, channels_stub, managed_stub, None)
+    cfg = _create_config_with_runtime(store)
+    cfg["configurable"]["__pregel_read"] = read
+    return cfg
+
+
+def test_list_form_send_hydrates_state_from_channel_read() -> None:
+    """Send('tools', [tool_call]) with no inlined state should hydrate
+    ToolRuntime.state from CONFIG_KEY_READ (full state read)."""
+    state_seen = []
+
+    def state_inspector_handler(
+        request: ToolCallRequest,
+        execute: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        state_seen.append(request.state)
+        return execute(request)
+
+    channel_values = {
+        "messages": [AIMessage("from channels")],
+        "files": {"/a.md": "body"},
+    }
+
+    tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
+
+    tool_call: ToolCall = {
+        "name": "add",
+        "args": {"a": 1, "b": 2},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+
+    tool_node.invoke([tool_call], config=_config_with_channel_read(channel_values))
+
+    assert len(state_seen) == 1
+    got = state_seen[0]
+    assert got == channel_values
+    assert "messages" in got and "files" in got
+
+
+async def test_list_form_send_hydrates_state_async() -> None:
+    state_seen = []
+
+    def state_inspector_handler(
+        request: ToolCallRequest,
+        execute: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        state_seen.append(request.state)
+        return execute(request)
+
+    channel_values = {"messages": [AIMessage("from channels")], "files": {}}
+
+    tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
+
+    tool_call: ToolCall = {
+        "name": "add",
+        "args": {"a": 1, "b": 2},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+
+    await tool_node.ainvoke(
+        [tool_call], config=_config_with_channel_read(channel_values)
+    )
+
+    assert len(state_seen) == 1
+    assert state_seen[0] == channel_values
+
+
 def test_tool_call_request_is_frozen() -> None:
    """Test that ToolCallRequest raises deprecation warnings on direct attribute reassignment."""
    tool_call: ToolCall = {"name": "add", "args": {"a": 1, "b": 2}, "id": "call_1"}
@@ -0,0 +1,391 @@
+"""Tests for ToolCallTransformer and the ToolCallStream projection."""
+
+from __future__ import annotations
+
+import time
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    MessagesTransformer,
+    ValuesTransformer,
+)
+from typing_extensions import TypedDict
+
+from langgraph.prebuilt import (
+    ToolCallTransformer,
+    ToolNode,
+    ToolRuntime,
+)
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
+
+TS = int(time.time() * 1000)
+
+
+def _unstamped(items):
+    """Strip push stamps from a StreamChannel's internal buffer."""
+    return [item for _stamp, item in items]
+
+
+def _tool_event(
+    event: str,
+    tool_call_id: str,
+    *,
+    tool_name: str = "",
+    input: dict[str, Any] | None = None,
+    delta: Any = None,
+    output: Any = None,
+    message: str = "",
+    namespace: list[str] | None = None,
+) -> ProtocolEvent:
+    data: dict[str, Any] = {"event": event, "tool_call_id": tool_call_id}
+    if event == "tool-started":
+        data["tool_name"] = tool_name
+        if input is not None:
+            data["input"] = input
+    elif event == "tool-output-delta":
+        data["delta"] = delta
+    elif event == "tool-finished":
+        data["output"] = output
+    elif event == "tool-error":
+        data["message"] = message
+    return {
+        "type": "event",
+        "method": "tools",
+        "params": {
+            "namespace": namespace or [],
+            "timestamp": TS,
+            "data": data,
+        },
+    }
+
+
+def _subscribe(log: StreamChannel) -> None:
+    log._subscribed = True
+
+
+def _mux() -> tuple[StreamMux, ToolCallTransformer]:
+    transformer = ToolCallTransformer()
+    mux = StreamMux(
+        [
+            ValuesTransformer(),
+            MessagesTransformer(),
+            transformer,
+        ],
+        is_async=False,
+    )
+    _subscribe(transformer._log)
+    return mux, transformer
+
+
+class TestToolCallTransformerUnit:
+    def test_required_stream_modes_declares_tools(self) -> None:
+        assert ToolCallTransformer.required_stream_modes == ("tools",)
+
+    def test_tool_started_yields_handle(self) -> None:
+        mux, transformer = _mux()
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc1",
+                tool_name="echo",
+                input={"text": "hi"},
+            )
+        )
+        handles = _unstamped(transformer._log._items)
+        assert len(handles) == 1
+        h = handles[0]
+        assert isinstance(h, ToolCallStream)
+        assert h.tool_call_id == "tc1"
+        assert h.tool_name == "echo"
+        assert h.input == {"text": "hi"}
+        assert h.completed is False
+
+    def test_delta_accumulates_on_active_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        _subscribe(transformer._active["tc1"]._output_deltas)
+        mux.push(_tool_event("tool-output-delta", "tc1", delta="a"))
+        mux.push(_tool_event("tool-output-delta", "tc1", delta="b"))
+        stream = transformer._active["tc1"]
+        assert _unstamped(stream._output_deltas._items) == ["a", "b"]
+
+    def test_finish_closes_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        stream = transformer._active["tc1"]
+        mux.push(_tool_event("tool-finished", "tc1", output="done"))
+        assert stream.completed is True
+        assert stream.output == "done"
+        assert stream.error is None
+        assert "tc1" not in transformer._active
+
+    def test_error_closes_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="boom"))
+        stream = transformer._active["tc1"]
+        mux.push(_tool_event("tool-error", "tc1", message="nope"))
+        assert stream.completed is True
+        assert stream.output is None
+        assert stream.error == "nope"
+        assert "tc1" not in transformer._active
+
+    def test_concurrent_tool_calls_do_not_bleed(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "a", tool_name="t"))
+        mux.push(_tool_event("tool-started", "b", tool_name="t"))
+        for tc in ("a", "b"):
+            _subscribe(transformer._active[tc]._output_deltas)
+        mux.push(_tool_event("tool-output-delta", "a", delta="A1"))
+        mux.push(_tool_event("tool-output-delta", "b", delta="B1"))
+        mux.push(_tool_event("tool-output-delta", "a", delta="A2"))
+        assert _unstamped(transformer._active["a"]._output_deltas._items) == [
+            "A1",
+            "A2",
+        ]
+        assert _unstamped(transformer._active["b"]._output_deltas._items) == ["B1"]
+
+    def test_tools_event_passes_through_main_log(self) -> None:
+        mux, transformer = _mux()
+        _subscribe(mux._events)
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        kept = [e for e in _unstamped(mux._events._items) if e["method"] == "tools"]
+        assert len(kept) == 1
+
+    def test_out_of_scope_event_skipped(self) -> None:
+        """Subgraph-scoped `tools` events must not project into a parent
+        transformer's `tool_calls` log.
+
+        The parent's main event log keeps the event (so wire consumers
+        still see it) but the parent's `ToolCallTransformer` only owns
+        the projection at its own scope. Per-scope `ToolCallTransformer`
+        instances on child mini-muxes are responsible for projecting
+        events at their own depth.
+        """
+        # Root-scope transformer (`scope == ()`).
+        mux, transformer = _mux()
+        _subscribe(mux._events)
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc1",
+                tool_name="inner_echo",
+                namespace=["child:abc"],
+            )
+        )
+        # No `ToolCallStream` was projected into the root's log.
+        assert _unstamped(transformer._log._items) == []
+        assert "tc1" not in transformer._active
+        # The event still passes through the main event log so consumers
+        # of the raw `tools` channel see it untouched.
+        kept = [e for e in _unstamped(mux._events._items) if e["method"] == "tools"]
+        assert len(kept) == 1
+
+    def test_in_scope_event_projected_when_scope_set(self) -> None:
+        """A non-root transformer projects only events at its own scope."""
+        scope: tuple[str, ...] = ("child:abc",)
+        transformer = ToolCallTransformer(scope=scope)
+        mux = StreamMux(
+            [ValuesTransformer(), MessagesTransformer(), transformer],
+            scope=scope,
+            is_async=False,
+        )
+        _subscribe(transformer._log)
+        # Event at this scope: projected.
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc1",
+                tool_name="echo",
+                namespace=list(scope),
+            )
+        )
+        assert len(_unstamped(transformer._log._items)) == 1
+        # Event at a deeper scope: ignored.
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc2",
+                tool_name="grandchild",
+                namespace=[*scope, "grand:xyz"],
+            )
+        )
+        assert len(_unstamped(transformer._log._items)) == 1
+        # Event at root (above this scope): ignored.
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc3",
+                tool_name="root_tool",
+                namespace=[],
+            )
+        )
+        assert len(_unstamped(transformer._log._items)) == 1
+
+
+# ---------------------------------------------------------------------------
+# End-to-end tests with a real graph
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def _build_graph(caller, tools):
+    sg = StateGraph(_State)
+    sg.add_node("caller", caller)
+    sg.add_node("tools", ToolNode(tools))
+    sg.add_edge(START, "caller")
+    sg.add_edge("caller", "tools")
+    sg.add_edge("tools", END)
+    return sg.compile()
+
+
+class TestToolCallTransformerEndToEnd:
+    def test_sync_streaming_tool_populates_tool_calls(self) -> None:
+        @tool
+        def streamer(text: str, runtime: ToolRuntime) -> str:
+            """streams chunks."""
+            for chunk in ("one", "two"):
+                runtime.emit_output_delta(chunk)
+            return text
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "streamer", "args": {"text": "x"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [streamer])
+        run = graph.stream_events(
+            {"messages": []}, transformers=[ToolCallTransformer], version="v3"
+        )
+
+        tool_calls: list[ToolCallStream] = []
+        for tc in run.tool_calls:
+            tool_calls.append(tc)
+            deltas = list(tc.output_deltas)
+            assert deltas == ["one", "two"]
+        assert len(tool_calls) == 1
+        tc = tool_calls[0]
+        assert tc.tool_call_id == "tc1"
+        assert tc.tool_name == "streamer"
+        assert tc.completed is True
+        assert tc.error is None
+
+    def test_stream_modes_union_includes_tools(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return text
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "echo", "args": {"text": "x"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [echo])
+        # Without ToolCallTransformer, no tool_calls projection is
+        # exposed and no `tools` events flow through (required_stream_modes
+        # omits it).
+        run_no_tc = graph.stream_events({"messages": []}, version="v3")
+        assert "tool_calls" not in run_no_tc._mux.extensions  # type: ignore[attr-defined]
+
+        # With ToolCallTransformer, the projection is present.
+        run = graph.stream_events(
+            {"messages": []}, transformers=[ToolCallTransformer], version="v3"
+        )
+        assert "tool_calls" in run._mux.extensions  # type: ignore[attr-defined]
+        # Drain so the run closes cleanly.
+        list(run.tool_calls)
+
+    @pytest.mark.anyio
+    async def test_async_streaming_tool_populates_tool_calls(self) -> None:
+        @tool
+        async def astreamer(text: str, runtime: ToolRuntime) -> str:
+            """async streams."""
+            runtime.emit_output_delta(text)
+            runtime.emit_output_delta(text + "!")
+            return text
+
+        async def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "astreamer", "args": {"text": "hi"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [astreamer])
+        run = await graph.astream_events(
+            {"messages": []}, version="v3", transformers=[ToolCallTransformer]
+        )
+
+        collected: list[ToolCallStream] = []
+        async for tc in run.tool_calls:
+            collected.append(tc)
+            deltas = [d async for d in tc.output_deltas]
+            assert deltas == ["hi", "hi!"]
+        assert len(collected) == 1
+        assert collected[0].completed is True
+        assert collected[0].error is None
+
+    def test_tool_error_populates_error_field(self) -> None:
+        @tool
+        def boom() -> str:
+            """raises."""
+            raise ValueError("nope")
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[{"name": "boom", "args": {}, "id": "tc1"}],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [boom])
+        run = graph.stream_events(
+            {"messages": []}, transformers=[ToolCallTransformer], version="v3"
+        )
+
+        collected: list[ToolCallStream] = []
+        with pytest.raises(ValueError, match="nope"):
+            for tc in run.tool_calls:
+                collected.append(tc)
+                # Drain deltas so the error field is populated before we
+                # inspect it below.
+                list(tc.output_deltas)
+
+        assert len(collected) == 1
+        assert collected[0].error == "nope"
+        assert collected[0].output is None
+        assert collected[0].completed is True
@@ -2016,6 +2016,19 @@ 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_defaults_tools_to_empty_list() -> None:
+    runtime = ToolRuntime(
+        state={},
+        context=None,
+        config={},
+        stream_writer=lambda *args, **kwargs: None,
+        tool_call_id=None,
+        store=None,
+    )
+
+    assert runtime.tools == []
+
+
 def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
    """Test that execution_info, server_info, and tools are forwarded from Runtime to ToolRuntime."""
    from langgraph.runtime import ExecutionInfo, ServerInfo
@@ -1,6 +1,10 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]

 [[package]]
 name = "aiosqlite"
@@ -249,10 +253,11 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.1"
+version = "1.4.0a2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -261,14 +266,26 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
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 ]

 [[package]]
@@ -3,6 +3,6 @@ from langgraph_sdk.client import get_client, get_sync_client
 from langgraph_sdk.encryption import Encryption
 from langgraph_sdk.encryption.types import EncryptionContext

-__version__ = "0.3.13"
+__version__ = "0.3.14"

 __all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]
--- a/Show More
+++ b/Show More