Remove StreamChannel close/fail no-ops and channel tracking from mux

Remove unused timestamp from ProtocolEvent params (seq provides
ordering). Fix docstrings: remove async-specific language from sync
base class, correct tautological namespace comment, distinguish
sync/async projection sets in module docstring. Add double-iteration
tests for values and raw events on both sync and async paths.

.github/workflows/_integration_test.yml

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

.gitignore

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

README.md

@@ -16,7 +16,7 @@
   <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
   <a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
   <a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
-  <a href="https://x.com/langchain_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>
+  <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>
 </div>
 
 <br>

libs/checkpoint-conformance/uv.lock

@@ -306,7 +306,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.31"
+version = "0.7.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -319,9 +319,9 @@ dependencies = [
     { name = "xxhash" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/8d/bc/8172fefad4f2da888a6d564a27d1fb7d4dbf3c640899c2b40c46235cbe98/langsmith-0.7.3.tar.gz", hash = "sha256:0223b97021af62d2cf53c8a378a27bd22e90a7327e45b353e0069ae60d5d6f9e", size = 988575, upload-time = "2026-02-13T23:25:32.916Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/9d/5a68b6b5e313ffabbb9725d18a71edb48177fd6d3ad329c07801d2a8e862/langsmith-0.7.3-py3-none-any.whl", hash = "sha256:03659bf9274e6efcead361c9c31a7849ea565ae0d6c0d73e1d8b239029eff3be", size = 325718, upload-time = "2026-02-13T23:25:31.52Z" },
 ]
 
 [[package]]
@@ -623,7 +623,7 @@ wheels = [
 
 [[package]]
 name = "pytest"
-version = "9.0.3"
+version = "9.0.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
@@ -634,9 +634,9 @@ dependencies = [
     { name = "pygments" },
     { name = "tomli", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" },
 ]
 
 [[package]]

libs/checkpoint-postgres/README.md

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

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

@@ -4,35 +4,26 @@ import threading
 from collections import defaultdict
 from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any, cast
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
-    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
-    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
 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 (
-    SELECT_DELTA_STAGE1_SQL,
-    SELECT_DELTA_STAGE2_SQL,
-    BasePostgresSaver,
-    _DeltaStage1Row,
-    _DeltaStage2Row,
-)
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 
 Conn = _internal.Conn  # For backward compatibility
@@ -311,12 +302,7 @@ class PostgresSaver(BasePostgresSaver):
         # others are stored in blobs table
         blob_values = {}
         for k, v in checkpoint["channel_values"].items():
-            if v is DELTA_SENTINEL:
-                copy["channel_values"].pop(k)
-            elif isinstance(v, _DeltaSnapshot):
-                blob_values[k] = copy["channel_values"].pop(k)
-                copy["channel_values"][k] = True
-            elif v is None or isinstance(v, (str, int, float, bool)):
+            if v is None or isinstance(v, (str, int, float, bool)):
                 pass
             else:
                 blob_values[k] = copy["channel_values"].pop(k)
@@ -444,56 +430,6 @@ class PostgresSaver(BasePostgresSaver):
                 with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
-    def _get_channel_writes_history(
-        self, config: RunnableConfig, channel: str
-    ) -> _ChannelWritesHistory:
-        """Fast-path override of `BaseCheckpointSaver._get_channel_writes_history`.
-
-        Two-stage query: stage 1 scans checkpoint metadata to walk the parent
-        chain and locate the nearest snapshot; stage 2 fetches only the
-        chain-limited writes and single seed blob.
-        """
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        checkpoint_id = get_checkpoint_id(config)
-        if checkpoint_id is None:
-            target = self.get_tuple(config)
-            if target is None:
-                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
-            checkpoint_id = target.config["configurable"]["checkpoint_id"]
-
-        with self._cursor() as cur:
-            cur.execute(
-                SELECT_DELTA_STAGE1_SQL,
-                (channel, channel, thread_id, checkpoint_ns),
-            )
-            stage1_rows = cur.fetchall()
-        chain_cids, seed_version = self._walk_stage1(
-            cast("list[_DeltaStage1Row]", stage1_rows), checkpoint_id
-        )
-        seed_versions = [seed_version] if seed_version else []
-        with self._cursor() as cur:
-            cur.execute(
-                SELECT_DELTA_STAGE2_SQL,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    chain_cids,
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    seed_versions,
-                ),
-            )
-            stage2_rows = cur.fetchall()
-        return self._build_delta_channel_writes_history(
-            channel=channel,
-            chain_cids=chain_cids,
-            seed_version=seed_version,
-            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
-        )
-
     def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.

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

@@ -4,35 +4,26 @@ import asyncio
 from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import Any, cast
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
-    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
-    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
 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 (
-    SELECT_DELTA_STAGE1_SQL,
-    SELECT_DELTA_STAGE2_SQL,
-    BasePostgresSaver,
-    _DeltaStage1Row,
-    _DeltaStage2Row,
-)
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 
 Conn = _ainternal.Conn  # For backward compatibility
@@ -270,12 +261,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         # others are stored in blobs table
         blob_values = {}
         for k, v in checkpoint["channel_values"].items():
-            if v is DELTA_SENTINEL:
-                copy["channel_values"].pop(k)
-            elif isinstance(v, _DeltaSnapshot):
-                blob_values[k] = copy["channel_values"].pop(k)
-                copy["channel_values"][k] = True
-            elif v is None or isinstance(v, (str, int, float, bool)):
+            if v is None or isinstance(v, (str, int, float, bool)):
                 pass
             else:
                 blob_values[k] = copy["channel_values"].pop(k)
@@ -405,56 +391,6 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
-    async def _aget_channel_writes_history(
-        self, config: RunnableConfig, channel: str
-    ) -> _ChannelWritesHistory:
-        """Fast-path override of `BaseCheckpointSaver._aget_channel_writes_history`.
-
-        Two-stage query: stage 1 scans checkpoint metadata to walk the parent
-        chain and locate the nearest snapshot; stage 2 fetches only the
-        chain-limited writes and single seed blob.
-        """
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        checkpoint_id = get_checkpoint_id(config)
-        if checkpoint_id is None:
-            target = await self.aget_tuple(config)
-            if target is None:
-                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
-            checkpoint_id = target.config["configurable"]["checkpoint_id"]
-
-        async with self._cursor() as cur:
-            await cur.execute(
-                SELECT_DELTA_STAGE1_SQL,
-                (channel, channel, thread_id, checkpoint_ns),
-            )
-            stage1_rows = await cur.fetchall()
-        chain_cids, seed_version = self._walk_stage1(
-            cast("list[_DeltaStage1Row]", stage1_rows), checkpoint_id
-        )
-        seed_versions = [seed_version] if seed_version else []
-        async with self._cursor() as cur:
-            await cur.execute(
-                SELECT_DELTA_STAGE2_SQL,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    chain_cids,
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    seed_versions,
-                ),
-            )
-            stage2_rows = await cur.fetchall()
-        return self._build_delta_channel_writes_history(
-            channel=channel,
-            chain_cids=chain_cids,
-            seed_version=seed_version,
-            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
-        )
-
     async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.

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

@@ -4,16 +4,13 @@ import random
 import warnings
 from collections.abc import Sequence
 from importlib.metadata import version as get_version
-from typing import Any, TypedDict, cast
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
-    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
-    PendingWrite,
-    _ChannelWritesHistory,
     get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
@@ -156,62 +153,6 @@ INSERT_CHECKPOINT_WRITES_SQL = """
 """
 
 
-class _DeltaStage2Row(TypedDict, total=False):
-    """One row from `SELECT_DELTA_STAGE2_SQL` (a UNION ALL of writes and blobs)."""
-
-    _kind: str  # "w" or "b"
-    checkpoint_id: str | None  # "w" rows only
-    type: str | None
-    blob: bytes | None
-    task_id: str | None  # "w" rows only
-    idx: int | None  # "w" rows only
-    version: str | None  # "b" rows only
-
-
-# Two-stage DeltaChannel reconstruction.  Stage 1 scans checkpoint
-# metadata (no blob bytes) to walk the parent chain and locate the
-# nearest snapshot marker.  Stage 2 fetches only the chain-limited
-# writes and the single seed snapshot blob.
-#
-# Parameter order:
-#   stage1: (channel, channel, thread_id, checkpoint_ns)
-#   stage2: (thread_id, checkpoint_ns, channel, chain_cids[],
-#            thread_id, checkpoint_ns, channel, seed_versions[])
-
-SELECT_DELTA_STAGE1_SQL = """
-    SELECT checkpoint_id,
-           parent_checkpoint_id,
-           checkpoint -> 'channel_versions' ->> %s AS ver,
-           (checkpoint -> 'channel_values' -> %s) IS NOT NULL AS has_snapshot
-    FROM checkpoints
-    WHERE thread_id = %s AND checkpoint_ns = %s
-"""
-
-SELECT_DELTA_STAGE2_SQL = """
-    SELECT 'w'::text AS _kind,
-           checkpoint_id,
-           type, blob, task_id, idx, NULL::text AS version
-    FROM checkpoint_writes
-    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
-      AND checkpoint_id = ANY(%s)
-    UNION ALL
-    SELECT 'b', NULL,
-           type, blob, NULL, NULL, version
-    FROM checkpoint_blobs
-    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
-      AND version = ANY(%s)
-"""
-
-
-class _DeltaStage1Row(TypedDict):
-    """One row from `SELECT_DELTA_STAGE1_SQL`."""
-
-    checkpoint_id: str
-    parent_checkpoint_id: str | None
-    ver: str | None
-    has_snapshot: bool
-
-
 class BasePostgresSaver(BaseCheckpointSaver[str]):
     SELECT_SQL = SELECT_SQL
     SELECT_PENDING_SENDS_SQL = SELECT_PENDING_SENDS_SQL
@@ -254,88 +195,6 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
             if t.decode() != "empty"
         }
 
-    @staticmethod
-    def _walk_stage1(
-        stage1_rows: Sequence[_DeltaStage1Row],
-        target_id: str,
-    ) -> tuple[list[str], str | None]:
-        """Walk the parent chain from stage 1 metadata rows.
-
-        Returns (chain_cids, seed_version):
-          chain_cids: ancestor checkpoint IDs from target's parent down to
-                      the seed (or root), in newest-first order.
-          seed_version: the channel blob version at the nearest ancestor
-                        with has_snapshot=True, or None if pure delta.
-        """
-        parent_of: dict[str, str | None] = {}
-        ver_of: dict[str, str | None] = {}
-        snapshot_of: dict[str, bool] = {}
-        for r in stage1_rows:
-            cid = r["checkpoint_id"]
-            parent_of[cid] = r["parent_checkpoint_id"]
-            ver_of[cid] = r["ver"]
-            snapshot_of[cid] = r["has_snapshot"]
-
-        chain_cids: list[str] = []
-        seed_version: str | None = None
-        cur_cid: str | None = parent_of.get(target_id)
-        while cur_cid is not None:
-            chain_cids.append(cur_cid)
-            if snapshot_of.get(cur_cid, False):
-                seed_version = ver_of.get(cur_cid)
-                break
-            cur_cid = parent_of.get(cur_cid)
-        return chain_cids, seed_version
-
-    def _build_delta_channel_writes_history(
-        self,
-        *,
-        channel: str,
-        chain_cids: list[str],
-        seed_version: str | None,
-        stage2_rows: Sequence[_DeltaStage2Row],
-    ) -> _ChannelWritesHistory:
-        """Reconstruct delta channel history from two-stage query results.
-
-        chain_cids are in newest-first order (target's parent first).
-        stage2_rows contain only writes for chain_cids and the single
-        seed blob at seed_version.
-        """
-        writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
-        seed_blob: tuple[str, bytes] | None = None
-
-        for r in stage2_rows:
-            kind = r["_kind"]
-            if kind == "w":
-                cid = cast(str, r["checkpoint_id"])
-                writes_by_cid.setdefault(cid, []).append(
-                    cast(
-                        "tuple[str, bytes, str, int]",
-                        (r["type"], r["blob"], r["task_id"], r["idx"]),
-                    )
-                )
-            else:  # kind == "b"
-                seed_blob = cast("tuple[str, bytes]", (r["type"], r["blob"]))
-
-        for ws in writes_by_cid.values():
-            ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
-
-        if not chain_cids:
-            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
-
-        collected: list[PendingWrite] = []
-        for cid in chain_cids:
-            for type_tag, write_blob, task_id, _idx in writes_by_cid.get(cid, []):
-                val = self.serde.loads_typed((type_tag, write_blob))
-                collected.append((task_id, channel, val))
-
-        seed: Any = DELTA_SENTINEL
-        if seed_blob is not None and seed_blob[0] != "empty":
-            seed = self.serde.loads_typed(seed_blob)
-
-        collected.reverse()
-        return _ChannelWritesHistory(seed=seed, writes=collected)
-
     def _dump_blobs(
         self,
         thread_id: str,

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.1.0a3"
+version = "3.0.5"
 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>=4.1.0a3,<5.0.0",
+  "langgraph-checkpoint>=2.1.2,<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_oss"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint-postgres/tests/test_async.py

@@ -361,9 +361,9 @@ async def test_get_checkpoint_no_channel_values(
 
         load_checkpoint_tuple = saver._load_checkpoint_tuple
 
-        async def patched_load_checkpoint_tuple(value):
+        def patched_load_checkpoint_tuple(value):
             value["checkpoint"].pop("channel_values", None)
-            return await load_checkpoint_tuple(value)
+            return load_checkpoint_tuple(value)
 
         monkeypatch.setattr(
             saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
@@ -371,47 +371,3 @@ async def test_get_checkpoint_no_channel_values(
 
         checkpoint = await saver.aget_tuple(config)
         assert checkpoint.checkpoint["channel_values"] == {}
-
-
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
-async def test_delta_channel_chain_reconstruction(saver_name: str) -> None:
-    """AsyncPostgresSaver reconstructs DeltaChannel chain via point-lookup traversal."""
-    pytest.importorskip(
-        "langgraph.channels.delta", reason="langgraph core not installed"
-    )
-
-    from typing import Annotated
-
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import _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"

libs/checkpoint-postgres/uv.lock

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 name = "langgraph-checkpoint"
-version = "4.1.0a3"
+version = "4.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -307,7 +307,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.1.0a3"
+version = "3.0.5"
 source = { editable = "." }
 dependencies = [
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 version = "0.25.0"

libs/checkpoint-sqlite/README.md

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

libs/checkpoint-sqlite/pyproject.toml

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

libs/checkpoint-sqlite/uv.lock

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+version = "1.2.22"
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 [[package]]
 name = "langgraph-checkpoint"
-version = "4.1.0a3"
+version = "4.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
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@@ -385,7 +385,7 @@ test = [
 
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+version = "0.6.4"
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-
 [[package]]
 name = "zstandard"
 version = "0.25.0"

libs/checkpoint/README.md

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

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

@@ -3,7 +3,7 @@ from __future__ import annotations
 import copy
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
-from typing import (
+from typing import (  # noqa: UP035
     Any,
     Generic,
     Literal,
@@ -18,9 +18,6 @@ from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-from langgraph.checkpoint.serde.types import (
-    DELTA_SENTINEL as DELTA_SENTINEL,
-)
 from langgraph.checkpoint.serde.types import (
     ERROR,
     INTERRUPT,
@@ -31,8 +28,6 @@ from langgraph.checkpoint.serde.types import (
 
 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
-
-
 logger = logging.getLogger(__name__)
 
 
@@ -124,30 +119,6 @@ class CheckpointTuple(NamedTuple):
     pending_writes: list[PendingWrite] | None = None
 
 
-class _ChannelWritesHistory(NamedTuple):
-    """Result of `BaseCheckpointSaver._get_channel_writes_history`.
-
-    Storage-level view of what one channel wrote across the ancestor chain
-    of a target checkpoint:
-
-      * `seed` — the nearest ancestor's stored blob value for this channel,
-        or `DELTA_SENTINEL` if the walk reached the root without finding a
-        stored value. A non-sentinel seed typically indicates a pre-delta
-        snapshot preserved across a channel-type migration (e.g.
-        `BinaryOperatorAggregate` storage extended under `DeltaChannel`).
-      * `writes` — on-path deltas oldest→newest, one `PendingWrite` per
-        step that wrote to this channel. Writes stored at the target
-        checkpoint itself are pending for the next super-step and are
-        excluded.
-
-    Experimental: method surface may change; the NamedTuple shape is the
-    contract.
-    """
-
-    seed: Any
-    writes: list[PendingWrite]
-
-
 class BaseCheckpointSaver(Generic[V]):
     """Base class for creating a graph checkpointer.
 
@@ -486,104 +457,6 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
-    def _get_tuple_raw(self, config: RunnableConfig) -> CheckpointTuple | None:
-        """Pure storage read used by `_get_channel_writes_history`.
-
-        Must return the same value as `get_tuple` but must NOT trigger channel
-        reconstruction; otherwise the channel-hydration path would re-enter
-        `_get_channel_writes_history`. Override only if `get_tuple` itself
-        performs channel hydration.
-        """
-        return self.get_tuple(config)
-
-    async def _aget_tuple_raw(self, config: RunnableConfig) -> CheckpointTuple | None:
-        """Async version of `_get_tuple_raw`. See docstring there."""
-        return await self.aget_tuple(config)
-
-    def _get_channel_writes_history(
-        self, config: RunnableConfig, channel: str
-    ) -> _ChannelWritesHistory:
-        """**Experimental.** Query one channel's writes along the parent chain.
-
-        Storage-level query, not channel semantics: returns `(seed, writes)`
-        reflecting what storage knows about a single channel across the
-        ancestor chain of the target checkpoint identified by `config`.
-
-        * `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
-          Writes stored at the target `checkpoint_id` itself are pending
-          for the next super-step and are excluded.
-        * `seed` — the nearest ancestor's stored blob value for this
-          channel; `DELTA_SENTINEL` if the walk reached the root without
-          finding a stored value. A non-sentinel seed typically indicates
-          a pre-delta snapshot preserved across a channel-type migration.
-
-        Walks the **parent chain** (not `list(before=...)`): for forked
-        threads, only on-path ancestors contribute.
-
-        Reference implementation walks `get_tuple` + `parent_config`,
-        inspecting each ancestor's `channel_values[channel]` for the seed
-        terminator. Savers with direct storage access (`InMemorySaver`,
-        `PostgresSaver`) override for performance; the return contract is
-        fixed here.
-
-        Underscore-prefixed because the method surface is experimental.
-        """
-        collected: list[PendingWrite] = []  # newest first; reversed at the end
-        target_tuple = self._get_tuple_raw(config)
-        cursor_config: RunnableConfig | None = (
-            target_tuple.parent_config if target_tuple else None
-        )
-        while cursor_config is not None:
-            tup = self._get_tuple_raw(cursor_config)
-            if tup is None:
-                break
-            # Collect this ancestor's writes FIRST — they encode the
-            # transition from this ancestor's state to its child's, so
-            # they must be included whether or not this ancestor is the
-            # seed terminator.
-            if tup.pending_writes:
-                # Within a superstep, pending_writes are oldest→newest;
-                # reverse to scan newest-first.
-                for write in reversed(tup.pending_writes):
-                    if write[1] != channel:
-                        continue
-                    collected.append(write)
-            # Seed terminator: any non-sentinel blob on an ancestor
-            # establishes the reconstruction base. Stop here.
-            ancestor_value = tup.checkpoint["channel_values"].get(channel)
-            if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
-                collected.reverse()
-                return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
-            cursor_config = tup.parent_config
-        collected.reverse()
-        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
-
-    async def _aget_channel_writes_history(
-        self, config: RunnableConfig, channel: str
-    ) -> _ChannelWritesHistory:
-        """Async version of `_get_channel_writes_history`. See docstring there."""
-        collected: list[PendingWrite] = []
-        target_tuple = await self._aget_tuple_raw(config)
-        cursor_config: RunnableConfig | None = (
-            target_tuple.parent_config if target_tuple else None
-        )
-        while cursor_config is not None:
-            tup = await self._aget_tuple_raw(cursor_config)
-            if tup is None:
-                break
-            if tup.pending_writes:
-                for write in reversed(tup.pending_writes):
-                    if write[1] != channel:
-                        continue
-                    collected.append(write)
-            ancestor_value = tup.checkpoint["channel_values"].get(channel)
-            if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
-                collected.reverse()
-                return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
-            cursor_config = tup.parent_config
-        collected.reverse()
-        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
-
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.
 

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

@@ -14,20 +14,16 @@ from typing import Any
 from langchain_core.runnables import RunnableConfig
 
 from langgraph.checkpoint.base import (
-    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
-    PendingWrite,
     SerializerProtocol,
-    _ChannelWritesHistory,
     get_checkpoint_id,
     get_checkpoint_metadata,
 )
-from langgraph.checkpoint.serde.types import _DeltaSnapshot
 
 logger = logging.getLogger(__name__)
 
@@ -125,114 +121,16 @@ 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]:
-        result: dict[str, Any] = {}
-        for k, ver in versions.items():
-            kk = (thread_id, checkpoint_ns, k, ver)
-            if kk not in self.blobs:
-                continue
-            vv = self.blobs[kk]
-            if vv[0] == "empty":
-                continue
-            result[k] = self.serde.loads_typed(vv)
-        return result
-
-    def _get_channel_writes_history(
-        self, config: RunnableConfig, channel: str
-    ) -> _ChannelWritesHistory:
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        checkpoint_id = config["configurable"].get("checkpoint_id", "")
-        ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
-        # Walk the parent chain newest→oldest. Skip the target itself —
-        # writes stored AT `checkpoint_id` are pending for the next step
-        # (pregel applies them via `apply_writes`; they aren't part of the
-        # snapshot value AT `checkpoint_id`).
-        chain: list[str] = []
-        target_entry = ns_storage.get(checkpoint_id)
-        current: str | None = target_entry[2] if target_entry is not None else None
-        while current is not None:
-            entry = ns_storage.get(current)
-            if entry is None:
-                break
-            chain.append(current)
-            _, _, parent = entry
-            current = parent
-        # Scan newest→oldest. A pre-delta blob on an ancestor terminates the
-        # walk and is bound as `seed`; without this, a thread migrated from
-        # pre-delta storage would replay ancestor writes all the way to the
-        # root AND miss any value that lived only in the old blob (e.g. from
-        # `update_state`).
-        #
-        # At each ancestor, check the blob BEFORE processing its pending
-        # writes: a pre-delta blob represents the state AT that ancestor,
-        # which already subsumes any writes stored under it. Processing
-        # those writes first would fold them into the reconstructed value
-        # twice (once via the blob, once via replay).
-        collected: list[PendingWrite] = []  # newest first
-        for cp_id in chain:  # newest → oldest
-            entry = ns_storage.get(cp_id)
-            if entry is not None:
-                ckpt = self.serde.loads_typed(entry[0])
-                ver = ckpt.get("channel_versions", {}).get(channel)
-                if ver is not None:
-                    blob_entry = self.blobs.get(
-                        (thread_id, checkpoint_ns, channel, ver)
-                    )
-                    if blob_entry is not None and blob_entry[0] != "empty":
-                        blob_value = self.serde.loads_typed(blob_entry)
-                        if blob_value is not DELTA_SENTINEL:
-                            if isinstance(blob_value, _DeltaSnapshot):
-                                # Step-based snapshot: the blob is state AT this
-                                # ancestor, but the ancestor's pending_writes
-                                # encode the NEXT step's transition and are NOT
-                                # subsumed by the snapshot — collect them first.
-                                step_writes = self.writes.get(
-                                    (thread_id, checkpoint_ns, cp_id), {}
-                                )
-                                for (_task_id, _idx), (
-                                    tid,
-                                    ch,
-                                    serialized,
-                                    _,
-                                ) in sorted(step_writes.items(), reverse=True):
-                                    if ch != channel:
-                                        continue
-                                    collected.append(
-                                        (tid, ch, self.serde.loads_typed(serialized))
-                                    )
-                                collected.reverse()
-                                return _ChannelWritesHistory(
-                                    seed=blob_value, writes=collected
-                                )
-                            # Pre-delta blob: state AT this ancestor already
-                            # subsumes its pending_writes — skip them.
-                            collected.reverse()
-                            return _ChannelWritesHistory(
-                                seed=blob_value, writes=collected
-                            )
-
-            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
-            # Within a superstep, sorted by (task_id, idx) = oldest → newest;
-            # reverse for newest-first scan.
-            for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
-                step_writes.items(), reverse=True
-            ):
-                if ch != channel:
-                    continue
-                val = self.serde.loads_typed(serialized)
-                collected.append((tid, ch, val))
-        collected.reverse()
-        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
-
-    async def _aget_channel_writes_history(
-        self, config: RunnableConfig, channel: str
-    ) -> _ChannelWritesHistory:
-        return self._get_channel_writes_history(config, channel)
+        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
 
     def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the in-memory storage.
@@ -452,9 +350,7 @@ class InMemorySaver(
         values: dict[str, Any] = c.pop("channel_values")  # type: ignore[misc]
         for k, v in new_versions.items():
             self.blobs[(thread_id, checkpoint_ns, k, v)] = (
-                self.serde.dumps_typed(values[k])
-                if k in values and values[k] is not DELTA_SENTINEL
-                else ("empty", b"")
+                self.serde.dumps_typed(values[k]) if k in values else ("empty", b"")
             )
         self.storage[thread_id][checkpoint_ns].update(
             {

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

@@ -1,10 +1,3 @@
-"""Msgpack deserialization safety controls.
-
-Set ``LANGGRAPH_STRICT_MSGPACK=true`` to restrict checkpoint deserialization
-to the types listed in ``SAFE_MSGPACK_TYPES``.  Without this, any Python
-callable stored in checkpoint data will be imported and executed on load.
-"""
-
 import os
 from collections.abc import Iterable
 from typing import cast
@@ -73,7 +66,6 @@ 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"),

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

@@ -33,51 +33,19 @@ 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,
-    _DeltaSnapshot,
-)
+from langgraph.checkpoint.serde.types import SendProtocol
 from langgraph.store.base import Item
 
 if TYPE_CHECKING:
     from langgraph.checkpoint.serde._msgpack import (
         AllowedMsgpackModules,
     )
+    from langgraph.checkpoint.serde.types import SendProtocol
 
 LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
 logger = logging.getLogger(__name__)
 
-# Dedup log warnings across process lifetime; cap bounds state if types are
-# dynamically generated (also acts as a circuit breaker on warning volume).
-# Dedup is best-effort: racing threads may each emit once for the same key,
-# and warnings are silently dropped once _MAX_WARNED_TYPES is reached.
-_MAX_WARNED_TYPES = 1000
-_warned_unregistered_types: set[tuple[str, str]] = set()
-_warned_blocked_types: set[tuple[str, str]] = set()
-
-
-def _is_safe_json_type(id_list: list[str]) -> bool:
-    """Return True if an lc=2 id refers to a type in SAFE_MSGPACK_TYPES.
-
-    Safe types bypass the ``allowed_json_modules`` gate so that old "json" format
-    checkpoints (written before the msgpack migration) can be resumed without
-    requiring users to configure an explicit allowlist.
-    """
-    if len(id_list) < 2:
-        return False
-    module_name = ".".join(id_list[:-1])
-    return (module_name, id_list[-1]) in _lg_msgpack.SAFE_MSGPACK_TYPES
-
-
-def _warn_once(
-    seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
-) -> None:
-    if key in seen or len(seen) >= _MAX_WARNED_TYPES:
-        return
-    seen.add(key)
-    logger.warning(msg, *args)
-
 
 class JsonPlusSerializer(SerializerProtocol):
     """Serializer that uses ormsgpack, with optional fallbacks.
@@ -88,10 +56,6 @@ class JsonPlusSerializer(SerializerProtocol):
         class and called within the Pregel loop. It should not be used on untrusted
         python objects. If an attacker can write directly to your checkpoint database,
         they may be able to trigger code execution when data is deserialized.
-
-        Set the environment variable ``LANGGRAPH_STRICT_MSGPACK=true`` to restrict
-        deserialization to a built-in allowlist of safe types.  You can also pass
-        an explicit ``allowed_msgpack_modules`` to the constructor.
     """
 
     def __init__(
@@ -106,11 +70,8 @@ class JsonPlusSerializer(SerializerProtocol):
     ) -> None:
         if allowed_msgpack_modules is _lg_msgpack._SENTINEL:
             if _lg_msgpack.STRICT_MSGPACK_ENABLED:
-                # Strict: only SAFE_MSGPACK_TYPES are allowed.
                 allowed_msgpack_modules = None
             else:
-                # Permissive (default): all types allowed with a warning.
-                # Set LANGGRAPH_STRICT_MSGPACK=true to lock this down.
                 allowed_msgpack_modules = True
         self.pickle_fallback = pickle_fallback
         self._allowed_json_modules: set[tuple[str, ...]] | Literal[True] | None = (
@@ -179,23 +140,19 @@ class JsonPlusSerializer(SerializerProtocol):
         return out
 
     def _reviver(self, value: dict[str, Any]) -> Any:
-        if (
+        if self._allowed_json_modules and (
             value.get("lc", None) == 2
             and value.get("type", None) == "constructor"
             and value.get("id", None) is not None
         ):
-            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,
-                        )
+            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,
+                )
 
         return LC_REVIVER(value)
 
@@ -243,13 +200,6 @@ 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}). "
@@ -319,13 +269,10 @@ 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 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
+    if hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
         return ormsgpack.Ext(
             EXT_PYDANTIC_V2,
             _msgpack_enc(
@@ -497,13 +444,10 @@ 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__, args),
+                (obj.__class__.__module__, obj.__class__.__name__, (obj.node, obj.arg)),
             ),
         )
     elif dataclasses.is_dataclass(obj):
@@ -554,15 +498,6 @@ 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]:
@@ -592,13 +527,10 @@ def _create_msgpack_ext_hook(
                     "name": name,
                 }
             )
-            _warn_once(
-                _warned_unregistered_types,
-                key,
+            logger.warning(
                 "Deserializing unregistered type %s.%s from checkpoint. "
                 "This will be blocked in a future version. "
-                "Set LANGGRAPH_STRICT_MSGPACK=true to block now, or add "
-                "to allowed_msgpack_modules to allow explicitly: [(%r, %r)]",
+                "Add to allowed_msgpack_modules to silence: [(%r, %r)]",
                 module,
                 name,
                 module,
@@ -616,9 +548,7 @@ def _create_msgpack_ext_hook(
                 "name": name,
             }
         )
-        _warn_once(
-            _warned_blocked_types,
-            key,
+        logger.warning(
             "Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
             "Add to allowed_msgpack_modules to allow: [(%r, %r)]",
             module,
@@ -651,13 +581,7 @@ def _create_msgpack_ext_hook(
         return False
 
     def ext_hook(code: int, data: bytes) -> Any:
-        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:
+        if code == EXT_CONSTRUCTOR_SINGLE_ARG:
             try:
                 tup = ormsgpack.unpackb(
                     data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
@@ -678,8 +602,6 @@ 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:
@@ -793,7 +715,9 @@ 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":
-                return _send_from_args(tup[2])
+                from langgraph.types import Send  # type: ignore
+
+                return Send(*tup[2])
             # module, name, args
             return tup[2]
         except Exception:

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

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

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "4.1.0a3"
+version = "4.0.1"
 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_oss"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint/tests/test_encrypted.py

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

libs/checkpoint/tests/test_jsonplus.py

@@ -35,8 +35,6 @@ from langgraph.checkpoint.serde.jsonplus import (
     JsonPlusSerializer,
     _msgpack_enc,
     _msgpack_ext_hook_to_json,
-    _warned_blocked_types,
-    _warned_unregistered_types,
 )
 from langgraph.store.base import Item
 
@@ -333,57 +331,6 @@ 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 = {
@@ -633,14 +580,6 @@ def test_msgpack_safe_types_no_warning(caplog: pytest.LogCaptureFixture) -> None
         assert result is not None
 
 
-@pytest.fixture(autouse=True)
-def _reset_warned_types() -> None:
-    # Warning dedup state is process-global; reset per-test so each case sees
-    # a fresh slate and assertions about warning emission are stable.
-    _warned_unregistered_types.clear()
-    _warned_blocked_types.clear()
-
-
 def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) -> None:
     """Pydantic models not in allowlist should log warning but still deserialize."""
     current = _lg_msgpack.STRICT_MSGPACK_ENABLED
@@ -656,12 +595,6 @@ def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) ->
     assert "unregistered type" in caplog.text.lower()
     assert "allowed_msgpack_modules" in caplog.text
     assert result == obj
-
-    # Second deserialization of the same type should NOT produce another warning
-    caplog.clear()
-    result2 = serde.loads_typed(dumped)
-    assert "unregistered type" not in caplog.text.lower()
-    assert result2 == obj
     _lg_msgpack.STRICT_MSGPACK_ENABLED = current
 
 
@@ -706,6 +639,7 @@ def test_msgpack_allowlist_silences_warning(caplog: pytest.LogCaptureFixture) ->
 
 def test_msgpack_none_blocks_unregistered(caplog: pytest.LogCaptureFixture) -> None:
     """allowed_msgpack_modules=None should block unregistered types."""
+
     serde = JsonPlusSerializer(allowed_msgpack_modules=None)
 
     obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
@@ -723,6 +657,7 @@ def test_msgpack_allowlist_blocks_non_listed(
     caplog: pytest.LogCaptureFixture,
 ) -> None:
     """Allowlists should block unregistered types even if msgpack is enabled."""
+
     serde = JsonPlusSerializer(
         allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
     )

libs/checkpoint/tests/test_memory.py

@@ -6,32 +6,19 @@ from langchain_core.runnables import RunnableConfig
 from pydantic import BaseModel
 
 from langgraph.checkpoint.base import (
-    DELTA_SENTINEL,
     Checkpoint,
     CheckpointMetadata,
     create_checkpoint,
     empty_checkpoint,
 )
 from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.checkpoint.serde.jsonplus import (
-    JsonPlusSerializer,
-    _warned_blocked_types,
-    _warned_unregistered_types,
-)
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 
 
 class MemoryPydantic(BaseModel):
     foo: str
 
 
-@pytest.fixture(autouse=True)
-def _reset_warned_types() -> None:
-    # Warning dedup state is process-global; reset per-test so each case sees
-    # a fresh slate and assertions about warning emission are stable.
-    _warned_unregistered_types.clear()
-    _warned_blocked_types.clear()
-
-
 class TestMemorySaver:
     @pytest.fixture(autouse=True)
     def setup(self) -> None:
@@ -209,6 +196,8 @@ class TestMemorySaver:
 
 
 async def test_memory_saver() -> None:
+    from langgraph.checkpoint.memory import InMemorySaver
+
     memory_saver = InMemorySaver()
     assert isinstance(memory_saver, InMemorySaver)
 
@@ -319,338 +308,3 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
     assert direct is not None
     expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
     assert direct.checkpoint["channel_values"]["foo"] == expected
-
-
-class TestInMemorySaverDeltaChannel:
-    def test_load_blobs_omits_delta_channel(self) -> None:
-        """_load_blobs omits delta channels (stored as 'empty'); reconstruction deferred."""
-        saver = InMemorySaver()
-
-        thread_id, ns, channel = "t1", "", "messages"
-        v1 = "00000000000000000000000000000001.0000000000000000"
-
-        saver.blobs[(thread_id, ns, channel, v1)] = ("empty", b"")
-
-        result = saver._load_blobs(thread_id, ns, {channel: v1})
-        assert channel not in result
-
-    def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
-        """_get_channel_writes_history collects ancestor writes oldest→newest,
-        and excludes writes stored at the target checkpoint itself (those are
-        pending writes for the next step, applied separately by pregel)."""
-        saver = InMemorySaver()
-        serde = JsonPlusSerializer()
-
-        thread_id, ns, channel = "t1", "", "messages"
-
-        cp1 = empty_checkpoint()
-        cp1["id"] = "cp1"
-        cp2 = empty_checkpoint()
-        cp2["id"] = "cp2"
-        saver.storage[thread_id][ns] = {
-            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
-            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
-        }
-        # Writes stored at cp1 produced the cp1 snapshot; part of history.
-        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
-            "task1",
-            channel,
-            serde.dumps_typed({"content": "hi"}),
-            "",
-        )
-        # Writes stored at cp2 are pending — they will produce cp3 when the
-        # step that loaded cp2 completes. They MUST NOT appear in the
-        # reconstructed snapshot value at cp2.
-        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
-            "task2",
-            channel,
-            serde.dumps_typed({"content": "pending"}),
-            "",
-        )
-
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": "cp2",
-            }
-        }
-        result = saver._get_channel_writes_history(config, channel)
-        assert result.seed is DELTA_SENTINEL
-        values = [v for _, _, v in result.writes]
-        assert values == [{"content": "hi"}]
-
-    def test_get_channel_writes_at_root_returns_empty(self) -> None:
-        """Reconstructing the root checkpoint's state: no ancestors → []."""
-        saver = InMemorySaver()
-        serde = JsonPlusSerializer()
-        thread_id, ns, channel = "t1", "", "messages"
-
-        cp1 = empty_checkpoint()
-        cp1["id"] = "cp1"
-        saver.storage[thread_id][ns] = {
-            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
-        }
-        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
-            "task1",
-            channel,
-            serde.dumps_typed({"content": "pending"}),
-            "",
-        )
-
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": "cp1",
-            }
-        }
-        result = saver._get_channel_writes_history(config, channel)
-        assert result.seed is DELTA_SENTINEL
-        assert result.writes == []
-
-
-class TestBaseFallbackGetChannelWrites:
-    """Exercises the `BaseCheckpointSaver._get_channel_writes_history` default
-    implementation — the path third-party savers inherit when they don't
-    override `_get_channel_writes_history` themselves.
-
-    Regression guard for a bug where the fallback passed the caller's config
-    (with `checkpoint_id`) straight to `self.list()`, which most savers
-    collapse to a single row — causing the fallback to return `[]`.
-    """
-
-    def _build_saver_with_chain(self) -> tuple[InMemorySaver, str, str]:
-        """Build an InMemorySaver with a 3-checkpoint chain and per-step writes
-        for a `messages` channel.
-
-        Returns `(saver, thread_id, namespace)`. The saver subclass deletes the
-        InMemorySaver override so the base class fallback is exercised.
-        """
-
-        class _ThirdPartyStyleSaver(InMemorySaver):
-            _get_channel_writes_history = (
-                InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
-            )
-            _aget_channel_writes_history = (
-                InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
-            )
-
-        saver = _ThirdPartyStyleSaver()
-        serde = JsonPlusSerializer()
-        thread_id, ns, channel = "t1", "", "messages"
-
-        cp0 = empty_checkpoint()
-        cp0["id"] = "00000000000000000000000000000001.0000000000000000"
-        cp1 = empty_checkpoint()
-        cp1["id"] = "00000000000000000000000000000002.0000000000000000"
-        cp2 = empty_checkpoint()
-        cp2["id"] = "00000000000000000000000000000003.0000000000000000"
-        saver.storage[thread_id][ns] = {
-            cp0["id"]: (serde.dumps_typed(cp0), serde.dumps_typed({}), None),
-            cp1["id"]: (serde.dumps_typed(cp1), serde.dumps_typed({}), cp0["id"]),
-            cp2["id"]: (serde.dumps_typed(cp2), serde.dumps_typed({}), cp1["id"]),
-        }
-        # Writes under cp0 produced cp1's state; writes under cp1 produced cp2's.
-        saver.writes[(thread_id, ns, cp0["id"])][("task1", 0)] = (
-            "task1",
-            channel,
-            serde.dumps_typed({"content": "first"}),
-            "",
-        )
-        saver.writes[(thread_id, ns, cp1["id"])][("task2", 0)] = (
-            "task2",
-            channel,
-            serde.dumps_typed({"content": "second"}),
-            "",
-        )
-        return saver, thread_id, ns
-
-    def test_fallback_returns_ancestor_writes_oldest_first(self) -> None:
-        saver, thread_id, ns = self._build_saver_with_chain()
-        target_id = "00000000000000000000000000000003.0000000000000000"
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": target_id,
-            }
-        }
-
-        result = saver._get_channel_writes_history(config, "messages")
-
-        assert result.seed is DELTA_SENTINEL
-        values = [v for _, _, v in result.writes]
-        assert values == [{"content": "first"}, {"content": "second"}]
-
-    async def test_async_fallback_returns_ancestor_writes_oldest_first(self) -> None:
-        saver, thread_id, ns = self._build_saver_with_chain()
-        target_id = "00000000000000000000000000000003.0000000000000000"
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": target_id,
-            }
-        }
-
-        result = await saver._aget_channel_writes_history(config, "messages")
-
-        assert result.seed is DELTA_SENTINEL
-        values = [v for _, _, v in result.writes]
-        assert values == [{"content": "first"}, {"content": "second"}]
-
-    async def test_async_fallback_concurrent_tasks_do_not_interfere(self) -> None:
-        """Regression: the re-entrancy guard must be task-local, not thread-local.
-
-        Two concurrent `_aget_channel_writes_history` calls on the same
-        event-loop thread must each see their full reconstructed writes. A
-        `threading.local()` guard would let whichever task set it first
-        short-circuit the other to `writes=[]`.
-        """
-        import asyncio
-
-        saver, thread_id, ns = self._build_saver_with_chain()
-
-        # Force the two tasks to interleave across the `set(True)` boundary:
-        # each `aget_tuple` yields control, so if the guard were thread-local
-        # the second task would observe `active=True` set by the first.
-        orig_aget_tuple = saver.aget_tuple
-
-        async def slow_aget_tuple(config: RunnableConfig) -> Any:
-            await asyncio.sleep(0)
-            return await orig_aget_tuple(config)
-
-        saver.aget_tuple = slow_aget_tuple  # type: ignore[method-assign]
-
-        target_id = "00000000000000000000000000000003.0000000000000000"
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": target_id,
-            }
-        }
-
-        results = await asyncio.gather(
-            saver._aget_channel_writes_history(config, "messages"),
-            saver._aget_channel_writes_history(config, "messages"),
-        )
-
-        expected_values = [{"content": "first"}, {"content": "second"}]
-        for result in results:
-            assert result.seed is DELTA_SENTINEL
-            values = [v for _, _, v in result.writes]
-            assert values == expected_values
-
-
-class TestPreDeltaBlobTerminator:
-    """Verify the pre-delta blob terminator: when the ancestor walk hits a
-    checkpoint whose blob for the channel is a real value (not
-    DELTA_SENTINEL), reconstruction seeds from it and stops. This guards
-
-      * back-compat: a thread written by pre-delta code, then extended under
-        delta — reconstruction must return the correct value without walking
-        past the last pre-delta ancestor;
-      * perf: without the terminator, every reconstruct-after-migration would
-        walk all the way to the thread root.
-    """
-
-    def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
-        """Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
-        write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
-        seed=[A] + writes=[B, C].
-
-        Returns `(saver, thread_id, ns, channel, cp3_id)`.
-        """
-        saver = InMemorySaver()
-        serde = JsonPlusSerializer()
-        thread_id, ns, channel = "t1", "", "messages"
-
-        v1 = "00000000000000000000000000000001.0"
-        v2 = "00000000000000000000000000000002.0"
-        v3 = "00000000000000000000000000000003.0"
-
-        # Pre-delta: cp1 stored a real blob for the channel.
-        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
-        # Delta-era: cp2 and cp3 store "empty"; real writes in checkpoint_writes.
-        saver.blobs[(thread_id, ns, channel, v2)] = ("empty", b"")
-        saver.blobs[(thread_id, ns, channel, v3)] = ("empty", b"")
-
-        cp1 = empty_checkpoint()
-        cp1["id"] = "cp1"
-        cp1["channel_versions"][channel] = v1
-        cp2 = empty_checkpoint()
-        cp2["id"] = "cp2"
-        cp2["channel_versions"][channel] = v2
-        cp3 = empty_checkpoint()
-        cp3["id"] = "cp3"
-        cp3["channel_versions"][channel] = v3
-
-        saver.storage[thread_id][ns] = {
-            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
-            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
-            "cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
-        }
-        # Write under cp1 would be from the pre-delta era and MUST be ignored
-        # (the blob already captures it). We add one and assert it is not
-        # folded into the reconstructed result.
-        saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
-            "task0",
-            channel,
-            serde.dumps_typed("PRE-DELTA-WRITE"),
-            "",
-        )
-        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
-            "task2",
-            channel,
-            serde.dumps_typed("B"),
-            "",
-        )
-        saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
-            "task3",
-            channel,
-            serde.dumps_typed("PENDING-AT-TARGET"),
-            "",
-        )
-        return saver, thread_id, ns, channel, "cp3"
-
-    def test_seed_from_pre_delta_ancestor_blob(self) -> None:
-        saver, thread_id, ns, channel, target = self._build_mixed_thread()
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": target,
-            }
-        }
-
-        result = saver._get_channel_writes_history(config, channel)
-
-        # Seed came from the pre-delta blob at cp1.
-        assert result.seed == ["A"]
-        # Delta-era writes from cp2 replay through the reducer on top of seed.
-        # cp3 is the target — its own write is pending for the NEXT step and
-        # must be excluded.
-        values = [v for _, _, v in result.writes]
-        assert values == ["B"]
-
-    def test_pre_delta_blob_terminates_walk_before_older_writes(self) -> None:
-        """Writes stored at the pre-delta ancestor itself must not be replayed
-        (the blob subsumes them)."""
-        saver, thread_id, ns, channel, target = self._build_mixed_thread()
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": target,
-            }
-        }
-
-        result = saver._get_channel_writes_history(config, channel)
-
-        values = [v for _, _, v in result.writes]
-        # The pre-delta write under cp1 must not appear (the blob subsumes it).
-        assert "PRE-DELTA-WRITE" not in values
-        # And the pending write at the target is never folded in.
-        assert "PENDING-AT-TARGET" not in values

libs/checkpoint/uv.lock

@@ -286,7 +286,7 @@ wheels = [
 
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+version = "4.0.1"
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@@ -379,12 +379,11 @@ dependencies = [
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@@ -1129,9 +1128,9 @@ dependencies = [
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-]
-
 [[package]]
 name = "zstandard"
 version = "0.25.0"

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

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

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

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

libs/cli/examples/graph_prerelease_reqs_fail/pyproject.toml

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

libs/cli/js-examples/yarn.lock

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

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

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

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.24"
+__version__ = "0.4.21"

libs/cli/langgraph_cli/_ignore.py

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

libs/cli/langgraph_cli/analytics.py

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

libs/cli/langgraph_cli/archive.py

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

libs/cli/langgraph_cli/uv_lock.py

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

libs/cli/pyproject.toml

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

libs/cli/tests/unit_tests/test_archive.py

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

libs/cli/tests/unit_tests/test_config.py

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

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

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

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

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

libs/langgraph/README.md

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

libs/langgraph/langgraph/_internal/_config.py

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

libs/langgraph/langgraph/_internal/_constants.py

@@ -12,9 +12,6 @@ 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")
@@ -59,8 +56,6 @@ 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")
@@ -71,13 +66,6 @@ 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")
@@ -105,7 +93,6 @@ RESERVED = {
     INTERRUPT,
     RESUME,
     ERROR,
-    ERROR_SOURCE_NODE,
     NO_WRITES,
     # reserved config.configurable keys
     CONFIG_KEY_SEND,
@@ -119,9 +106,7 @@ RESERVED = {
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_ID,
     CONFIG_KEY_CHECKPOINT_NS,
-    CONFIG_KEY_TIMED_ATTEMPT_OBSERVER,
     CONFIG_KEY_RESUME_MAP,
-    CONFIG_KEY_STREAM_MESSAGES_V2,
     # other constants
     PUSH,
     PULL,

libs/langgraph/langgraph/_internal/_runnable.py

@@ -51,11 +51,9 @@ 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:
@@ -119,19 +117,6 @@ 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."""
@@ -196,15 +181,6 @@ 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.
@@ -378,8 +354,6 @@ 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
@@ -452,8 +426,6 @@ class RunnableCallable(Runnable):
             kw_value: Any = MISSING
             if kw == "config":
                 kw_value = config
-            elif kw == "error":
-                kw_value = config.get(CONF, {}).get(CONFIG_KEY_NODE_ERROR, MISSING)
             elif runtime:
                 if kw == "runtime":
                     kw_value = runtime

libs/langgraph/langgraph/_internal/_timeout.py

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

libs/langgraph/langgraph/callbacks.py

@@ -1,394 +0,0 @@
-"""Graph lifecycle callback interfaces and event payloads.
-
-This module defines the public callback surface for observing LangGraph-specific
-lifecycle transitions such as interrupt and resume.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Sequence
-from dataclasses import dataclass
-from typing import Any, Literal, TypeAlias, TypeVar
-from uuid import UUID
-
-from langchain_core.callbacks import BaseCallbackHandler, BaseCallbackManager
-from langchain_core.callbacks.manager import ahandle_event, handle_event
-from langchain_core.runnables import RunnableConfig
-
-from langgraph.types import Interrupt
-
-__all__ = (
-    "GraphCallbackHandler",
-    "GraphInterruptEvent",
-    "GraphLifecycleEvent",
-    "GraphLifecycleStatus",
-    "GraphResumeEvent",
-    "get_async_graph_callback_manager_for_config",
-    "get_sync_graph_callback_manager_for_config",
-)
-
-
-GraphLifecycleStatus: TypeAlias = Literal[
-    "input",
-    "pending",
-    "done",
-    "interrupt_before",
-    "interrupt_after",
-    "out_of_steps",
-]
-"""Allowed lifecycle statuses reported in graph lifecycle callback events."""
-
-
-@dataclass(frozen=True)
-class GraphInterruptEvent:
-    """Graph lifecycle event emitted when execution pauses for interrupts."""
-
-    run_id: UUID | None
-    """Run id for the current graph execution, if available."""
-
-    status: GraphLifecycleStatus
-    """Loop status when the interrupt was captured."""
-
-    checkpoint_id: str
-    """Checkpoint id associated with the interrupted execution."""
-
-    checkpoint_ns: tuple[str, ...]
-    """Checkpoint namespace path for the current graph or subgraph."""
-
-    interrupts: tuple[Interrupt, ...]
-    """Interrupt payloads that caused the graph to pause."""
-
-
-@dataclass(frozen=True)
-class GraphResumeEvent:
-    """Graph lifecycle event emitted when execution resumes from a checkpoint."""
-
-    run_id: UUID | None
-    """Run id for the current graph execution, if available."""
-
-    status: GraphLifecycleStatus
-    """Loop status when the resume was captured."""
-
-    checkpoint_id: str
-    """Checkpoint id the graph resumed from."""
-
-    checkpoint_ns: tuple[str, ...]
-    """Checkpoint namespace path for the current graph or subgraph."""
-
-
-GraphLifecycleEvent: TypeAlias = GraphInterruptEvent | GraphResumeEvent
-"""Union of all public graph lifecycle callback event payloads.
-
-Use this alias when a callback or helper can receive either interrupt or resume
-lifecycle events.
-"""
-
-
-class GraphCallbackHandler(BaseCallbackHandler):
-    """Base class for graph-level lifecycle callbacks.
-
-    Subclass this handler to observe graph lifecycle transitions that are
-    specific to LangGraph execution, rather than generic LangChain runnable
-    callbacks.
-
-    Instances can be passed through `config["callbacks"]` when invoking a
-    graph. Only handlers that inherit from `GraphCallbackHandler` receive these
-    lifecycle events.
-    """
-
-    def on_interrupt(self, event: GraphInterruptEvent) -> Any:
-        """Run when graph execution pauses due to one or more interrupts.
-
-        Args:
-            event: Interrupt lifecycle event payload.
-        """
-
-    def on_resume(self, event: GraphResumeEvent) -> Any:
-        """Run when graph execution resumes from a persisted checkpoint.
-
-        Args:
-            event: Resume lifecycle event payload.
-        """
-
-
-_MISSING = object()
-
-
-def _filter_graph_handlers(
-    handlers: list[BaseCallbackHandler],
-) -> list[GraphCallbackHandler]:
-    return [h for h in handlers if isinstance(h, GraphCallbackHandler)]
-
-
-def _init_base_manager(
-    manager: BaseCallbackManager,
-    handlers: Sequence[GraphCallbackHandler] | None,
-    inheritable_handlers: Sequence[GraphCallbackHandler] | None,
-    parent_run_id: UUID | None,
-    *,
-    tags: list[str] | None,
-    inheritable_tags: list[str] | None,
-    metadata: dict[str, Any] | None,
-    inheritable_metadata: dict[str, Any] | None,
-    run_id: UUID | None,
-) -> None:
-    base_handlers: list[BaseCallbackHandler] = []
-    base_inheritable_handlers: list[BaseCallbackHandler] = []
-    if handlers is not None:
-        base_handlers.extend(handlers)
-    if inheritable_handlers is not None:
-        base_inheritable_handlers.extend(inheritable_handlers)
-    BaseCallbackManager.__init__(
-        manager,
-        handlers=base_handlers,
-        inheritable_handlers=base_inheritable_handlers,
-        parent_run_id=parent_run_id,
-        tags=tags,
-        inheritable_tags=inheritable_tags,
-        metadata=metadata,
-        inheritable_metadata=inheritable_metadata,
-    )
-    manager.run_id = run_id  # type: ignore[attr-defined]
-
-
-def _configure_graph_callbacks(
-    cls: type[_GraphManagerT],
-    callbacks: object | None,
-    *,
-    run_id: UUID | None,
-) -> _GraphManagerT:
-    if callbacks is None:
-        return cls(run_id=run_id)
-    if isinstance(callbacks, cls):
-        return callbacks.copy(run_id=run_id)
-    if isinstance(callbacks, (_GraphCallbackManager, _AsyncGraphCallbackManager)):
-        # Cross-type: extract handlers into the requested cls.
-        return cls(
-            handlers=_filter_graph_handlers(callbacks.handlers),
-            inheritable_handlers=_filter_graph_handlers(callbacks.inheritable_handlers),
-            parent_run_id=callbacks.parent_run_id,
-            tags=callbacks.tags.copy(),
-            inheritable_tags=callbacks.inheritable_tags.copy(),
-            metadata=callbacks.metadata.copy(),
-            inheritable_metadata=callbacks.inheritable_metadata.copy(),
-            run_id=run_id,
-        )
-    if isinstance(callbacks, BaseCallbackManager):
-        return cls(
-            handlers=_filter_graph_handlers(callbacks.handlers),
-            inheritable_handlers=_filter_graph_handlers(callbacks.inheritable_handlers),
-            parent_run_id=callbacks.parent_run_id,
-            tags=callbacks.tags.copy(),
-            inheritable_tags=callbacks.inheritable_tags.copy(),
-            metadata=callbacks.metadata.copy(),
-            inheritable_metadata=callbacks.inheritable_metadata.copy(),
-            run_id=run_id,
-        )
-    if isinstance(callbacks, GraphCallbackHandler):
-        return cls((callbacks,), run_id=run_id)
-    if isinstance(callbacks, (str, bytes)) or not isinstance(callbacks, Sequence):
-        raise TypeError("callbacks must be a handler, sequence, or manager")
-    return cls(_filter_graph_handlers(list(callbacks)), run_id=run_id)
-
-
-def _copy_graph_manager(
-    manager: _GraphCallbackManager | _AsyncGraphCallbackManager,
-    cls: type[_GraphManagerT],
-    run_id: UUID | None | object,
-) -> _GraphManagerT:
-    resolved_run_id: UUID | None
-    if run_id is _MISSING:
-        resolved_run_id = manager.run_id
-    else:
-        if run_id is not None and not isinstance(run_id, UUID):
-            raise TypeError("run_id must be a UUID or None")
-        resolved_run_id = run_id
-
-    return cls(
-        handlers=_filter_graph_handlers(manager.handlers),
-        inheritable_handlers=_filter_graph_handlers(manager.inheritable_handlers),
-        parent_run_id=manager.parent_run_id,
-        tags=manager.tags.copy(),
-        inheritable_tags=manager.inheritable_tags.copy(),
-        metadata=manager.metadata.copy(),
-        inheritable_metadata=manager.inheritable_metadata.copy(),
-        run_id=resolved_run_id,
-    )
-
-
-class _GraphCallbackManager(BaseCallbackManager):
-    """Sync dispatcher for graph lifecycle events."""
-
-    run_id: UUID | None
-
-    def __init__(
-        self,
-        handlers: Sequence[GraphCallbackHandler] | None = None,
-        inheritable_handlers: Sequence[GraphCallbackHandler] | None = None,
-        parent_run_id: UUID | None = None,
-        *,
-        tags: list[str] | None = None,
-        inheritable_tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        inheritable_metadata: dict[str, Any] | None = None,
-        run_id: UUID | None = None,
-    ) -> None:
-        _init_base_manager(
-            self,
-            handlers,
-            inheritable_handlers,
-            parent_run_id,
-            tags=tags,
-            inheritable_tags=inheritable_tags,
-            metadata=metadata,
-            inheritable_metadata=inheritable_metadata,
-            run_id=run_id,
-        )
-
-    def copy(
-        self,
-        *,
-        run_id: UUID | None | object = _MISSING,
-    ) -> _GraphCallbackManager:
-        return _copy_graph_manager(self, _GraphCallbackManager, run_id)
-
-    @classmethod
-    def configure(
-        cls,
-        callbacks: object | None = None,
-        *,
-        run_id: UUID | None = None,
-    ) -> _GraphCallbackManager:
-        return _configure_graph_callbacks(cls, callbacks, run_id=run_id)
-
-    def on_interrupt(self, event: GraphInterruptEvent) -> None:
-        handle_event(
-            self.handlers,
-            "on_interrupt",
-            None,
-            event,
-        )
-
-    def on_resume(self, event: GraphResumeEvent) -> None:
-        handle_event(
-            self.handlers,
-            "on_resume",
-            None,
-            event,
-        )
-
-
-class _AsyncGraphCallbackManager(BaseCallbackManager):
-    """Async dispatcher for graph lifecycle events."""
-
-    run_id: UUID | None
-
-    @property
-    def is_async(self) -> bool:
-        """Return whether the manager is async."""
-        return True
-
-    def __init__(
-        self,
-        handlers: Sequence[GraphCallbackHandler] | None = None,
-        inheritable_handlers: Sequence[GraphCallbackHandler] | None = None,
-        parent_run_id: UUID | None = None,
-        *,
-        tags: list[str] | None = None,
-        inheritable_tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        inheritable_metadata: dict[str, Any] | None = None,
-        run_id: UUID | None = None,
-    ) -> None:
-        _init_base_manager(
-            self,
-            handlers,
-            inheritable_handlers,
-            parent_run_id,
-            tags=tags,
-            inheritable_tags=inheritable_tags,
-            metadata=metadata,
-            inheritable_metadata=inheritable_metadata,
-            run_id=run_id,
-        )
-
-    def copy(
-        self,
-        *,
-        run_id: UUID | None | object = _MISSING,
-    ) -> _AsyncGraphCallbackManager:
-        return _copy_graph_manager(self, _AsyncGraphCallbackManager, run_id)
-
-    @classmethod
-    def configure(
-        cls,
-        callbacks: object | None = None,
-        *,
-        run_id: UUID | None = None,
-    ) -> _AsyncGraphCallbackManager:
-        return _configure_graph_callbacks(cls, callbacks, run_id=run_id)
-
-    async def on_interrupt(self, event: GraphInterruptEvent) -> None:
-        await ahandle_event(
-            self.handlers,
-            "on_interrupt",
-            None,
-            event,
-        )
-
-    async def on_resume(self, event: GraphResumeEvent) -> None:
-        await ahandle_event(
-            self.handlers,
-            "on_resume",
-            None,
-            event,
-        )
-
-
-_GraphManagerT = TypeVar(
-    "_GraphManagerT", _GraphCallbackManager, _AsyncGraphCallbackManager
-)
-
-GraphCallbacks: TypeAlias = (
-    _GraphCallbackManager
-    | _AsyncGraphCallbackManager
-    | BaseCallbackManager
-    | GraphCallbackHandler
-    | Sequence[BaseCallbackHandler]
-    | Sequence[GraphCallbackHandler]
-    | None
-)
-
-
-def get_sync_graph_callback_manager_for_config(
-    config: RunnableConfig,
-    *,
-    run_id: UUID | None = None,
-) -> _GraphCallbackManager:
-    """Build a sync graph lifecycle callback manager from a runnable config.
-
-    This helper filters `config["callbacks"]` down to handlers that inherit
-    from `GraphCallbackHandler` and binds the provided `run_id` onto the
-    returned manager.
-    """
-    return _GraphCallbackManager.configure(
-        config.get("callbacks"),
-        run_id=run_id,
-    )
-
-
-def get_async_graph_callback_manager_for_config(
-    config: RunnableConfig,
-    *,
-    run_id: UUID | None = None,
-) -> _AsyncGraphCallbackManager:
-    """Build an async graph lifecycle callback manager from a runnable config.
-
-    This helper filters `config["callbacks"]` down to handlers that inherit
-    from `GraphCallbackHandler` and binds the provided `run_id` onto the
-    returned manager.
-    """
-    return _AsyncGraphCallbackManager.configure(
-        config.get("callbacks"),
-        run_id=run_id,
-    )

libs/langgraph/langgraph/channels/__init__.py

@@ -1,7 +1,6 @@
 from langgraph.channels.any_value import AnyValue
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
-from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -21,7 +20,6 @@ __all__ = (
     "UntrackedValue",
     "EphemeralValue",
     "BinaryOperatorAggregate",
-    "DeltaChannel",
     "NamedBarrierValue",
     "NamedBarrierValueAfterFinish",
     # topics

libs/langgraph/langgraph/channels/binop.py

@@ -22,9 +22,10 @@ __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
 
 
@@ -32,22 +33,11 @@ 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 len(value) == 1 and OVERWRITE in value:
+    if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
         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.
 
@@ -78,8 +68,11 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
             self.value = MISSING
 
     def __eq__(self, value: object) -> bool:
-        return isinstance(value, BinaryOperatorAggregate) and _operators_equal(
-            self.operator, value.operator
+        return isinstance(value, BinaryOperatorAggregate) and (
+            value.operator is self.operator
+            if value.operator.__name__ != "<lambda>"
+            and self.operator.__name__ != "<lambda>"
+            else True
         )
 
     @property

libs/langgraph/langgraph/channels/delta.py

@@ -1,197 +0,0 @@
-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 DELTA_SENTINEL, PendingWrite
-from langgraph.checkpoint.serde.types import _DeltaSnapshot
-from typing_extensions import Self
-
-from langgraph._internal._typing import MISSING
-from langgraph.channels.base import BaseChannel, Value
-from langgraph.channels.binop import _get_overwrite, _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_frequency=None` (default): pure delta; stores only
-    `DELTA_SENTINEL` in checkpoint blobs; reads replay all ancestor writes.
-
-    `snapshot_frequency=N`: `create_checkpoint` writes a full `_DeltaSnapshot`
-    blob every N steps, bounding replay depth to N.
-
-    Parameters:
-        reducer: `(state, list[writes]) -> new_state`. Must be deterministic
-            and batching-invariant as described above.
-        typ: The value type (e.g. `list`, `dict`). Inferred automatically
-            from the outer type when used inside `Annotated[T, DeltaChannel(...)]`.
-        snapshot_frequency: Every Nth pregel step writes a snapshot blob.
-            `None` (default) = pure delta, never snapshot.
-    """
-
-    __slots__ = ("value", "reducer", "snapshot_frequency")
-    value: Value | Any
-
-    def __init__(
-        self,
-        reducer: Callable[[Any, Sequence[Any]], Any],
-        typ: type[Value] | None = None,
-        *,
-        snapshot_frequency: int | None = None,
-    ) -> None:
-        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 is_snapshot_step(self, step: int) -> bool:
-        """True if pregel should write a snapshot blob at this step."""
-        return (
-            self.snapshot_frequency is not None
-            and step > 0
-            and step % self.snapshot_frequency == 0
-        )
-
-    def copy(self) -> Self:
-        new = 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 or sentinel.
-
-        Blob types (dispatched via serde ext code, not dict key inspection):
-          * `DELTA_SENTINEL` / `MISSING`: start empty; caller replays writes.
-          * `_DeltaSnapshot(value)`: restore value directly from snapshot.
-          * plain value (migration from old BinOp blobs): use directly.
-        """
-        new = self.__class__(
-            self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
-        )
-        new.key = self.key
-        if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
-            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 `DELTA_SENTINEL`.
-
-        Snapshot decisions are made by `create_checkpoint` in pregel (which
-        has the step number) via `is_snapshot_step`. `checkpoint()` is only
-        called for non-snapshot steps or when no checkpointer is available.
-        """
-        if self.value is MISSING:
-            return MISSING
-        return DELTA_SENTINEL

libs/langgraph/langgraph/errors.py

@@ -1,9 +1,8 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Literal
+from typing import Any
 from warnings import warn
 
 # EmptyChannelError is re-exported from langgraph.channels.base
@@ -16,14 +15,11 @@ from langgraph.warnings import LangGraphDeprecatedSinceV10
 __all__ = (
     "EmptyChannelError",
     "ErrorCode",
-    "GraphDrained",
     "GraphRecursionError",
     "InvalidUpdateError",
     "GraphBubbleUp",
     "GraphInterrupt",
-    "NodeError",
     "NodeInterrupt",
-    "NodeTimeoutError",
     "ParentCommand",
     "EmptyInputError",
     "TaskNotFound",
@@ -46,23 +42,6 @@ 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.
 
@@ -98,6 +77,10 @@ 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."""
@@ -142,77 +125,3 @@ class TaskNotFound(Exception):
     """Raised when the executor is unable to find a task (for distributed mode)."""
 
     pass
-
-
-@dataclass(frozen=True, slots=True)
-class NodeError:
-    """Failure context passed to a node-level error handler.
-
-    Inject by adding a parameter typed `NodeError` to a handler registered via
-    `StateGraph.add_node(..., error_handler=...)`:
-
-    ```python
-    def handler(state: State, error: NodeError) -> Command:
-        return Command(update={"status": f"recovered from {error.node}: {error.error}"})
-    ```
-    """
-
-    node: str
-    """Name of the node whose execution failed."""
-
-    error: BaseException
-    """Exception raised by the failed node."""
-
-
-class NodeTimeoutError(Exception):
-    """Raised when a node invocation exceeds one of its configured timeouts.
-
-    Does **not** inherit from the built-in `TimeoutError` (a subclass of
-    `OSError`) so that the default `RetryPolicy` treats it as retryable.
-
-    Both `idle_timeout` and `run_timeout` reflect the configured policy at the
-    time of the failure (each is `None` if not configured). `kind` and
-    `timeout` identify which one fired.
-    """
-
-    node: str
-    timeout: float
-    run_timeout: float | None
-    idle_timeout: float | None
-    elapsed: float
-    kind: Literal["idle", "run"]
-
-    def __init__(
-        self,
-        node: str,
-        elapsed: float,
-        *,
-        kind: Literal["idle", "run"],
-        idle_timeout: float | None = None,
-        run_timeout: float | None = None,
-    ) -> None:
-        if kind == "idle":
-            if idle_timeout is None:
-                raise ValueError("idle_timeout is required when kind='idle'")
-            message = (
-                f"Node '{node}' exceeded its idle timeout of "
-                f"{idle_timeout:.3f}s without making progress "
-                f"(elapsed: {elapsed:.3f}s)."
-            )
-            self.timeout = idle_timeout
-        elif kind == "run":
-            if run_timeout is None:
-                raise ValueError("run_timeout is required when kind='run'")
-            message = (
-                f"Node '{node}' exceeded its run timeout of "
-                f"{run_timeout:.3f}s (elapsed: {elapsed:.3f}s)."
-            )
-            self.timeout = run_timeout
-        else:
-            raise ValueError("kind must be 'idle' or 'run'")
-        super().__init__(message)
-        self.node = node
-        self.elapsed = elapsed
-        self.kind = kind
-        self.idle_timeout = idle_timeout
-        self.run_timeout = run_timeout

libs/langgraph/langgraph/func/__init__.py

@@ -5,7 +5,6 @@ import inspect
 import warnings
 from collections.abc import Awaitable, Callable, Sequence
 from dataclasses import dataclass
-from datetime import timedelta
 from typing import (
     Any,
     Generic,
@@ -23,11 +22,6 @@ 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
@@ -37,19 +31,13 @@ from langgraph.pregel._call import (
     P,
     SyncAsyncFuture,
     T,
-    _call_with_options,
+    call,
     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,
-    TimeoutPolicy,
-)
+from langgraph.types import _DC_KWARGS, CachePolicy, RetryPolicy, StreamMode
 from langgraph.typing import ContextT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
 
@@ -63,7 +51,6 @@ 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:
@@ -80,17 +67,15 @@ 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_with_options(
+        return call(
             self.func,
-            args,
-            kwargs,
             retry_policy=self.retry_policy,
             cache_policy=self.cache_policy,
-            timeout=self.timeout,
+            *args,
+            **kwargs,
         )
 
     def clear_cache(self, cache: BaseCache) -> None:
@@ -113,7 +98,6 @@ 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]],
@@ -135,7 +119,6 @@ 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]]
@@ -159,14 +142,6 @@ 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.
@@ -221,7 +196,6 @@ def task(
         )
         if retry_policy is None:
             retry_policy = retry  # type: ignore[assignment]
-    timeout_policy = coerce_timeout_policy(timeout)
 
     retry_policies: Sequence[RetryPolicy] = (
         ()
@@ -234,15 +208,8 @@ 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,
-            timeout=timeout_policy,
-            name=name,
+            func, retry_policy=retry_policies, cache_policy=cache_policy, name=name
         )
 
     if __func_or_none__ is not None:
@@ -301,15 +268,6 @@ 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.
@@ -442,7 +400,6 @@ 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."""
@@ -469,7 +426,6 @@ 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)
@@ -579,7 +535,6 @@ class entrypoint(Generic[ContextT]):
                     bound=bound,
                     triggers=[START],
                     channels=START,
-                    timeout=self.timeout,
                     writers=[
                         ChannelWrite(
                             [

libs/langgraph/langgraph/graph/_node.py

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

libs/langgraph/langgraph/graph/message.py

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

libs/langgraph/langgraph/graph/state.py

@@ -7,7 +7,6 @@ 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
@@ -46,11 +45,9 @@ 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 (
@@ -84,7 +81,6 @@ from langgraph.types import (
     Command,
     RetryPolicy,
     Send,
-    TimeoutPolicy,
     ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
@@ -303,9 +299,7 @@ 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.
@@ -372,9 +366,7 @@ 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.
@@ -446,9 +438,7 @@ 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.
@@ -515,9 +505,7 @@ 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.
@@ -591,9 +579,7 @@ 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`.
@@ -612,7 +598,6 @@ 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.
@@ -624,14 +609,6 @@ 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
@@ -685,7 +662,6 @@ 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
@@ -772,25 +748,6 @@ 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]
@@ -798,10 +755,8 @@ 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(
@@ -810,10 +765,8 @@ 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](
@@ -822,10 +775,8 @@ 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
@@ -1080,6 +1031,7 @@ 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
 
@@ -1093,7 +1045,6 @@ 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.
 
@@ -1126,19 +1077,11 @@ 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]] = [
@@ -1193,11 +1136,6 @@ 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,
@@ -1220,9 +1158,7 @@ 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
 
@@ -1395,10 +1331,7 @@ 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
@@ -1734,20 +1667,6 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
         # Search through all annotated medata to find channel annotations
         for item in meta:
             if isinstance(item, BaseChannel):
-                if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
-                    origin = typ.__origin__
-                    # Unwrap parameterized Required[X]/NotRequired[X] to X
-                    # (e.g. Annotated[NotRequired[dict[...]], ...]).
-                    if hasattr(origin, "__origin__") and origin.__origin__ in (
-                        Required,
-                        NotRequired,
-                    ):
-                        origin = origin.__args__[0]
-                    item = item.__class__(
-                        item.reducer,
-                        origin,
-                        snapshot_frequency=item.snapshot_frequency,
-                    )
                 return item
             elif isclass(item) and issubclass(item, BaseChannel):
                 # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]

libs/langgraph/langgraph/pregel/_algo.py

@@ -39,7 +39,6 @@ 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,
@@ -48,7 +47,6 @@ from langgraph._internal._constants import (
     CONFIG_KEY_TASK_ID,
     CONFIG_KEY_THREAD_ID,
     ERROR,
-    ERROR_SOURCE_NODE,
     INTERRUPT,
     NO_WRITES,
     NS_END,
@@ -68,7 +66,6 @@ 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
@@ -83,7 +80,6 @@ from langgraph.types import (
     PregelTask,
     RetryPolicy,
     Send,
-    TimeoutPolicy,
 )
 
 GetNextVersion = Callable[[V | None, None], V]
@@ -118,21 +114,13 @@ class PregelTaskWrites(NamedTuple):
 
 
 class Call:
-    __slots__ = (
-        "func",
-        "input",
-        "retry_policy",
-        "cache_policy",
-        "callbacks",
-        "timeout",
-    )
+    __slots__ = ("func", "input", "retry_policy", "cache_policy", "callbacks")
 
     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,
@@ -142,14 +130,12 @@ 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(
@@ -295,15 +281,7 @@ 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,
-                ERROR_SOURCE_NODE,
-            ):
+            if chan in (NO_WRITES, PUSH, RESUME, INTERRUPT, RETURN, ERROR):
                 pass
             elif chan in channels:
                 pending_writes_by_channel[chan].append(val)
@@ -755,48 +733,11 @@ 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)
@@ -929,7 +870,6 @@ 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)
@@ -1101,153 +1041,11 @@ 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:
@@ -1457,4 +1255,4 @@ 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, timeout=packet.timeout)
+    return Send(node=packet.node, arg=sanitized_arg)

libs/langgraph/langgraph/pregel/_call.py

@@ -8,7 +8,6 @@ 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
@@ -21,13 +20,9 @@ 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, TimeoutPolicy
+from langgraph.types import CachePolicy, RetryPolicy
 
 ##
 # Utilities borrowed from cloudpickle.
@@ -260,31 +255,8 @@ 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(
@@ -293,6 +265,5 @@ def _call_with_options(
         retry_policy=retry_policy,
         cache_policy=cache_policy,
         callbacks=config["callbacks"],
-        timeout=timeout,
     )
     return fut

libs/langgraph/langgraph/pregel/_checkpoint.py

@@ -1,23 +1,17 @@
 from __future__ import annotations
 
-from collections.abc import Callable, Mapping
+from collections.abc import Mapping
 from datetime import datetime, timezone
-from typing import Any, cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.checkpoint.base import DELTA_SENTINEL, BaseCheckpointSaver, Checkpoint
+from langgraph.checkpoint.base import Checkpoint
 from langgraph.checkpoint.base.id import uuid6
-from langgraph.checkpoint.serde.types import _DeltaSnapshot
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.base import BaseChannel
-from langgraph.channels.delta import DeltaChannel
 from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec
 
 LATEST_VERSION = 4
 
-GetNextVersion = Callable[[Any, None], Any]
-
 
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
@@ -37,87 +31,35 @@ def create_checkpoint(
     *,
     id: str | None = None,
     updated_channels: set[str] | None = None,
-    get_next_version: GetNextVersion | None = None,
-    force_delta_snapshot: bool = False,
 ) -> Checkpoint:
-    """Create a checkpoint for the given channels.
-
-    For `DeltaChannel` with `snapshot_frequency=N`, snapshot steps write a
-    `_DeltaSnapshot` blob rather than `DELTA_SENTINEL`, bounding the ancestor
-    walk to at most N steps. Snapshots are eager: even if the channel had no
-    write this step, a version bump is forced (via `get_next_version`) so the
-    blob is stored by `put()`. Without `get_next_version` (e.g. static
-    contexts), snapshot steps gracefully fall back to sentinel.
-
-    `force_delta_snapshot` writes available `DeltaChannel` values as snapshots
-    regardless of `snapshot_frequency`. This is used by `durability="exit"`,
-    where intermediate writes are not stored as ancestor `checkpoint_writes`.
-    """
+    """Create a checkpoint for the given channels."""
     ts = datetime.now(timezone.utc).isoformat()
     if channels is None:
         values = checkpoint["channel_values"]
-        channel_versions = checkpoint["channel_versions"]
     else:
         values = {}
-        channel_versions = dict(checkpoint["channel_versions"])
         for k in channels:
-            if k not in channel_versions:
+            if k not in checkpoint["channel_versions"]:
                 continue
-            ch = channels[k]
-            if (
-                isinstance(ch, DeltaChannel)
-                and (force_delta_snapshot or ch.is_snapshot_step(step))
-                and ch.is_available()
-            ):
-                # Eager snapshot: bump version if not already written this step
-                # so put() includes this channel in new_versions and stores blob.
-                if get_next_version is not None and (
-                    updated_channels is None or k not in updated_channels
-                ):
-                    channel_versions[k] = get_next_version(channel_versions[k], None)
-                values[k] = _DeltaSnapshot(ch.get())
-            else:
-                v = ch.checkpoint()
-                if v is not MISSING:
-                    values[k] = v
+            v = channels[k].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=channel_versions,
+        channel_versions=checkpoint["channel_versions"],
         versions_seen=checkpoint["versions_seen"],
         updated_channels=None if updated_channels is None else sorted(updated_channels),
     )
 
 
-def _needs_replay(spec: BaseChannel, stored: object) -> bool:
-    """True if `spec` is a `DeltaChannel` and the stored blob is a sentinel,
-    requiring an ancestor walk to reconstruct.
-
-    `_DeltaSnapshot` blobs and plain values (migration) resolve directly via
-    `from_checkpoint` — only `DELTA_SENTINEL` / `MISSING` trigger replay.
-    """
-    if not isinstance(spec, DeltaChannel):
-        return False
-    return stored is MISSING or stored is DELTA_SENTINEL
-
-
 def channels_from_checkpoint(
     specs: Mapping[str, BaseChannel | ManagedValueSpec],
     checkpoint: Checkpoint,
-    *,
-    saver: BaseCheckpointSaver | None = None,
-    config: RunnableConfig | None = None,
 ) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Hydrate channels from a checkpoint.
-
-    For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
-    is sufficient. `DeltaChannel` is the exception: sentinel blobs require an
-    ancestor walk via `saver._get_channel_writes_history`. The walk terminates
-    at the nearest `_DeltaSnapshot` blob (step-based) or a pre-migration plain
-    value, so read depth is bounded by `snapshot_frequency`.
-    """
+    """Get channels from a checkpoint."""
     channel_specs: dict[str, BaseChannel] = {}
     managed_specs: dict[str, ManagedValueSpec] = {}
     for k, v in specs.items():
@@ -125,53 +67,13 @@ def channels_from_checkpoint(
             channel_specs[k] = v
         else:
             managed_specs[k] = v
-
-    channels: dict[str, BaseChannel] = {}
-    for k, spec in channel_specs.items():
-        ch: BaseChannel
-        stored = checkpoint["channel_values"].get(k, MISSING)
-        if _needs_replay(spec, stored) and saver is not None and config is not None:
-            delta_spec = cast(DeltaChannel, spec)
-            history = saver._get_channel_writes_history(config, k)
-            replay_ch = delta_spec.from_checkpoint(history.seed)
-            replay_ch.replay_writes(history.writes)
-            ch = replay_ch
-        else:
-            ch = spec.from_checkpoint(stored)
-        channels[k] = ch
-    return channels, managed_specs
-
-
-async def achannels_from_checkpoint(
-    specs: Mapping[str, BaseChannel | ManagedValueSpec],
-    checkpoint: Checkpoint,
-    *,
-    saver: BaseCheckpointSaver | None = None,
-    config: RunnableConfig | None = None,
-) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Async version of `channels_from_checkpoint`. See docstring there."""
-    channel_specs: dict[str, BaseChannel] = {}
-    managed_specs: dict[str, ManagedValueSpec] = {}
-    for k, v in specs.items():
-        if isinstance(v, BaseChannel):
-            channel_specs[k] = v
-        else:
-            managed_specs[k] = v
-
-    channels: dict[str, BaseChannel] = {}
-    for k, spec in channel_specs.items():
-        ch: BaseChannel
-        stored = checkpoint["channel_values"].get(k, MISSING)
-        if _needs_replay(spec, stored) and saver is not None and config is not None:
-            delta_spec = cast(DeltaChannel, spec)
-            history = await saver._aget_channel_writes_history(config, k)
-            replay_ch = delta_spec.from_checkpoint(history.seed)
-            replay_ch.replay_writes(history.writes)
-            ch = replay_ch
-        else:
-            ch = spec.from_checkpoint(stored)
-        channels[k] = ch
-    return channels, managed_specs
+    return (
+        {
+            k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
+            for k, v in channel_specs.items()
+        },
+        managed_specs,
+    )
 
 
 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:

libs/langgraph/langgraph/pregel/_loop.py

@@ -45,13 +45,11 @@ 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,
@@ -64,13 +62,7 @@ from langgraph._internal._constants import (
 from langgraph._internal._replay import ReplayState
 from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import EMPTY_SEQ, MISSING
-from langgraph.callbacks import (
-    GraphInterruptEvent,
-    GraphLifecycleEvent,
-    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 (
@@ -89,14 +81,12 @@ 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,
@@ -122,13 +112,11 @@ 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,
     Command,
     Durability,
-    Interrupt,
     PregelExecutableTask,
     RetryPolicy,
     Send,
@@ -194,8 +182,6 @@ 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
@@ -210,17 +196,13 @@ 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
-    _graph_lifecycle_events: deque[GraphLifecycleEvent]
-    _has_graph_lifecycle_callbacks: bool
 
     # public
 
@@ -246,7 +228,6 @@ class PregelLoop:
         migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
         retry_policy: Sequence[RetryPolicy] = (),
         cache_policy: CachePolicy | None = None,
-        has_graph_lifecycle_callbacks: bool = False,
     ) -> None:
         self.stream = stream
         self.config = config
@@ -271,8 +252,6 @@ class PregelLoop:
         self.retry_policy = retry_policy
         self.cache_policy = cache_policy
         self.durability = durability
-        self._has_graph_lifecycle_callbacks = has_graph_lifecycle_callbacks
-        self._graph_lifecycle_events = deque()
         if self.stream is not None and CONFIG_KEY_STREAM in config[CONF]:
             self.stream = DuplexStream(self.stream, config[CONF][CONFIG_KEY_STREAM])
         scratchpad: PregelScratchpad | None = config[CONF].get(CONFIG_KEY_SCRATCHPAD)
@@ -323,47 +302,6 @@ 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,
-        kind: Literal["resume", "interrupt"],
-        *,
-        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=status,
-                    checkpoint_id=self.checkpoint["id"],
-                    checkpoint_ns=self.checkpoint_ns,
-                )
-            )
-        elif kind == "interrupt":
-            self._graph_lifecycle_events.append(
-                GraphInterruptEvent(
-                    run_id=None,
-                    status=status,
-                    checkpoint_id=self.checkpoint["id"],
-                    checkpoint_ns=self.checkpoint_ns,
-                    interrupts=interrupts,
-                )
-            )
-        else:
-            msg = f"Unknown graph lifecycle event type: {kind}"
-            raise AssertionError(msg)
-
-    def _pop_lifecycle_event(self) -> GraphLifecycleEvent | None:
-        if not self._graph_lifecycle_events:
-            return None
-        return self._graph_lifecycle_events.popleft()
 
     def put_writes(self, task_id: str, writes: WritesT) -> None:
         """Put writes for a task, to be read by the next tick."""
@@ -423,7 +361,7 @@ class PregelLoop:
                     task = self.tasks.get(task_id)
                 else:
                     task = None
-                fut = self.submit(
+                self.submit(
                     self.checkpointer_put_writes,
                     config,
                     writes_to_save,
@@ -431,16 +369,12 @@ class PregelLoop:
                     task_path_str(task.path) if task else "",
                 )
             else:
-                fut = self.submit(
+                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)
@@ -524,16 +458,6 @@ 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.
 
@@ -592,10 +516,6 @@ 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)
@@ -662,7 +582,7 @@ class PregelLoop:
 
     def _match_writes(self, tasks: Mapping[str, PregelExecutableTask]) -> None:
         for tid, k, v in self.checkpoint_pending_writes:
-            if k in (ERROR, ERROR_SOURCE_NODE, INTERRUPT, RESUME):
+            if k in (ERROR, INTERRUPT, RESUME):
                 continue
             if task := tasks.get(tid):
                 task.writes.append((k, v))
@@ -727,7 +647,7 @@ class PregelLoop:
         # writes so that interrupt() calls re-fire instead of returning
         # stale values. But if we're actively resuming, keep them —
         # multi-interrupt scenarios need previously resolved values preserved.
-        is_time_traveling = self.is_replaying and (
+        if self.is_replaying and (
             # Time-travel to a subgraph checkpoint: the parent sets
             # RESUMING=True (it can't distinguish time-travel from resume),
             # so we check if this subgraph's own ns is in checkpoint_map.
@@ -745,8 +665,7 @@ class PregelLoop:
                 # (subgraph input is a Send arg, not a Command)
                 or configurable.get(CONFIG_KEY_RESUMING, False)
             )
-        )
-        if is_time_traveling:
+        ):
             self.checkpoint_pending_writes = [
                 w for w in self.checkpoint_pending_writes if w[1] != RESUME
             ]
@@ -801,26 +720,6 @@ class PregelLoop:
                 if k in self.checkpoint["channel_versions"]:
                     version = self.checkpoint["channel_versions"][k]
                     self.checkpoint["versions_seen"][INTERRUPT][k] = version
-            # When time-traveling (replaying from a specific checkpoint),
-            # save a fork checkpoint so the replayed execution creates a
-            # new branch. Without this, if the execution hits an interrupt
-            # before after_tick() runs, no new checkpoint is created —
-            # the parent's latest checkpoint remains the old one and
-            # subsequent resumes load the wrong state.
-            # Skip for update_state forks (source=update/fork) since they
-            # already have their own fork checkpoint.
-            if is_time_traveling and self.checkpoint_metadata.get("source") not in (
-                "update",
-                "fork",
-            ):
-                # Clear old INTERRUPT writes from the loaded checkpoint.
-                # The fork will have a new checkpoint_id which changes
-                # task IDs — stale interrupt writes would accumulate and
-                # confuse the multiple-interrupt check in future resumes.
-                self.checkpoint_pending_writes = [
-                    w for w in self.checkpoint_pending_writes if w[1] != INTERRUPT
-                ]
-                self._put_checkpoint({"source": "fork"})
             # produce values output
             self._emit(
                 "values", map_output_values, self.output_keys, True, self.channels
@@ -863,28 +762,14 @@ class PregelLoop:
         if not self.is_nested:
             # Pass the resolved before-bound checkpoint ID so subgraphs can
             # find their corresponding checkpoint without re-fetching the
-            # parent. For forks (source=update/fork), use the fork's parent
+            # parent. For forks (source=update), use the fork's parent
             # checkpoint ID since the fork was created after the subgraph's
             # checkpoints from the original execution.
-            #
-            # Only gate on is_time_traveling (not is_replaying). When the
-            # client resumes with an explicit checkpoint_id that happens to
-            # point at the current head (e.g. LangGraph Studio sending
-            # `checkpoint: {checkpoint_id}` alongside Command(resume=...)),
-            # is_replaying is True but is_time_traveling is False. In that
-            # case subgraphs should load their latest checkpoint normally,
-            # not go through ReplayState's before-bound lookup which would
-            # miss subgraph checkpoints created during processing of the
-            # current parent step.
             replay_state: ReplayState | None = None
-            if is_time_traveling:
+            if self.is_replaying:
                 replay_checkpoint_id = self.checkpoint["id"]
                 if (
-                    self.checkpoint_metadata.get("source")
-                    in (
-                        "update",
-                        "fork",
-                    )
+                    self.checkpoint_metadata.get("source") == "update"
                     and self.prev_checkpoint_config
                 ):
                     replay_checkpoint_id = self.prev_checkpoint_config[CONF].get(
@@ -900,8 +785,6 @@ class PregelLoop:
             )
         # set flag
         self.status = "pending"
-        if is_resuming:
-            self._push_graph_lifecycle_event("resume")
         return updated_channels
 
     def _put_checkpoint(self, metadata: CheckpointMetadata) -> None:
@@ -925,10 +808,6 @@ 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",
         )
         # sanitize TASK channel in the checkpoint before saving (durability=="exit")
         if TASKS in self.checkpoint["channel_values"] and any(
@@ -1006,10 +885,8 @@ class PregelLoop:
             self._put_checkpoint(self.checkpoint_metadata)
             self._put_pending_writes()
         # suppress interrupt
-        if isinstance(exc_value, GraphInterrupt) and not self.is_nested:
-            interrupt = exc_value
-            interrupts = tuple(interrupt.args[0]) if interrupt.args else ()
-            self._push_graph_lifecycle_event("interrupt", interrupts=interrupts)
+        suppress = isinstance(exc_value, GraphInterrupt) and not self.is_nested
+        if suppress:
             # emit one last "values" event, with pending writes applied
             if (
                 hasattr(self, "tasks")
@@ -1036,11 +913,12 @@ class PregelLoop:
                         self.channels,
                     )
             # emit INTERRUPT if exception is empty (otherwise emitted by put_writes)
-            if not interrupt.args or not interrupt.args[0]:
-                interrupt_payload = interrupt.args[0] if interrupt.args else ()
+            if exc_value is not None and (not exc_value.args or not exc_value.args[0]):
                 self._emit(
                     "updates",
-                    lambda: iter([{INTERRUPT: interrupt_payload}]),
+                    lambda: iter(
+                        [{INTERRUPT: cast(GraphInterrupt, exc_value).args[0]}]
+                    ),
                 )
             # save final output
             self.output = read_channels(self.channels, self.output_keys)
@@ -1162,7 +1040,6 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
         migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
         retry_policy: Sequence[RetryPolicy] = (),
         cache_policy: CachePolicy | None = None,
-        has_graph_lifecycle_callbacks: bool = False,
     ) -> None:
         super().__init__(
             input,
@@ -1184,7 +1061,6 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
             retry_policy=retry_policy,
             cache_policy=cache_policy,
             durability=durability,
-            has_graph_lifecycle_callbacks=has_graph_lifecycle_callbacks,
         )
         self.stack = ExitStack()
         if checkpointer:
@@ -1239,45 +1115,6 @@ 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)
@@ -1299,7 +1136,6 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
     # context manager
 
     def __enter__(self) -> Self:
-        self._graph_lifecycle_events = deque()
         if not self.checkpointer:
             saved = None
         elif self.checkpoint_config[CONF].get(CONFIG_KEY_CHECKPOINT_ID):
@@ -1351,10 +1187,7 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
         )
         self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
         self.channels, self.managed = channels_from_checkpoint(
-            self.specs,
-            self.checkpoint,
-            saver=self.checkpointer,
-            config=self.checkpoint_config,
+            self.specs, self.checkpoint
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"
@@ -1403,7 +1236,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
         migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
         retry_policy: Sequence[RetryPolicy] = (),
         cache_policy: CachePolicy | None = None,
-        has_graph_lifecycle_callbacks: bool = False,
     ) -> None:
         super().__init__(
             input,
@@ -1425,7 +1257,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
             retry_policy=retry_policy,
             cache_policy=cache_policy,
             durability=durability,
-            has_graph_lifecycle_callbacks=has_graph_lifecycle_callbacks,
         )
         self.stack = AsyncExitStack()
         if checkpointer:
@@ -1449,11 +1280,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
         metadata: CheckpointMetadata,
         new_versions: ChannelVersions,
     ) -> RunnableConfig:
-        # Drain DeltaChannel write futures before committing the checkpoint so
-        # DELTA_SENTINEL blobs are never saved ahead of their backing writes.
-        if self._delta_write_futs:
-            futs, self._delta_write_futs = self._delta_write_futs, []
-            await asyncio.gather(*futs)
         try:
             if prev is not None:
                 await prev
@@ -1485,45 +1311,6 @@ 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)
@@ -1548,7 +1335,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
     # context manager
 
     async def __aenter__(self) -> Self:
-        self._graph_lifecycle_events = deque()
         if not self.checkpointer:
             saved = None
         elif self.checkpoint_config[CONF].get(CONFIG_KEY_CHECKPOINT_ID):
@@ -1598,15 +1384,11 @@ 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 = await achannels_from_checkpoint(
-            self.specs,
-            self.checkpoint,
-            saver=self.checkpointer,
-            config=self.checkpoint_config,
+        self.channels, self.managed = channels_from_checkpoint(
+            self.specs, self.checkpoint
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"

libs/langgraph/langgraph/pregel/_messages.py

@@ -24,11 +24,6 @@ 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]]
 
@@ -253,126 +248,3 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         **kwargs: Any,
     ) -> Any:
         self.metadata.pop(run_id, None)
-
-
-class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler):
-    """v2 variant of `StreamMessagesHandler`.
-
-    Declaring `_V2StreamingCallbackHandler` as a base flips
-    `BaseChatModel.invoke` to route through `_stream_chat_model_events`
-    (firing `on_stream_event`) instead of `_stream` (firing
-    `on_llm_new_token`). Inherits `on_stream_event` from the parent,
-    which forwards protocol events onto the messages stream channel.
-
-    Pregel attaches this class instead of the v1 handler only when
-    `StreamingHandler` opts in via the internal
-    `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
-    `graph.stream(stream_mode="messages")` callers keep the v1
-    AIMessageChunk shape.
-    """
-
-    def on_llm_new_token(
-        self,
-        token: str,
-        *,
-        chunk: ChatGenerationChunk | None = None,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Intentional no-op — v1 chunks are not used on v2-flagged runs.
-
-        The v2 marker already steers `invoke` to the event generator, so
-        `on_llm_new_token` should not fire under normal routing. This
-        override stays a pass-through (no call to `super()`) to make
-        the intent explicit and to guard against any caller (e.g. a
-        node that calls `model.stream()` directly, which still fires
-        the v1 callback) leaking AIMessageChunks onto a v2-flagged
-        messages stream.
-        """
-        # Intentionally empty: v2 handler does not forward v1 chunks.
-
-    def __init__(
-        self,
-        stream: Callable[[StreamChunk], None],
-        subgraphs: bool,
-        *,
-        parent_ns: tuple[str, ...] | None = None,
-    ) -> None:
-        super().__init__(stream, subgraphs, parent_ns=parent_ns)
-        self._streamed_run_ids: set[UUID] = set()
-
-    def on_llm_end(
-        self,
-        response: LLMResult,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        if meta := self.metadata.get(run_id):
-            if response.generations and response.generations[0]:
-                gen = response.generations[0][0]
-                if isinstance(gen, ChatGeneration):
-                    if run_id in self._streamed_run_ids:
-                        if gen.message.id is None:
-                            gen.message.id = str(uuid4())
-                        self.seen.add(gen.message.id)
-                    else:
-                        self._emit(meta, gen.message, dedupe=True)
-        self._streamed_run_ids.discard(run_id)
-        self.metadata.pop(run_id, None)
-
-    def on_llm_error(
-        self,
-        error: BaseException,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        self._streamed_run_ids.discard(run_id)
-        super().on_llm_error(
-            error,
-            run_id=run_id,
-            parent_run_id=parent_run_id,
-            **kwargs,
-        )
-
-    def on_stream_event(
-        self,
-        event: dict[str, Any],
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Forward a protocol event from `stream_events(version="v3")` as a messages stream part.
-
-        Fires once per `MessagesData` event (`message-start`, per-block
-        `content-block-*`, `message-finish`). The transformer layer
-        correlates events back to a single `ChatModelStream` via
-        `metadata["run_id"]` — attached here so the v1
-        `stream_mode="messages"` output (which emits
-        `(AIMessageChunk, metadata)` via `on_llm_new_token`) keeps its
-        original metadata shape.
-
-        Lives on the v2 handler rather than the v1 base: content-block
-        events are a v2-only concept, and forwarding them only when the
-        v2 handler is attached keeps the message channel's shape
-        predictable for v1 callers.
-        """
-        if meta := self.metadata.get(run_id):
-            # Record message_id on message-start so on_chain_end's
-            # dedupe skips the finalized AIMessage the node returns
-            # (otherwise the messages projection double-counts: once
-            # from streaming, once from the chain output).
-            if event.get("event") == "message-start":
-                self._streamed_run_ids.add(run_id)
-                msg_id = event.get("message_id")
-                if msg_id:
-                    self.seen.add(msg_id)
-            v2_meta = {**meta[1], "run_id": str(run_id)}
-            self.stream((meta[0], "messages", (event, v2_meta)))

libs/langgraph/langgraph/pregel/_messages_v2.py

@@ -0,0 +1,807 @@
+"""Protocol-native content-block message handler for StreamingHandler.
+
+Emits structured content-block lifecycle events (message-start,
+content-block-start/delta/finish, message-finish) instead of raw
+``(AIMessageChunk, metadata)`` tuples.  The existing
+:class:`~langgraph.pregel._messages.StreamMessagesHandler` is NOT
+modified — this handler is only activated when
+``__protocol_messages_stream`` is ``True`` in the run's configurable.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncIterator, Callable, Iterator, Sequence
+from dataclasses import dataclass, field
+from typing import Any, TypeVar, cast
+from uuid import UUID, uuid4
+
+from langchain_core.callbacks import BaseCallbackHandler
+from langchain_core.messages import AIMessageChunk, BaseMessage
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
+
+from langgraph._internal._constants import NS_SEP
+from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
+from langgraph.pregel.protocol import StreamChunk
+from langgraph.stream._types import (
+    ContentBlockDeltaData,
+    ContentBlockFinishData,
+    ContentBlockStartData,
+    FinishReason,
+    InvalidToolCallBlock,
+    MessageErrorData,
+    MessageStartData,
+    ReasoningBlock,
+    TextBlock,
+    ToolCallBlock,
+    UsageInfo,
+)
+
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = object  # type: ignore
+
+T = TypeVar("T")
+Meta = tuple[tuple[str, ...], dict[str, Any]]
+
+PROTOCOL_MESSAGES_STREAM_KEY = "__protocol_messages_stream"
+
+# ---------------------------------------------------------------------------
+# Content-block accumulation helpers
+# ---------------------------------------------------------------------------
+
+# A "compatible content block" is a dict matching one of the protocol block
+# TypedDicts (TextBlock, ReasoningBlock, ToolCallChunkBlock, etc.).
+CompatBlock = dict[str, Any]
+
+
+@dataclass
+class _ProtocolRunState:
+    """Per-run state for tracking the active message lifecycle."""
+
+    message_id: str | None = None
+    started: bool = False
+    blocks: dict[int, CompatBlock] = field(default_factory=dict)
+    usage: dict[str, Any] | None = None
+
+
+def _accumulate_block(accumulated: CompatBlock, delta: CompatBlock) -> CompatBlock:
+    """Merge *delta* into *accumulated*, returning the updated block."""
+    btype = accumulated.get("type", "text")
+    if btype == "text" and delta.get("type", "text") == "text":
+        accumulated["text"] = accumulated.get("text", "") + delta.get("text", "")
+    elif btype == "reasoning" and delta.get("type") == "reasoning":
+        accumulated["reasoning"] = accumulated.get("reasoning", "") + delta.get(
+            "reasoning", ""
+        )
+    elif btype == "tool_call_chunk" and delta.get("type") == "tool_call_chunk":
+        accumulated["args"] = accumulated.get("args", "") + delta.get("args", "")
+        if delta.get("id") is not None:
+            accumulated["id"] = delta["id"]
+        if delta.get("name") is not None:
+            accumulated["name"] = delta["name"]
+    return accumulated
+
+
+def _delta_block(previous: CompatBlock, current: CompatBlock) -> CompatBlock | None:
+    """Compute the delta between *previous* and *current*.
+
+    Returns ``None`` if there is nothing new to emit.
+    """
+    btype = current.get("type", "text")
+    if btype == "text":
+        prev_text = previous.get("text", "")
+        cur_text = current.get("text", "")
+        delta_text = cur_text[len(prev_text) :]
+        if not delta_text:
+            return None
+        return TextBlock(type="text", text=delta_text)
+    elif btype == "reasoning":
+        prev_r = previous.get("reasoning", "")
+        cur_r = current.get("reasoning", "")
+        delta_r = cur_r[len(prev_r) :]
+        if not delta_r:
+            return None
+        return ReasoningBlock(type="reasoning", reasoning=delta_r)
+    elif btype == "tool_call_chunk":
+        prev_args = previous.get("args", "")
+        cur_args = current.get("args", "")
+        delta_args = cur_args[len(prev_args) :]
+        has_meta = current.get("id") is not None or current.get("name") is not None
+        if not delta_args and not has_meta:
+            return None
+        result: CompatBlock = {"type": "tool_call_chunk", "args": delta_args}
+        if current.get("id") is not None and previous.get("id") is None:
+            result["id"] = current["id"]
+        if current.get("name") is not None and previous.get("name") is None:
+            result["name"] = current["name"]
+        return result
+    # Unrecognized block type — pass through unchanged
+    return current
+
+
+def _finalize_block(block: CompatBlock) -> CompatBlock:
+    """Convert a ``tool_call_chunk`` block to a finalized ``tool_call`` or
+    ``invalid_tool_call`` block.  Other block types pass through unchanged.
+    """
+    if block.get("type") != "tool_call_chunk":
+        return block
+    raw_args = block.get("args", "{}")
+    try:
+        parsed_args = json.loads(raw_args) if raw_args else {}
+        return ToolCallBlock(
+            type="tool_call",
+            id=block.get("id", ""),
+            name=block.get("name", ""),
+            args=parsed_args,
+        )
+    except (json.JSONDecodeError, TypeError):
+        return InvalidToolCallBlock(
+            type="invalid_tool_call",
+            id=block.get("id"),
+            name=block.get("name"),
+            args=raw_args,
+            error="Failed to parse tool call arguments as JSON",
+        )
+
+
+def _normalize_finish_reason(value: Any) -> FinishReason:
+    """Map provider-specific stop reasons to protocol finish reasons."""
+    if value == "length":
+        return "length"
+    if value == "content_filter":
+        return "content_filter"
+    if value in ("tool_use", "tool_calls"):
+        return "tool_use"
+    # "end_turn", "stop", None, and anything else → "stop"
+    return "stop"
+
+
+def _accumulate_usage(
+    current: dict[str, Any] | None, delta: Any
+) -> dict[str, Any] | None:
+    """Accumulate usage metadata from streamed chunks."""
+    if not isinstance(delta, dict):
+        return current
+    if current is None:
+        return dict(delta)
+    for key in ("input_tokens", "output_tokens", "total_tokens", "cached_tokens"):
+        if key in delta:
+            current[key] = current.get(key, 0) + delta[key]
+    # Merge detail dicts
+    for detail_key in ("input_token_details", "output_token_details"):
+        if detail_key in delta and isinstance(delta[detail_key], dict):
+            if detail_key not in current:
+                current[detail_key] = {}
+            current[detail_key].update(delta[detail_key])
+    return current
+
+
+def _to_protocol_usage(usage: dict[str, Any] | None) -> UsageInfo | None:
+    """Convert LangChain usage metadata to protocol ``UsageInfo``."""
+    if usage is None:
+        return None
+    result: dict[str, Any] = {}
+    if "input_tokens" in usage:
+        result["input_tokens"] = usage["input_tokens"]
+    if "output_tokens" in usage:
+        result["output_tokens"] = usage["output_tokens"]
+    if "total_tokens" in usage:
+        result["total_tokens"] = usage["total_tokens"]
+    if "cached_tokens" in usage:
+        result["cached_tokens"] = usage["cached_tokens"]
+    return UsageInfo(**result) if result else None
+
+
+# ---------------------------------------------------------------------------
+# Extracting content blocks from LangChain messages
+# ---------------------------------------------------------------------------
+
+
+def _extract_blocks_from_chunk(msg: AIMessageChunk) -> list[tuple[int, CompatBlock]]:
+    """Extract ``(index, block)`` pairs from an ``AIMessageChunk``.
+
+    LangChain stores content in several places:
+    - ``content: str`` — a single text block at index 0
+    - ``content: list[dict]`` — explicit content blocks with their own types
+    - ``tool_call_chunks`` — separate list for streamed tool call deltas
+    """
+    blocks: list[tuple[int, CompatBlock]] = []
+    content = msg.content
+    if isinstance(content, str) and content:
+        blocks.append((0, dict(TextBlock(type="text", text=content))))
+    elif isinstance(content, list):
+        for i, item in enumerate(content):
+            if not isinstance(item, dict):
+                continue
+            ctype = item.get("type", "")
+            if ctype == "text" and item.get("text"):
+                blocks.append(
+                    (
+                        item.get("index", i),
+                        dict(TextBlock(type="text", text=item["text"])),
+                    )
+                )
+            elif ctype in ("reasoning_content", "reasoning", "thinking"):
+                reasoning_text = (
+                    item.get("reasoning_content")
+                    or item.get("reasoning")
+                    or item.get("thinking", "")
+                )
+                if reasoning_text:
+                    blocks.append(
+                        (
+                            item.get("index", i),
+                            dict(
+                                ReasoningBlock(
+                                    type="reasoning", reasoning=reasoning_text
+                                )
+                            ),
+                        )
+                    )
+
+    # Tool call chunks live in a separate field
+    for tc in msg.tool_call_chunks or []:
+        idx = tc.get("index")
+        if idx is None:
+            # Assign indices after text content blocks
+            idx = len(blocks)
+        block: CompatBlock = {"type": "tool_call_chunk", "args": tc.get("args", "")}
+        if tc.get("id") is not None:
+            block["id"] = tc["id"]
+        if tc.get("name") is not None:
+            block["name"] = tc["name"]
+        blocks.append((idx, block))
+
+    return blocks
+
+
+# ---------------------------------------------------------------------------
+# The handler
+# ---------------------------------------------------------------------------
+
+
+class StreamProtocolMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
+    """Callback handler that emits content-block protocol events.
+
+    Activated when ``__protocol_messages_stream`` is ``True`` in the run's
+    configurable metadata.  Emits ``StreamChunk`` tuples of the form
+    ``(namespace, "messages", data)`` where *data* is one of the
+    ``MessagesData`` event types (``message-start``, ``content-block-start``,
+    etc.).
+    """
+
+    run_inline = True
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        self.stream = stream
+        self.subgraphs = subgraphs
+        self.parent_ns = parent_ns
+        # Per-run metadata: run_id → (namespace, metadata_dict)
+        self.metadata: dict[UUID, Meta] = {}
+        # Per-run protocol state for streamed messages
+        self.protocol_runs: dict[UUID, _ProtocolRunState] = {}
+        # Stable message ID mapping: run_id → message_id
+        self.stable_message_ids: dict[UUID, str] = {}
+        # Seen message IDs for deduplication of chain-emitted messages
+        self.seen: set[str | int] = set()
+
+    def _emit(self, meta: Meta, data: Any) -> None:
+        """Emit a protocol event as a StreamChunk.
+
+        The node name from *meta* is embedded at ``"__node__"`` so the
+        stream pump can lift it into ``params.node`` without changing the
+        ``StreamChunk`` tuple shape.
+        """
+        node = meta[1].get("langgraph_node")
+        if node and isinstance(data, dict):
+            data = {**data, "__node__": node}
+        self.stream((meta[0], "messages", data))
+
+    # -- Chat model callbacks -----------------------------------------------
+
+    def on_chat_model_start(
+        self,
+        serialized: dict[str, Any],
+        messages: list[list[BaseMessage]],
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        metadata: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        if metadata and (not tags or (TAG_NOSTREAM not in tags)):
+            ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
+                :-1
+            ]
+            if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
+                return
+            if tags:
+                if filtered := [t for t in tags if not t.startswith("seq:step")]:
+                    metadata["tags"] = filtered
+            self.metadata[run_id] = (ns, metadata)
+            self.protocol_runs[run_id] = _ProtocolRunState()
+
+    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:
+        if not isinstance(chunk, ChatGenerationChunk):
+            return
+        meta = self.metadata.get(run_id)
+        if meta is None:
+            return
+        state = self.protocol_runs.get(run_id)
+        if state is None:
+            return
+
+        msg = chunk.message
+        if not isinstance(msg, AIMessageChunk):
+            return
+
+        # Emit message-start on first token
+        if not state.started:
+            message_id = self._normalize_message_id(msg, run_id)
+            state.message_id = message_id
+            state.started = True
+            start_data = dict(
+                MessageStartData(
+                    event="message-start",
+                    role="ai",
+                )
+            )
+            if message_id:
+                start_data["message_id"] = message_id
+            self._emit(meta, start_data)
+
+        # Extract content blocks from this chunk
+        extracted = _extract_blocks_from_chunk(msg)
+        for idx, delta_block in extracted:
+            if idx not in state.blocks:
+                # New block — emit content-block-start
+                state.blocks[idx] = dict(delta_block)
+                # Start block has empty content placeholder
+                start_block = _make_start_block(delta_block)
+                self._emit(
+                    meta,
+                    ContentBlockStartData(
+                        event="content-block-start",
+                        index=idx,
+                        content_block=start_block,
+                    ),
+                )
+                # Then emit the first delta
+                first_delta = _delta_block(
+                    _make_start_block(delta_block), state.blocks[idx]
+                )
+                if first_delta is not None:
+                    self._emit(
+                        meta,
+                        ContentBlockDeltaData(
+                            event="content-block-delta",
+                            index=idx,
+                            content_block=first_delta,
+                        ),
+                    )
+            else:
+                # Existing block — compute delta, accumulate, emit
+                previous = dict(state.blocks[idx])
+                state.blocks[idx] = _accumulate_block(state.blocks[idx], delta_block)
+                delta = _delta_block(previous, state.blocks[idx])
+                if delta is not None:
+                    self._emit(
+                        meta,
+                        ContentBlockDeltaData(
+                            event="content-block-delta",
+                            index=idx,
+                            content_block=delta,
+                        ),
+                    )
+
+        # Accumulate usage from chunk
+        if msg.usage_metadata:
+            state.usage = _accumulate_usage(state.usage, msg.usage_metadata)
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        meta = self.metadata.pop(run_id, None)
+        state = self.protocol_runs.pop(run_id, None)
+        if meta is None or state is None:
+            return
+
+        # Extract finish reason and usage from the final generation
+        finish_reason: FinishReason = "stop"
+        final_usage = state.usage
+
+        if response.generations and response.generations[0]:
+            gen = response.generations[0][0]
+            if isinstance(gen, ChatGeneration):
+                final_msg = gen.message
+                # Get finish reason from response_metadata
+                rm = getattr(final_msg, "response_metadata", {}) or {}
+                raw_reason = rm.get("finish_reason") or rm.get("stop_reason")
+                if raw_reason:
+                    finish_reason = _normalize_finish_reason(raw_reason)
+                # If we have tool calls in the final message, infer tool_use
+                if (
+                    finish_reason == "stop"
+                    and hasattr(final_msg, "tool_calls")
+                    and final_msg.tool_calls
+                ):
+                    finish_reason = "tool_use"
+                # Get usage from final message if not accumulated from chunks
+                if final_usage is None and hasattr(final_msg, "usage_metadata"):
+                    final_usage = (
+                        dict(final_msg.usage_metadata)
+                        if final_msg.usage_metadata
+                        else None
+                    )
+
+                # If we never got streaming tokens (non-streamed model call),
+                # emit the full message lifecycle now
+                if not state.started:
+                    self._emit_full_message(meta, final_msg, finish_reason, final_usage)
+                    return
+
+        # Close out any open content blocks
+        for idx in sorted(state.blocks):
+            finalized = _finalize_block(state.blocks[idx])
+            self._emit(
+                meta,
+                ContentBlockFinishData(
+                    event="content-block-finish",
+                    index=idx,
+                    content_block=finalized,
+                ),
+            )
+
+        # Emit message-finish
+        finish_data: dict[str, Any] = {
+            "event": "message-finish",
+            "reason": finish_reason,
+        }
+        usage_info = _to_protocol_usage(final_usage)
+        if usage_info is not None:
+            finish_data["usage"] = usage_info
+        self._emit(meta, finish_data)
+
+        # Track the message as seen for dedup
+        if state.message_id:
+            self.seen.add(state.message_id)
+
+    def on_llm_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        meta = self.metadata.pop(run_id, None)
+        state = self.protocol_runs.pop(run_id, None)
+        self.stable_message_ids.pop(run_id, None)
+        if meta is None or state is None:
+            return
+        if state.started:
+            self._emit(
+                meta,
+                MessageErrorData(
+                    event="error",
+                    message=str(error),
+                ),
+            )
+
+    # -- Chain callbacks (for node-level message dedup) ---------------------
+
+    def on_chain_start(
+        self,
+        serialized: dict[str, Any],
+        inputs: dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        metadata: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        if (
+            metadata
+            and kwargs.get("name") == metadata.get("langgraph_node")
+            and (not tags or TAG_HIDDEN not in tags)
+        ):
+            ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
+                :-1
+            ]
+            if not self.subgraphs and len(ns) > 0:
+                return
+            self.metadata[run_id] = (ns, metadata)
+            # Record input message IDs for deduplication
+            self._record_seen_messages(inputs)
+
+    def on_chain_end(
+        self,
+        response: Any,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        meta = self.metadata.pop(run_id, None)
+        if meta is None:
+            return
+        # Emit protocol events for any new messages in the node's output
+        self._emit_chain_messages(meta, response)
+
+    def on_chain_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self.metadata.pop(run_id, None)
+
+    # -- Iterator taps (required by _StreamingCallbackHandler) ---------------
+
+    def tap_output_aiter(
+        self, run_id: UUID, output: AsyncIterator[T]
+    ) -> AsyncIterator[T]:
+        return output
+
+    def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
+        return output
+
+    # -- Internal helpers ---------------------------------------------------
+
+    def _normalize_message_id(self, msg: BaseMessage, run_id: UUID) -> str | None:
+        """Return a stable message ID for this run, creating one if needed."""
+        msg_id = msg.id
+        if msg_id is None:
+            msg_id = self.stable_message_ids.get(run_id)
+        if msg_id is None:
+            msg_id = f"run-{run_id}"
+        self.stable_message_ids[run_id] = msg_id
+        # Mutate the message for consistency downstream
+        if msg.id != msg_id:
+            msg.id = msg_id
+        return msg_id
+
+    def _emit_full_message(
+        self,
+        meta: Meta,
+        msg: BaseMessage,
+        finish_reason: FinishReason,
+        usage: dict[str, Any] | None,
+        role: str = "ai",
+    ) -> None:
+        """Emit a complete message lifecycle for a non-streamed model call."""
+        message_id = msg.id or str(uuid4())
+        if message_id in self.seen:
+            return
+        self.seen.add(message_id)
+
+        # message-start
+        start_data = dict(
+            MessageStartData(
+                event="message-start",
+                role=role,
+            )
+        )
+        start_data["message_id"] = message_id
+        self._emit(meta, start_data)
+
+        # Extract all blocks from the final message
+        blocks = _extract_final_blocks(msg)
+        for idx, block in blocks:
+            # content-block-start with the full content
+            self._emit(
+                meta,
+                ContentBlockStartData(
+                    event="content-block-start",
+                    index=idx,
+                    content_block=_make_start_block(block),
+                ),
+            )
+            # content-block-delta with the full content
+            delta = _delta_block(_make_start_block(block), block)
+            if delta is not None:
+                self._emit(
+                    meta,
+                    ContentBlockDeltaData(
+                        event="content-block-delta",
+                        index=idx,
+                        content_block=delta,
+                    ),
+                )
+            # content-block-finish
+            finalized = _finalize_block(block)
+            self._emit(
+                meta,
+                ContentBlockFinishData(
+                    event="content-block-finish",
+                    index=idx,
+                    content_block=finalized,
+                ),
+            )
+
+        # message-finish
+        finish_data: dict[str, Any] = {
+            "event": "message-finish",
+            "reason": finish_reason,
+        }
+        usage_info = _to_protocol_usage(usage)
+        if usage_info is not None:
+            finish_data["usage"] = usage_info
+        self._emit(meta, finish_data)
+
+    def _record_seen_messages(self, obj: Any) -> None:
+        """Record message IDs from node inputs for deduplication."""
+        if isinstance(obj, BaseMessage):
+            if obj.id is not None:
+                self.seen.add(obj.id)
+        elif isinstance(obj, dict):
+            for value in obj.values():
+                self._record_seen_messages(value)
+        elif isinstance(obj, Sequence) and not isinstance(obj, (str, bytes)):
+            for item in obj:
+                self._record_seen_messages(item)
+
+    def _emit_chain_messages(self, meta: Meta, response: Any) -> None:
+        """Emit protocol events for messages found in chain output."""
+        from langgraph.types import Command
+
+        if isinstance(response, Command):
+            self._emit_chain_messages(meta, response.update)
+        elif isinstance(response, BaseMessage):
+            self._emit_message_from_chain(meta, response)
+        elif isinstance(response, Sequence) and not isinstance(response, (str, bytes)):
+            for item in response:
+                if isinstance(item, Command):
+                    self._emit_chain_messages(meta, item.update)
+                elif isinstance(item, BaseMessage):
+                    self._emit_message_from_chain(meta, item)
+        elif isinstance(response, dict):
+            for value in response.values():
+                if isinstance(value, BaseMessage):
+                    self._emit_message_from_chain(meta, value)
+                elif isinstance(value, Sequence) and not isinstance(
+                    value, (str, bytes)
+                ):
+                    for item in value:
+                        if isinstance(item, BaseMessage):
+                            self._emit_message_from_chain(meta, item)
+
+    def _emit_message_from_chain(self, meta: Meta, msg: BaseMessage) -> None:
+        """Emit a full message lifecycle for a message from a chain output,
+        deduplicating against previously-seen messages."""
+        if msg.id is not None and msg.id in self.seen:
+            return
+        if msg.id is None:
+            msg.id = str(uuid4())
+
+        # Determine role and finish reason
+        role = "ai"
+        if hasattr(msg, "type"):
+            if msg.type == "human":
+                role = "human"
+            elif msg.type == "system":
+                role = "system"
+
+        finish_reason: FinishReason = "stop"
+        rm = getattr(msg, "response_metadata", {}) or {}
+        raw_reason = rm.get("finish_reason") or rm.get("stop_reason")
+        if raw_reason:
+            finish_reason = _normalize_finish_reason(raw_reason)
+        if finish_reason == "stop" and hasattr(msg, "tool_calls") and msg.tool_calls:
+            finish_reason = "tool_use"
+
+        raw_usage = getattr(msg, "usage_metadata", None)
+        usage = dict(raw_usage) if raw_usage else None
+
+        self._emit_full_message(meta, msg, finish_reason, usage, role=role)
+
+
+# ---------------------------------------------------------------------------
+# Block extraction for finalized (non-streamed) messages
+# ---------------------------------------------------------------------------
+
+
+def _extract_final_blocks(msg: BaseMessage) -> list[tuple[int, CompatBlock]]:
+    """Extract ``(index, block)`` pairs from a finalized ``AIMessage``."""
+    blocks: list[tuple[int, CompatBlock]] = []
+    content = msg.content
+
+    if isinstance(content, str) and content:
+        blocks.append((0, dict(TextBlock(type="text", text=content))))
+    elif isinstance(content, list):
+        for i, item in enumerate(content):
+            if not isinstance(item, dict):
+                continue
+            ctype = item.get("type", "")
+            if ctype == "text" and item.get("text"):
+                blocks.append((i, dict(TextBlock(type="text", text=item["text"]))))
+            elif ctype in ("reasoning_content", "reasoning", "thinking"):
+                reasoning_text = (
+                    item.get("reasoning_content")
+                    or item.get("reasoning")
+                    or item.get("thinking", "")
+                )
+                if reasoning_text:
+                    blocks.append(
+                        (
+                            i,
+                            dict(
+                                ReasoningBlock(
+                                    type="reasoning", reasoning=reasoning_text
+                                )
+                            ),
+                        )
+                    )
+
+    # Finalized tool calls (already parsed, not chunks)
+    for tc in getattr(msg, "tool_calls", None) or []:
+        idx = len(blocks)
+        blocks.append(
+            (
+                idx,
+                dict(
+                    ToolCallBlock(
+                        type="tool_call",
+                        id=tc.get("id", ""),
+                        name=tc.get("name", ""),
+                        args=tc.get("args", {}),
+                    )
+                ),
+            )
+        )
+
+    return blocks
+
+
+def _make_start_block(block: CompatBlock) -> CompatBlock:
+    """Create an empty start placeholder for a content block."""
+    btype = block.get("type", "text")
+    if btype == "text":
+        return TextBlock(type="text", text="")
+    elif btype == "reasoning":
+        return ReasoningBlock(type="reasoning", reasoning="")
+    elif btype == "tool_call_chunk":
+        result: CompatBlock = {"type": "tool_call_chunk", "args": ""}
+        if "id" in block:
+            result["id"] = block["id"]
+        if "name" in block:
+            result["name"] = block["name"]
+        return result
+    elif btype == "tool_call":
+        # Already finalized — return as-is for start event
+        return ToolCallBlock(
+            type="tool_call",
+            id=block.get("id", ""),
+            name=block.get("name", ""),
+            args=block.get("args", {}),
+        )
+    return dict(block)
+
+
+__all__ = ["PROTOCOL_MESSAGES_STREAM_KEY", "StreamProtocolMessagesHandler"]

libs/langgraph/langgraph/pregel/_read.py

@@ -1,7 +1,6 @@
 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,
@@ -12,11 +11,10 @@ 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, TimeoutPolicy
+from langgraph.types import CachePolicy, RetryPolicy
 
 READ_TYPE = Callable[[str | Sequence[str], bool], Any | dict[str, Any]]
 INPUT_CACHE_KEY_TYPE = tuple[Callable[..., Any], tuple[str, ...]]
@@ -125,25 +123,12 @@ 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."""
 
@@ -159,10 +144,7 @@ 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)
@@ -174,11 +156,8 @@ class PregelNode:
             self.retry_policy = (retry_policy,)
         else:
             self.retry_policy = retry_policy
-        self.timeout = coerce_timeout_policy(timeout)
         self.tags = tags
         self.metadata = metadata
-        self.is_error_handler = is_error_handler
-        self.error_handler_node = error_handler_node
         if subgraphs is not None:
             self.subgraphs = subgraphs
         elif self.bound is not DEFAULT_BOUND:

libs/langgraph/langgraph/pregel/_retry.py

@@ -4,487 +4,32 @@ import asyncio
 import logging
 import random
 import sys
-import threading
 import time
-import weakref
 from collections.abc import Awaitable, Callable, Sequence
-from contextlib import suppress
-from dataclasses import dataclass, replace
-from datetime import datetime, timedelta, timezone
-from typing import Any, Literal, NamedTuple
+from dataclasses import replace
+from typing import Any
 
-from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.runnables import RunnableConfig
 
-from langgraph._internal._config import (
-    merge_configs,
-    patch_configurable,
-    recast_checkpoint_ns,
-)
+from langgraph._internal._config import 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._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.errors import GraphBubbleUp, ParentCommand
 from langgraph.runtime import ExecutionInfo, Runtime
-from langgraph.types import Command, PregelExecutableTask, RetryPolicy, TimeoutPolicy
+from langgraph.types import Command, PregelExecutableTask, RetryPolicy
 
 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:
@@ -545,10 +90,6 @@ 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
@@ -654,9 +195,6 @@ 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
@@ -691,53 +229,35 @@ 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()
-            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)
+            # run the task
             if stream:
+                async for _ in task.proc.astream(task.input, config):
+                    pass
                 # if successful, end
                 break
-            return result
+            else:
+                return await task.proc.ainvoke(task.input, config)
         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):
-                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)
+                # this command is for the current graph, handle it
+                for w in task.writers:
+                    w.invoke(cmd, config)
                 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)),)
-            _finish_timed_attempt(config, attempt_ctx)
-            # bubble up the exception to the parent graph
+            # bubble up
             raise
         except GraphBubbleUp:
             # if interrupted, end
-            _finish_timed_attempt(config, attempt_ctx)
             raise
         except Exception as exc:
-            _finish_timed_attempt(config, attempt_ctx, exc)
             if SUPPORTS_EXC_NOTES:
                 exc.add_note(f"During task with name '{task.name}' and id '{task.id}'")
             if not retry_policy:

libs/langgraph/langgraph/pregel/_runner.py

@@ -10,10 +10,8 @@ from collections.abc import (
     AsyncIterator,
     Awaitable,
     Callable,
-    Collection,
     Iterable,
     Iterator,
-    Mapping,
     Sequence,
 )
 from functools import partial
@@ -48,7 +46,6 @@ from langgraph.types import (
     CachePolicy,
     PregelExecutableTask,
     RetryPolicy,
-    TimeoutPolicy,
 )
 
 F = TypeVar("F", concurrent.futures.Future, asyncio.Future)
@@ -74,10 +71,6 @@ 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
@@ -88,7 +81,6 @@ 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:
@@ -96,7 +88,6 @@ 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()
 
@@ -117,7 +108,6 @@ 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))
@@ -125,9 +115,7 @@ class FuturesDict(Generic[F, E], dict[F, PregelExecutableTask | None]):
             with self.lock:
                 self.done.add(fut)
                 self.counter -= 1
-                # 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):
+                if self.counter == 0 or _should_stop_others(self.done):
                     self.event.set()
 
 
@@ -143,34 +131,11 @@ 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,
@@ -189,9 +154,6 @@ 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
@@ -201,7 +163,6 @@ 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,
@@ -220,23 +181,12 @@ 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:
-                    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:
+                    # 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 tb := exc.__traceback__:
                         while tb.tb_next is not None and any(
                             tb.tb_frame.f_code.co_filename.endswith(name)
@@ -245,12 +195,10 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures and not scheduled_error_handler:
-                # maybe `t` scheduled another task
+            if not futures:  # maybe `t` scheduled another task
                 return
             else:
-                if not scheduled_error_handler:
-                    tasks = ()  # don't reschedule this task
+                tasks = ()  # don't reschedule this task
         # add waiter task if requested
         if get_waiter is not None:
             futures[get_waiter()] = None
@@ -277,7 +225,6 @@ 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,
@@ -286,49 +233,17 @@ 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_for_stop, handled_exception_ids=self._handled_exception_ids
-            ):
+            if _should_stop_others(done):
                 break
             # give control back to the caller
             yield
@@ -343,8 +258,6 @@ 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__:
@@ -378,9 +291,6 @@ 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
@@ -390,7 +300,6 @@ 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,
@@ -412,22 +321,12 @@ 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:
-                    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:
+                    # 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 tb := exc.__traceback__:
                         while tb.tb_next is not None and any(
                             tb.tb_frame.f_code.co_filename.endswith(name)
@@ -436,12 +335,10 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures and not scheduled_error_handler:
-                # maybe `t` scheduled another task
+            if not futures:  # maybe `t` scheduled another task
                 return
             else:
-                if not scheduled_error_handler:
-                    tasks = ()  # don't reschedule this task
+                tasks = ()  # don't reschedule this task
         # add waiter task if requested
         if get_waiter is not None:
             futures[get_waiter()] = None
@@ -476,7 +373,6 @@ 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,
@@ -485,59 +381,17 @@ 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_for_stop, handled_exception_ids=self._handled_exception_ids
-            ):
+            if _should_stop_others(done):
                 break
             # give control back to the caller
             yield
@@ -557,8 +411,6 @@ 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__:
@@ -594,11 +446,6 @@ 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 (
@@ -614,8 +461,6 @@ 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."""
@@ -623,11 +468,7 @@ def _should_stop_others(
         if fut.cancelled():
             continue
         elif exc := fut.exception():
-            if (
-                id(exc) not in (handled_exception_ids or set())
-                and not isinstance(exc, GraphBubbleUp)
-                and fut not in SKIP_RERAISE_SET
-            ):
+            if not isinstance(exc, GraphBubbleUp) and fut not in SKIP_RERAISE_SET:
                 return True
 
     return False
@@ -651,9 +492,6 @@ 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()
@@ -670,10 +508,6 @@ 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()
@@ -703,7 +537,6 @@ 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[
@@ -727,7 +560,6 @@ def _call(
             retry_policy=retry_policy,
             cache_policy=cache_policy,
             callbacks=callbacks,
-            timeout=timeout,
         ),
     ):
         if fut := next(
@@ -792,7 +624,6 @@ 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],
@@ -826,7 +657,6 @@ def _acall(
             input,
             retry_policy=retry_policy,
             cache_policy=cache_policy,
-            timeout=timeout,
             callbacks=callbacks,
             futures=futures,
             schedule_task=schedule_task,
@@ -848,7 +678,6 @@ 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]],
@@ -874,7 +703,6 @@ async def _acall_impl(
                 retry_policy=retry_policy,
                 cache_policy=cache_policy,
                 callbacks=callbacks,
-                timeout=timeout,
             ),
         ):
             if fut := next(

libs/langgraph/langgraph/pregel/_tools.py

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

libs/langgraph/langgraph/pregel/_utils.py

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

libs/langgraph/langgraph/runtime.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from collections.abc import Callable
 from dataclasses import dataclass, field, replace
 from typing import Any, Generic, cast
 
@@ -16,7 +15,6 @@ from langgraph.typing import ContextT
 __all__ = (
     "BaseUser",
     "ExecutionInfo",
-    "RunControl",
     "Runtime",
     "ServerInfo",
     "get_runtime",
@@ -76,49 +74,16 @@ 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)
@@ -197,7 +162,7 @@ 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)
@@ -206,19 +171,9 @@ 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.
     """
 
@@ -230,13 +185,6 @@ 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.
 
@@ -248,13 +196,9 @@ 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(
@@ -273,23 +217,13 @@ class Runtime(Generic[ContextT]):
             execution_info=self.execution_info.patch(**overrides),
         )
 
-    @property
-    def drain_requested(self) -> bool:
-        return self.control.drain_requested if self.control is not None else False
-
-    @property
-    def drain_reason(self) -> str | None:
-        return self.control.drain_reason if self.control is not None else None
-
 
 DEFAULT_RUNTIME = Runtime(
     context=None,
     store=None,
     stream_writer=_no_op_stream_writer,
-    heartbeat=_no_op_heartbeat,
     previous=None,
     execution_info=None,
-    control=None,
 )
 
 

libs/langgraph/langgraph/stream/__init__.py

@@ -1,45 +1,45 @@
-"""Streaming infrastructure for LangGraph.
+"""Stream protocol types and 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._convert import STREAM_V2_MODES, convert_to_protocol_event
+from langgraph.stream._mux import AsyncStreamMux, StreamMux
+from langgraph.stream._types import (
+    InterruptPayload,
+    ProtocolEvent,
+    StreamTransformer,
+)
+from langgraph.stream.chat_model_stream import AsyncChatModelStream, ChatModelStream
 from langgraph.stream.run_stream import (
     AsyncGraphRunStream,
     AsyncSubgraphRunStream,
     GraphRunStream,
-    SubgraphRunStream,
+    create_async_graph_run_stream,
+    create_graph_run_stream,
 )
-from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.stream_channel import StreamChannel, is_stream_channel
+from langgraph.stream.streaming_handler import StreamingHandler
 from langgraph.stream.transformers import (
-    CheckpointsTransformer,
-    CustomTransformer,
-    DebugTransformer,
-    LifecyclePayload,
-    LifecycleTransformer,
-    SubgraphStatus,
-    SubgraphTransformer,
-    TasksTransformer,
-    UpdatesTransformer,
+    MessagesTransformer,
+    ValuesTransformer,
 )
 
 __all__ = [
+    "STREAM_V2_MODES",
+    "AsyncChatModelStream",
     "AsyncGraphRunStream",
+    "AsyncStreamMux",
     "AsyncSubgraphRunStream",
-    "CheckpointsTransformer",
-    "CustomTransformer",
-    "DebugTransformer",
+    "ChatModelStream",
     "GraphRunStream",
-    "LifecyclePayload",
-    "LifecycleTransformer",
+    "InterruptPayload",
+    "MessagesTransformer",
     "ProtocolEvent",
     "StreamChannel",
+    "StreamMux",
     "StreamTransformer",
-    "SubgraphRunStream",
-    "SubgraphStatus",
-    "SubgraphTransformer",
-    "TasksTransformer",
-    "UpdatesTransformer",
+    "StreamingHandler",
+    "ValuesTransformer",
+    "convert_to_protocol_event",
+    "create_async_graph_run_stream",
+    "create_graph_run_stream",
+    "is_stream_channel",
 ]

libs/langgraph/langgraph/stream/_convert.py

@@ -1,32 +1,67 @@
+"""Convert raw ``StreamChunk`` tuples to ``ProtocolEvent`` envelopes.
+
+Each ``StreamMode`` is mapped to a ``ProtocolEvent`` whose ``method``
+field matches the mode name and whose ``params.data`` wraps the
+original payload.
+"""
+
 from __future__ import annotations
 
-import time
-from typing import Any, cast
+from typing import Any
 
 from langgraph.stream._types import ProtocolEvent, _ProtocolEventParams
-from langgraph.types import StreamPart
+from langgraph.types import StreamMode
+
+#: All stream modes requested by ``StreamingHandler`` when calling the
+#: underlying ``stream()`` / ``astream()``.
+STREAM_V2_MODES: list[StreamMode] = [
+    "values",
+    "updates",
+    "messages",
+    "custom",
+    "checkpoints",
+    "tasks",
+    "debug",
+]
+
+_SUPPORTED_MODES: set[str] = set(STREAM_V2_MODES)
 
 
-def convert_to_protocol_event(part: StreamPart) -> ProtocolEvent:
-    """Convert a v2 StreamPart to a ProtocolEvent.
+def convert_to_protocol_event(
+    ns: tuple[str, ...],
+    mode: str,
+    payload: Any,
+    *,
+    node: str | None = None,
+) -> ProtocolEvent | None:
+    """Convert a ``StreamChunk`` to a ``ProtocolEvent``.
+
+    Returns ``None`` for unsupported or unknown modes.
+
+    The ``seq`` field is left as ``0`` here; the :class:`StreamMux` is
+    the sole seq assigner and overwrites it inside ``push()``.
 
     Args:
-        part: A stream part with keys `type`, `ns`, `data`, and
-            optionally `interrupts` (present on values events).
-
-    Returns:
-        The equivalent ProtocolEvent.
+        ns: Namespace tuple from the ``StreamChunk``.
+        mode: Stream mode string (``"values"``, ``"updates"``, etc.).
+        payload: The raw payload from the stream.
+        node: Optional node name for provenance.
     """
-    part_dict = cast(dict[str, Any], part)
+    if mode not in _SUPPORTED_MODES:
+        return None
+
     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,
+        "namespace": list(ns),
+        "data": payload,
     }
+    if node is not None:
+        params["node"] = node
+
+    return ProtocolEvent(
+        type="event",
+        method=mode,
+        params=params,
+    )
+
+
+__all__ = ["STREAM_V2_MODES", "convert_to_protocol_event"]

libs/langgraph/langgraph/stream/_mux.py

@@ -1,498 +1,385 @@
+"""Central event dispatcher with transformer pipeline for StreamingHandler.
+
+``StreamMux`` is the synchronous core: it holds the main
+event log (a plain list), tracks discovered namespaces for subgraph stream
+creation, and pipes every event through the registered
+:class:`StreamTransformer` pipeline before appending it to the log.
+
+``AsyncStreamMux`` extends ``StreamMux`` with async consumer APIs
+(output futures, async event subscriptions, subgraph discovery).
+"""
+
 from __future__ import annotations
 
 import asyncio
-import time
-from collections.abc import Awaitable, Callable
+from collections.abc import AsyncIterator
 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=...)`.
-"""
+from langgraph.stream._types import InterruptPayload, ProtocolEvent, StreamTransformer
+from langgraph.stream.stream_channel import StreamChannel, is_stream_channel
 
 
 class StreamMux:
-    """Central event dispatcher for the streaming infrastructure.
+    """Synchronous event dispatcher for the StreamingHandler 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.
+    The mux owns the main event log, applies the transformer pipeline to
+    every incoming event, and tracks namespace discovery and latest values.
 
-    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`.
+    For async consumer APIs (output futures, async event subscriptions,
+    subgraph discovery) use :class:`AsyncStreamMux`.
     """
 
-    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.
+    def __init__(self, transformers: list[StreamTransformer] | None = None) -> None:
+        self._event_log: list[ProtocolEvent] = []
+        self._transformers: list[StreamTransformer] = list(transformers or [])
+        self._current_namespace: list[str] = []
+        self._next_emit_seq: int = 0
 
-        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.
+        # Namespace discovery: maps top-level ns segment → True
+        self._discovered_ns: dict[str, bool] = {}
 
-        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.
+        # Latest values per namespace (list-of-strings key)
+        self._latest_values: dict[str, Any] = {}
 
-        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
+        # Interrupt tracking
+        self._interrupts: list[InterruptPayload] = []
+        self._interrupted = False
 
-        self.extensions: dict[str, Any] = {}
-        self.native_keys: set[str] = set()
-        self._projection_owners: dict[str, str] = {}
-        self._transformer_by_key: dict[str, StreamTransformer] = {}
+        # Closed state
+        self._closed = False
+        self._error: BaseException | None = None
 
-        # 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)
+    # -- Producer API -------------------------------------------------------
 
     def push(self, event: ProtocolEvent) -> None:
-        """Route an event through all transformers, then append to the main log.
+        """Push an event through the transformer pipeline and into the 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.
+        Each registered transformer's ``process()`` is called in order.
+        If any transformer returns ``False``, the event is suppressed
+        (not appended to the main log).
         """
+        if self._closed:
+            return
+
+        # Mux is the sole seq assigner — ensures all events in the log
+        # (including those from StreamChannel forwarders) share a single
+        # monotonically increasing counter.
+        event["seq"] = self._next_emit_seq
+        self._next_emit_seq += 1
+
+        # Track namespace
+        ns = event["params"].get("namespace", [])
+        if ns:
+            top_segment = ns[0]
+            if top_segment not in self._discovered_ns:
+                self._discovered_ns[top_segment] = True
+
+        # Track values
+        if event["method"] == "values":
+            ns_key = _ns_key(ns)
+            self._latest_values[ns_key] = event["params"]["data"]
+
+        # Track interrupts from values events
+        if event["method"] == "values":
+            data = event["params"]["data"]
+            if isinstance(data, dict) and "__interrupt__" in data:
+                interrupt_info = data["__interrupt__"]
+                if isinstance(interrupt_info, (list, tuple)):
+                    for item in interrupt_info:
+                        iid = getattr(item, "id", None) or str(id(item))
+                        self._interrupts.append(
+                            InterruptPayload(
+                                interrupt_id=iid,
+                                payload=item,
+                            )
+                        )
+                    self._interrupted = True
+
+        # Run transformer pipeline
+        self._current_namespace = ns
         keep = True
         for transformer in self._transformers:
-            if not transformer.process(event):
+            result = transformer.process(event)
+            if result is False:
                 keep = False
+        self._current_namespace = []
+
+        # Append to main log if not suppressed
         if keep:
-            if self._assign_seq:
-                self._seq += 1
-                event["seq"] = self._seq
-            self._events.push(event)
+            self._event_log.append(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)
+    def close(self, output: Any = None) -> None:
+        """Close the mux and finalize all transformers."""
+        if self._closed:
+            return
+        self._closed = 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)
+            transformer.finalize()
 
-    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()
-        ]
+    def fail(self, error: BaseException) -> None:
+        """Fail the mux and propagate the error to all consumers."""
+        if self._closed:
+            return
+        self._closed = True
+        self._error = error
 
-    # ------------------------------------------------------------------
-    # Binding and StreamChannel auto-wiring
-    # ------------------------------------------------------------------
+        for transformer in self._transformers:
+            transformer.fail(error)
 
-    def _bind_and_wire(
-        self, projection: dict[str, Any], *, native: bool = False
-    ) -> None:
-        """Bind and optionally wire StreamChannel instances in a projection.
+    # -- Inspection ---------------------------------------------------------
 
-        All StreamChannels are bound and tracked. Channels with a name
-        are additionally wired for protocol auto-forwarding.
+    @property
+    def interrupted(self) -> bool:
+        return self._interrupted
 
-        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:`.
+    @property
+    def interrupts(self) -> list[InterruptPayload]:
+        return list(self._interrupts)
+
+    @property
+    def event_log(self) -> list[ProtocolEvent]:
+        return self._event_log
+
+    def get_latest_values(self, ns: list[str] | None = None) -> Any:
+        """Return the most recent values for a namespace."""
+        return self._latest_values.get(_ns_key(ns or []))
+
+    # -- Internal -----------------------------------------------------------
+
+    def register_transformer(self, transformer: StreamTransformer) -> None:
+        """Register a new transformer and replay all buffered events through it.
+
+        This is the safe way to add a late-arriving transformer after the mux
+        has already started processing events.  The sequence is:
+
+        1. Snapshot the current log length.
+        2. Append the transformer so future ``push()`` calls reach it.
+        3. Replay events ``[0, snapshot)`` through the transformer.
+        4. If the mux is already closed, call ``finalize()`` immediately so
+           the transformer's log/channel terminates cleanly.
+
+        No namespace filtering is applied — all buffered events are
+        replayed.  Transformers that need namespace filtering should do
+        so inside their ``process()`` implementation.
         """
-        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}"
+        snapshot = len(self._event_log)
+        self._transformers.append(transformer)
+        for i in range(snapshot):
+            transformer.process(self._event_log[i])
+        if self._closed:
+            transformer.finalize()
 
-                    def _make_forward(method_name: str) -> Callable[[Any], None]:
-                        def _forward(item: Any) -> None:
-                            self._forward(method_name, item)
+    def wire_channels(self, projection: Any) -> None:
+        """Scan *projection* for :class:`StreamChannel` instances and wire them.
 
-                        return _forward
+        For each ``StreamChannel`` found, registers a push callback that
+        appends a :class:`ProtocolEvent` directly to the main event log
+        with ``method`` set to the channel's name.
 
-                    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.
+        Channel events bypass the transformer pipeline (matching the JS
+        implementation).  They are visible to raw event iteration and
+        remote SDK clients but not to other transformers' ``process()``.
         """
-        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)
+        if projection is None:
+            return
+        items: dict[str, Any] = {}
+        if isinstance(projection, dict):
+            items = projection
+        elif hasattr(projection, "__dict__"):
+            items = vars(projection)
+        for _key, value in items.items():
+            if is_stream_channel(value):
+                channel: StreamChannel[Any] = value
+                def _make_forwarder(ch: StreamChannel[Any]) -> Any:
+                    def _forward(item: Any) -> None:
+                        if self._closed:
+                            return
+                        # Append directly to the event log, bypassing
+                        # the transformer pipeline.  This matches the JS
+                        # implementation and avoids re-entrancy bugs
+                        # (namespace clobbering, infinite recursion).
+                        self._event_log.append(
+                            ProtocolEvent(
+                                type="event",
+                                seq=self._next_emit_seq,
+                                method=ch.channel_name,
+                                params={
+                                    "namespace": list(self._current_namespace),
+                                    "data": item,
+                                },
+                            )
+                        )
+                        self._next_emit_seq += 1
+
+                    return _forward
+
+                channel._wire(_make_forwarder(channel))
+
+
+# ---------------------------------------------------------------------------
+# AsyncStreamMux — async consumer APIs on top of the sync core
+# ---------------------------------------------------------------------------
+
+
+class AsyncStreamMux(StreamMux):
+    """Async extension of :class:`StreamMux`.
+
+    Adds output futures, async event subscriptions, and subgraph
+    discovery on top of the synchronous producer/transformer core.
+    """
+
+    def __init__(self, transformers: list[StreamTransformer] | None = None) -> None:
+        super().__init__(transformers=transformers)
+        # Notification event — set on every push/close/fail to wake async consumers
+        self._notify: asyncio.Event = asyncio.Event()
+        # Waiters for new namespace discovery
+        self._ns_waiters: list[asyncio.Future[None]] = []
+        # Output promise tracking
+        self._output_futures: dict[str, asyncio.Future[Any]] = {}
+
+    # -- Producer overrides (extend to resolve async primitives) -------------
+
+    def push(self, event: ProtocolEvent) -> None:
+        # Peek at namespace before super().push() so we can detect new
+        # discoveries and wake waiters.
+        ns = event["params"].get("namespace", [])
+        is_new_ns = bool(ns) and ns[0] not in self._discovered_ns
+        super().push(event)
+        if is_new_ns and ns[0] in self._discovered_ns:
+            self._wake_ns_waiters()
+        self._notify.set()
+
+    def close(self, output: Any = None) -> None:
+        super().close(output)
+        self._notify.set()
+        # Resolve output futures
+        for ns_key, fut in self._output_futures.items():
+            if not fut.done():
+                value = self._latest_values.get(ns_key)
+                try:
+                    fut.get_loop().call_soon_threadsafe(fut.set_result, value)
+                except RuntimeError:
+                    pass
+        # Wake namespace waiters
+        self._wake_ns_waiters()
+
+    def fail(self, error: BaseException) -> None:
+        super().fail(error)
+        self._notify.set()
+        # Reject output futures
+        for fut in self._output_futures.values():
+            if not fut.done():
+                try:
+                    fut.get_loop().call_soon_threadsafe(fut.set_exception, error)
+                except RuntimeError:
+                    pass
+        # Wake namespace waiters
+        self._wake_ns_waiters()
+
+    # -- Async consumer API -------------------------------------------------
+
+    async def subscribe_events(
+        self, path: list[str] | None = None, offset: int = 0
+    ) -> AsyncIterator[ProtocolEvent]:
+        """Async iterate over events matching *path*.
+
+        If *path* is ``None`` or empty, all events are yielded.
+        Otherwise, only events whose namespace starts with *path*
+        are yielded.
+
+        Uses the list + ``asyncio.Event`` notification pattern: poll
+        the event log, yield what's new, await the notify event for more.
+        """
+        cursor = offset
+        while True:
+            while cursor < len(self._event_log):
+                event = self._event_log[cursor]
+                cursor += 1
+                if not path or _ns_starts_with(
+                    event["params"].get("namespace", []), path
+                ):
+                    yield event
+            if self._closed:
+                if self._error is not None:
+                    raise self._error
+                return
+            self._notify.clear()
+            await self._notify.wait()
+
+    async def subscribe_subgraphs(
+        self, path: list[str] | None = None, offset: int = 0
+    ) -> AsyncIterator[str]:
+        """Yield top-level namespace segments as they are discovered.
+
+        Each yielded value is the first namespace segment of a newly
+        discovered subgraph (e.g. ``"agent:0"``).
+        """
+        yielded: set[str] = set()
+        while True:
+            # Yield any newly discovered namespaces
+            for ns_segment in list(self._discovered_ns):
+                if ns_segment not in yielded:
+                    # Filter by path prefix if specified
+                    if path:
+                        if not ns_segment.startswith(path[0]):
+                            continue
+                    yielded.add(ns_segment)
+                    yield ns_segment
+
+            if self._closed:
+                return
+
+            # Wait for new namespaces
+            loop = asyncio.get_running_loop()
+            fut: asyncio.Future[None] = loop.create_future()
+            self._ns_waiters.append(fut)
+            await fut
+
+    def get_output_future(self, ns: list[str] | None = None) -> asyncio.Future[Any]:
+        """Get or create an output future for a namespace.
+
+        The future resolves to the latest ``values`` event data when
+        the mux is closed.
+        """
+        ns_key = _ns_key(ns or [])
+        if ns_key not in self._output_futures:
+            loop = asyncio.get_running_loop()
+            self._output_futures[ns_key] = loop.create_future()
+
+            # If already closed, resolve immediately
+            if self._closed:
+                value = self._latest_values.get(ns_key)
+                if self._error is not None:
+                    self._output_futures[ns_key].set_exception(self._error)
+                else:
+                    self._output_futures[ns_key].set_result(value)
+
+        return self._output_futures[ns_key]
+
+    # -- Internal -----------------------------------------------------------
+
+    def _wake_ns_waiters(self) -> None:
+        for fut in self._ns_waiters:
+            if not fut.done():
+                try:
+                    fut.get_loop().call_soon_threadsafe(fut.set_result, None)
+                except RuntimeError:
+                    pass
+        self._ns_waiters.clear()
+
+
+
+def _ns_key(ns: list[str] | tuple[str, ...]) -> str:
+    """Convert a namespace list to a hashable key."""
+    return "|".join(ns)
+
+
+def _ns_starts_with(ns: list[str], prefix: list[str]) -> bool:
+    """Check if *ns* starts with *prefix*."""
+    if len(ns) < len(prefix):
+        return False
+    return ns[: len(prefix)] == prefix
+
+
+__all__ = ["AsyncStreamMux", "StreamMux"]

libs/langgraph/langgraph/stream/_types.py

@@ -1,313 +1,166 @@
+"""Protocol types for StreamingHandler.
+
+Re-exports CDDL-derived types from ``langchain-protocol`` and defines
+in-process-only types needed by the LangGraph streaming infrastructure.
+"""
+
 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 import Any
 
+# ---------------------------------------------------------------------------
+# Re-exports from langchain-protocol (CDDL-derived)
+# ---------------------------------------------------------------------------
+# Primitives
+# Content blocks
+# Messages data
+# Tools data
+from langchain_protocol import (
+    Annotation,
+    Citation,
+    ContentBlock,
+    ContentBlockDeltaData,
+    ContentBlockFinishData,
+    ContentBlockStartData,
+    FinalizedContentBlock,
+    FinishReason,
+    InvalidToolCallBlock,
+    MessageErrorData,
+    MessageFinishData,
+    MessageMetadata,
+    MessageRole,
+    MessagesData,
+    MessageStartData,
+    MetadataScalar,
+    Namespace,
+    ReasoningBlock,
+    TextBlock,
+    ToolCallBlock,
+    ToolCallChunkBlock,
+    ToolErrorData,
+    ToolFinishedData,
+    ToolOutputDeltaData,
+    ToolsData,
+    ToolStartedData,
+    UsageInfo,
+)
 from typing_extensions import NotRequired, TypedDict
 
-_logger = logging.getLogger(__name__)
+# ---------------------------------------------------------------------------
+# In-process types (not in the CDDL spec)
+# ---------------------------------------------------------------------------
 
 
 class _ProtocolEventParams(TypedDict):
-    """Parameters for a protocol event.
+    """Payload envelope for a :class:`ProtocolEvent`."""
 
-    `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
+    namespace: Namespace
+    node: NotRequired[str]
     data: Any
-    interrupts: NotRequired[tuple[Any, ...]]
 
 
 class ProtocolEvent(TypedDict):
-    """A protocol event emitted by the streaming infrastructure.
+    """A single protocol event emitted by the StreamingHandler 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).
+    ``method`` corresponds to a
+    :pydata:`~langgraph.types.StreamMode` value (``"messages"``,
+    ``"updates"``, etc.).
     """
 
-    type: Literal["event"]
-    eventId: NotRequired[str]
-    seq: NotRequired[int]
-    method: str  # StreamMode value: "values", "messages", "custom", etc.
+    type: str  # always "event"
+    seq: NotRequired[int]  # assigned by StreamMux.push(); absent before push()
+    method: str  # StreamMode value
     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.).
+    Implementations are registered with ``StreamingHandler`` and receive every
+    :class:`ProtocolEvent` before it is appended to the event log.
 
-    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`).
+    Any :class:`~langgraph.stream.stream_channel.StreamChannel` instances
+    returned by ``init()`` are automatically wired to the protocol event
+    stream by the mux.
 
-    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.
+    def init(self) -> Any:
+        """Return the initial projection value.
 
-        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.
+        Called once before the run.  Any
+        :class:`~langgraph.stream.stream_channel.StreamChannel` instances
+        in the return value are automatically wired by the mux.
         """
         ...
 
-    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`).
-        """
-
+    @abstractmethod
     def process(self, event: ProtocolEvent) -> bool:
-        """Handle an event on the sync lane.
+        """Process an event.
 
-        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.
+        Return ``True`` to keep the event in the 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).
+        """Called once when the run completes successfully.
 
-        Override to close StreamChannels, resolve promises, or perform
-        other teardown. StreamChannel instances in the projection dict
-        are auto-closed by the mux.
+        Optional — the mux auto-closes any :class:`StreamChannel` instances,
+        so transformers that only use channels can omit this.
         """
 
-    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).
+        """Called once when the run fails.
 
-        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.
+        Optional — the mux auto-fails any :class:`StreamChannel` instances,
+        so transformers that only use channels can omit this.
         """
 
-    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.
+class InterruptPayload(TypedDict):
+    """An interrupt produced during a StreamingHandler run."""
 
-        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
+    interrupt_id: str
+    payload: Any
 
 
-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
+__all__ = [
+    # Primitives (re-exported)
+    "Namespace",
+    "MessageRole",
+    "MessageMetadata",
+    "MetadataScalar",
+    # Content blocks (re-exported)
+    "TextBlock",
+    "ReasoningBlock",
+    "ToolCallBlock",
+    "ToolCallChunkBlock",
+    "InvalidToolCallBlock",
+    "ContentBlock",
+    "FinalizedContentBlock",
+    "Annotation",
+    "Citation",
+    # Messages data (re-exported)
+    "MessagesData",
+    "MessageStartData",
+    "ContentBlockStartData",
+    "ContentBlockDeltaData",
+    "ContentBlockFinishData",
+    "MessageFinishData",
+    "MessageErrorData",
+    "FinishReason",
+    "UsageInfo",
+    # Tools data (re-exported)
+    "ToolsData",
+    "ToolStartedData",
+    "ToolOutputDeltaData",
+    "ToolFinishedData",
+    "ToolErrorData",
+    # In-process types
+    "ProtocolEvent",
+    "StreamTransformer",
+    "InterruptPayload",
+]

libs/langgraph/langgraph/stream/chat_model_stream.py

@@ -0,0 +1,324 @@
+"""Per-message streaming objects for StreamingHandler.
+
+``ChatModelStream`` is the synchronous variant returned by
+``GraphRunStream.messages``.  Properties (``.text``, ``.reasoning``,
+``.usage``) return final accumulated values.
+
+``AsyncChatModelStream`` is the asynchronous variant returned by
+``AsyncGraphRunStream.messages``.  Projections are dual
+async-iterable + awaitable (e.g. ``async for delta in msg.text``
+or ``full = await msg.text``).
+"""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Generator
+from typing import Any
+
+from langgraph.stream._types import UsageInfo
+
+# ---------------------------------------------------------------------------
+# Sync variant
+# ---------------------------------------------------------------------------
+
+
+class ChatModelStream:
+    """Synchronous per-message object for a single LLM response.
+
+    Created by :class:`~langgraph.stream.transformers.MessagesTransformer`
+    and yielded by ``GraphRunStream.messages``.  By the time the sync
+    iterator yields a ``ChatModelStream``, the message lifecycle is
+    complete and all properties contain their final values.
+
+    Projections:
+
+    - ``.text`` — accumulated text content (``str``)
+    - ``.reasoning`` — accumulated reasoning content (``str``)
+    - ``.usage`` — :class:`UsageInfo` or ``None``
+    - ``.namespace`` / ``.node`` — provenance metadata
+    """
+
+    def __init__(
+        self,
+        *,
+        namespace: list[str] | None = None,
+        node: str | None = None,
+        message_id: str | None = None,
+    ) -> None:
+        self._namespace = namespace or []
+        self._node = node
+        self._message_id = message_id
+
+        # Accumulated state
+        self._text_acc = ""
+        self._reasoning_acc = ""
+        self._usage_value: UsageInfo | None = None
+        self._done = False
+
+    # -- Public projections ------------------------------------------------
+
+    @property
+    def text(self) -> str:
+        """Accumulated text content."""
+        return self._text_acc
+
+    @property
+    def reasoning(self) -> str:
+        """Accumulated reasoning content."""
+        return self._reasoning_acc
+
+    @property
+    def usage(self) -> UsageInfo | None:
+        """Usage info, available after the message finishes."""
+        return self._usage_value
+
+    @property
+    def namespace(self) -> list[str]:
+        return self._namespace
+
+    @property
+    def node(self) -> str | None:
+        return self._node
+
+    @property
+    def message_id(self) -> str | None:
+        return self._message_id
+
+    @property
+    def done(self) -> bool:
+        return self._done
+
+    # -- Internal API (called by MessagesTransformer) ----------------------
+
+    def _push_content_block_delta(self, data: dict[str, Any]) -> None:
+        """Process a ``content-block-delta`` event."""
+        block = data.get("content_block", {})
+        btype = block.get("type", "")
+
+        if btype == "text":
+            delta_text = block.get("text", "")
+            if delta_text:
+                self._text_acc += delta_text
+        elif btype == "reasoning":
+            delta_r = block.get("reasoning", "")
+            if delta_r:
+                self._reasoning_acc += delta_r
+
+    def _push_content_block_finish(self, data: dict[str, Any]) -> None:
+        """Process a ``content-block-finish`` event."""
+        block = data.get("content_block", {})
+        btype = block.get("type", "")
+
+        if btype == "text":
+            full_text = block.get("text", "")
+            if full_text and full_text != self._text_acc:
+                self._text_acc = full_text
+        elif btype == "reasoning":
+            full_r = block.get("reasoning", "")
+            if full_r and full_r != self._reasoning_acc:
+                self._reasoning_acc = full_r
+
+    def _finish(self, data: dict[str, Any]) -> None:
+        """Process a ``message-finish`` event."""
+        self._done = True
+        self._usage_value = data.get("usage")
+
+    def _fail(self, error: BaseException) -> None:
+        """Process a ``message-error`` event."""
+        self._done = True
+
+
+# ---------------------------------------------------------------------------
+# Dual-projection helpers — sync data container + async notification layer
+# ---------------------------------------------------------------------------
+
+
+class _DualProjection:
+    """Sync data container for incremental deltas and a final value.
+
+    Stores deltas as they arrive and tracks the final accumulated value.
+    No async primitives — see :class:`_AsyncDualProjection` for the
+    async-iterable + awaitable extension.
+    """
+
+    def __init__(self) -> None:
+        self._deltas: list[Any] = []
+        self._done = False
+        self._error: BaseException | None = None
+        self._final_value: Any = None
+        self._final_set = False
+
+    # -- Producer API (called by AsyncChatModelStream) ---------------------
+
+    def _push(self, delta: Any) -> None:
+        """Add a new delta value."""
+        self._deltas.append(delta)
+
+    def _finish(self, accumulated: Any) -> None:
+        """Set the final accumulated value and mark as done."""
+        self._final_value = accumulated
+        self._final_set = True
+        self._done = True
+
+    def _fail(self, error: BaseException) -> None:
+        self._error = error
+        self._done = True
+
+
+class _AsyncDualProjection(_DualProjection):
+    """Async extension of :class:`_DualProjection`.
+
+    Async iterable of deltas that is also awaitable for the final value.
+    Uses an ``asyncio.Event`` to notify async consumers when new data
+    arrives — the same pattern as :class:`AsyncStreamMux`.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._notify: asyncio.Event = asyncio.Event()
+
+    # -- Producer overrides (extend to notify) -----------------------------
+
+    def _push(self, delta: Any) -> None:
+        super()._push(delta)
+        self._notify.set()
+
+    def _finish(self, accumulated: Any) -> None:
+        super()._finish(accumulated)
+        self._notify.set()
+
+    def _fail(self, error: BaseException) -> None:
+        super()._fail(error)
+        self._notify.set()
+
+    # -- Async iterable (yields deltas) ------------------------------------
+
+    def __aiter__(self) -> _AsyncDualProjectionIterator:
+        return _AsyncDualProjectionIterator(self)
+
+    # -- Awaitable (returns final value) -----------------------------------
+
+    def __await__(self) -> Generator[Any, None, Any]:
+        return self._await_impl().__await__()
+
+    async def _await_impl(self) -> Any:
+        while not self._final_set:
+            if self._error is not None:
+                raise self._error
+            self._notify.clear()
+            await self._notify.wait()
+        if self._error is not None:
+            raise self._error
+        return self._final_value
+
+
+class _AsyncDualProjectionIterator:
+    """Async iterator over an :class:`_AsyncDualProjection`'s deltas."""
+
+    __slots__ = ("_proj", "_offset")
+
+    def __init__(self, proj: _AsyncDualProjection) -> None:
+        self._proj = proj
+        self._offset = 0
+
+    def __aiter__(self) -> _AsyncDualProjectionIterator:
+        return self
+
+    async def __anext__(self) -> Any:
+        while True:
+            if self._offset < len(self._proj._deltas):
+                item = self._proj._deltas[self._offset]
+                self._offset += 1
+                return item
+            if self._proj._error is not None:
+                raise self._proj._error
+            if self._proj._done:
+                raise StopAsyncIteration
+            self._proj._notify.clear()
+            await self._proj._notify.wait()
+
+
+# ---------------------------------------------------------------------------
+# Async variant
+# ---------------------------------------------------------------------------
+
+
+class AsyncChatModelStream(ChatModelStream):
+    """Asynchronous per-message streaming object for a single LLM response.
+
+    Created by :class:`~langgraph.stream.transformers.MessagesTransformer`
+    and yielded by ``AsyncGraphRunStream.messages``.  Content-block events
+    are fed into this object until ``message-finish``.
+
+    Projections:
+
+    - ``.text`` — async iterable of text deltas; awaitable for full text
+    - ``.reasoning`` — async iterable of reasoning deltas; awaitable for
+      full reasoning text
+    - ``.usage`` — awaitable for :class:`UsageInfo`
+    - ``.namespace`` / ``.node`` — provenance metadata
+    """
+
+    def __init__(
+        self,
+        *,
+        namespace: list[str] | None = None,
+        node: str | None = None,
+        message_id: str | None = None,
+    ) -> None:
+        super().__init__(namespace=namespace, node=node, message_id=message_id)
+        self._text_proj = _AsyncDualProjection()
+        self._reasoning_proj = _AsyncDualProjection()
+        self._usage_proj = _AsyncDualProjection()
+
+    # -- Public projections (override sync properties) ---------------------
+
+    @property
+    def text(self) -> _AsyncDualProjection:
+        """Text content — async iterable of deltas, awaitable for full text."""
+        return self._text_proj
+
+    @property
+    def reasoning(self) -> _AsyncDualProjection:
+        """Reasoning content — async iterable of deltas, awaitable for full text."""
+        return self._reasoning_proj
+
+    @property
+    def usage(self) -> _AsyncDualProjection:
+        """Usage info — awaitable for :class:`UsageInfo`."""
+        return self._usage_proj
+
+    # -- Internal API (extend base to also drive projections) --------------
+
+    def _push_content_block_delta(self, data: dict[str, Any]) -> None:
+        """Process a ``content-block-delta`` event."""
+        super()._push_content_block_delta(data)
+        block = data.get("content_block", {})
+        btype = block.get("type", "")
+
+        if btype == "text":
+            delta_text = block.get("text", "")
+            if delta_text:
+                self._text_proj._push(delta_text)
+        elif btype == "reasoning":
+            delta_r = block.get("reasoning", "")
+            if delta_r:
+                self._reasoning_proj._push(delta_r)
+
+    def _finish(self, data: dict[str, Any]) -> None:
+        """Process a ``message-finish`` event."""
+        super()._finish(data)
+        self._text_proj._finish(self._text_acc)
+        self._reasoning_proj._finish(self._reasoning_acc)
+        self._usage_proj._finish(self._usage_value)
+
+    def _fail(self, error: BaseException) -> None:
+        """Process a ``message-error`` event."""
+        super()._fail(error)
+        self._text_proj._fail(error)
+        self._reasoning_proj._fail(error)
+        self._usage_proj._fail(error)
+
+
+__all__ = ["AsyncChatModelStream", "ChatModelStream"]

libs/langgraph/langgraph/stream/stream_channel.py

@@ -1,341 +1,49 @@
+"""StreamChannel — typed push-based channel for StreamTransformer projections.
+
+A ``StreamChannel`` wraps a list and declares a protocol channel name.
+When the :class:`StreamMux` detects a ``StreamChannel`` in a transformer's
+``init()`` return, it wires every ``push()`` call to inject a
+:class:`ProtocolEvent` into the main event stream using the channel's
+name as the ``method``.
+"""
+
 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
+from collections.abc import Callable
+from typing import Any, Generic, TypeVar
 
 T = TypeVar("T")
 
 
 class StreamChannel(Generic[T]):
-    """Single-consumer drainable queue for streaming events, with optional
-    protocol auto-forwarding.
+    """A typed push-based channel that integrates with the mux.
 
-    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.
+    Transformer authors create a ``StreamChannel`` in ``init()`` and
+    call ``push()`` inside ``process()`` to emit domain objects.  The
+    mux auto-wires pushes to protocol events.
     """
 
-    def __init__(self, name: str | None = None, *, maxlen: int | None = None) -> None:
-        """Initialize the channel.
+    __slots__ = ("channel_name", "_items", "_on_push")
 
-        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 __init__(self, name: str) -> None:
+        self.channel_name = name
+        self._items: list[T] = []
+        self._on_push: Callable[[Any], None] | None = None
 
     def push(self, item: T) -> None:
-        """Append an item. Auto-forwards if wired.
+        """Push an item to the channel."""
+        self._items.append(item)
+        if self._on_push is not None:
+            self._on_push(item)
 
-        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.
+    def _wire(self, fn: Callable[[Any], None]) -> None:
+        """Wire a callback invoked on every ``push()``.  Called by the mux."""
+        self._on_push = fn
 
-        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 is_stream_channel(value: object) -> bool:
+    """Check if *value* is a :class:`StreamChannel` instance."""
+    return isinstance(value, StreamChannel)
 
-    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))
+__all__ = ["StreamChannel", "is_stream_channel"]

libs/langgraph/langgraph/stream/streaming_handler.py

@@ -0,0 +1,168 @@
+"""Experimental streaming wrapper for CompiledGraph.
+
+``StreamingHandler`` wraps a compiled graph and exposes the new streaming
+API without adding methods to the ``CompiledGraph`` class itself.
+
+Usage::
+
+    from langgraph.stream import StreamingHandler
+
+    s = StreamingHandler(graph)
+
+    # async
+    run = await s.astream(input)
+    async for msg in run.messages:
+        ...
+
+    # sync
+    run = s.stream(input)
+    for event in run:
+        ...
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Iterator, Sequence
+from typing import TYPE_CHECKING, Any, cast
+
+from langchain_core.runnables import RunnableConfig
+
+from langgraph._internal._config import patch_configurable
+from langgraph.stream._convert import STREAM_V2_MODES
+from langgraph.stream._types import StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    GraphRunStream,
+    create_async_graph_run_stream,
+    create_graph_run_stream,
+)
+from langgraph.types import All
+
+if TYPE_CHECKING:
+    from langgraph.pregel import Pregel
+
+#: Config key that activates the protocol messages handler.
+#: Duplicated here to avoid a circular import with ``pregel._messages_v2``.
+PROTOCOL_MESSAGES_STREAM_KEY = "__protocol_messages_stream"
+
+
+class StreamingHandler:
+    """Experimental streaming wrapper around a compiled graph.
+
+    Provides ``.stream()`` and ``.astream()`` returning
+    :class:`GraphRunStream` / :class:`AsyncGraphRunStream` with
+    ergonomic projections (``run.values``, ``run.messages``,
+    ``run.subgraphs``, ``run.output``).
+
+    Args:
+        graph: A compiled LangGraph (``Pregel`` instance).
+    """
+
+    def __init__(self, graph: Pregel) -> None:
+        self._graph = graph
+
+    async def astream(
+        self,
+        input: Any,
+        config: RunnableConfig | None = None,
+        *,
+        context: Any | None = None,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        debug: bool | None = None,
+        transformers: list[StreamTransformer] | None = None,
+    ) -> AsyncGraphRunStream:
+        """Stream graph execution, returning an
+        :class:`~langgraph.stream.run_stream.AsyncGraphRunStream`.
+
+        The returned stream provides ergonomic projections:
+
+        - ``await run.output`` -- final state
+        - ``async for v in run.values`` -- intermediate state snapshots
+        - ``async for msg in run.messages`` -- per-message
+          :class:`~langgraph.stream.chat_model_stream.AsyncChatModelStream`
+          objects
+        - ``async for sub in run.subgraphs`` -- child
+          :class:`~langgraph.stream.run_stream.AsyncSubgraphRunStream`
+          instances
+        - ``async for event in run`` -- raw
+          :class:`~langgraph.stream._types.ProtocolEvent` objects
+
+        Args:
+            input: The input to the graph.
+            config: The configuration to use for the run.
+            context: The static context to use for the run.
+            interrupt_before: Nodes to interrupt before.
+            interrupt_after: Nodes to interrupt after.
+            debug: Whether to emit debug events.
+            transformers: Optional user-supplied
+                :class:`~langgraph.stream._types.StreamTransformer` instances
+                for custom projections (available on ``run.extensions``).
+
+        Returns:
+            An :class:`~langgraph.stream.run_stream.AsyncGraphRunStream`.
+        """
+        merged_config = patch_configurable(config, {PROTOCOL_MESSAGES_STREAM_KEY: True})
+
+        source = cast(
+            AsyncIterator[tuple[tuple[str, ...], str, Any]],
+            self._graph.astream(
+                input,
+                merged_config,
+                context=context,
+                stream_mode=STREAM_V2_MODES,
+                subgraphs=True,
+                interrupt_before=interrupt_before,
+                interrupt_after=interrupt_after,
+                debug=debug,
+                version="v1",
+            ),
+        )
+
+        return await create_async_graph_run_stream(
+            source,
+            transformers=transformers,
+            output_mapper=self._graph._output_mapper,
+        )
+
+    def stream(
+        self,
+        input: Any,
+        config: RunnableConfig | None = None,
+        *,
+        context: Any | None = None,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        debug: bool | None = None,
+        transformers: list[StreamTransformer] | None = None,
+    ) -> GraphRunStream:
+        """Synchronous variant of :meth:`astream`.
+
+        Returns a :class:`~langgraph.stream.run_stream.GraphRunStream`
+        immediately.  The underlying source is consumed lazily as
+        projections are iterated.
+
+        See :meth:`astream` for full documentation.
+        """
+        merged_config = patch_configurable(config, {PROTOCOL_MESSAGES_STREAM_KEY: True})
+
+        source = cast(
+            Iterator[tuple[tuple[str, ...], str, Any]],
+            self._graph.stream(
+                input,
+                merged_config,
+                context=context,
+                stream_mode=STREAM_V2_MODES,
+                subgraphs=True,
+                interrupt_before=interrupt_before,
+                interrupt_after=interrupt_after,
+                debug=debug,
+                version="v1",
+            ),
+        )
+
+        return create_graph_run_stream(
+            source,
+            transformers=transformers,
+            output_mapper=self._graph._output_mapper,
+        )

libs/langgraph/langgraph/types.py

@@ -4,7 +4,6 @@ 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,
@@ -68,7 +67,6 @@ __all__ = (
     "CheckpointPayload",
     "DebugPayload",
     "RetryPolicy",
-    "TimeoutPolicy",
     "CachePolicy",
     "Interrupt",
     "StateUpdate",
@@ -425,83 +423,6 @@ 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])
 
 
@@ -627,7 +548,6 @@ class PregelExecutableTask:
     path: tuple[str | int | tuple, ...]
     writers: Sequence[Runnable] = ()
     subgraphs: Sequence[PregelProtocol] = ()
-    timeout: TimeoutPolicy | None = None
 
 
 class StateSnapshot(NamedTuple):
@@ -667,8 +587,6 @@ 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
 
@@ -698,47 +616,33 @@ class Send:
         ```
     """
 
-    __slots__ = ("node", "arg", "timeout")
+    __slots__ = ("node", "arg")
 
     node: str
     arg: Any
-    timeout: TimeoutPolicy | None
 
-    def __init__(
-        self,
-        /,
-        node: str,
-        arg: Any,
-        *,
-        timeout: float | timedelta | TimeoutPolicy | None = None,
-    ) -> None:
+    def __init__(self, /, node: str, arg: Any) -> 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, self.timeout))
+        return hash((self.node, self.arg))
 
     def __repr__(self) -> str:
-        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})"
+        return f"Send(node={self.node!r}, arg={self.arg!r})"
 
     def __eq__(self, value: object) -> bool:
         return (
             isinstance(value, Send)
             and self.node == value.node
             and self.arg == value.arg
-            and self.timeout == value.timeout
         )
 
 

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.2.0a4"
+version = "1.1.6"
 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>=1.4.0a2,<2",
-    "langgraph-checkpoint>=4.1.0a3,<5.0.0",
+    "langchain-core>=0.1",
+    "langgraph-checkpoint>=2.1.0,<5.0.0",
     "langgraph-sdk>=0.3.0,<0.4.0",
-    "langgraph-prebuilt>=1.1.0a1,<1.2.0",
+    "langgraph-prebuilt>=1.0.9,<1.1.0",
     "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_oss"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/langgraph/tests/test_channels.py

@@ -1,34 +1,18 @@
 import operator
 from collections.abc import Sequence
-from typing import Annotated
 
 import pytest
-from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
-from langgraph.checkpoint.base import DELTA_SENTINEL
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.checkpoint.serde.types import _DeltaSnapshot
-from typing_extensions import NotRequired, TypedDict
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.binop import BinaryOperatorAggregate
-from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
-from langgraph.graph import START, StateGraph
-from langgraph.graph.message import _messages_delta_reducer
-from langgraph.graph.state import _get_channel
-from langgraph.types import Overwrite
 
 pytestmark = pytest.mark.anyio
 
 
-# ---------------------------------------------------------------------------
-# Core channel primitives
-# ---------------------------------------------------------------------------
-
-
 def test_last_value() -> None:
     channel = LastValue(int).from_checkpoint(MISSING)
     assert channel.ValueType is int
@@ -111,542 +95,25 @@ 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 DELTA_SENTINEL
-
-    ch.update([AIMessage(content="hello", id="a1")])
-    d2 = ch.checkpoint()
-    assert d2 is DELTA_SENTINEL
-
-    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(DELTA_SENTINEL)
-    ch.replay_writes(
-        [
-            ("t0", "messages", HumanMessage(content="hi", id="h1")),
-            ("t1", "messages", AIMessage(content="hello", id="a1")),
-            ("t2", "messages", HumanMessage(content="bye", id="h2")),
-        ]
-    )
-    msgs = ch.get()
-    assert len(msgs) == 3
-    assert msgs[0].content == "hi"
-    assert msgs[1].content == "hello"
-    assert msgs[2].content == "bye"
-
-
-def test_delta_channel_from_checkpoint_backwards_compat() -> None:
-    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 DELTA_SENTINEL
-    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(DELTA_SENTINEL)
-    ch2.replay_writes(
-        [
-            ("t0", "messages", HumanMessage(content="hi", id="h1")),
-            ("t1", "messages", AIMessage(content="hello", id="a1")),
-            ("t2", "messages", RemoveMessage(id="a1")),
-        ]
-    )
-    assert ch2.get() == [HumanMessage(content="hi", id="h1")]
-
-
-def test_delta_channel_update_by_id_and_replay() -> None:
-    """Updating a message by ID must round-trip correctly through writes replay."""
-    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(DELTA_SENTINEL)
-    ch2.replay_writes(
-        [
-            ("t0", "messages", HumanMessage(content="original", id="h1")),
-            ("t1", "messages", HumanMessage(content="updated", id="h1")),
-        ]
-    )
-    assert len(ch2.get()) == 1
-    assert ch2.get()[0].content == "updated"
-
-
-def test_delta_channel_checkpoint_returns_sentinel() -> None:
-    """checkpoint() always returns DELTA_SENTINEL regardless of state."""
-    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
-    assert ch.checkpoint() is DELTA_SENTINEL
-
-    ch.update([HumanMessage(content="hi", id="h1")])
-    assert ch.checkpoint() is DELTA_SENTINEL
-
-
-# ---------------------------------------------------------------------------
-# DeltaChannel — snapshot frequency
-# ---------------------------------------------------------------------------
-
-
-def test_delta_channel_snapshot_step_based() -> None:
-    """Snapshots fire on every Nth step regardless of whether the channel was written.
-
-    With snapshot_frequency=N, every Nth pregel step produces a _DeltaSnapshot
-    blob — even if the channel had no write that step (eager snapshot). This
-    bounds the ancestor walk to at most N steps on any read.
-    """
-
-    class State(TypedDict):
-        messages: Annotated[
-            list, DeltaChannel(_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
-
-
-def test_delta_channel_snapshot_fires_even_when_not_written() -> None:
-    """Eager snapshot: _DeltaSnapshot stored at snapshot step even when the
-    channel had no write that step (node_b doesn't touch messages).
-    """
-
-    class State(TypedDict):
-        messages: Annotated[
-            list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=3)
-        ]
-        tick: int
-
-    def writer(state: State) -> dict:
-        i = len(state["messages"]) // 2
-        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
-
-    def ticker(state: State) -> dict:
-        return {"tick": state["tick"] + 1}
-
-    g = StateGraph(State)
-    g.add_node("writer", writer)
-    g.add_node("ticker", ticker)
-    g.add_edge(START, "writer")
-    g.add_edge("writer", "ticker")
-    saver = InMemorySaver()
-    graph = g.compile(checkpointer=saver)
-
-    config = {"configurable": {"thread_id": "t1"}}
-    for i in range(5):
-        graph.invoke(
-            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "tick": 0},
-            config,
-        )
-
-    msg_blobs = {
-        k: saver.serde.loads_typed((t, b))
-        for k, (t, b) in saver.blobs.items()
-        if k[2] == "messages" and t == "msgpack" and b
-    }
-    snapshots = {k: v for k, v in msg_blobs.items() if isinstance(v, _DeltaSnapshot)}
-    assert snapshots, (
-        "eager snapshots must fire even on steps where messages wasn't written"
-    )
-
-    state = graph.get_state(config)
-    assert len(state.values["messages"]) == 10  # 5 human + 5 AI
-
-
-# ---------------------------------------------------------------------------
-# DeltaChannel — end-to-end (InMemorySaver)
-# ---------------------------------------------------------------------------
-
-
-def test_delta_channel_inmemory_saver_assembles_writes() -> None:
-    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(_messages_delta_reducer, list)]
-
-    n = {"v": 0}
-
-    def respond(state: State) -> dict:
-        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 DELTA_SENTINEL
-
-    ch.update([{"b": 2}])
-    d2 = ch.checkpoint()
-    assert d2 is DELTA_SENTINEL
-
-    assert ch.get() == {"a": 1, "b": 2}
-
-
-def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
-    """replay_writes on a fresh channel replays through a dict merge reducer."""
-
-    def merge_dicts(state: dict, writes: list) -> dict:
-        result = dict(state)
-        for w in writes:
-            result.update(w)
-        return result
-
-    spec = _delta_channel_with_type(merge_dicts, dict)
-    ch = spec.from_checkpoint(DELTA_SENTINEL)
-    ch.replay_writes(
-        [
-            ("t0", "files", {"a": 1}),
-            ("t1", "files", {"b": 2}),
-            ("t2", "files", {"c": 3}),
-        ]
-    )
-    assert ch.get() == {"a": 1, "b": 2, "c": 3}
-
-
-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(DELTA_SENTINEL)
-    ch2.replay_writes(
-        [
-            ("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
-            ("t1", "files", {"file1.py": None, "file3.py": "content3"}),
-        ]
-    )
-    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
-
-
-def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
-    """Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
-
-    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(DELTA_SENTINEL)
-    ch.replay_writes(
-        [
-            ("t0", "files", {"a": 1}),
-            ("t1", "files", Overwrite({"x": 10, "y": 20})),
-            ("t2", "files", {"z": 30}),
-        ]
-    )
-    assert ch.get() == {"x": 10, "y": 20, "z": 30}
-
-
-def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
-    """DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`."""
-
-    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 DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
-    explicitly should feed None to the reducer as the left operand.
-    """
-
-    def replace(state, writes):
-        return writes[-1] if writes else state
-
-    spec = DeltaChannel(replace, list)
-    ch = spec.from_checkpoint(None)
-    ch.replay_writes([("t0", "x", "after")])
-    assert ch.get() == "after"

libs/langgraph/tests/test_delta_channel_benchmark.py

@@ -1,478 +0,0 @@
-"""Benchmark: DeltaChannel snapshot_frequency — storage vs. read-depth tradeoff.
-
-Run directly:  python tests/test_delta_channel_benchmark.py
-Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
-
-Part 1 — baseline (original): DeltaChannel(inf) vs add_messages (BinOp).
-Part 2 — snapshot_frequency sweep: shows the storage/read-latency tradeoff
-  across frequencies [1, 5, 10, 50, inf] at scale.
-
-Key insight:
-  snapshot_frequency=inf  → O(N) storage, O(N) read depth (pure delta)
-  snapshot_frequency=N    → O(N²/N) storage, O(N) read depth bounded by freq
-  snapshot_frequency=1    → O(N²) storage, O(1) read depth (full snapshot)
-"""
-
-from __future__ import annotations
-
-import contextlib
-import math
-import os
-import sys
-import time
-from typing import Annotated, Any
-
-import pytest
-from langchain_core.messages import AIMessage, HumanMessage
-from langgraph.checkpoint.memory import MemorySaver
-from typing_extensions import TypedDict
-
-from langgraph.channels.delta import DeltaChannel
-from langgraph.graph import END, StateGraph
-from langgraph.graph.message import _messages_delta_reducer, add_messages
-
-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. "
-    "We've had prior incidents in this area and want to be deliberate. "
-    "What should we prioritize first, and are there known failure modes we should design around from the start?"
-)
-
-_AI_TEMPLATE = (
-    "Great question about {topic}. The key insight here is that {concern} introduces "
-    "a subtle ordering dependency that most teams overlook until they hit it in production. "
-    "For your {component} layer specifically, I'd recommend starting with a careful audit "
-    "of the interface boundaries before making any structural changes. This will give you "
-    "a clear picture of the blast radius and let you sequence the migration safely."
-)
-
-_TOPICS = [
-    "distributed tracing",
-    "eventual consistency",
-    "schema migration",
-    "backpressure handling",
-    "idempotency guarantees",
-    "cache invalidation",
-    "connection pooling",
-    "rate limiting",
-    "circuit breaking",
-    "observability pipelines",
-]
-
-_CONCERNS = [
-    "concurrency model",
-    "retry semantics",
-    "state management",
-    "error propagation",
-    "latency budget",
-]
-
-_COMPONENTS = [
-    "persistence",
-    "routing",
-    "ingestion",
-    "aggregation",
-    "serialization",
-]
-
-
-def _human_content(i: int) -> str:
-    return _HUMAN_TEMPLATE.format(
-        topic=_TOPICS[i % len(_TOPICS)],
-        concern=_CONCERNS[i % len(_CONCERNS)],
-        component=_COMPONENTS[i % len(_COMPONENTS)],
-    )
-
-
-def _ai_content(i: int) -> str:
-    return _AI_TEMPLATE.format(
-        topic=_TOPICS[i % len(_TOPICS)],
-        concern=_CONCERNS[i % len(_CONCERNS)],
-        component=_COMPONENTS[i % len(_COMPONENTS)],
-    )
-
-
-# ---------------------------------------------------------------------------
-# State definitions
-# ---------------------------------------------------------------------------
-
-
-class BinaryState(TypedDict):
-    messages: Annotated[list, add_messages]
-
-
-class DeltaState(TypedDict):
-    messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
-
-
-def _make_delta_state(snapshot_frequency: int | float) -> type:
-    """Create a TypedDict with DeltaChannel at the given snapshot_frequency."""
-    channel = DeltaChannel(
-        _messages_delta_reducer, snapshot_frequency=snapshot_frequency
-    )
-    # Use the functional TypedDict form so the Annotated type is stored as an
-    # already-evaluated object rather than a forward-reference string (which
-    # would fail when get_type_hints tries to resolve 'snapshot_frequency').
-    return TypedDict(  # type: ignore[return-value]
-        f"DeltaState_freq{snapshot_frequency}",
-        {"messages": Annotated[list, channel]},
-    )
-
-
-# ---------------------------------------------------------------------------
-# Graph factory
-# ---------------------------------------------------------------------------
-
-
-def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
-    def human_node(state: Any) -> dict:
-        return {}
-
-    def ai_node(state: Any) -> dict:
-        i = len(state["messages"]) // 2
-        return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
-
-    g = StateGraph(state_cls)
-    g.add_node("human", human_node)
-    g.add_node("ai", ai_node)
-    g.add_edge("human", "ai")
-    g.add_edge("ai", END)
-    g.set_entry_point("human")
-    return g.compile(checkpointer=checkpointer or MemorySaver())
-
-
-# ---------------------------------------------------------------------------
-# Measurement helpers
-# ---------------------------------------------------------------------------
-
-
-def _total_blob_bytes(saver: MemorySaver) -> int:
-    total = 0
-    for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
-        if blob is not None:
-            total += len(blob)
-    return total
-
-
-def _run_turns(
-    n_turns: int,
-    state_cls: type,
-    checkpointer: Any = None,
-) -> tuple[float, float, int]:
-    """Run n_turns conversation turns.
-
-    Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
-    Read latency is the average of 5 get_state calls after the full history
-    is built — forces state rehydration including ancestor replay if needed.
-    """
-    graph = _make_graph(state_cls, checkpointer)
-    config = {"configurable": {"thread_id": "bench"}}
-
-    t0 = time.perf_counter()
-    for i in range(n_turns):
-        graph.invoke(
-            {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
-            config,
-        )
-    write_elapsed = time.perf_counter() - t0
-
-    t1 = time.perf_counter()
-    for _ in range(5):
-        graph.get_state(config)
-    read_elapsed = (time.perf_counter() - t1) / 5
-
-    blob_bytes = (
-        _total_blob_bytes(graph.checkpointer)
-        if isinstance(graph.checkpointer, MemorySaver)
-        else -1
-    )
-    return write_elapsed, read_elapsed, blob_bytes
-
-
-def _fmt_bytes(n: int) -> str:
-    if n >= 1_000_000:
-        return f"{n / 1_000_000:.1f} MB"
-    if n >= 1_000:
-        return f"{n / 1_000:.1f} KB"
-    return f"{n} B"
-
-
-def _approx_tokens(n_turns: int) -> str:
-    tokens = n_turns * 200
-    if tokens >= 1_000_000:
-        return f"~{tokens / 1_000_000:.1f}M tok"
-    if tokens >= 1_000:
-        return f"~{tokens / 1_000:.0f}K tok"
-    return f"~{tokens} tok"
-
-
-# ---------------------------------------------------------------------------
-# Checkpointer factories
-# ---------------------------------------------------------------------------
-
-
-@contextlib.contextmanager
-def _pg_saver(thread_id: str = "bench"):
-    """Context manager that yields a fresh PostgresSaver and cleans up after."""
-    with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
-        saver.setup()
-        with saver._cursor() as cur:
-            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
-                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
-        yield saver
-        with saver._cursor() as cur:
-            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
-                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
-
-
-def _checkpointers() -> list[tuple[str, Any]]:
-    """Return (label, saver_or_None) pairs for available checkpointers."""
-    result: list[tuple[str, Any]] = [("InMemory", None)]
-    if _POSTGRES_AVAILABLE:
-        try:
-            import psycopg
-
-            psycopg.connect(_POSTGRES_URI).close()
-            result.append(("Postgres", "postgres"))
-        except Exception:
-            pass
-    return result
-
-
-# ---------------------------------------------------------------------------
-# Part 1: baseline DeltaChannel(inf) vs add_messages
-# ---------------------------------------------------------------------------
-
-BASELINE_TURN_COUNTS = [10, 25, 50, 100, 500]
-DELTA_ONLY_TURN_COUNTS = [1000]
-
-
-def _run_baseline_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
-    W = 72
-
-    def _make_saver():
-        if cp_hint is None:
-            return contextlib.nullcontext(None)
-        return _pg_saver()
-
-    rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
-    for turns in BASELINE_TURN_COUNTS:
-        with _make_saver() as saver:
-            b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
-        with _make_saver() as saver:
-            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
-        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt))
-    for turns in DELTA_ONLY_TURN_COUNTS:
-        with _make_saver() as saver:
-            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
-        rows.append((turns, None, d_bytes, None, d_rt, None, d_wt))
-
-    def _bytes_or_na(v: Any) -> str:
-        if v is None or v < 0:
-            return "n/a"
-        return _fmt_bytes(v)
-
-    def _ms_or_na(v: Any) -> str:
-        return "n/a" if v is None else f"{v * 1000:.1f}ms"
-
-    print(f"\n  [{cp_label}] Storage (blob bytes)")
-    print(
-        f"  {'turns':>6}  {'ctx':>10}  {'add_msgs':>12}  {'delta(inf)':>12}  {'savings':>8}"
-    )
-    print("  " + "-" * (W - 2))
-    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
-        if b_bytes is None or b_bytes < 0 or d_bytes is None or d_bytes < 0:
-            ratio_str = "n/a"
-        else:
-            ratio = b_bytes / d_bytes if d_bytes else float("inf")
-            ratio_str = f"{ratio:.0f}x"
-        print(
-            f"  {turns:>6}  {_approx_tokens(turns):>10}  "
-            f"{_bytes_or_na(b_bytes):>12}  {_bytes_or_na(d_bytes):>12}  {ratio_str:>8}"
-        )
-
-    print(f"\n  [{cp_label}] Read latency (avg of 5 get_state calls)")
-    print(f"  {'turns':>6}  {'ctx':>10}  {'add_msgs':>12}  {'delta(inf)':>12}")
-    print("  " + "-" * (W - 2))
-    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
-        print(
-            f"  {turns:>6}  {_approx_tokens(turns):>10}  "
-            f"{_ms_or_na(b_rt):>12}  {_ms_or_na(d_rt):>12}"
-        )
-
-
-def run_baseline_benchmark() -> None:
-    print()
-    print("Part 1 — DeltaChannel(inf) vs add_messages: storage & latency")
-    print("=" * 72)
-    for cp_label, cp_hint in _checkpointers():
-        _run_baseline_for_checkpointer(cp_label, cp_hint)
-    print()
-
-
-# ---------------------------------------------------------------------------
-# Part 2: snapshot_frequency sweep
-# ---------------------------------------------------------------------------
-
-# Frequencies to test. 1 = always snapshot (like BinOp), inf = pure delta.
-SNAPSHOT_FREQUENCIES: list[int | float] = [1, 5, 10, 50, math.inf]
-
-# Turn counts for the sweep — high enough to show storage divergence.
-SWEEP_TURN_COUNTS = [50, 100, 500]
-
-
-def _freq_label(freq: int | float) -> str:
-    if freq == math.inf:
-        return "inf"
-    return str(int(freq))
-
-
-def _run_sweep_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
-    def _make_saver():
-        if cp_hint is None:
-            return contextlib.nullcontext(None)
-        return _pg_saver()
-
-    # Collect results: {turns: {freq_label: (write_s, read_s, bytes)}}
-    results: dict[int, dict[str, tuple[float, float, int]]] = {}
-    for turns in SWEEP_TURN_COUNTS:
-        results[turns] = {}
-        for freq in SNAPSHOT_FREQUENCIES:
-            state_cls = _make_delta_state(freq)
-            with _make_saver() as saver:
-                wt, rt, bb = _run_turns(turns, state_cls, saver)
-            results[turns][_freq_label(freq)] = (wt, rt, bb)
-
-    freq_labels = [_freq_label(f) for f in SNAPSHOT_FREQUENCIES]
-    col_w = 12
-
-    header = f"  {'turns':>6}  {'ctx':>10}" + "".join(
-        f"  {f'freq={freq_label}':>{col_w}}" for freq_label in freq_labels
-    )
-
-    print(f"\n  [{cp_label}] Storage (blob bytes) — lower is better")
-    print(header)
-    print("  " + "-" * (len(header) - 2))
-    for turns in SWEEP_TURN_COUNTS:
-        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
-        for label in freq_labels:
-            _, _, bb = results[turns][label]
-            row += f"  {_fmt_bytes(bb) if bb >= 0 else 'n/a':>{col_w}}"
-        print(row)
-
-    print(f"\n  [{cp_label}] Read latency (avg of 5 get_state) — lower is better")
-    print(header)
-    print("  " + "-" * (len(header) - 2))
-    for turns in SWEEP_TURN_COUNTS:
-        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
-        for label in freq_labels:
-            _, rt, _ = results[turns][label]
-            row += f"  {f'{rt * 1000:.1f}ms':>{col_w}}"
-        print(row)
-
-    print(
-        f"\n  [{cp_label}] Per-invoke write latency (total / turns) — lower is better"
-    )
-    print(header)
-    print("  " + "-" * (len(header) - 2))
-    for turns in SWEEP_TURN_COUNTS:
-        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
-        for label in freq_labels:
-            wt, _, _ = results[turns][label]
-            row += f"  {f'{(wt / turns) * 1000:.1f}ms':>{col_w}}"
-        print(row)
-
-
-def run_snapshot_freq_benchmark() -> None:
-    print()
-    print("Part 2 — DeltaChannel snapshot_frequency sweep")
-    print("Lower freq → fewer snapshots → less storage but deeper read replay")
-    print("=" * 80)
-    for cp_label, cp_hint in _checkpointers():
-        _run_sweep_for_checkpointer(cp_label, cp_hint)
-    print()
-    print("Legend:")
-    print(
-        "  freq=1    snapshot every write (full blob always — same as add_messages / BinOp)"
-    )
-    print("  freq=N    snapshot every N writes; read walks at most N ancestor writes")
-    print("  freq=inf  pure delta; read walks entire ancestor chain")
-    print()
-
-
-# ---------------------------------------------------------------------------
-# Pytest entry points
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.skip(
-    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
-)
-def test_delta_channel_baseline_benchmark(capsys: Any) -> None:
-    """DeltaChannel(inf) uses less storage than add_messages at scale."""
-    with capsys.disabled():
-        run_baseline_benchmark()
-
-    for turns in [25, 50]:
-        _, _, b_bytes = _run_turns(turns, BinaryState)
-        _, _, d_bytes = _run_turns(turns, DeltaState)
-        assert d_bytes < b_bytes, (
-            f"DeltaChannel should use less storage at {turns} turns, "
-            f"got delta={d_bytes} binary={b_bytes}"
-        )
-
-
-@pytest.mark.skip(
-    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
-)
-def test_snapshot_freq_benchmark(capsys: Any) -> None:
-    """snapshot_frequency trades storage for bounded read depth."""
-    with capsys.disabled():
-        run_snapshot_freq_benchmark()
-
-    # Correctness: results at all frequencies should agree on final state.
-    n_turns = 20
-    states: dict[str, list] = {}
-    for freq in SNAPSHOT_FREQUENCIES:
-        state_cls = _make_delta_state(freq)
-        graph = _make_graph(state_cls)
-        config = {"configurable": {"thread_id": "correctness"}}
-        for i in range(n_turns):
-            graph.invoke(
-                {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
-                config,
-            )
-        state = graph.get_state(config)
-        states[_freq_label(freq)] = [m.id for m in state.values["messages"]]
-
-    ref = states["inf"]
-    for label, msg_ids in states.items():
-        assert msg_ids == ref, (
-            f"freq={label} produced different message IDs than freq=inf"
-        )
-
-
-# ---------------------------------------------------------------------------
-# Script entry point
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    run_baseline_benchmark()
-    run_snapshot_freq_benchmark()
-    sys.exit(0)

libs/langgraph/tests/test_delta_channel_migration.py

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

libs/langgraph/tests/test_graph_callbacks.py

@@ -1,344 +0,0 @@
-from __future__ import annotations
-
-import sys
-from typing import Any
-
-import pytest
-from langchain_core.callbacks.base import BaseCallbackHandler
-from langchain_core.callbacks.manager import CallbackManager
-from langgraph.checkpoint.memory import InMemorySaver
-from typing_extensions import TypedDict
-
-from langgraph.callbacks import (
-    GraphCallbackHandler,
-    GraphInterruptEvent,
-    GraphResumeEvent,
-)
-from langgraph.graph import START, StateGraph
-from langgraph.types import Command, Interrupt, interrupt
-
-NEEDS_CONTEXTVARS = pytest.mark.skipif(
-    sys.version_info < (3, 11),
-    reason="Python 3.11+ is required for async contextvars support",
-)
-
-
-class _GraphEventHandler(GraphCallbackHandler):
-    def __init__(self) -> None:
-        self.interrupt_events: list[GraphInterruptEvent] = []
-        self.resume_events: list[GraphResumeEvent] = []
-
-    def on_interrupt(self, event: GraphInterruptEvent) -> Any:
-        self.interrupt_events.append(event)
-
-    def on_resume(self, event: GraphResumeEvent) -> Any:
-        self.resume_events.append(event)
-
-
-class _LangChainCustomEventHandler(BaseCallbackHandler):
-    run_inline = True
-
-    def __init__(self) -> None:
-        self.events: list[str] = []
-
-    def on_custom_event(self, name: str, data: Any, **kwargs: Any) -> Any:
-        self.events.append(name)
-
-
-class _RaisingGraphEventHandler(GraphCallbackHandler):
-    def __init__(
-        self,
-        *,
-        raise_on_interrupt: bool = False,
-        raise_on_resume: bool = False,
-        raise_error: bool = False,
-    ) -> None:
-        self.raise_on_interrupt = raise_on_interrupt
-        self.raise_on_resume = raise_on_resume
-        self.raise_error = raise_error
-
-    def on_interrupt(self, event: GraphInterruptEvent) -> Any:
-        if self.raise_on_interrupt:
-            raise ValueError("boom-interrupt")
-
-    def on_resume(self, event: GraphResumeEvent) -> Any:
-        if self.raise_on_resume:
-            raise ValueError("boom-resume")
-
-
-class _AsyncRaisingGraphEventHandler(GraphCallbackHandler):
-    def __init__(
-        self,
-        *,
-        raise_on_interrupt: bool = False,
-        raise_on_resume: bool = False,
-        raise_error: bool = False,
-    ) -> None:
-        self.raise_on_interrupt = raise_on_interrupt
-        self.raise_on_resume = raise_on_resume
-        self.raise_error = raise_error
-
-    async def on_interrupt(self, event: GraphInterruptEvent) -> Any:
-        if self.raise_on_interrupt:
-            raise ValueError("boom-interrupt")
-
-    async def on_resume(self, event: GraphResumeEvent) -> Any:
-        if self.raise_on_resume:
-            raise ValueError("boom-resume")
-
-
-class _State(TypedDict):
-    answer: str | None
-
-
-def _build_interrupt_graph() -> Any:
-    def ask(state: _State) -> _State:
-        answer = interrupt("Provide value")
-        return {"answer": answer}
-
-    builder = StateGraph(_State)
-    builder.add_node("ask", ask)
-    builder.add_edge(START, "ask")
-    return builder.compile(checkpointer=InMemorySaver())
-
-
-def test_graph_callbacks_interrupt_and_resume_sync() -> None:
-    graph = _build_interrupt_graph()
-    handler = _GraphEventHandler()
-    langchain_handler = _LangChainCustomEventHandler()
-    config = {
-        "configurable": {"thread_id": "graph-callback-sync"},
-        "callbacks": [langchain_handler, handler],
-    }
-
-    first = graph.invoke({"answer": None}, config)
-    assert "__interrupt__" in first
-
-    assert len(handler.interrupt_events) == 1
-    assert handler.interrupt_events[0].interrupts
-    assert isinstance(handler.interrupt_events[0].interrupts[0], Interrupt)
-    assert handler.interrupt_events[0].checkpoint_ns == ()
-    assert langchain_handler.events == []
-
-    handler.resume_events.clear()
-    resumed = graph.invoke(Command(resume="done"), config)
-    assert resumed == {"answer": "done"}
-
-    assert len(handler.resume_events) == 1
-    assert handler.resume_events[0].checkpoint_ns == ()
-    assert langchain_handler.events == []
-
-
-@pytest.mark.anyio
-@NEEDS_CONTEXTVARS
-async def test_graph_callbacks_interrupt_and_resume_async() -> None:
-    graph = _build_interrupt_graph()
-    handler = _GraphEventHandler()
-    langchain_handler = _LangChainCustomEventHandler()
-    config = {
-        "configurable": {"thread_id": "graph-callback-async"},
-        "callbacks": [langchain_handler, handler],
-    }
-
-    first = await graph.ainvoke({"answer": None}, config)
-    assert "__interrupt__" in first
-
-    assert len(handler.interrupt_events) == 1
-    assert handler.interrupt_events[0].interrupts
-    assert isinstance(handler.interrupt_events[0].interrupts[0], Interrupt)
-    assert handler.interrupt_events[0].checkpoint_ns == ()
-    assert langchain_handler.events == []
-
-    handler.resume_events.clear()
-    resumed = await graph.ainvoke(Command(resume="done"), config)
-    assert resumed == {"answer": "done"}
-
-    assert len(handler.resume_events) == 1
-    assert handler.resume_events[0].checkpoint_ns == ()
-    assert langchain_handler.events == []
-
-
-def test_graph_callbacks_continue_when_interrupt_handler_raises_sync() -> None:
-    graph = _build_interrupt_graph()
-    raising_handler = _RaisingGraphEventHandler(raise_on_interrupt=True)
-    recording_handler = _GraphEventHandler()
-
-    first = graph.invoke(
-        {"answer": None},
-        {
-            "configurable": {"thread_id": "graph-callback-sync-raises"},
-            "callbacks": [raising_handler, recording_handler],
-        },
-    )
-
-    assert "__interrupt__" in first
-    assert len(recording_handler.interrupt_events) == 1
-
-
-def test_graph_callbacks_continue_when_resume_handler_raises_sync() -> None:
-    graph = _build_interrupt_graph()
-    raising_handler = _RaisingGraphEventHandler(raise_on_resume=True)
-    recording_handler = _GraphEventHandler()
-    config = {
-        "configurable": {"thread_id": "graph-callback-sync-raises-resume"},
-        "callbacks": [raising_handler, recording_handler],
-    }
-
-    first = graph.invoke({"answer": None}, config)
-    assert "__interrupt__" in first
-
-    resumed = graph.invoke(Command(resume="done"), config)
-    assert resumed == {"answer": "done"}
-    assert len(recording_handler.resume_events) == 1
-
-
-def test_graph_callbacks_raise_error_propagates_sync() -> None:
-    graph = _build_interrupt_graph()
-    raising_handler = _RaisingGraphEventHandler(
-        raise_on_interrupt=True,
-        raise_error=True,
-    )
-
-    with pytest.raises(ValueError, match="boom-interrupt"):
-        graph.invoke(
-            {"answer": None},
-            {
-                "configurable": {"thread_id": "graph-callback-sync-raise-error"},
-                "callbacks": [raising_handler],
-            },
-        )
-
-
-@pytest.mark.anyio
-@NEEDS_CONTEXTVARS
-async def test_graph_callbacks_continue_when_handler_raises_async() -> None:
-    graph = _build_interrupt_graph()
-    raising_interrupt_handler = _AsyncRaisingGraphEventHandler(raise_on_interrupt=True)
-    recording_handler = _GraphEventHandler()
-    config = {
-        "configurable": {"thread_id": "graph-callback-async-raises-interrupt"},
-        "callbacks": [raising_interrupt_handler, recording_handler],
-    }
-
-    first = await graph.ainvoke({"answer": None}, config)
-    assert "__interrupt__" in first
-    assert len(recording_handler.interrupt_events) == 1
-
-    graph = _build_interrupt_graph()
-    raising_resume_handler = _AsyncRaisingGraphEventHandler(raise_on_resume=True)
-    recording_handler = _GraphEventHandler()
-    config = {
-        "configurable": {"thread_id": "graph-callback-async-raises-resume"},
-        "callbacks": [raising_resume_handler, recording_handler],
-    }
-
-    first = await graph.ainvoke({"answer": None}, config)
-    assert "__interrupt__" in first
-    resumed = await graph.ainvoke(Command(resume="done"), config)
-    assert resumed == {"answer": "done"}
-    assert len(recording_handler.resume_events) == 1
-
-
-@pytest.mark.anyio
-@NEEDS_CONTEXTVARS
-async def test_graph_callbacks_raise_error_propagates_async() -> None:
-    graph = _build_interrupt_graph()
-    raising_handler = _AsyncRaisingGraphEventHandler(
-        raise_on_interrupt=True,
-        raise_error=True,
-    )
-
-    with pytest.raises(ValueError, match="boom-interrupt"):
-        await graph.ainvoke(
-            {"answer": None},
-            {
-                "configurable": {"thread_id": "graph-callback-async-raise-error"},
-                "callbacks": [raising_handler],
-            },
-        )
-
-
-def test_graph_callbacks_accept_base_callback_manager() -> None:
-    graph = _build_interrupt_graph()
-    graph_handler = _GraphEventHandler()
-    custom_handler = _LangChainCustomEventHandler()
-    manager = CallbackManager.configure(inheritable_callbacks=[custom_handler])
-    manager.add_handler(graph_handler)
-
-    first = graph.invoke(
-        {"answer": None},
-        {
-            "configurable": {"thread_id": "graph-callback-base-manager"},
-            "callbacks": manager,
-        },
-    )
-
-    assert "__interrupt__" in first
-    assert len(graph_handler.interrupt_events) == 1
-
-
-def test_non_graph_handler_via_add_handler_does_not_crash() -> None:
-    """Non-GraphCallbackHandler added via add_handler should not raise.
-
-    Libraries like opentelemetry-instrumentation-langchain monkey-patch
-    BaseCallbackManager.__init__ and inject handlers via add_handler().
-    These handlers inherit from BaseCallbackHandler, not
-    GraphCallbackHandler. They must be silently accepted — graph lifecycle
-    events will simply not be dispatched to them.
-    """
-    from langgraph.callbacks import _GraphCallbackManager
-
-    manager = _GraphCallbackManager()
-    plain_handler = _LangChainCustomEventHandler()
-
-    manager.add_handler(plain_handler, inherit=True)
-    assert plain_handler in manager.handlers
-
-
-def test_non_graph_handler_does_not_receive_lifecycle_events() -> None:
-    """Non-GraphCallbackHandler added alongside a GraphCallbackHandler
-    should not interfere with lifecycle event dispatch."""
-    graph = _build_interrupt_graph()
-    graph_handler = _GraphEventHandler()
-    plain_handler = _LangChainCustomEventHandler()
-
-    config = {
-        "configurable": {"thread_id": "graph-callback-mixed-handlers"},
-        "callbacks": [plain_handler, graph_handler],
-    }
-
-    first = graph.invoke({"answer": None}, config)
-    assert "__interrupt__" in first
-
-    assert len(graph_handler.interrupt_events) == 1
-    assert plain_handler.events == []
-
-    resumed = graph.invoke(Command(resume="done"), config)
-    assert resumed == {"answer": "done"}
-    assert len(graph_handler.resume_events) == 1
-    assert plain_handler.events == []
-
-
-@pytest.mark.anyio
-@NEEDS_CONTEXTVARS
-async def test_non_graph_handler_does_not_receive_lifecycle_events_async() -> None:
-    """Async variant: non-GraphCallbackHandler should not interfere."""
-    graph = _build_interrupt_graph()
-    graph_handler = _GraphEventHandler()
-    plain_handler = _LangChainCustomEventHandler()
-
-    config = {
-        "configurable": {"thread_id": "graph-callback-mixed-handlers-async"},
-        "callbacks": [plain_handler, graph_handler],
-    }
-
-    first = await graph.ainvoke({"answer": None}, config)
-    assert "__interrupt__" in first
-
-    assert len(graph_handler.interrupt_events) == 1
-    assert plain_handler.events == []
-
-    resumed = await graph.ainvoke(Command(resume="done"), config)
-    assert resumed == {"answer": "done"}
-    assert len(graph_handler.resume_events) == 1
-    assert plain_handler.events == []

libs/langgraph/tests/test_graph_run_stream.py

@@ -0,0 +1,623 @@
+import asyncio
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from pydantic import BaseModel
+from typing_extensions import TypedDict
+
+from langgraph.config import get_stream_writer
+from langgraph.graph import END, START, MessagesState, StateGraph
+from langgraph.stream import AsyncChatModelStream, StreamingHandler
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from tests.fake_chat import FakeChatModel
+
+
+class State(TypedDict):
+    value: str
+    items: Annotated[list[str], lambda a, b: a + b]
+
+
+def make_simple_graph():
+    def node_a(state):
+        return {"value": state["value"] + "_a", "items": ["a"]}
+
+    def node_b(state):
+        return {"value": state["value"] + "_b", "items": ["b"]}
+
+    graph = StateGraph(State)
+    graph.add_node("node_a", node_a)
+    graph.add_node("node_b", node_b)
+    graph.add_edge(START, "node_a")
+    graph.add_edge("node_a", "node_b")
+    graph.add_edge("node_b", END)
+    return graph.compile()
+
+
+@pytest.mark.anyio
+async def test_output():
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+    output = await run.output
+    assert output == {"value": "x_a_b", "items": ["a", "b"]}
+
+
+@pytest.mark.anyio
+async def test_values_iteration():
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+
+    snapshots = []
+    async for v in run.values:
+        snapshots.append(v)
+
+    assert len(snapshots) == 3
+    assert snapshots[0]["value"] == "x"
+    assert snapshots[1]["value"] == "x_a"
+    assert snapshots[2]["value"] == "x_a_b"
+
+
+@pytest.mark.anyio
+async def test_updates_in_raw_events():
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+
+    updates = []
+    async for event in run:
+        if event["method"] == "updates":
+            updates.append(event["params"]["data"])
+
+    assert len(updates) == 2
+    assert "node_a" in updates[0]
+    assert "node_b" in updates[1]
+
+
+@pytest.mark.anyio
+async def test_messages_with_chat_model():
+    model = FakeChatModel(messages=[AIMessage(content="Hello world")])
+
+    def agent(state):
+        return {"messages": [model.invoke(state["messages"])]}
+
+    graph = StateGraph(MessagesState)
+    graph.add_node("agent", agent)
+    graph.add_edge(START, "agent")
+    graph.add_edge("agent", END)
+    compiled = graph.compile()
+
+    run = await StreamingHandler(compiled).astream(
+        {"messages": [HumanMessage(content="hi")]}
+    )
+    await asyncio.sleep(0.1)
+
+    messages_seen = []
+    async for msg in run.messages:
+        messages_seen.append(msg)
+
+    assert len(messages_seen) >= 1
+    msg = messages_seen[0]
+    assert isinstance(msg, AsyncChatModelStream)
+    text = await msg.text
+    assert text == "Hello world"
+
+
+@pytest.mark.anyio
+async def test_custom_events():
+    def node(state):
+        writer = get_stream_writer()
+        writer("hello")
+        writer(42)
+        return {"value": state["value"] + "_a", "items": ["a"]}
+
+    graph = StateGraph(State)
+    graph.add_node("node_a", node)
+    graph.add_edge(START, "node_a")
+    graph.add_edge("node_a", END)
+    compiled = graph.compile()
+
+    run = await StreamingHandler(compiled).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+
+    custom_payloads = []
+    async for event in run:
+        if event["method"] == "custom":
+            custom_payloads.append(event["params"]["data"])
+
+    assert "hello" in custom_payloads
+    assert 42 in custom_payloads
+
+
+@pytest.mark.anyio
+async def test_multiple_modes_present():
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+
+    methods = set()
+    async for event in run:
+        methods.add(event["method"])
+
+    assert {"values", "updates", "tasks", "debug"} <= methods
+
+
+@pytest.mark.anyio
+async def test_interrupted_false():
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+    async for _ in run:
+        pass
+    assert run.interrupted is False
+
+
+@pytest.mark.anyio
+async def test_regression_v1_stream_unchanged():
+    graph = make_simple_graph()
+    chunks = []
+    async for chunk in graph.astream(
+        {"value": "x", "items": []}, stream_mode="values", version="v1"
+    ):
+        chunks.append(chunk)
+    for chunk in chunks:
+        assert isinstance(chunk, dict)
+
+
+@pytest.mark.anyio
+async def test_regression_v2_stream_unchanged():
+    graph = make_simple_graph()
+    chunks = []
+    async for chunk in graph.astream(
+        {"value": "x", "items": []}, stream_mode="values", version="v2"
+    ):
+        chunks.append(chunk)
+    assert len(chunks) >= 1
+    for chunk in chunks:
+        assert isinstance(chunk, dict)
+        assert "type" in chunk
+        assert chunk["type"] == "values"
+
+
+@pytest.mark.anyio
+async def test_regression_invoke_unchanged():
+    graph = make_simple_graph()
+    result = await graph.ainvoke({"value": "x", "items": []})
+    assert result == {"value": "x_a_b", "items": ["a", "b"]}
+
+
+def test_sync_stream_output():
+    graph = make_simple_graph()
+    run = StreamingHandler(graph).stream({"value": "x", "items": []})
+    assert run.output == {"value": "x_a_b", "items": ["a", "b"]}
+
+
+def test_sync_stream_values():
+    graph = make_simple_graph()
+    run = StreamingHandler(graph).stream({"value": "x", "items": []})
+    snapshots = list(run.values)
+    assert len(snapshots) == 3
+    assert snapshots[0]["value"] == "x"
+    assert snapshots[2]["value"] == "x_a_b"
+
+
+def test_sync_stream_raw_events():
+    graph = make_simple_graph()
+    run = StreamingHandler(graph).stream({"value": "x", "items": []})
+    methods = {e["method"] for e in run}
+    assert {"values", "updates", "tasks", "debug"} <= methods
+
+
+# ---------------------------------------------------------------------------
+# Typed output (pydantic)
+# ---------------------------------------------------------------------------
+
+
+class ModelState(BaseModel):
+    value: str
+    items: Annotated[list[str], lambda a, b: a + b]
+
+
+def _make_model_state_graph():
+    def node_a(state):
+        return {"value": state.value + "_a", "items": ["a"]}
+
+    graph = StateGraph(ModelState)
+    graph.add_node("node_a", node_a)
+    graph.add_edge(START, "node_a")
+    graph.add_edge("node_a", END)
+    return graph.compile()
+
+
+@pytest.mark.anyio
+async def test_pydantic_output():
+    graph = _make_model_state_graph()
+    run = await StreamingHandler(graph).astream(ModelState(value="x", items=[]))
+    await asyncio.sleep(0.1)
+    output = await run.output
+    assert isinstance(output, ModelState)
+    assert output.value == "x_a"
+
+
+@pytest.mark.anyio
+async def test_pydantic_values():
+    graph = _make_model_state_graph()
+    run = await StreamingHandler(graph).astream(ModelState(value="x", items=[]))
+    await asyncio.sleep(0.1)
+    snapshots = []
+    async for v in run.values:
+        snapshots.append(v)
+    for v in snapshots:
+        assert isinstance(v, ModelState)
+
+
+def test_sync_pydantic_output():
+    graph = _make_model_state_graph()
+    run = StreamingHandler(graph).stream(ModelState(value="x", items=[]))
+    assert isinstance(run.output, ModelState)
+    assert run.output.value == "x_a"
+
+
+# ---------------------------------------------------------------------------
+# Interrupts
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+async def test_interrupts():
+    from langgraph.checkpoint.memory import MemorySaver
+
+    from langgraph.types import interrupt
+
+    def ask_human(state: State):
+        answer = interrupt("what do you want?")
+        return {"value": state["value"] + f"_{answer}", "items": [answer]}
+
+    graph = StateGraph(State)
+    graph.add_node("ask", ask_human)
+    graph.add_edge(START, "ask")
+    graph.add_edge("ask", END)
+    compiled = graph.compile(checkpointer=MemorySaver())
+
+    config = {"configurable": {"thread_id": "t1"}}
+    run = await StreamingHandler(compiled).astream(
+        {"value": "x", "items": []}, config=config
+    )
+    await asyncio.sleep(0.1)
+    # Drain events
+    async for _ in run:
+        pass
+    assert run.interrupted is True
+    assert len(run.interrupts) > 0
+
+
+# ---------------------------------------------------------------------------
+# messages_from(node)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+async def test_messages_from_node():
+    model = FakeChatModel(messages=[AIMessage(content="from agent")])
+
+    def agent(state):
+        return {"messages": [model.invoke(state["messages"])]}
+
+    def postprocess(state):
+        return {"messages": state["messages"]}
+
+    graph = StateGraph(MessagesState)
+    graph.add_node("agent", agent)
+    graph.add_node("postprocess", postprocess)
+    graph.add_edge(START, "agent")
+    graph.add_edge("agent", "postprocess")
+    graph.add_edge("postprocess", END)
+    compiled = graph.compile()
+
+    run = await StreamingHandler(compiled).astream(
+        {"messages": [HumanMessage(content="hi")]}
+    )
+    await asyncio.sleep(0.1)
+
+    # All messages
+    all_msgs = []
+    async for m in run.messages:
+        all_msgs.append(m)
+    assert len(all_msgs) >= 1
+    # Node provenance should be set
+    assert all_msgs[0].node == "agent"
+
+
+# ---------------------------------------------------------------------------
+# Subgraph child stream
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+async def test_subgraph_child_output():
+    """AsyncSubgraphRunStream.output should contain the child graph's final state."""
+
+    class ChildState(TypedDict):
+        value: str
+
+    class ParentState(TypedDict):
+        value: str
+
+    def child_node(state):
+        return {"value": state["value"] + "_child"}
+
+    child_graph = StateGraph(ChildState)
+    child_graph.add_node("child_node", child_node)
+    child_graph.add_edge(START, "child_node")
+    child_graph.add_edge("child_node", END)
+    # Add the compiled child as a node — this triggers LangGraph's
+    # subgraph streaming mechanism and emits child namespace events.
+    child_compiled = child_graph.compile()
+
+    parent_graph = StateGraph(ParentState)
+    parent_graph.add_node("child_node", child_compiled)
+    parent_graph.add_edge(START, "child_node")
+    parent_graph.add_edge("child_node", END)
+    parent_compiled = parent_graph.compile()
+
+    run = await StreamingHandler(parent_compiled).astream({"value": "x"})
+    await asyncio.sleep(0.1)
+
+    subgraph_streams = []
+    async for sub in run.subgraphs:
+        subgraph_streams.append(sub)
+
+    assert len(subgraph_streams) >= 1
+    child_output = await subgraph_streams[0].output
+    assert child_output is not None
+    assert child_output["value"] == "x_child"
+
+
+# ---------------------------------------------------------------------------
+# Custom reducers / .extensions
+# ---------------------------------------------------------------------------
+
+
+class _CountTransformer(StreamTransformer):
+    """Counts events. Exposes count via .value for extensions."""
+
+    name = "event_count"
+
+    def __init__(self) -> None:
+        self.value = 0
+
+    def init(self) -> Any:
+        return None
+
+    def process(self, event: ProtocolEvent) -> bool:
+        self.value += 1
+        return True
+
+    def finalize(self) -> None:
+        pass
+
+    def fail(self, err: BaseException) -> None:
+        pass
+
+
+@pytest.mark.anyio
+async def test_custom_reducer_extensions():
+    graph = make_simple_graph()
+    counter = _CountTransformer()
+    run = await StreamingHandler(graph).astream(
+        {"value": "x", "items": []}, transformers=[counter]
+    )
+    await asyncio.sleep(0.1)
+    async for _ in run:
+        pass
+    assert counter.value > 0
+    assert run.extensions["event_count"] == counter.value
+
+
+def test_sync_custom_reducer_extensions():
+    graph = make_simple_graph()
+    counter = _CountTransformer()
+    run = StreamingHandler(graph).stream(
+        {"value": "x", "items": []}, transformers=[counter]
+    )
+    for _ in run:
+        pass
+    assert counter.value > 0
+    assert run.extensions["event_count"] == counter.value
+
+
+# ---------------------------------------------------------------------------
+# Double iteration over .values
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+async def test_async_values_double_iteration():
+    """Iterating over run.values twice should yield the same snapshots both times."""
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+
+    first = []
+    async for v in run.values:
+        first.append(v)
+
+    second = []
+    async for v in run.values:
+        second.append(v)
+
+    assert len(first) == 3
+    assert first == second
+
+
+def test_sync_values_double_iteration():
+    """Iterating over run.values twice should yield the same snapshots both times."""
+    graph = make_simple_graph()
+    run = StreamingHandler(graph).stream({"value": "x", "items": []})
+
+    first = list(run.values)
+    second = list(run.values)
+
+    assert len(first) == 3
+    assert first == second
+
+
+@pytest.mark.anyio
+async def test_async_raw_events_double_iteration():
+    """Iterating over the raw event stream twice should yield the same events."""
+    graph = make_simple_graph()
+    run = await StreamingHandler(graph).astream({"value": "x", "items": []})
+    await asyncio.sleep(0.1)
+
+    first = []
+    async for event in run:
+        first.append(event)
+
+    second = []
+    async for event in run:
+        second.append(event)
+
+    assert len(first) > 0
+    assert first == second
+
+
+def test_sync_raw_events_double_iteration():
+    """Iterating over the raw event stream twice should yield the same events."""
+    graph = make_simple_graph()
+    run = StreamingHandler(graph).stream({"value": "x", "items": []})
+
+    first = list(run)
+    second = list(run)
+
+    assert len(first) > 0
+    assert first == second
+
+
+# ---------------------------------------------------------------------------
+# Tool transformer via extensions
+# ---------------------------------------------------------------------------
+
+
+class _ToolExecution:
+    def __init__(self, tool_call_id: str, tool_name: str, input: Any, output: Any):
+        self.tool_call_id = tool_call_id
+        self.tool_name = tool_name
+        self.input = input
+        self.output = output
+
+
+class _ToolsTransformer(StreamTransformer):
+    """Groups tool-started/tool-finished custom events into _ToolExecution objects."""
+
+    name = "tools"
+
+    def __init__(self) -> None:
+        self._log: list[_ToolExecution] = []
+        self._pending: dict[str, dict] = {}
+        self.value = self._log
+
+    def init(self) -> Any:
+        return None
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "custom":
+            return True
+        data = event["params"]["data"]
+        if not isinstance(data, dict) or "event" not in data:
+            return True
+
+        tool_call_id = data.get("tool_call_id")
+        if tool_call_id is None:
+            return True
+
+        if data["event"] == "tool-started":
+            self._pending[tool_call_id] = data
+            return False
+
+        if data["event"] == "tool-finished":
+            started = self._pending.pop(tool_call_id, {})
+            self._log.append(_ToolExecution(
+                tool_call_id=tool_call_id,
+                tool_name=started.get("tool_name", ""),
+                input=started.get("input"),
+                output=data["output"],
+            ))
+            return False
+
+        return True
+
+    def finalize(self) -> None:
+        pass
+
+    def fail(self, err: BaseException) -> None:
+        pass
+
+
+def _make_tool_graph():
+    """Graph: agent emits a tool call, custom_tools executes it with writer events."""
+    from langgraph.types import StreamWriter
+
+    def agent(state):
+        return {
+            "value": "called",
+            "items": ["agent"],
+        }
+
+    def custom_tools(state, *, writer: StreamWriter):
+        writer({
+            "event": "tool-started",
+            "tool_call_id": "call_1",
+            "tool_name": "get_weather",
+            "input": {"city": "SF"},
+        })
+        writer({
+            "event": "tool-finished",
+            "tool_call_id": "call_1",
+            "output": {"temp_f": 64},
+        })
+        return {"value": "done", "items": ["tools"]}
+
+    graph = StateGraph(State)
+    graph.add_node("agent", agent)
+    graph.add_node("custom_tools", custom_tools)
+    graph.add_edge(START, "agent")
+    graph.add_edge("agent", "custom_tools")
+    graph.add_edge("custom_tools", END)
+    return graph.compile()
+
+
+def test_sync_tool_transformer_via_extensions():
+    """Tool events flow through extensions and are iterable without draining raw events."""
+    graph = _make_tool_graph()
+    run = StreamingHandler(graph).stream(
+        {"value": "", "items": []},
+        transformers=[_ToolsTransformer()],
+    )
+
+    # Iterating extensions drives the pump — no need to drain raw events first
+    executions = list(run.extensions["tools"])
+    assert len(executions) == 1
+    assert executions[0].tool_name == "get_weather"
+    assert executions[0].input == {"city": "SF"}
+    assert executions[0].output == {"temp_f": 64}
+
+
+@pytest.mark.anyio
+async def test_async_tool_transformer_via_extensions():
+    """Tool events flow through extensions in async mode."""
+    graph = _make_tool_graph()
+    run = await StreamingHandler(graph).astream(
+        {"value": "", "items": []},
+        transformers=[_ToolsTransformer()],
+    )
+    await asyncio.sleep(0.1)
+
+    # Drain main stream so transformer processes all events
+    async for _ in run:
+        pass
+
+    tools_log = run.extensions["tools"]
+    assert len(tools_log) == 1
+    assert tools_log[0].tool_name == "get_weather"
+    assert tools_log[0].output == {"temp_f": 64}

libs/langgraph/tests/test_interleave_arrival_order.py

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

libs/langgraph/tests/test_large_cases.py

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

libs/langgraph/tests/test_large_cases_async.py

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

libs/langgraph/tests/test_messages_v2.py

@@ -0,0 +1,531 @@
+from uuid import uuid4
+
+import pytest
+from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
+
+from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
+from langgraph.pregel._messages_v2 import StreamProtocolMessagesHandler
+from langgraph.types import Command
+
+META = {"langgraph_checkpoint_ns": "root:", "langgraph_node": "agent"}
+
+
+def make_handler(subgraphs=True):
+    events = []
+    handler = StreamProtocolMessagesHandler(events.append, subgraphs)
+    return handler, events
+
+
+def test_streamed_text():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    for token_text in ("Hello", " ", "world"):
+        chunk = ChatGenerationChunk(
+            message=AIMessageChunk(content=token_text, id=f"run-{run_id}")
+        )
+        handler.on_llm_new_token(token_text, chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(content="Hello world", id=f"run-{run_id}")
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    assert data_events[0]["event"] == "message-start"
+    assert data_events[1]["event"] == "content-block-start"
+    assert data_events[1]["index"] == 0
+
+    deltas = [d for d in data_events if d["event"] == "content-block-delta"]
+    assert len(deltas) == 3
+    assert deltas[0]["content_block"]["text"] == "Hello"
+    assert deltas[1]["content_block"]["text"] == " "
+    assert deltas[2]["content_block"]["text"] == "world"
+
+    finish_blocks = [d for d in data_events if d["event"] == "content-block-finish"]
+    assert len(finish_blocks) == 1
+    assert finish_blocks[0]["content_block"]["text"] == "Hello world"
+
+    assert data_events[-1]["event"] == "message-finish"
+    assert data_events[-1]["reason"] == "stop"
+
+
+def test_tool_calls():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    chunk1 = ChatGenerationChunk(
+        message=AIMessageChunk(
+            content="",
+            tool_call_chunks=[
+                {"name": "search", "args": '{"q', "id": "call_1", "index": 0}
+            ],
+            id=f"run-{run_id}",
+        )
+    )
+    handler.on_llm_new_token("", chunk=chunk1, run_id=run_id)
+
+    chunk2 = ChatGenerationChunk(
+        message=AIMessageChunk(
+            content="",
+            tool_call_chunks=[
+                {"name": None, "args": 'uery":"hi"}', "id": None, "index": 0}
+            ],
+            id=f"run-{run_id}",
+        )
+    )
+    handler.on_llm_new_token("", chunk=chunk2, run_id=run_id)
+
+    final_msg = AIMessage(
+        content="",
+        tool_calls=[{"name": "search", "args": {"query": "hi"}, "id": "call_1"}],
+        id=f"run-{run_id}",
+    )
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    finish_blocks = [d for d in data_events if d["event"] == "content-block-finish"]
+    assert len(finish_blocks) == 1
+    fb = finish_blocks[0]["content_block"]
+    assert fb["type"] == "tool_call"
+    assert fb["args"] == {"query": "hi"}
+    assert fb["name"] == "search"
+    assert fb["id"] == "call_1"
+
+
+def test_invalid_tool_call_json():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    chunk = ChatGenerationChunk(
+        message=AIMessageChunk(
+            content="",
+            tool_call_chunks=[
+                {
+                    "name": "search",
+                    "args": "{not valid json",
+                    "id": "call_2",
+                    "index": 0,
+                }
+            ],
+            id=f"run-{run_id}",
+        )
+    )
+    handler.on_llm_new_token("", chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(content="", id=f"run-{run_id}")
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    finish_blocks = [d for d in data_events if d["event"] == "content-block-finish"]
+    assert len(finish_blocks) == 1
+    fb = finish_blocks[0]["content_block"]
+    assert fb["type"] == "invalid_tool_call"
+    assert "Failed to parse" in fb["error"]
+
+
+def test_reasoning_blocks():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    chunk = ChatGenerationChunk(
+        message=AIMessageChunk(
+            content=[{"type": "reasoning_content", "reasoning_content": "thinking..."}],
+            id=f"run-{run_id}",
+        )
+    )
+    handler.on_llm_new_token("", chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(content="", id=f"run-{run_id}")
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    block_starts = [d for d in data_events if d["event"] == "content-block-start"]
+    assert len(block_starts) == 1
+    assert block_starts[0]["content_block"]["type"] == "reasoning"
+
+    deltas = [d for d in data_events if d["event"] == "content-block-delta"]
+    assert len(deltas) == 1
+    assert deltas[0]["content_block"]["reasoning"] == "thinking..."
+
+
+def test_multiple_content_blocks():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    chunk1 = ChatGenerationChunk(
+        message=AIMessageChunk(content="hello", id=f"run-{run_id}")
+    )
+    handler.on_llm_new_token("hello", chunk=chunk1, run_id=run_id)
+
+    chunk2 = ChatGenerationChunk(
+        message=AIMessageChunk(
+            content="",
+            tool_call_chunks=[
+                {"name": "lookup", "args": '{"x":1}', "id": "call_3", "index": 1}
+            ],
+            id=f"run-{run_id}",
+        )
+    )
+    handler.on_llm_new_token("", chunk=chunk2, run_id=run_id)
+
+    final_msg = AIMessage(
+        content="hello",
+        tool_calls=[{"name": "lookup", "args": {"x": 1}, "id": "call_3"}],
+        id=f"run-{run_id}",
+    )
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    finish_blocks = [d for d in data_events if d["event"] == "content-block-finish"]
+    assert len(finish_blocks) == 2
+
+
+def test_usage_metadata():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    chunk = ChatGenerationChunk(
+        message=AIMessageChunk(content="hi", id=f"run-{run_id}")
+    )
+    handler.on_llm_new_token("hi", chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(
+        content="hi",
+        id=f"run-{run_id}",
+        usage_metadata={"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
+    )
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    finish_event = [d for d in data_events if d["event"] == "message-finish"][0]
+    assert "usage" in finish_event
+    assert finish_event["usage"]["input_tokens"] == 10
+
+
+@pytest.mark.parametrize(
+    "raw_reason,expected",
+    [
+        ("stop", "stop"),
+        ("tool_calls", "tool_use"),
+        ("length", "length"),
+        ("content_filter", "content_filter"),
+        ("end_turn", "stop"),
+    ],
+)
+def test_finish_reason_normalization(raw_reason, expected):
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+    chunk = ChatGenerationChunk(message=AIMessageChunk(content="x", id=f"run-{run_id}"))
+    handler.on_llm_new_token("x", chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(
+        content="x",
+        id=f"run-{run_id}",
+        response_metadata={"finish_reason": raw_reason},
+    )
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    finish_event = [d for d in data_events if d["event"] == "message-finish"][0]
+    assert finish_event["reason"] == expected
+
+
+def test_tag_nostream():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[TAG_NOSTREAM]
+    )
+    chunk = ChatGenerationChunk(
+        message=AIMessageChunk(content="secret", id=f"run-{run_id}")
+    )
+    handler.on_llm_new_token("secret", chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(content="secret", id=f"run-{run_id}")
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+    assert events == []
+
+
+def test_tag_hidden_chain():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chain_start(
+        serialized={},
+        inputs={},
+        run_id=run_id,
+        metadata=META,
+        tags=[TAG_HIDDEN],
+        name="agent",
+    )
+    handler.on_chain_end(
+        {"messages": [AIMessage(content="hidden", id="msg-1")]},
+        run_id=run_id,
+    )
+    assert events == []
+
+
+def test_subgraph_filtering():
+    handler, events = make_handler(subgraphs=False)
+    run_id = uuid4()
+
+    subgraph_meta = {
+        "langgraph_checkpoint_ns": "root:|child:",
+        "langgraph_node": "agent",
+    }
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=subgraph_meta, tags=[]
+    )
+    chunk = ChatGenerationChunk(
+        message=AIMessageChunk(content="sub", id=f"run-{run_id}")
+    )
+    handler.on_llm_new_token("sub", chunk=chunk, run_id=run_id)
+
+    final_msg = AIMessage(content="sub", id=f"run-{run_id}")
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+    assert events == []
+
+
+def test_chain_emits_messages():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chain_start(
+        serialized={}, inputs={}, run_id=run_id, metadata=META, tags=[], name="agent"
+    )
+    handler.on_chain_end(
+        {"messages": [AIMessage(content="hello", id="msg-chain-1")]},
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    assert len(data_events) > 0
+    assert data_events[0]["event"] == "message-start"
+    assert data_events[-1]["event"] == "message-finish"
+
+
+def test_llm_error_after_start():
+    """on_llm_error should emit a message-error event for a started stream."""
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    chunk = ChatGenerationChunk(
+        message=AIMessageChunk(content="partial", id=f"run-{run_id}")
+    )
+    handler.on_llm_new_token("partial", chunk=chunk, run_id=run_id)
+
+    handler.on_llm_error(RuntimeError("connection lost"), run_id=run_id)
+
+    data_events = [e[2] for e in events]
+    assert data_events[0]["event"] == "message-start"
+    error_events = [d for d in data_events if d["event"] == "error"]
+    assert len(error_events) == 1
+    assert "connection lost" in error_events[0]["message"]
+
+
+def test_llm_error_before_start_no_emit():
+    """on_llm_error before any tokens should not emit error events."""
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    # Error before any token — state.started is False
+    handler.on_llm_error(RuntimeError("immediate fail"), run_id=run_id)
+
+    data_events = [e[2] for e in events]
+    error_events = [d for d in data_events if d.get("event") == "error"]
+    assert len(error_events) == 0
+
+
+def test_non_streamed_model():
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id, metadata=META, tags=[]
+    )
+
+    final_msg = AIMessage(
+        content="full response",
+        id=f"run-{run_id}",
+        response_metadata={"finish_reason": "stop"},
+    )
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    assert len(data_events) > 0
+    assert data_events[0]["event"] == "message-start"
+
+    deltas = [d for d in data_events if d["event"] == "content-block-delta"]
+    assert len(deltas) == 1
+    assert deltas[0]["content_block"]["text"] == "full response"
+
+    assert data_events[-1]["event"] == "message-finish"
+    assert data_events[-1]["reason"] == "stop"
+
+
+def test_chain_emits_command_with_message():
+    """on_chain_end should emit protocol events for messages inside a Command."""
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chain_start(
+        serialized={}, inputs={}, run_id=run_id, metadata=META, tags=[], name="agent"
+    )
+    handler.on_chain_end(
+        Command(update={"messages": [AIMessage(content="from command", id="cmd-1")]}),
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    assert len(data_events) > 0
+    assert data_events[0]["event"] == "message-start"
+    deltas = [d for d in data_events if d["event"] == "content-block-delta"]
+    assert len(deltas) == 1
+    assert deltas[0]["content_block"]["text"] == "from command"
+    assert data_events[-1]["event"] == "message-finish"
+
+
+def test_chain_emits_command_in_list():
+    """on_chain_end should handle a list containing Command objects."""
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chain_start(
+        serialized={}, inputs={}, run_id=run_id, metadata=META, tags=[], name="agent"
+    )
+    handler.on_chain_end(
+        [Command(update={"messages": [AIMessage(content="listed", id="cmd-2")]})],
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    starts = [d for d in data_events if d["event"] == "message-start"]
+    assert len(starts) == 1
+
+
+def test_chain_deduplicates_seen_messages():
+    """Messages already seen from LLM streaming should not be re-emitted by chain end."""
+    handler, events = make_handler()
+    run_id_llm = uuid4()
+    run_id_chain = uuid4()
+    msg_id = f"run-{run_id_llm}"
+
+    # Simulate LLM streaming
+    handler.on_chat_model_start(
+        serialized={}, messages=[[]], run_id=run_id_llm, metadata=META, tags=[]
+    )
+    chunk = ChatGenerationChunk(message=AIMessageChunk(content="hello", id=msg_id))
+    handler.on_llm_new_token("hello", chunk=chunk, run_id=run_id_llm)
+
+    final_msg = AIMessage(content="hello", id=msg_id)
+    handler.on_llm_end(
+        LLMResult(generations=[[ChatGeneration(message=final_msg)]]),
+        run_id=run_id_llm,
+    )
+
+    events_before = len(events)
+
+    # Now chain end with the same message ID
+    handler.on_chain_start(
+        serialized={},
+        inputs={},
+        run_id=run_id_chain,
+        metadata=META,
+        tags=[],
+        name="agent",
+    )
+    handler.on_chain_end(
+        {"messages": [AIMessage(content="hello", id=msg_id)]},
+        run_id=run_id_chain,
+    )
+
+    # No new events should have been emitted for the duplicate
+    data_events_after = [e[2] for e in events[events_before:]]
+    starts = [d for d in data_events_after if d.get("event") == "message-start"]
+    assert len(starts) == 0
+
+
+def test_chain_emits_human_message_role():
+    """Non-AI messages from chain output should have the correct role."""
+    handler, events = make_handler()
+    run_id = uuid4()
+
+    handler.on_chain_start(
+        serialized={}, inputs={}, run_id=run_id, metadata=META, tags=[], name="agent"
+    )
+    handler.on_chain_end(
+        {"messages": [HumanMessage(content="user msg", id="hmsg-1")]},
+        run_id=run_id,
+    )
+
+    data_events = [e[2] for e in events]
+    starts = [d for d in data_events if d["event"] == "message-start"]
+    assert len(starts) == 1
+    assert starts[0]["role"] == "human"

libs/langgraph/tests/test_pregel.py

@@ -16,7 +16,7 @@ from typing import Annotated, Any, Literal, get_type_hints
 
 import pytest
 from langchain_core.language_models import GenericFakeChatModel
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
 from langchain_core.runnables import (
     RunnableConfig,
     RunnableLambda,
@@ -25,7 +25,6 @@ from langchain_core.runnables import (
 from langchain_core.runnables.graph import Edge
 from langgraph.cache.base import BaseCache
 from langgraph.checkpoint.base import (
-    DELTA_SENTINEL,
     BaseCheckpointSaver,
     Checkpoint,
     CheckpointMetadata,
@@ -42,7 +41,6 @@ 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
@@ -51,7 +49,7 @@ 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, _messages_delta_reducer, add_messages
+from langgraph.graph.message import MessagesState, add_messages
 from langgraph.pregel import (
     NodeBuilder,
     Pregel,
@@ -122,29 +120,6 @@ 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
@@ -640,11 +615,8 @@ def test_run_from_checkpoint_id_retains_previous_writes(
         )
     ]
 
-    # +2: one fork checkpoint from time travel, one from the new execution
-    assert len(new_history) == len(history) + 2
-    # new_history[0] is the new execution result, new_history[1] is the fork
-    assert new_history[1].metadata["source"] == "fork"
-    for original, new in zip(history, new_history[2:]):
+    assert len(new_history) == len(history) + 1
+    for original, new in zip(history, new_history[1:]):
         assert original.values == new.values
         assert original.next == new.next
         assert original.metadata["step"] == new.metadata["step"]
@@ -652,7 +624,7 @@ def test_run_from_checkpoint_id_retains_previous_writes(
     def _get_tasks(hist: list, start: int):
         return [h.tasks for h in hist[start:]]
 
-    assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
+    assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
 
 
 def test_batch_two_processes_in_out() -> None:
@@ -6299,7 +6271,7 @@ def test_sync_streaming_with_functional_api() -> None:
     @task()
     def slow() -> dict:
         time.sleep(time_delay)  # Simulate a delay of 10 ms
-        return {"tic": time.monotonic()}
+        return {"tic": time.time()}
 
     @entrypoint()
     def graph(inputs: dict) -> list:
@@ -6312,7 +6284,7 @@ def test_sync_streaming_with_functional_api() -> None:
     for chunk in graph.stream({}):
         if "slow" not in chunk:  # We'll just look at the updates from `slow`
             continue
-        arrival_times.append(time.monotonic())
+        arrival_times.append(time.time())
 
     assert len(arrival_times) == 2
     delta = arrival_times[1] - arrival_times[0]
@@ -6921,6 +6893,7 @@ def test_tags_stream_mode_messages() -> None:
                 "langgraph_path": ("__pregel_pull", "call_model"),
                 "langgraph_checkpoint_ns": AnyStr("call_model:"),
                 "checkpoint_ns": AnyStr("call_model:"),
+                "_type": "generic-fake-chat-model",
                 "ls_provider": "genericfakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -6930,60 +6903,6 @@ def test_tags_stream_mode_messages() -> None:
     ]
 
 
-def test_configurable_propagates_to_stream_metadata() -> None:
-    """Regression: thread_id, run_id, assistant_id, graph_id,
-    and langgraph_auth_user_id from configurable must appear
-    in stream_mode='messages' metadata."""
-
-    def my_node(state):
-        return {"messages": HumanMessage(content="hello")}
-
-    graph = (
-        StateGraph(MessagesState)
-        .add_node("my_node", my_node)
-        .add_edge(START, "my_node")
-        .compile()
-    )
-
-    config = {
-        "configurable": {
-            "thread_id": "th-123",
-            "checkpoint_id": "ckpt-1",
-            "checkpoint_ns": "ns-1",
-            "task_id": "task-1",
-            "run_id": "run-456",
-            "assistant_id": "asst-789",
-            "graph_id": "graph-0",
-            "model": "gpt-4o",
-            "user_id": "uid-1",
-            "cron_id": "cron-1",
-            "langgraph_auth_user_id": "user-1",
-            # these should NOT be propagated into metadata
-            "some_api_key": "secret",
-            "custom_setting": {"nested": True},
-        },
-    }
-    results = list(graph.stream({"messages": []}, config, stream_mode="messages"))
-    assert len(results) == 1
-    _, metadata = results[0]
-    # propagated keys
-    assert metadata["thread_id"] == "th-123"
-    assert metadata["checkpoint_id"] == "ckpt-1"
-    assert metadata["checkpoint_ns"] == "ns-1"
-    assert metadata["task_id"] == "task-1"
-    assert metadata["run_id"] == "run-456"
-    assert metadata["assistant_id"] == "asst-789"
-    assert metadata["graph_id"] == "graph-0"
-    # These are only present in trace metadata by default as of langgraph 1.2
-    # assert metadata["model"] == "gpt-4o"
-    # assert metadata["user_id"] == "uid-1"
-    # assert metadata["cron_id"] == "cron-1"
-    # assert metadata["langgraph_auth_user_id"] == "user-1"
-    # non-allowlisted keys must not appear
-    assert "some_api_key" not in metadata
-    assert "custom_setting" not in metadata
-
-
 def test_stream_mode_messages_command() -> None:
     from langchain_core.messages import HumanMessage
 
@@ -9425,254 +9344,3 @@ def test_fork_does_not_apply_pending_writes(
 
     # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
     assert result == {"value": 121}
-
-
-async def test_delta_channel_end_to_end_inmemory() -> None:
-    """Full graph run: DeltaChannel accumulates correctly across multiple turns."""
-
-    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 sentinel checkpoints."""
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
-
-    def respond(state: State) -> dict:
-        i = len(state["messages"])
-        return {"messages": [AIMessage(content=f"r{i}", id=f"ai{i}")]}
-
-    order: list[str] = []
-    original_aput_writes = InMemorySaver.aput_writes
-    original_aput = InMemorySaver.aput
-
-    async def tracked_aput_writes(self, config, writes, task_id, task_path=""):
-        result = await original_aput_writes(self, config, writes, task_id, task_path)
-        order.append("aput_writes")
-        return result
-
-    async def tracked_aput(self, config, checkpoint, metadata, new_versions):
-        has_sentinel = any(
-            v is DELTA_SENTINEL for v in checkpoint.get("channel_values", {}).values()
-        )
-        order.append("aput_sentinel" if has_sentinel else "aput_other")
-        return await original_aput(self, config, checkpoint, metadata, new_versions)
-
-    InMemorySaver.aput_writes = tracked_aput_writes
-    InMemorySaver.aput = tracked_aput
-    try:
-        builder = StateGraph(State)
-        builder.add_node("respond", respond)
-        builder.add_edge(START, "respond")
-        graph = builder.compile(checkpointer=InMemorySaver())
-        config = {"configurable": {"thread_id": "async-ordering-test"}}
-
-        for i in range(3):
-            await graph.ainvoke(
-                {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config
-            )
-
-        # Every aput_sentinel must be preceded by at least one aput_writes
-        for i, event in enumerate(order):
-            if event == "aput_sentinel":
-                preceding = order[:i]
-                assert "aput_writes" in preceding, (
-                    f"aput_sentinel at {i} had no preceding aput_writes: {order}"
-                )
-                last_write_idx = max(
-                    j for j, e in enumerate(order[:i]) if e == "aput_writes"
-                )
-                assert last_write_idx < i, (
-                    f"aput_writes at {last_write_idx} should precede aput_sentinel at {i}: {order}"
-                )
-    finally:
-        InMemorySaver.aput_writes = original_aput_writes
-        InMemorySaver.aput = original_aput
-
-    state = await graph.aget_state(config)
-    assert len(state.values["messages"]) == 6  # 3 human + 3 AI

libs/langgraph/tests/test_pregel_async.py

@@ -16,12 +16,10 @@ from typing import (
     Literal,
     Optional,
 )
-from unittest.mock import patch
 from uuid import UUID
 
 import pytest
 from langchain_core.language_models import GenericFakeChatModel
-from langchain_core.messages import HumanMessage
 from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnablePassthrough
 from langchain_core.utils.aiter import aclosing
 from langgraph.cache.base import BaseCache
@@ -49,7 +47,6 @@ from langgraph.channels.topic import Topic
 from langgraph.errors import (
     GraphRecursionError,
     InvalidUpdateError,
-    NodeError,
     ParentCommand,
 )
 from langgraph.func import entrypoint, task
@@ -217,30 +214,6 @@ 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."""
@@ -2112,11 +2085,8 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
         )
     ]
 
-    # +2: one fork checkpoint from time travel, one from the new execution
-    assert len(new_history) == len(history) + 2
-    # new_history[0] is the new execution result, new_history[1] is the fork
-    assert new_history[1].metadata["source"] == "fork"
-    for original, new in zip(history, new_history[2:]):
+    assert len(new_history) == len(history) + 1
+    for original, new in zip(history, new_history[1:]):
         assert original.values == new.values
         assert original.next == new.next
         assert original.metadata["step"] == new.metadata["step"]
@@ -2124,7 +2094,7 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
     def _get_tasks(hist: list, start: int):
         return [h.tasks for h in hist[start:]]
 
-    assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
+    assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
 
 
 async def test_cond_edge_after_send() -> None:
@@ -6127,36 +6097,6 @@ 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):
@@ -7601,6 +7541,7 @@ async def test_tags_stream_mode_messages() -> None:
                 "langgraph_path": ("__pregel_pull", "call_model"),
                 "langgraph_checkpoint_ns": AnyStr("call_model:"),
                 "checkpoint_ns": AnyStr("call_model:"),
+                "_type": "generic-fake-chat-model",
                 "ls_provider": "genericfakechatmodel",
                 "ls_model_type": "chat",
                 "ls_integration": "langchain_chat_model",
@@ -7610,67 +7551,6 @@ async def test_tags_stream_mode_messages() -> None:
     ]
 
 
-async def test_configurable_propagates_to_stream_metadata() -> None:
-    """Regression: thread_id, run_id, assistant_id, graph_id,
-    and langgraph_auth_user_id from configurable must appear
-    in stream_mode='messages' metadata."""
-
-    def my_node(state):
-        return {"messages": HumanMessage(content="hello")}
-
-    graph = (
-        StateGraph(MessagesState)
-        .add_node("my_node", my_node)
-        .add_edge(START, "my_node")
-        .compile()
-    )
-
-    config = {
-        "configurable": {
-            "thread_id": "th-123",
-            "checkpoint_id": "ckpt-1",
-            "checkpoint_ns": "ns-1",
-            "task_id": "task-1",
-            "run_id": "run-456",
-            "assistant_id": "asst-789",
-            "graph_id": "graph-0",
-            "model": "gpt-4o",
-            "user_id": "uid-1",
-            "cron_id": "cron-1",
-            "langgraph_auth_user_id": "user-1",
-            # these should NOT be propagated into metadata
-            "some_api_key": "secret",
-            "custom_setting": {"nested": True},
-        },
-    }
-    results = [
-        chunk
-        async for chunk in graph.astream(
-            {"messages": []}, config, stream_mode="messages"
-        )
-    ]
-    assert len(results) == 1
-    _, metadata = results[0]
-    # propagated keys
-    assert metadata["thread_id"] == "th-123"
-    assert metadata["checkpoint_id"] == "ckpt-1"
-    assert metadata["checkpoint_ns"] == "ns-1"
-    assert metadata["task_id"] == "task-1"
-    assert metadata["run_id"] == "run-456"
-    assert metadata["assistant_id"] == "asst-789"
-    assert metadata["graph_id"] == "graph-0"
-
-    # These will only be traced as of langgraph 1.2 and not present by default in
-    # metadata
-    # assert metadata["model"] == "gpt-4o"
-    # assert metadata["user_id"] == "uid-1"
-    # assert metadata["cron_id"] == "cron-1"
-    # assert metadata["langgraph_auth_user_id"] == "user-1"
-    # non-allowlisted keys must not appear
-    assert "some_api_key" not in metadata
-    assert "custom_setting" not in metadata
-
-
 async def test_stream_mode_messages_command() -> None:
     from langchain_core.messages import HumanMessage
 
@@ -9765,126 +9645,3 @@ async def test_fork_does_not_apply_pending_writes(
 
     # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
     assert result == {"value": 121}
-
-
-async def test_graph_error_handler_async_runtime_info() -> None:
-    class State(TypedDict):
-        foo: str
-
-    attempts = 0
-    captured: dict[str, object] = {}
-
-    async def always_failing_node(state: State) -> State:
-        nonlocal attempts
-        attempts += 1
-        raise ValueError("Always fails async")
-
-    async def err_handler_node(state: State, error: NodeError) -> State:
-        captured["from_node_name"] = error.node
-        captured["from_node_error"] = error.error
-        return {"foo": "handled_async"}
-
-    graph = (
-        StateGraph(State)
-        .add_node(
-            "always_failing",
-            always_failing_node,
-            retry_policy=RetryPolicy(
-                max_attempts=2,
-                initial_interval=0.01,
-                jitter=False,
-                retry_on=ValueError,
-            ),
-            error_handler=err_handler_node,
-        )
-        .add_edge(START, "always_failing")
-        .compile()
-    )
-
-    with patch("asyncio.sleep"):
-        result = await graph.ainvoke({"foo": ""})
-
-    assert attempts == 2
-    assert result["foo"] == "handled_async"
-    assert captured["from_node_name"] == "always_failing"
-    assert isinstance(captured["from_node_error"], BaseException)
-
-
-@NEEDS_CONTEXTVARS
-async def test_graph_error_handler_does_not_swallow_interrupt_concurrent() -> None:
-    """When a graph error handler is configured and a node calls interrupt()
-    concurrently with other nodes, the interrupt must still be raised — not
-    silently swallowed."""
-
-    class State(TypedDict):
-        foo: str
-
-    async def node_a(state: State) -> State:
-        val = interrupt("need human input")
-        return {"foo": f"a_{val}"}
-
-    async def node_b(state: State) -> State:
-        return {}
-
-    async def err_handler(state: State) -> State:
-        return {"foo": "handled"}
-
-    checkpointer = InMemorySaver()
-    graph = (
-        StateGraph(State)
-        .add_node("node_a", node_a, error_handler=err_handler)
-        .add_node("node_b", node_b)
-        .add_edge(START, "node_a")
-        .add_edge(START, "node_b")
-        .compile(checkpointer=checkpointer)
-    )
-
-    config = {"configurable": {"thread_id": "test-interrupt-concurrent-async"}}
-
-    await graph.ainvoke({"foo": ""}, config)
-
-    state = await graph.aget_state(config)
-    assert len(state.tasks) > 0
-
-    interrupts = [t for t in state.tasks if hasattr(t, "interrupts") and t.interrupts]
-    assert len(interrupts) > 0, (
-        "GraphInterrupt was swallowed — interrupt() in node_a "
-        "should have paused execution"
-    )
-
-
-async def test_node_error_handler_handles_subgraph_internal_failure_async() -> None:
-    class SubState(TypedDict):
-        foo: str
-
-    class ParentState(TypedDict):
-        foo: str
-
-    captured: dict[str, object] = {}
-
-    async def sub_fail_node(state: SubState) -> SubState:
-        raise ValueError("async subgraph boom")
-
-    async def parent_handler(state: ParentState, error: NodeError) -> ParentState:
-        captured["from_node_name"] = error.node
-        captured["from_node_error"] = error.error
-        return {"foo": "handled_async_subgraph"}
-
-    subgraph = (
-        StateGraph(SubState)
-        .add_node("sub_fail_node", sub_fail_node)
-        .add_edge(START, "sub_fail_node")
-        .compile()
-    )
-
-    parent_graph = (
-        StateGraph(ParentState)
-        .add_node("subgraph_node", subgraph, error_handler=parent_handler)
-        .add_edge(START, "subgraph_node")
-        .compile()
-    )
-
-    result = await parent_graph.ainvoke({"foo": ""})
-    assert result["foo"] == "handled_async_subgraph"
-    assert captured["from_node_name"] == "subgraph_node"
-    assert isinstance(captured["from_node_error"], BaseException)

libs/langgraph/tests/test_runtime.py

@@ -1,6 +1,3 @@
-import asyncio
-import threading
-import time
 from dataclasses import dataclass
 from typing import Any
 
@@ -9,15 +6,8 @@ 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,
-    RunControl,
-    Runtime,
-    ServerInfo,
-    get_runtime,
-)
+from langgraph.runtime import ExecutionInfo, Runtime, ServerInfo, get_runtime
 
 
 def test_injected_runtime() -> None:
@@ -89,183 +79,6 @@ 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:
@@ -579,334 +392,6 @@ 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 ---
 
 
@@ -1016,13 +501,13 @@ async def test_execution_info_populated_in_graph_async() -> None:
     assert isinstance(info.node_first_attempt_time, float)
 
 
-def test_server_info_from_configurable() -> None:
-    """server_info is built from assistant_id/graph_id in config configurable."""
+def test_server_info_from_metadata() -> None:
+    """server_info is built from assistant_id/graph_id in config metadata."""
     captured: dict[str, Any] = {}
     compiled = _make_capture_graph(captured)
     compiled.invoke(
         {"message": "hi"},
-        config={"configurable": {"assistant_id": "asst-abc", "graph_id": "my-graph"}},
+        config={"metadata": {"assistant_id": "asst-abc", "graph_id": "my-graph"}},
     )
     si = captured["server_info"]
     assert si is not None
@@ -1031,8 +516,8 @@ def test_server_info_from_configurable() -> None:
     assert si.user is None
 
 
-def test_server_info_none_without_configurable() -> None:
-    """server_info is None when no assistant_id/graph_id in configurable."""
+def test_server_info_none_without_metadata() -> None:
+    """server_info is None when no assistant_id/graph_id in metadata."""
     captured: dict[str, Any] = {}
     compiled = _make_capture_graph(captured)
     compiled.invoke({"message": "hi"})
@@ -1094,11 +579,8 @@ def test_server_info_user_from_auth_user() -> None:
     compiled.invoke(
         {"message": "hi"},
         config={
-            "configurable": {
-                "langgraph_auth_user": proxy,
-                "assistant_id": "asst-proxy",
-                "graph_id": "graph-proxy",
-            },
+            "configurable": {"langgraph_auth_user": proxy},
+            "metadata": {"assistant_id": "asst-proxy", "graph_id": "graph-proxy"},
         },
     )
     si = captured["server_info"]

libs/langgraph/tests/test_stream_chat_model_stream.py

@@ -0,0 +1,245 @@
+import pytest
+
+from langgraph.stream.chat_model_stream import AsyncChatModelStream, ChatModelStream
+
+
+def _text_delta(text: str) -> dict:
+    return {"content_block": {"type": "text", "text": text}}
+
+
+def _reasoning_delta(text: str) -> dict:
+    return {"content_block": {"type": "reasoning", "reasoning": text}}
+
+
+# ---------------------------------------------------------------------------
+# Sync ChatModelStream tests
+# ---------------------------------------------------------------------------
+
+
+def test_sync_text_accumulates():
+    stream = ChatModelStream()
+    stream._push_content_block_delta(_text_delta("Hello"))
+    stream._push_content_block_delta(_text_delta(", world"))
+    stream._finish({"reason": "stop"})
+
+    assert stream.text == "Hello, world"
+    assert isinstance(stream.text, str)
+
+
+def test_sync_reasoning_accumulates():
+    stream = ChatModelStream()
+    stream._push_content_block_delta(_reasoning_delta("step 1"))
+    stream._push_content_block_delta(_reasoning_delta(" -> step 2"))
+    stream._finish({"reason": "stop"})
+
+    assert stream.reasoning == "step 1 -> step 2"
+    assert isinstance(stream.reasoning, str)
+
+
+def test_sync_usage():
+    stream = ChatModelStream()
+    usage = {"input_tokens": 10, "output_tokens": 5}
+    stream._finish({"reason": "stop", "usage": usage})
+    assert stream.usage == usage
+
+
+def test_sync_mixed_blocks():
+    stream = ChatModelStream()
+    stream._push_content_block_delta(_text_delta("answer"))
+    stream._push_content_block_delta(
+        {"content_block": {"type": "tool_call", "name": "search"}}
+    )
+    stream._push_content_block_delta(_text_delta(" here"))
+    stream._finish({"reason": "stop"})
+
+    assert stream.text == "answer here"
+
+
+def test_sync_tool_call_only_text_empty():
+    stream = ChatModelStream()
+    stream._push_content_block_delta(
+        {"content_block": {"type": "tool_call", "name": "search"}}
+    )
+    stream._finish({"reason": "stop"})
+    assert stream.text == ""
+
+
+def test_sync_fail_marks_done():
+    stream = ChatModelStream()
+    assert not stream.done
+    stream._fail(RuntimeError("err"))
+    assert stream.done
+
+
+def test_sync_namespace_and_node():
+    stream = ChatModelStream(
+        namespace=["agent:0", "tools:1"],
+        node="chat_model",
+        message_id="msg-123",
+    )
+    assert stream.namespace == ["agent:0", "tools:1"]
+    assert stream.node == "chat_model"
+    assert stream.message_id == "msg-123"
+
+
+def test_sync_content_block_finish_authoritative():
+    """content-block-finish with authoritative text overrides accumulated."""
+    stream = ChatModelStream()
+    stream._push_content_block_delta(_text_delta("partial"))
+    stream._push_content_block_finish(
+        {"content_block": {"type": "text", "text": "full text"}}
+    )
+    assert stream.text == "full text"
+
+
+# ---------------------------------------------------------------------------
+# Async ChatModelStream tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.anyio
+async def test_async_text_iterable_yields_deltas():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_text_delta("Hello"))
+    stream._push_content_block_delta(_text_delta(", world"))
+    stream._finish({"reason": "stop"})
+
+    collected = []
+    async for delta in stream.text:
+        collected.append(delta)
+    assert collected == ["Hello", ", world"]
+
+
+@pytest.mark.anyio
+async def test_async_text_awaitable_returns_full():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_text_delta("Hello"))
+    stream._push_content_block_delta(_text_delta(", world"))
+    stream._finish({"reason": "stop"})
+
+    result = await stream.text
+    assert result == "Hello, world"
+
+
+@pytest.mark.anyio
+async def test_async_reasoning_dual_pattern():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_reasoning_delta("step 1"))
+    stream._push_content_block_delta(_reasoning_delta(" -> step 2"))
+    stream._finish({"reason": "stop"})
+
+    collected = []
+    async for delta in stream.reasoning:
+        collected.append(delta)
+    assert collected == ["step 1", " -> step 2"]
+
+    stream2 = AsyncChatModelStream()
+    stream2._push_content_block_delta(_reasoning_delta("thinking"))
+    stream2._finish({"reason": "stop"})
+    full = await stream2.reasoning
+    assert full == "thinking"
+
+
+@pytest.mark.anyio
+async def test_async_usage_resolves():
+    stream = AsyncChatModelStream()
+    usage = {"input_tokens": 10, "output_tokens": 5}
+    stream._finish({"reason": "stop", "usage": usage})
+    result = await stream.usage
+    assert result == usage
+
+
+@pytest.mark.anyio
+async def test_async_mixed_blocks_text_only():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_text_delta("answer"))
+    stream._push_content_block_delta(
+        {"content_block": {"type": "tool_call", "name": "search"}}
+    )
+    stream._push_content_block_delta(_text_delta(" here"))
+    stream._finish({"reason": "stop"})
+
+    collected = []
+    async for delta in stream.text:
+        collected.append(delta)
+    assert collected == ["answer", " here"]
+
+
+@pytest.mark.anyio
+async def test_async_tool_call_only_text_empty():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(
+        {"content_block": {"type": "tool_call", "name": "search"}}
+    )
+    stream._finish({"reason": "stop"})
+    result = await stream.text
+    assert result == ""
+
+
+@pytest.mark.anyio
+async def test_async_fail_raises_on_text_await():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_text_delta("partial"))
+    stream._fail(RuntimeError("model error"))
+
+    with pytest.raises(RuntimeError, match="model error"):
+        await stream.text
+
+
+@pytest.mark.anyio
+async def test_async_fail_raises_on_reasoning_await():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_reasoning_delta("thinking"))
+    stream._fail(RuntimeError("model error"))
+
+    with pytest.raises(RuntimeError, match="model error"):
+        await stream.reasoning
+
+
+@pytest.mark.anyio
+async def test_async_fail_raises_on_usage_await():
+    stream = AsyncChatModelStream()
+    stream._fail(RuntimeError("model error"))
+
+    with pytest.raises(RuntimeError, match="model error"):
+        await stream.usage
+
+
+@pytest.mark.anyio
+async def test_async_fail_raises_during_text_iteration():
+    stream = AsyncChatModelStream()
+    stream._push_content_block_delta(_text_delta("partial"))
+    stream._fail(RuntimeError("model error"))
+
+    collected = []
+    with pytest.raises(RuntimeError, match="model error"):
+        async for delta in stream.text:
+            collected.append(delta)
+    assert collected == ["partial"]
+
+
+@pytest.mark.anyio
+async def test_async_fail_marks_done():
+    stream = AsyncChatModelStream()
+    assert not stream.done
+    stream._fail(RuntimeError("err"))
+    assert stream.done
+
+
+@pytest.mark.anyio
+async def test_async_namespace_and_node():
+    stream = AsyncChatModelStream(
+        namespace=["agent:0", "tools:1"],
+        node="chat_model",
+        message_id="msg-123",
+    )
+    assert stream.namespace == ["agent:0", "tools:1"]
+    assert stream.node == "chat_model"
+    assert stream.message_id == "msg-123"
+
+
+@pytest.mark.anyio
+async def test_async_inherits_from_sync():
+    """AsyncChatModelStream is a subclass of ChatModelStream."""
+    stream = AsyncChatModelStream()
+    assert isinstance(stream, ChatModelStream)

libs/langgraph/tests/test_stream_convert.py

@@ -0,0 +1,79 @@
+from langgraph.stream._convert import STREAM_V2_MODES, convert_to_protocol_event
+
+
+def test_values_mode():
+    evt = convert_to_protocol_event((), "values", {"x": 1})
+    assert evt is not None
+    assert evt["method"] == "values"
+    assert evt["params"]["data"] == {"x": 1}
+
+
+def test_updates_mode():
+    evt = convert_to_protocol_event((), "updates", {"node": "out"})
+    assert evt is not None
+    assert evt["method"] == "updates"
+
+
+def test_messages_mode():
+    evt = convert_to_protocol_event((), "messages", {"event": "msg"})
+    assert evt is not None
+    assert evt["method"] == "messages"
+
+
+def test_custom_mode():
+    evt = convert_to_protocol_event((), "custom", "hello")
+    assert evt is not None
+    assert evt["method"] == "custom"
+    assert evt["params"]["data"] == "hello"
+
+
+def test_debug_mode():
+    evt = convert_to_protocol_event((), "debug", {})
+    assert evt is not None
+    assert evt["method"] == "debug"
+
+
+def test_checkpoints_mode():
+    evt = convert_to_protocol_event((), "checkpoints", {})
+    assert evt is not None
+    assert evt["method"] == "checkpoints"
+
+
+def test_tasks_mode():
+    evt = convert_to_protocol_event((), "tasks", {})
+    assert evt is not None
+    assert evt["method"] == "tasks"
+
+
+def test_namespace_passthrough():
+    evt = convert_to_protocol_event(("agent", "0"), "values", {})
+    assert evt is not None
+    assert evt["params"]["namespace"] == ["agent", "0"]
+
+
+def test_unknown_mode_returns_none():
+    assert convert_to_protocol_event((), "unknown_mode", {}) is None
+
+
+def test_node_parameter():
+    evt = convert_to_protocol_event((), "values", {}, node="agent")
+    assert evt is not None
+    assert evt["params"]["node"] == "agent"
+
+
+def test_type_is_event():
+    evt = convert_to_protocol_event((), "values", {})
+    assert evt is not None
+    assert evt["type"] == "event"
+
+
+def test_stream_v2_modes_complete():
+    assert set(STREAM_V2_MODES) == {
+        "values",
+        "updates",
+        "messages",
+        "custom",
+        "checkpoints",
+        "tasks",
+        "debug",
+    }

libs/langgraph/tests/test_stream_data_transformers.py

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

libs/langgraph/tests/test_stream_events_v3_e2e.py

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

libs/langgraph/tests/test_stream_lifecycle_transformer.py

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

libs/langgraph/tests/test_stream_messages_transformer.py

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

libs/langgraph/tests/test_stream_mux.py

@@ -0,0 +1,293 @@
+from typing import Any
+
+import pytest
+
+from langgraph.stream._convert import convert_to_protocol_event
+from langgraph.stream._mux import AsyncStreamMux, StreamMux
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.stream_channel import StreamChannel
+
+
+def _event(mode: str, data: Any, ns: list[str] | None = None) -> ProtocolEvent:
+    ev = convert_to_protocol_event(tuple(ns or []), mode, data)
+    assert ev is not None
+    return ev
+
+
+class _MockTransformer(StreamTransformer):
+    def __init__(self, *, suppress: bool = False):
+        self.calls: list[ProtocolEvent] = []
+        self._suppress = suppress
+
+    def init(self) -> Any:
+        return None
+
+    def process(self, event: ProtocolEvent) -> bool:
+        self.calls.append(event)
+        return not self._suppress
+
+    def finalize(self) -> None:
+        pass
+
+    def fail(self, err: BaseException) -> None:
+        pass
+
+
+@pytest.mark.anyio
+async def test_events_through_reducer_pipeline():
+    reducer = _MockTransformer()
+    mux = StreamMux(transformers=[reducer])
+    event = _event("values", {"key": "val"})
+    mux.push(event)
+    assert len(reducer.calls) == 1
+    assert reducer.calls[0] is event
+
+
+@pytest.mark.anyio
+async def test_reducer_suppresses_event():
+    reducer = _MockTransformer(suppress=True)
+    mux = StreamMux(transformers=[reducer])
+    mux.push(_event("values", {"x": 1}))
+    mux.close()
+    assert len(reducer.calls) == 1
+    assert len(mux.event_log) == 0
+
+
+@pytest.mark.anyio
+async def test_namespace_discovery():
+    mux = StreamMux()
+    mux.push(_event("values", {"a": 1}, ns=["child:0"]))
+    assert "child:0" in mux._discovered_ns
+
+
+@pytest.mark.anyio
+async def test_top_level_ns_only():
+    mux = StreamMux()
+    mux.push(_event("values", {"a": 1}, ns=["agent:0", "tools:1"]))
+    assert "agent:0" in mux._discovered_ns
+    assert "tools:1" not in mux._discovered_ns
+
+
+@pytest.mark.anyio
+async def test_subscribe_events_filter():
+    mux = AsyncStreamMux()
+    mux.push(_event("values", {"a": 1}, ns=["child:0"]))
+    mux.push(_event("values", {"b": 2}, ns=["other:1"]))
+    mux.push(_event("values", {"c": 3}, ns=["child:0"]))
+    mux.close()
+
+    collected = []
+    async for ev in mux.subscribe_events(["child:0"]):
+        collected.append(ev)
+    assert len(collected) == 2
+    assert collected[0]["params"]["data"] == {"a": 1}
+    assert collected[1]["params"]["data"] == {"c": 3}
+
+
+@pytest.mark.anyio
+async def test_close_resolves_output():
+    mux = AsyncStreamMux()
+    fut = mux.get_output_future()
+    mux.push(_event("values", {"v": 1}))
+    mux.push(_event("values", {"v": 2}))
+    mux.close()
+    result = await fut
+    assert result == {"v": 2}
+
+
+@pytest.mark.anyio
+async def test_fail_rejects_output():
+    mux = AsyncStreamMux()
+    fut = mux.get_output_future()
+    mux.fail(ValueError("boom"))
+    with pytest.raises(ValueError, match="boom"):
+        await fut
+
+
+@pytest.mark.anyio
+async def test_latest_values_tracked():
+    mux = StreamMux()
+    mux.push(_event("values", {"v": 1}, ns=["child:0"]))
+    mux.push(_event("values", {"v": 2}, ns=["child:0"]))
+    assert mux.get_latest_values(["child:0"]) == {"v": 2}
+
+
+@pytest.mark.anyio
+async def test_interrupt_tracking():
+    """StreamMux should track __interrupt__ payloads in values events."""
+
+    class _FakeInterrupt:
+        def __init__(self, id: str, payload: Any):
+            self.id = id
+            self.payload = payload
+
+    mux = StreamMux()
+    interrupt_obj = _FakeInterrupt("int-1", "what do you want?")
+    mux.push(
+        _event(
+            "values",
+            {"__interrupt__": [interrupt_obj]},
+        )
+    )
+    assert mux.interrupted is True
+    assert len(mux.interrupts) == 1
+    assert mux.interrupts[0]["interrupt_id"] == "int-1"
+    assert mux.interrupts[0]["payload"] is interrupt_obj
+
+
+@pytest.mark.anyio
+async def test_no_interrupt_by_default():
+    mux = StreamMux()
+    mux.push(_event("values", {"x": 1}))
+    mux.close()
+    assert mux.interrupted is False
+    assert mux.interrupts == []
+
+
+@pytest.mark.anyio
+async def test_push_after_close_ignored():
+    mux = StreamMux()
+    mux.push(_event("values", {"a": 1}))
+    mux.close()
+    mux.push(_event("values", {"b": 2}))
+    assert len(mux.event_log) == 1
+
+
+@pytest.mark.anyio
+async def test_fail_rejects_all_futures():
+    mux = AsyncStreamMux()
+    fut1 = mux.get_output_future([])
+    fut2 = mux.get_output_future(["child:0"])
+    mux.fail(ValueError("boom"))
+    with pytest.raises(ValueError, match="boom"):
+        await fut1
+    with pytest.raises(ValueError, match="boom"):
+        await fut2
+
+
+@pytest.mark.anyio
+async def test_channel_events_bypass_transformer_pipeline():
+    """Events emitted via ``StreamChannel.push()`` are appended directly
+    to the event log, bypassing the transformer pipeline.  This matches
+    the JS implementation and avoids re-entrancy bugs.
+    """
+    mock = _MockTransformer()
+    mux = AsyncStreamMux(transformers=[mock])
+
+    channel: StreamChannel[str] = StreamChannel("my_channel")
+    mux.wire_channels({"ch": channel})
+
+    # Regular push — transformer sees it
+    mux.push(_event("values", {"a": 1}))
+    assert len(mock.calls) == 1
+
+    # Channel push — bypasses transformers, goes straight to event log
+    channel.push("hello from channel")
+
+    assert len(mock.calls) == 1, (
+        f"Transformer saw {len(mock.calls)} events (expected 1).  "
+        "Channel events should bypass the transformer pipeline."
+    )
+
+    # But the event IS in the log
+    mux.close()
+    events = []
+    async for ev in mux.subscribe_events():
+        events.append(ev)
+    assert len(events) == 2
+    assert events[1]["method"] == "my_channel"
+    assert events[1]["params"]["data"] == "hello from channel"
+
+
+@pytest.mark.anyio
+async def test_event_log_has_monotonic_seq_numbers():
+    """All events in the event log should have strictly monotonically
+    increasing seq numbers so consumers can reason about ordering.
+
+    Events from ``mux.push()`` carry seq numbers assigned by the pump
+    while channel-emitted events use a separate counter
+    (``_next_emit_seq``).  When interleaved, seq numbers can duplicate.
+    """
+    mux = AsyncStreamMux()
+    channel: StreamChannel[str] = StreamChannel("test_ch")
+    mux.wire_channels({"ch": channel})
+
+    mux.push(_event("values", {"a": 1}))  # log seq: 0
+    channel.push("from_channel")  # log seq: 0 (from _next_emit_seq)
+    mux.push(_event("values", {"b": 2}))  # log seq: 1
+    mux.close()
+
+    seqs: list[int] = []
+    async for event in mux.subscribe_events():
+        seqs.append(event["seq"])
+
+    assert len(seqs) == 3, f"Expected 3 events but got {len(seqs)}"
+
+    for i in range(1, len(seqs)):
+        assert seqs[i] > seqs[i - 1], (
+            f"Seq numbers not strictly monotonic: {seqs}.  "
+            f"seq[{i}]={seqs[i]} <= seq[{i - 1}]={seqs[i - 1]}.  "
+            "Channel events use a separate counter from push() events."
+        )
+
+
+@pytest.mark.anyio
+async def test_channel_push_during_process_preserves_namespace():
+    """When two transformers both call channel.push() during the same
+    outer mux.push(), the second transformer's channel event should
+    still carry the original event's namespace.
+
+    Bug: the first channel.push() re-enters mux.push(), which resets
+    ``_current_namespace`` to ``[]`` on exit.  The second transformer's
+    channel.push() then reads the clobbered value and its event gets
+    ``namespace: []`` instead of the original.
+    """
+
+    class _ChannelTransformer(StreamTransformer):
+        """Pushes to its channel whenever it sees a ``values`` event."""
+
+        def __init__(self, name: str) -> None:
+            self.name = name
+            self.channel: StreamChannel[str] = StreamChannel(name)
+
+        def init(self) -> Any:
+            return {self.name: self.channel}
+
+        def process(self, event: ProtocolEvent) -> bool:
+            if event["method"] == "values":
+                self.channel.push(f"from_{self.name}")
+            return True
+
+        def finalize(self) -> None:
+            pass
+
+        def fail(self, err: BaseException) -> None:
+            pass
+
+    t1 = _ChannelTransformer("first")
+    t2 = _ChannelTransformer("second")
+    mux = AsyncStreamMux(transformers=[t1, t2])
+    mux.wire_channels({"first": t1.channel})
+    mux.wire_channels({"second": t2.channel})
+
+    # Push a values event with a non-root namespace
+    mux.push(_event("values", {"x": 1}, ns=["agent:0"]))
+    mux.close()
+
+    # Collect channel events emitted by each transformer
+    channel_events: list[ProtocolEvent] = []
+    async for ev in mux.subscribe_events():
+        if ev["method"] in ("first", "second"):
+            channel_events.append(ev)
+
+    assert len(channel_events) == 2, (
+        f"Expected 2 channel events but got {len(channel_events)}"
+    )
+
+    for ev in channel_events:
+        assert ev["params"]["namespace"] == ["agent:0"], (
+            f"Channel event for method={ev['method']!r} has "
+            f"namespace={ev['params']['namespace']!r}, expected ['agent:0'].  "
+            "The nested mux.push() from the first channel.push() clobbered "
+            "_current_namespace before the second transformer ran."
+        )