Broaden compile(transformers=...) signature to accept scope-aware factories

# Conflicts:
#	libs/langgraph/langgraph/stream/__init__.py
#	libs/langgraph/langgraph/stream/graph_streamer.py
#	libs/langgraph/langgraph/stream/run_stream.py
#	libs/langgraph/tests/test_graph_streamer.py
#	libs/langgraph/tests/test_pregel_stream_v2.py
#	libs/langgraph/tests/test_stream_messages_transformer.py
#	libs/langgraph/tests/test_stream_subgraph_transformer.py
#	libs/langgraph/tests/test_streaming_handler.py

.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]]

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

@@ -4,17 +4,15 @@ 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,
 )
@@ -25,11 +23,7 @@ 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_COMBINED_SQL,
-    BasePostgresSaver,
-    _DeltaCombinedRow,
-)
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 
 Conn = _internal.Conn  # For backward compatibility
@@ -436,48 +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`.
-
-        One combined UNION ALL query (`SELECT_DELTA_COMBINED_SQL`) fetches rows
-        from `checkpoints`, `checkpoint_writes`, and `checkpoint_blobs` in a
-        single roundtrip; the ancestor walk runs in Python.
-        """
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        checkpoint_id = get_checkpoint_id(config)
-        if checkpoint_id is None:
-            # Caller didn't specify a target — resolve to the latest
-            # checkpoint on the thread. `get_tuple` without `checkpoint_id`
-            # returns the newest; its config carries the resolved id.
-            target = self.get_tuple(config)
-            if target is None:
-                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
-            checkpoint_id = target.config["configurable"]["checkpoint_id"]
-        with self._cursor() as cur:
-            cur.execute(
-                SELECT_DELTA_COMBINED_SQL,
-                (
-                    channel,
-                    thread_id,
-                    checkpoint_ns,
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                ),
-            )
-            rows = cur.fetchall()
-        return self._build_delta_channel_writes_history(
-            channel=channel,
-            target_id=checkpoint_id,
-            rows=cast("list[_DeltaCombinedRow]", 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

@@ -4,17 +4,15 @@ 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,
 )
@@ -25,11 +23,7 @@ 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_COMBINED_SQL,
-    BasePostgresSaver,
-    _DeltaCombinedRow,
-)
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 
 Conn = _ainternal.Conn  # For backward compatibility
@@ -397,46 +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`.
-
-        One combined UNION ALL query (`SELECT_DELTA_COMBINED_SQL`) fetches rows
-        from `checkpoints`, `checkpoint_writes`, and `checkpoint_blobs` in a
-        single roundtrip; rows are assembled by the shared pure helper on
-        `BasePostgresSaver`.
-        """
-        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_COMBINED_SQL,
-                (
-                    channel,
-                    thread_id,
-                    checkpoint_ns,
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                    thread_id,
-                    checkpoint_ns,
-                    channel,
-                ),
-            )
-            rows = await cur.fetchall()
-        return self._build_delta_channel_writes_history(
-            channel=channel,
-            target_id=checkpoint_id,
-            rows=cast("list[_DeltaCombinedRow]", 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 _DeltaCombinedRow(TypedDict, total=False):
-    """One row from `SELECT_DELTA_COMBINED_SQL` (a UNION ALL of three tables).
-
-    Every row carries `_kind` ("p" / "w" / "b") plus whichever columns are
-    relevant for that kind; irrelevant columns are NULL and typed as `None`.
-    """
-
-    _kind: str  # always present: "p", "w", or "b"
-    # checkpoint row ("p")
-    checkpoint_id: str | None
-    parent_checkpoint_id: str | None
-    ver: str | None
-    # write / blob rows ("w", "b")
-    type: str | None
-    blob: bytes | None
-    # write row only ("w")
-    task_id: str | None
-    idx: int | None
-    # blob row only ("b")
-    version: str | None
-
-
-# DeltaChannel reconstruction: one UNION ALL query fetches checkpoints,
-# writes, and blobs for `channel` in one roundtrip; the ancestor walk runs
-# in Python in `_build_delta_channel_writes_history`.
-#
-# Parameter order: (channel, thread_id, checkpoint_ns,
-#                   thread_id, checkpoint_ns, channel,
-#                   thread_id, checkpoint_ns, channel)
-SELECT_DELTA_COMBINED_SQL = """
-    SELECT 'p'::text       AS _kind,
-           checkpoint_id,
-           parent_checkpoint_id,
-           checkpoint -> 'channel_versions' ->> %s AS ver,
-           NULL::text       AS type,
-           NULL::bytea      AS blob,
-           NULL::text       AS task_id,
-           NULL::int        AS idx,
-           NULL::text       AS version
-    FROM checkpoints
-    WHERE thread_id = %s AND checkpoint_ns = %s
-    UNION ALL
-    SELECT 'w',
-           checkpoint_id, NULL, NULL,
-           type, blob, task_id, idx, NULL
-    FROM checkpoint_writes
-    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
-    UNION ALL
-    SELECT 'b',
-           NULL, NULL, NULL,
-           type, blob, NULL, NULL, version
-    FROM checkpoint_blobs
-    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
-"""
-
-
 class BasePostgresSaver(BaseCheckpointSaver[str]):
     SELECT_SQL = SELECT_SQL
     SELECT_PENDING_SENDS_SQL = SELECT_PENDING_SENDS_SQL
@@ -254,83 +195,6 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
             if t.decode() != "empty"
         }
 
-    def _build_delta_channel_writes_history(
-        self,
-        *,
-        channel: str,
-        target_id: str,
-        rows: Sequence[_DeltaCombinedRow],
-    ) -> _ChannelWritesHistory:
-        """Reconstruct one delta channel's history from the combined UNION ALL rows.
-
-        Pure data transform shared by sync (`PostgresSaver`) and async
-        (`AsyncPostgresSaver`); both paths run `SELECT_DELTA_COMBINED_SQL`
-        and feed the tagged rows here.
-
-        Walk is newest → oldest from the target's parent. A non-sentinel
-        blob in `checkpoint_blobs` (a pre-delta snapshot) terminates the
-        walk and is returned as the seed so replay starts from it.
-
-        Writes stored at `target_id` itself are pending writes for the next
-        step and are excluded — the walk begins at the target's parent.
-        """
-        parent_of: dict[str, str | None] = {}
-        ver_of: dict[str, str | None] = {}
-        writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
-        blob_by_ver: dict[str, tuple[str, bytes]] = {}
-
-        for r in rows:
-            kind = r["_kind"]
-            if kind == "p":
-                cid = cast(str, r["checkpoint_id"])
-                parent_of[cid] = r["parent_checkpoint_id"]
-                ver_of[cid] = r["ver"]
-            elif 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"
-                blob_by_ver[cast(str, r["version"])] = cast(
-                    "tuple[str, bytes]", (r["type"], r["blob"])
-                )
-
-        # newest write first per ancestor (task_id DESC, idx DESC)
-        for ws in writes_by_cid.values():
-            ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
-
-        ancestors: list[str] = []
-        cur_cid: str | None = parent_of.get(target_id)
-        while cur_cid is not None:
-            ancestors.append(cur_cid)
-            cur_cid = parent_of.get(cur_cid)
-        if not ancestors:
-            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
-
-        collected: list[PendingWrite] = []  # newest first; reversed at the end
-        for cid in ancestors:
-            # Collect writes first — they encode the transition FROM this
-            # ancestor's state to its child's and must be included even if
-            # this ancestor is also the seed checkpoint.
-            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))
-            # Then check seed terminator.
-            ver = ver_of.get(cid)
-            if ver is not None:
-                seed_blob = blob_by_ver.get(ver)
-                if seed_blob is not None and seed_blob[0] != "empty":
-                    blob_value = self.serde.loads_typed(seed_blob)
-                    if blob_value is not DELTA_SENTINEL:
-                        collected.reverse()
-                        return _ChannelWritesHistory(seed=blob_value, writes=collected)
-
-        collected.reverse()  # oldest → newest
-        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
-
     def _dump_blobs(
         self,
         thread_id: str,

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.1.0a1"
+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>=4.1.0a1,<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

@@ -259,7 +259,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.1.0a1"
+version = "4.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -307,7 +307,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.1.0a1"
+version = "3.0.5"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -382,7 +382,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.31"
+version = "0.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -392,12 +392,11 @@ dependencies = [
     { name = "requests" },
     { name = "requests-toolbelt" },
     { name = "uuid-utils" },
-    { 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/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
 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/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
 ]
 
 [[package]]
@@ -1285,124 +1284,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" },
 ]
 
-[[package]]
-name = "xxhash"
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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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 source = { editable = "../checkpoint" }
 dependencies = [
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-    { url = "https://files.pythonhosted.org/packages/7b/d9/8d95e906764a386a3d3b596f3c68bb63687dfca806373509f51ce8eea81f/xxhash-3.6.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:15e0dac10eb9309508bfc41f7f9deaa7755c69e35af835db9cb10751adebc35d", size = 31565, upload-time = "2025-10-02T14:37:06.966Z" },
-]
-
 [[package]]
 name = "zstandard"
 version = "0.25.0"

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.

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

@@ -73,7 +73,6 @@ 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

@@ -33,53 +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 (
-    DELTA_SENTINEL,
-    SendProtocol,
-    _DeltaSentinel,
-    _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.
@@ -181,23 +147,19 @@ 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)
 
@@ -245,13 +207,6 @@ 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}). "
@@ -321,16 +276,10 @@ EXT_METHOD_SINGLE_ARG = 3
 EXT_PYDANTIC_V1 = 4
 EXT_PYDANTIC_V2 = 5
 EXT_NUMPY_ARRAY = 6
-EXT_DELTA_SNAPSHOT = 7
-EXT_DELTA_SENTINEL = 8
 
 
 def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
-    if isinstance(obj, _DeltaSnapshot):
-        return ormsgpack.Ext(EXT_DELTA_SNAPSHOT, _msgpack_enc(obj.value))
-    elif isinstance(obj, _DeltaSentinel):
-        return ormsgpack.Ext(EXT_DELTA_SENTINEL, b"")
-    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(
@@ -502,13 +451,10 @@ 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):
@@ -559,15 +505,6 @@ 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]:
@@ -597,9 +534,7 @@ 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 "
@@ -621,9 +556,7 @@ 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,
@@ -656,15 +589,7 @@ def _create_msgpack_ext_hook(
         return False
 
     def ext_hook(code: int, data: bytes) -> Any:
-        if code == EXT_DELTA_SENTINEL:
-            return DELTA_SENTINEL
-        elif 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
@@ -685,8 +610,6 @@ 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:
@@ -800,7 +723,9 @@ 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,
@@ -15,39 +14,6 @@ INTERRUPT = "__interrupt__"
 RESUME = "__resume__"
 TASKS = "__pregel_tasks"
 
-
-class _DeltaSentinel:
-    """Singleton marker stored (as zero bytes) in checkpoint_blobs for a
-    DeltaChannel field. The actual per-step writes live in checkpoint_writes
-    and are replayed through the reducer at load time.
-
-    Compare with `is DELTA_SENTINEL` — `loads_typed` always returns the same
-    module-level instance.
-    """
-
-    __slots__ = ()
-
-    def __repr__(self) -> str:
-        return "DELTA_SENTINEL"
-
-
-DELTA_SENTINEL = _DeltaSentinel()
-
-
-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 = "4.1.0a1"
+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")]
     )
@@ -1048,15 +983,3 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
     # No blocking should occur - inner is serialized as dict, not ext
     assert "blocked" not in caplog.text.lower()
     assert result == obj
-
-
-def test_delta_sentinel_serde_round_trip() -> None:
-    from langgraph.checkpoint.base import DELTA_SENTINEL
-    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
-    serde = JsonPlusSerializer()
-    type_tag, blob = serde.dumps_typed(DELTA_SENTINEL)
-    assert type_tag == "msgpack"
-    assert blob  # non-empty ext envelope
-    loaded = serde.loads_typed((type_tag, blob))
-    assert loaded is DELTA_SENTINEL

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

libs/checkpoint/uv.lock

@@ -286,7 +286,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.1.0a1"
+version = "4.0.1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -369,7 +369,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.31"
+version = "0.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -379,12 +379,11 @@ dependencies = [
     { name = "requests" },
     { name = "requests-toolbelt" },
     { name = "uuid-utils" },
-    { name = "xxhash" },
     { name = "zstandard" },
 ]
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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

@@ -3676,9 +3676,9 @@ 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 "0.5.18"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
+  integrity sha512-3zuZUWffTHQ+73EAwnodADtf534VNEZUpXr9jC12qyG8/IQuJET7PRsCpTb9wX2lmBspakwLUpqpj3tNm/0bVA==
   dependencies:
     p-queue "6.6.2"
     uuid "10.0.0"

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

@@ -1328,9 +1328,9 @@ 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 "0.5.18"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
+  integrity sha512-3zuZUWffTHQ+73EAwnodADtf534VNEZUpXr9jC12qyG8/IQuJET7PRsCpTb9wX2lmBspakwLUpqpj3tNm/0bVA==
   dependencies:
     p-queue "6.6.2"
     uuid "10.0.0"

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/_constants.py

@@ -56,8 +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")
@@ -70,7 +68,7 @@ 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.
+# flow through stream_mode="messages"; set by GraphStreamer only.
 
 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")
@@ -111,7 +109,6 @@ 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

libs/langgraph/langgraph/_internal/_runnable.py

@@ -117,19 +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."""

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

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

libs/langgraph/langgraph/channels/__init__.py

@@ -1,7 +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/config.py

@@ -1,5 +1,7 @@
 import asyncio
 import sys
+from collections.abc import Callable
+from contextvars import ContextVar
 from typing import Any
 
 from langchain_core.runnables import RunnableConfig
@@ -9,6 +11,18 @@ from langgraph.store.base import BaseStore
 from langgraph._internal._constants import CONF, CONFIG_KEY_RUNTIME
 from langgraph.types import StreamWriter
 
+_tool_call_writer: ContextVar[Callable[[Any], None] | 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.
+Defined here (rather than alongside the handler in `pregel/_tools.py`)
+so `emit_tool_output_delta` can import it without triggering the
+pregel import chain — user tool code does
+`from langgraph.config import emit_tool_output_delta` at import time.
+"""
+
 
 def _no_op_stream_writer(c: Any) -> None:
     pass
@@ -194,3 +208,30 @@ def get_stream_writer() -> StreamWriter:
     """
     runtime = get_config()[CONF][CONFIG_KEY_RUNTIME]
     return runtime.stream_writer
+
+
+def emit_tool_output_delta(delta: Any) -> None:
+    """Emit a `tool-output-delta` event onto the `tools` stream mode.
+
+    Must be called from inside a tool's execution scope (sync or async).
+    While a tool is running, `StreamToolCallHandler.on_tool_start` sets a
+    writer closure on a ContextVar keyed to that call's `tool_call_id`
+    and namespace; this helper reads the ContextVar and forwards `delta`
+    through it.
+
+    When called outside any tool call, or when the graph was not
+    streamed with `"tools"` in `stream_mode`, this is a silent no-op —
+    tool authors can leave `emit_tool_output_delta` calls in place
+    without gating them on stream mode.
+
+    Args:
+        delta: The partial output chunk to stream. Shape is up to the
+            caller — strings are the common case, but any JSON-
+            serializable value is accepted and surfaced as-is on the
+            `tools` channel's `tool-output-delta` payload under
+            `"delta"`.
+    """
+    writer = _tool_call_writer.get()
+    if writer is None:
+        return
+    writer(delta)

libs/langgraph/langgraph/errors.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from collections.abc import Sequence
 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
@@ -15,13 +15,11 @@ from langgraph.warnings import LangGraphDeprecatedSinceV10
 __all__ = (
     "EmptyChannelError",
     "ErrorCode",
-    "GraphDrained",
     "GraphRecursionError",
     "InvalidUpdateError",
     "GraphBubbleUp",
     "GraphInterrupt",
     "NodeInterrupt",
-    "NodeTimeoutError",
     "ParentCommand",
     "EmptyInputError",
     "TaskNotFound",
@@ -44,23 +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.
 
@@ -96,6 +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."""
@@ -140,58 +125,3 @@ class TaskNotFound(Exception):
     """Raised when the executor is unable to find a task (for distributed mode)."""
 
     pass
-
-
-class NodeTimeoutError(TimeoutError):
-    """Raised when a node invocation exceeds one of its configured timeouts.
-
-    Subclasses the built-in `TimeoutError`, so existing `except TimeoutError`
-    handlers keep working. If the node has a `retry_policy` whose `retry_on`
-    permits `TimeoutError`, the attempt will be retried.
-
-    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
 
 
@@ -90,4 +90,3 @@ class StateNodeSpec(Generic[NodeInputT, ContextT]):
     cache_policy: CachePolicy | 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
@@ -304,7 +300,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | 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.
@@ -372,7 +367,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | 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.
@@ -445,7 +439,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | 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.
@@ -513,7 +506,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | 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.
@@ -588,7 +580,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | 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`.
@@ -618,14 +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
@@ -679,7 +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
@@ -775,7 +757,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 cache_policy=cache_policy,
                 ends=ends,
                 defer=defer,
-                timeout=timeout,
             )
         elif inferred_input_schema is not None:
             self.nodes[node] = StateNodeSpec(
@@ -786,7 +767,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 cache_policy=cache_policy,
                 ends=ends,
                 defer=defer,
-                timeout=timeout,
             )
         else:
             self.nodes[node] = StateNodeSpec[StateT, ContextT](
@@ -797,7 +777,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 cache_policy=cache_policy,
                 ends=ends,
                 defer=defer,
-                timeout=timeout,
             )
 
         input_schema = input_schema or inferred_input_schema
@@ -1066,7 +1045,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         interrupt_after: All | list[str] | None = None,
         debug: bool = False,
         name: str | None = None,
-        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
+        transformers: Sequence[Callable[..., Any]] | None = None,
     ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
         """Compiles the `StateGraph` into a `CompiledStateGraph` object.
 
@@ -1099,19 +1078,16 @@ 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_v2` / `astream_v2` 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.
+            transformers: Optional sequence of zero-arg factories returning
+                `StreamTransformer` instances. Registered on the compiled
+                graph and instantiated per-run whenever `stream_v2` /
+                `astream_v2` is called. Appended after the built-in
+                `ValuesTransformer` and `MessagesTransformer`.
 
         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]] = [
@@ -1363,7 +1339,6 @@ class CompiledStateGraph(
                 retry_policy=node.retry_policy,
                 cache_policy=node.cache_policy,
                 bound=node.runnable,  # type: ignore[arg-type]
-                timeout=node.timeout,
             )
         else:
             raise RuntimeError
@@ -1699,20 +1674,6 @@ 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

@@ -80,7 +80,6 @@ from langgraph.types import (
     PregelTask,
     RetryPolicy,
     Send,
-    TimeoutPolicy,
 )
 
 GetNextVersion = Callable[[V | None, None], V]
@@ -115,21 +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,
@@ -139,14 +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(
@@ -744,7 +733,6 @@ 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])
@@ -882,7 +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)
@@ -1054,7 +1041,6 @@ 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)
@@ -1269,4 +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/_lifecycle.py

@@ -0,0 +1,291 @@
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Callable, Iterator
+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.errors import GraphInterrupt
+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")
+
+
+_LANGGRAPH_SENTINEL_NODES = frozenset({"__start__", "__end__"})
+
+
+def _is_nested_pregel_start(
+    name: str | None,
+    metadata: dict[str, Any] | None,
+    parent_run_id: UUID | None,
+    task_run_ids: set[UUID],
+) -> bool:
+    """Recognize a nested `Pregel` invocation from its `on_chain_start` metadata.
+
+    When a compiled graph is added as a node, pregel fires two
+    `on_chain_start` callbacks at that task: first for the node chain
+    (whose `name` matches `metadata["langgraph_node"]`) and second for
+    the inner `Pregel` chain (whose `name` is the graph's `name`, not
+    the node name). Both share the same `langgraph_checkpoint_ns`.
+
+    Primary signal: a `langgraph_checkpoint_ns` is set AND `name`
+    differs from the owning task's `langgraph_node`. This covers the
+    common case where the compiled subgraph's name differs from the
+    node name it was registered under.
+
+    Fallback for name collisions (subgraph compiled with
+    `name == node_name`): the inner `Pregel` start's `parent_run_id`
+    is the run_id of the node chain's start event, which the handler
+    records in `task_run_ids` on the first start. Matching
+    `parent_run_id` to that set identifies the second start as the
+    nested `Pregel` even when names coincide.
+
+    Regular node chains are skipped; the root `Pregel` (which has no
+    `langgraph_node` metadata) isn't observed by this handler because
+    the root's start fires before the handler is attached.
+
+    Metadata-based detection is used because `on_chain_start`'s
+    `serialized` argument is `None` for compiled graphs in this
+    version of langchain-core, so class-based detection via
+    `serialized["id"]` isn't available.
+
+    Sentinel nodes (`__start__` / `__end__`) are excluded: conditional
+    edges from `START` fire an `on_chain_start` with `lg_node=__start__`
+    and the router function's name as `name`, which would otherwise
+    match the discriminator without representing an actual nested
+    `Pregel`.
+
+    Args:
+        name: The `name` kwarg from `on_chain_start`.
+        metadata: The `metadata` kwarg from `on_chain_start`.
+        parent_run_id: The `parent_run_id` kwarg from `on_chain_start`.
+        task_run_ids: The set of run_ids the handler has already seen
+            as node-chain starts (i.e. `name == langgraph_node`).
+    """
+    if not metadata:
+        return False
+    if not metadata.get("langgraph_checkpoint_ns"):
+        return False
+    lg_node = metadata.get("langgraph_node")
+    if lg_node is None or lg_node in _LANGGRAPH_SENTINEL_NODES:
+        return False
+    if name != lg_node:
+        return True
+    # Name collision fallback: the inner Pregel's parent_run_id is
+    # the node chain's run_id, which we recorded when that node
+    # chain's start fired.
+    return parent_run_id is not None and parent_run_id in task_run_ids
+
+
+class StreamLifecycleHandler(BaseCallbackHandler, _StreamingCallbackHandler):
+    """Callback handler that emits subgraph lifecycle events on the stream.
+
+    Pushes `LifecycleData`-shaped payloads onto the pregel stream under
+    the `"lifecycle"` mode, keyed by the subgraph's namespace tuple.
+    Drives the `started` → `running` → `completed` / `failed` /
+    `interrupted` state machine.
+
+    The handler is attached to `run_manager.inheritable_handlers` inside
+    a `Pregel.stream` / `astream` call, so it sees callbacks for every
+    descendant chain (nodes, nested `Pregel` subgraphs) but *not* for
+    the root `Pregel` whose start event has already fired. The root's
+    `started` event is emitted eagerly at construction; its terminal
+    state is emitted by `SubgraphTransformer.finalize` / `fail`.
+
+    `run_inline = True` keeps event ordering deterministic.
+    """
+
+    run_inline = True
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        *,
+        root_graph_name: str | None = None,
+    ) -> None:
+        """Initialize the handler and emit the root graph's `started` event.
+
+        Args:
+            stream: Callable that accepts a `StreamChunk` tuple
+                `(namespace, mode, payload)` and enqueues it.
+            root_graph_name: The root `Pregel` instance's `name`, emitted
+                with the root's `started` lifecycle payload.
+        """
+        self.stream = stream
+        # Namespaces awaiting the started→running transition.
+        self._pending_running: set[tuple[str, ...]] = set()
+        # run_id → subgraph namespace; populated only for Pregel chains.
+        self._run_to_ns: dict[UUID, tuple[str, ...]] = {}
+        # run_ids of node-chain starts (name == langgraph_node); used
+        # as the parent_run_id fallback when a subgraph's name equals
+        # its node name. Cleared as each chain ends.
+        self._task_run_ids: set[UUID] = set()
+
+        root_payload: dict[str, Any] = {"event": "started"}
+        if root_graph_name is not None:
+            root_payload["graph_name"] = root_graph_name
+        self.stream(((), "lifecycle", root_payload))
+        self._pending_running.add(())
+
+    @staticmethod
+    def _subgraph_ns_from_metadata(metadata: dict[str, Any] | None) -> tuple[str, ...]:
+        """Return the running subgraph's own namespace from task metadata.
+
+        For a nested `Pregel` invoked as a node, `langgraph_checkpoint_ns`
+        ends at the node segment (no inner task appended yet), so
+        splitting on `NS_SEP` gives the subgraph's own namespace.
+        """
+        if not metadata:
+            return ()
+        nskey = metadata.get("langgraph_checkpoint_ns")
+        if not nskey:
+            return ()
+        return tuple(cast(str, nskey).split(NS_SEP))
+
+    @staticmethod
+    def _containing_ns_from_metadata(
+        metadata: dict[str, Any] | None,
+    ) -> tuple[str, ...]:
+        """Return the namespace of the subgraph that contains this task.
+
+        For an inner task with `langgraph_checkpoint_ns`
+        `"seg_a|seg_b"`, the containing subgraph is `("seg_a",)`.
+        """
+        if not metadata:
+            return ()
+        nskey = metadata.get("langgraph_checkpoint_ns")
+        if not nskey:
+            return ()
+        return tuple(cast(str, nskey).split(NS_SEP))[:-1]
+
+    @staticmethod
+    def _trigger_call_id(metadata: dict[str, Any] | None) -> str | None:
+        """Extract `trigger_call_id` from task metadata if present.
+
+        The task that spawned a nested `Pregel` has its task id encoded
+        in `langgraph_checkpoint_ns`'s last segment as
+        `node_name:task_id`. Returns the `task_id` portion, which
+        parents can correlate with their `tools` / `tasks` events.
+        """
+        if not metadata:
+            return None
+        nskey = cast(str | None, metadata.get("langgraph_checkpoint_ns"))
+        if not nskey:
+            return None
+        last = nskey.split(NS_SEP)[-1]
+        _, sep, task_id = last.rpartition(":")
+        return task_id if sep else None
+
+    def _emit(self, ns: tuple[str, ...], payload: dict[str, Any]) -> None:
+        self.stream((ns, "lifecycle", payload))
+
+    def tap_output_aiter(
+        self, run_id: UUID, output: AsyncIterator[T]
+    ) -> AsyncIterator[T]:
+        """Pass-through — required by the `_StreamingCallbackHandler` protocol.
+
+        Returns the iterator unchanged. A missing implementation lets
+        langchain's default `Protocol` body return `None`, which breaks
+        the `_consume_aiter` code path in `_runnable.py:900`.
+        """
+        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
+
+    def _fire_running_if_pending(self, ns: tuple[str, ...]) -> None:
+        if ns in self._pending_running:
+            self._pending_running.discard(ns)
+            self._emit(ns, {"event": "running"})
+
+    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:
+        # Any descendant activity transitions the containing subgraph to running.
+        containing = self._containing_ns_from_metadata(metadata)
+        self._fire_running_if_pending(containing)
+
+        name = cast(str | None, kwargs.get("name"))
+        lg_node = (metadata or {}).get("langgraph_node")
+
+        # Record node-chain starts so the name-collision fallback in
+        # `_is_nested_pregel_start` can match the inner Pregel's
+        # parent_run_id to them.
+        if (
+            lg_node is not None
+            and lg_node not in _LANGGRAPH_SENTINEL_NODES
+            and name == lg_node
+        ):
+            self._task_run_ids.add(run_id)
+
+        if not _is_nested_pregel_start(
+            name, metadata, parent_run_id, self._task_run_ids
+        ):
+            return
+
+        ns = self._subgraph_ns_from_metadata(metadata)
+        if not ns:
+            return
+
+        self._run_to_ns[run_id] = ns
+        payload: dict[str, Any] = {"event": "started"}
+        if name:
+            payload["graph_name"] = name
+        trigger_call_id = self._trigger_call_id(metadata)
+        if trigger_call_id:
+            payload["trigger_call_id"] = trigger_call_id
+        self._emit(ns, payload)
+        self._pending_running.add(ns)
+
+    def on_chain_end(
+        self,
+        response: Any,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._task_run_ids.discard(run_id)
+        ns = self._run_to_ns.pop(run_id, None)
+        if ns is None:
+            return
+        # Ensure started→running fired even for empty subgraphs.
+        if ns in self._pending_running:
+            self._pending_running.discard(ns)
+            self._emit(ns, {"event": "running"})
+        self._emit(ns, {"event": "completed"})
+
+    def on_chain_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._task_run_ids.discard(run_id)
+        ns = self._run_to_ns.pop(run_id, None)
+        if ns is None:
+            return
+        self._pending_running.discard(ns)
+        if isinstance(error, GraphInterrupt):
+            self._emit(ns, {"event": "interrupted"})
+        else:
+            self._emit(ns, {"event": "failed", "error": str(error)})

libs/langgraph/langgraph/pregel/_loop.py

@@ -45,7 +45,6 @@ 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,
@@ -69,7 +68,6 @@ from langgraph.callbacks import (
     GraphResumeEvent,
 )
 from langgraph.channels.base import BaseChannel
-from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.constants import TAG_HIDDEN
 from langgraph.errors import (
@@ -94,7 +92,6 @@ from langgraph.pregel._algo import (
     task_path_str,
 )
 from langgraph.pregel._checkpoint import (
-    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -120,7 +117,6 @@ 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,
@@ -192,8 +188,6 @@ 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
@@ -208,12 +202,10 @@ 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
@@ -321,8 +313,6 @@ 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,
@@ -330,16 +320,11 @@ class PregelLoop:
         *,
         interrupts: tuple[Interrupt, ...] = (),
     ) -> None:
-        # drain status never reaches lifecycle events: tick() returns False
-        # before pushing, and interrupts are raised through GraphInterrupt
-        if self.status == "draining":
-            raise RuntimeError("Draining status cannot emit lifecycle events")
-        status = self.status
         if kind == "resume":
             self._graph_lifecycle_events.append(
                 GraphResumeEvent(
                     run_id=None,
-                    status=status,
+                    status=self.status,
                     checkpoint_id=self.checkpoint["id"],
                     checkpoint_ns=self.checkpoint_ns,
                 )
@@ -348,7 +333,7 @@ class PregelLoop:
             self._graph_lifecycle_events.append(
                 GraphInterruptEvent(
                     run_id=None,
-                    status=status,
+                    status=self.status,
                     checkpoint_id=self.checkpoint["id"],
                     checkpoint_ns=self.checkpoint_ns,
                     interrupts=interrupts,
@@ -421,7 +406,7 @@ class PregelLoop:
                     task = self.tasks.get(task_id)
                 else:
                     task = None
-                fut = self.submit(
+                self.submit(
                     self.checkpointer_put_writes,
                     config,
                     writes_to_save,
@@ -429,16 +414,12 @@ 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)
@@ -580,10 +561,6 @@ 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)
@@ -715,7 +692,7 @@ 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.
@@ -733,8 +710,7 @@ 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
             ]
@@ -789,26 +765,6 @@ 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
@@ -851,28 +807,14 @@ 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(
@@ -913,10 +855,6 @@ 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(
@@ -1300,10 +1238,7 @@ 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"
@@ -1398,11 +1333,6 @@ 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
@@ -1508,15 +1438,11 @@ 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

@@ -14,7 +14,7 @@ from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
 from pydantic import BaseModel
 
-from langgraph._internal._constants import NS_SEP
+from langgraph._internal._constants import NS_END, NS_SEP
 from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
 from langgraph.pregel.protocol import StreamChunk
 from langgraph.types import Command
@@ -137,15 +137,23 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         **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
-            ]
+            task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
+            checkpoint_ns = (
+                f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
+                if NS_END in task_checkpoint_ns
+                else task_checkpoint_ns
+            )
+            ns = tuple(task_checkpoint_ns.split(NS_SEP))[:-1]
             if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
                 return
+            stream_metadata = dict(metadata)
+            stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
+            # Preserve backwards-compatible streamed checkpoint metadata shape.
+            stream_metadata["checkpoint_ns"] = checkpoint_ns
             if tags:
                 if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
-                    metadata["tags"] = filtered_tags
-            self.metadata[run_id] = (ns, metadata)
+                    stream_metadata["tags"] = filtered_tags
+            self.metadata[run_id] = (ns, stream_metadata)
 
     def on_llm_new_token(
         self,
@@ -265,7 +273,7 @@ class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler
     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
+    `GraphStreamer` opts in via the internal
     `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
     `graph.stream(stream_mode="messages")` callers keep the v1
     AIMessageChunk shape.
@@ -293,53 +301,6 @@ class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler
         """
         # 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],
@@ -370,7 +331,6 @@ class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler
             # (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)

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, ...]]
@@ -125,13 +123,6 @@ 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."""
 
@@ -154,7 +145,6 @@ class PregelNode:
         retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         cache_policy: CachePolicy | None = None,
         subgraphs: Sequence[PregelProtocol] | None = None,
-        timeout: float | timedelta | TimeoutPolicy | None = None,
     ) -> None:
         self.channels = channels
         self.triggers = list(triggers)
@@ -166,7 +156,6 @@ 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
         if subgraphs is not None:

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

@@ -46,7 +46,6 @@ from langgraph.types import (
     CachePolicy,
     PregelExecutableTask,
     RetryPolicy,
-    TimeoutPolicy,
 )
 
 F = TypeVar("F", concurrent.futures.Future, asyncio.Future)
@@ -538,7 +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[
@@ -562,7 +560,6 @@ def _call(
             retry_policy=retry_policy,
             cache_policy=cache_policy,
             callbacks=callbacks,
-            timeout=timeout,
         ),
     ):
         if fut := next(
@@ -627,7 +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],
@@ -661,7 +657,6 @@ def _acall(
             input,
             retry_policy=retry_policy,
             cache_policy=cache_policy,
-            timeout=timeout,
             callbacks=callbacks,
             futures=futures,
             schedule_task=schedule_task,
@@ -683,7 +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]],
@@ -709,7 +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

@@ -1,14 +1,14 @@
 from __future__ import annotations
 
 from collections.abc import AsyncIterator, Callable, Iterator
-from contextvars import ContextVar, Token
+from contextvars import 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.config import _tool_call_writer
 from langgraph.pregel.protocol import StreamChunk
 
 try:
@@ -22,15 +22,6 @@ 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.
@@ -40,10 +31,10 @@ class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
     `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.
+    closure bound to that call's namespace and `tool_call_id`. The
+    `emit_tool_output_delta` helper in `langgraph.config` 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
@@ -52,31 +43,14 @@ class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
 
     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.
+    def __init__(self, stream: Callable[[StreamChunk], None]) -> None:
+        """Initialize the handler.
 
         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.
@@ -84,39 +58,24 @@ class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
             UUID, tuple[tuple[str, ...], str, Token[ToolCallWriter | None]]
         ] = {}
 
-    def _ns_for_emit(
-        self,
+    @staticmethod
+    def _containing_ns_from_metadata(
         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.
+    ) -> tuple[str, ...]:
+        """Return the namespace of the subgraph that contains this tool call.
 
-        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.
+        `langgraph_checkpoint_ns` on a tool's callback metadata ends with
+        the `node_name:task_id` segment of the node that invoked the
+        tool. Dropping that segment gives the subgraph's own namespace,
+        which matches what other `tools` / `lifecycle` / `messages`
+        emitters use.
         """
         if not metadata:
-            return None
-        if tags and TAG_NOSTREAM in tags:
-            return None
+            return ()
         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
+            return ()
+        return tuple(cast(str, nskey).split(NS_SEP))[:-1]
 
     def _start(
         self,
@@ -125,19 +84,16 @@ class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         *,
         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 ""
         )
+        ns = self._containing_ns_from_metadata(metadata)
 
         def writer(delta: Any) -> None:
             self.stream(
@@ -242,7 +198,6 @@ class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
             input_str,
             run_id=run_id,
             metadata=metadata,
-            tags=tags,
             inputs=inputs,
             kwargs=kwargs,
         )

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/pregel/main.py

@@ -17,7 +17,6 @@ from collections.abc import (
     Sequence,
 )
 from dataclasses import is_dataclass, replace
-from datetime import timedelta
 from functools import partial
 from inspect import isclass
 from typing import (
@@ -97,7 +96,6 @@ from langgraph._internal._runnable import (
     RunnableSeq,
     coerce_to_runnable,
 )
-from langgraph._internal._timeout import coerce_timeout_policy
 from langgraph._internal._typing import MISSING, DeprecatedKwargs
 from langgraph.callbacks import (
     GraphInterruptEvent,
@@ -111,7 +109,6 @@ from langgraph.config import get_config
 from langgraph.constants import END
 from langgraph.errors import (
     ErrorCode,
-    GraphDrained,
     GraphRecursionError,
     InvalidUpdateError,
     create_error_message,
@@ -126,7 +123,6 @@ from langgraph.pregel._algo import (
 )
 from langgraph.pregel._call import identifier
 from langgraph.pregel._checkpoint import (
-    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -134,6 +130,7 @@ from langgraph.pregel._checkpoint import (
 )
 from langgraph.pregel._draw import draw_graph
 from langgraph.pregel._io import map_input, read_channels
+from langgraph.pregel._lifecycle import StreamLifecycleHandler
 from langgraph.pregel._loop import (
     AsyncPregelLoop,
     SyncPregelLoop,
@@ -146,10 +143,7 @@ from langgraph.pregel._read import DEFAULT_BOUND, PregelNode
 from langgraph.pregel._retry import RetryPolicy
 from langgraph.pregel._runner import PregelRunner
 from langgraph.pregel._tools import StreamToolCallHandler
-from langgraph.pregel._utils import (
-    get_new_channel_versions,
-    validate_timeout_supported,
-)
+from langgraph.pregel._utils import get_new_channel_versions
 from langgraph.pregel._validate import validate_graph, validate_keys
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
 from langgraph.pregel.debug import get_bolded_text, get_colored_text, tasks_w_writes
@@ -157,19 +151,9 @@ from langgraph.pregel.protocol import PregelProtocol, StreamChunk, StreamProtoco
 from langgraph.runtime import (
     DEFAULT_RUNTIME,
     BaseUser,
-    RunControl,
     Runtime,
     ServerInfo,
 )
-from langgraph.stream._mux import StreamMux
-from langgraph.stream._types import StreamTransformer
-from langgraph.stream.run_stream import AsyncGraphRunStream, GraphRunStream
-from langgraph.stream.transformers import (
-    LifecycleTransformer,
-    MessagesTransformer,
-    SubgraphTransformer,
-    ValuesTransformer,
-)
 from langgraph.types import (
     All,
     CachePolicy,
@@ -183,7 +167,6 @@ from langgraph.types import (
     StateUpdate,
     StreamMode,
     StreamPart,
-    TimeoutPolicy,
     ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, OutputT, StateT
@@ -209,7 +192,6 @@ class NodeBuilder:
         "_bound",
         "_retry_policy",
         "_cache_policy",
-        "_timeout",
     )
 
     _channels: str | list[str]
@@ -220,7 +202,6 @@ class NodeBuilder:
     _bound: Runnable
     _retry_policy: list[RetryPolicy]
     _cache_policy: CachePolicy | None
-    _timeout: TimeoutPolicy | None
 
     def __init__(
         self,
@@ -233,7 +214,6 @@ class NodeBuilder:
         self._bound = DEFAULT_BOUND
         self._retry_policy = []
         self._cache_policy = None
-        self._timeout = None
 
     def subscribe_only(
         self,
@@ -352,11 +332,6 @@ class NodeBuilder:
         self._cache_policy = policy
         return self
 
-    def set_timeout(self, timeout: float | timedelta | TimeoutPolicy | None) -> Self:
-        """Set the per-attempt timeout policy for this node."""
-        self._timeout = coerce_timeout_policy(timeout)
-        return self
-
     def build(self) -> PregelNode:
         """Builds the node."""
         return PregelNode(
@@ -368,7 +343,6 @@ class NodeBuilder:
             bound=self._bound,
             retry_policy=self._retry_policy,
             cache_policy=self._cache_policy,
-            timeout=self._timeout,
         )
 
 
@@ -377,51 +351,56 @@ def _collect_stream_modes(mux: Any) -> list[StreamMode]:
 
     Transformers declare the stream modes they need to function, and
     `stream_v2` asks the graph for exactly that union — no hardcoded
-    default set. If zero transformers declare a given mode, the graph
-    does not stream events for it.
+    default set. If zero transformers are registered (or none declares
+    a given mode), the graph does not stream events for that mode.
     """
-    modes: set[StreamMode] = set()
+    modes: set[str] = set()
     for transformer in mux._transformers:
-        modes.update(
-            cast(
-                "tuple[StreamMode, ...]",
-                getattr(transformer, "required_stream_modes", ()),
-            )
-        )
-    return list(modes)
+        modes.update(transformer.required_stream_modes)
+    return cast("list[StreamMode]", list(modes))
 
 
-def _normalize_stream_transformer_factories(
-    specs: Sequence[Callable[[tuple[str, ...]], Any]] | None,
-) -> list[Callable[[tuple[str, ...]], Any]]:
-    """Normalize stream transformer specs to scoped factories.
+def _build_stream_factories(
+    compile_time: Sequence[Callable[..., Any]],
+    call_site: Sequence[Any] | None,
+) -> list[Callable[..., Any]]:
+    """Assemble the factory list handed to `StreamMux(factories=...)`.
 
-    A stream transformer spec is a callable that accepts
-    `scope: tuple[str, ...]` and returns a fresh `StreamTransformer`.
-    Transformer classes work when their constructor follows the same
-    shape. Pre-built instances are rejected because they cannot be
-    cloned into subgraph scopes.
+    Prepends the built-in `ValuesTransformer`, `MessagesTransformer`,
+    and `SubgraphTransformer` factories, then appends the graph's
+    compile-time `stream_transformers` followed by any call-site
+    additions. Factories flow down into subgraph mini-muxes, so
+    per-scope instances propagate automatically.
     """
-    factories: list[Callable[[tuple[str, ...]], Any]] = []
-    for spec in specs or ():
-        if isinstance(spec, StreamTransformer):
-            raise TypeError(
-                "stream_v2 transformers must be scope-aware callables, "
-                f"got pre-built instance {type(spec).__name__}. Pass the "
-                "transformer class or a factory like "
-                "`lambda scope: MyTransformer(scope, ...)`."
-            )
-        if not callable(spec):
-            raise TypeError(
-                "stream_v2 transformers must be scope-aware callables, "
-                f"got {type(spec).__name__}."
-            )
+    from langgraph.stream.transformers import (
+        MessagesTransformer,
+        SubgraphTransformer,
+        ValuesTransformer,
+    )
 
-        def factory(scope: tuple[str, ...], _spec: Callable[..., Any] = spec) -> Any:
-            return _spec(scope)
+    builtins: list[Callable[..., Any]] = [
+        ValuesTransformer,
+        MessagesTransformer,
+        SubgraphTransformer,
+    ]
+    return [*builtins, *compile_time, *(call_site or ())]
 
-        factories.append(factory)
-    return factories
+
+def _merge_v2_messages_flag(
+    config: RunnableConfig | None,
+) -> RunnableConfig:
+    """Return a config with the v2 messages flag set in `configurable`.
+
+    Signals to pregel that `stream_mode="messages"` should attach
+    `StreamMessagesHandlerV2` for this call so invoke-time model runs
+    route through the v2 event generator and their protocol events
+    reach the messages channel.
+    """
+    merged: RunnableConfig = dict(config or {})  # type: ignore[assignment]
+    configurable = dict(merged.get(CONF) or {})
+    configurable[CONFIG_KEY_STREAM_MESSAGES_V2] = True
+    merged[CONF] = configurable
+    return merged
 
 
 class Pregel(
@@ -755,7 +734,7 @@ class Pregel(
         config: RunnableConfig | None = None,
         trigger_to_nodes: Mapping[str, Sequence[str]] | None = None,
         name: str = "LangGraph",
-        stream_transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
+        stream_transformers: Sequence[Callable[..., Any]] | None = None,
         **deprecated_kwargs: Unpack[DeprecatedKwargs],
     ) -> None:
         if (
@@ -802,7 +781,7 @@ class Pregel(
         self.config = config
         self.trigger_to_nodes = trigger_to_nodes or {}
         self.name = name
-        self.stream_transformers: tuple[Callable[[tuple[str, ...]], Any], ...] = tuple(
+        self._stream_transformers: tuple[Callable[..., Any], ...] = tuple(
             stream_transformers or ()
         )
         self._serde_allowlist: set[tuple[str, ...]] | None = None
@@ -905,9 +884,6 @@ class Pregel(
         )
 
     def validate(self) -> Self:
-        for name, node in self.nodes.items():
-            if node.timeout is not None:
-                validate_timeout_supported(node.node or node.bound, name=name)
         validate_graph(
             self.nodes,
             {k: v for k, v in self.channels.items() if isinstance(v, BaseChannel)},
@@ -1143,10 +1119,6 @@ class Pregel(
         channels, managed = channels_from_checkpoint(
             self.channels,
             saved.checkpoint,
-            saver=self.checkpointer
-            if isinstance(self.checkpointer, BaseCheckpointSaver)
-            else None,
-            config=saved.config,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
@@ -1263,13 +1235,9 @@ class Pregel(
 
         step = saved.metadata.get("step", -1) + 1
         stop = step + 2
-        channels, managed = await achannels_from_checkpoint(
+        channels, managed = channels_from_checkpoint(
             self.channels,
             saved.checkpoint,
-            saver=self.checkpointer
-            if isinstance(self.checkpointer, BaseCheckpointSaver)
-            else None,
-            config=saved.config,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
@@ -1640,11 +1608,6 @@ class Pregel(
             channels, managed = channels_from_checkpoint(
                 self.channels,
                 checkpoint,
-                saver=self.checkpointer
-                if saved is not None
-                and isinstance(self.checkpointer, BaseCheckpointSaver)
-                else None,
-                config=saved.config if saved is not None else None,
             )
             values, as_node = updates[0][:2]
 
@@ -2088,14 +2051,9 @@ class Pregel(
             )
             if saved:
                 checkpoint_config = patch_configurable(config, saved.config[CONF])
-            channels, managed = await achannels_from_checkpoint(
+            channels, managed = channels_from_checkpoint(
                 self.channels,
                 checkpoint,
-                saver=self.checkpointer
-                if saved is not None
-                and isinstance(self.checkpointer, BaseCheckpointSaver)
-                else None,
-                config=saved.config if saved is not None else None,
             )
             values, as_node = updates[0][:2]
             # no values, just clear all tasks
@@ -2572,7 +2530,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         subgraphs: bool = False,
         debug: bool | None = None,
         version: Literal["v2"],
@@ -2592,7 +2549,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         subgraphs: bool = False,
         debug: bool | None = None,
         version: Literal["v1"] = ...,
@@ -2611,7 +2567,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         subgraphs: bool = False,
         debug: bool | None = None,
         version: Literal["v1", "v2"] = "v1",
@@ -2656,7 +2611,6 @@ class Pregel(
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            control: Optional run control used to request cooperative drain.
             subgraphs: Whether to stream events from inside subgraphs, defaults to `False`.
 
                 If `True`, the events will be emitted as tuples `(namespace, data)`,
@@ -2695,7 +2649,19 @@ class Pregel(
         stream = SyncQueue()
 
         config = ensure_config(self.config, config)
-        run_manager = None
+        callback_manager = get_callback_manager_for_config(config)
+        if "ls_integration" not in callback_manager.metadata:
+            callback_manager.add_metadata({"ls_integration": "langgraph"})
+        run_manager = callback_manager.on_chain_start(
+            None,
+            input,
+            name=config.get("run_name", self.get_name()),
+            run_id=config.get("run_id"),
+        )
+        graph_callback_manager = get_sync_graph_callback_manager_for_config(
+            config,
+            run_id=run_manager.run_id,
+        )
         try:
             # assign defaults
             (
@@ -2716,36 +2682,6 @@ class Pregel(
                 interrupt_after=interrupt_after,
                 durability=durability,
             )
-            callback_manager = get_callback_manager_for_config(config)
-            if "messages" in stream_modes and version != "v2":
-                # Strip any inherited v2 messages handler so a v1 stream
-                # does not get routed through the content-block event
-                # protocol. Leave v1 handlers in place — an outer
-                # stream(stream_mode="messages", subgraphs=True) relies
-                # on its inheritable handler to observe events emitted
-                # by inner stream(stream_mode="messages") calls.
-                callback_manager.handlers = [
-                    h
-                    for h in callback_manager.handlers
-                    if not isinstance(h, StreamMessagesHandlerV2)
-                ]
-                callback_manager.inheritable_handlers = [
-                    h
-                    for h in callback_manager.inheritable_handlers
-                    if not isinstance(h, StreamMessagesHandlerV2)
-                ]
-            if "ls_integration" not in callback_manager.metadata:
-                callback_manager.add_metadata({"ls_integration": "langgraph"})
-            run_manager = callback_manager.on_chain_start(
-                None,
-                input,
-                name=config.get("run_name", self.get_name()),
-                run_id=config.get("run_id"),
-            )
-            graph_callback_manager = get_sync_graph_callback_manager_for_config(
-                config,
-                run_id=run_manager.run_id,
-            )
             if checkpointer is None and durability is not None:
                 warnings.warn(
                     "`durability` has no effect when no checkpointer is present.",
@@ -2757,12 +2693,9 @@ class Pregel(
             # set up messages stream mode
             if "messages" in stream_modes:
                 ns_ = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
-                use_stream_messages_v2 = bool(
-                    version == "v2" and config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
-                )
                 messages_handler_cls = (
                     StreamMessagesHandlerV2
-                    if use_stream_messages_v2
+                    if config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
                     else StreamMessagesHandler
                 )
                 run_manager.inheritable_handlers.append(
@@ -2773,15 +2706,19 @@ class Pregel(
                     )
                 )
 
+            # set up lifecycle stream mode
+            if "lifecycle" in stream_modes:
+                run_manager.inheritable_handlers.append(
+                    StreamLifecycleHandler(
+                        stream.put,
+                        root_graph_name=self.name,
+                    )
+                )
+
             # set up tools stream mode
             if "tools" in stream_modes:
-                ns_tools = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
                 run_manager.inheritable_handlers.append(
-                    StreamToolCallHandler(
-                        stream.put,
-                        subgraphs,
-                        parent_ns=tuple(ns_tools.split(NS_SEP)) if ns_tools else None,
-                    )
+                    StreamToolCallHandler(stream.put)
                 )
 
             # set up custom stream mode
@@ -2821,7 +2758,6 @@ class Pregel(
                 previous=None,
                 execution_info=None,
                 server_info=server_info,
-                control=control or parent_runtime.control or RunControl(),
             )
             runtime = parent_runtime.merge(runtime)
             config[CONF][CONFIG_KEY_RUNTIME] = runtime
@@ -2952,15 +2888,10 @@ class Pregel(
                     error_code=ErrorCode.GRAPH_RECURSION_LIMIT,
                 )
                 raise GraphRecursionError(msg)
-            elif loop.status == "draining":
-                if loop.control is None:
-                    raise RuntimeError("Draining status requires run control")
-                raise GraphDrained(loop.control.drain_reason or "shutdown")
             # set final channel values as run output
             run_manager.on_chain_end(loop.output)
         except BaseException as e:
-            if run_manager is not None:
-                run_manager.on_chain_error(e)
+            run_manager.on_chain_error(e)
             raise
 
     @overload
@@ -2976,7 +2907,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         subgraphs: bool = False,
         debug: bool | None = None,
         version: Literal["v2"],
@@ -2996,7 +2926,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         subgraphs: bool = False,
         debug: bool | None = None,
         version: Literal["v1"] = ...,
@@ -3015,7 +2944,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         subgraphs: bool = False,
         debug: bool | None = None,
         version: Literal["v1", "v2"] = "v1",
@@ -3060,7 +2988,6 @@ class Pregel(
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            control: Optional run control used to request cooperative drain.
             subgraphs: Whether to stream events from inside subgraphs, defaults to `False`.
 
                 If `True`, the events will be emitted as tuples `(namespace, data)`,
@@ -3104,7 +3031,33 @@ class Pregel(
         )
 
         config = ensure_config(self.config, config)
-        run_manager = None
+        callback_manager = get_async_callback_manager_for_config(config)
+        if "ls_integration" not in callback_manager.metadata:
+            callback_manager.add_metadata({"ls_integration": "langgraph"})
+        run_manager = await callback_manager.on_chain_start(
+            None,
+            input,
+            name=config.get("run_name", self.get_name()),
+            run_id=config.get("run_id"),
+        )
+        graph_callback_manager = get_async_graph_callback_manager_for_config(
+            config,
+            run_id=run_manager.run_id,
+        )
+        # if running from astream_log() run each proc with streaming
+        do_stream = (
+            next(
+                (
+                    True
+                    for h in run_manager.handlers
+                    if isinstance(h, _StreamingCallbackHandler)
+                    and not isinstance(h, StreamMessagesHandler)
+                ),
+                False,
+            )
+            if _StreamingCallbackHandler is not None
+            else False
+        )
         try:
             # assign defaults
             (
@@ -3125,50 +3078,6 @@ class Pregel(
                 interrupt_after=interrupt_after,
                 durability=durability,
             )
-            callback_manager = get_async_callback_manager_for_config(config)
-            if "messages" in stream_modes and version != "v2":
-                # Strip any inherited v2 messages handler so a v1 stream
-                # does not get routed through the content-block event
-                # protocol. Leave v1 handlers in place — an outer
-                # astream(stream_mode="messages", subgraphs=True) relies
-                # on its inheritable handler to observe events emitted
-                # by inner astream(stream_mode="messages") calls.
-                callback_manager.handlers = [
-                    h
-                    for h in callback_manager.handlers
-                    if not isinstance(h, StreamMessagesHandlerV2)
-                ]
-                callback_manager.inheritable_handlers = [
-                    h
-                    for h in callback_manager.inheritable_handlers
-                    if not isinstance(h, StreamMessagesHandlerV2)
-                ]
-            if "ls_integration" not in callback_manager.metadata:
-                callback_manager.add_metadata({"ls_integration": "langgraph"})
-            run_manager = await callback_manager.on_chain_start(
-                None,
-                input,
-                name=config.get("run_name", self.get_name()),
-                run_id=config.get("run_id"),
-            )
-            graph_callback_manager = get_async_graph_callback_manager_for_config(
-                config,
-                run_id=run_manager.run_id,
-            )
-            # if running from astream_log() run each proc with streaming
-            do_stream = (
-                next(
-                    (
-                        True
-                        for h in run_manager.handlers
-                        if isinstance(h, _StreamingCallbackHandler)
-                        and not isinstance(h, StreamMessagesHandler)
-                    ),
-                    False,
-                )
-                if _StreamingCallbackHandler is not None
-                else False
-            )
             if checkpointer is None and durability is not None:
                 warnings.warn(
                     "`durability` has no effect when no checkpointer is present.",
@@ -3181,12 +3090,9 @@ class Pregel(
             if "messages" in stream_modes:
                 # namespace can be None in a root level graph?
                 ns_ = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
-                use_stream_messages_v2 = bool(
-                    version == "v2" and config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
-                )
                 messages_handler_cls = (
                     StreamMessagesHandlerV2
-                    if use_stream_messages_v2
+                    if config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
                     else StreamMessagesHandler
                 )
                 run_manager.inheritable_handlers.append(
@@ -3197,15 +3103,19 @@ class Pregel(
                     )
                 )
 
+            # set up lifecycle stream mode
+            if "lifecycle" in stream_modes:
+                run_manager.inheritable_handlers.append(
+                    StreamLifecycleHandler(
+                        stream_put,
+                        root_graph_name=self.name,
+                    )
+                )
+
             # set up tools stream mode
             if "tools" in stream_modes:
-                ns_tools = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
                 run_manager.inheritable_handlers.append(
-                    StreamToolCallHandler(
-                        stream_put,
-                        subgraphs,
-                        parent_ns=tuple(ns_tools.split(NS_SEP)) if ns_tools else None,
-                    )
+                    StreamToolCallHandler(stream_put)
                 )
 
             # set up custom stream mode
@@ -3260,7 +3170,6 @@ class Pregel(
                 previous=None,
                 execution_info=None,
                 server_info=server_info,
-                control=control or parent_runtime.control or RunControl(),
             )
             runtime = parent_runtime.merge(runtime)
             config[CONF][CONFIG_KEY_RUNTIME] = runtime
@@ -3429,15 +3338,10 @@ class Pregel(
                     error_code=ErrorCode.GRAPH_RECURSION_LIMIT,
                 )
                 raise GraphRecursionError(msg)
-            elif loop.status == "draining":
-                if loop.control is None:
-                    raise RuntimeError("Draining status requires run control")
-                raise GraphDrained(loop.control.drain_reason or "shutdown")
             # set final channel values as run output
             await run_manager.on_chain_end(loop.output)
         except BaseException as e:
-            if run_manager is not None:
-                await asyncio.shield(run_manager.on_chain_error(e))
+            await asyncio.shield(run_manager.on_chain_error(e))
             raise
 
     def stream_v2(
@@ -3447,72 +3351,43 @@ class Pregel(
         *,
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
-        control: RunControl | None = None,
-        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
+        transformers: Sequence[Any] | None = None,
     ) -> Any:
         """Start a sync v2 streaming run driven by transformer projections.
 
-        Builds a `StreamMux` from the built-in transformers, this
-        graph's compile-time `stream_transformers`, and any additional
-        `transformers=` supplied at the call site. Returns a
-        `GraphRunStream` that the caller drives by iterating any
-        projection — no background thread.
-
-        `run.output`, `run.interrupted` and `run.interrupts` work
-        regardless of which transformers are registered.
-
-        Note:
-            Nesting v1 `stream(stream_mode="messages")` inside a node
-            of a `stream_v2` run is not fully supported. The outer v2
-            messages handler reroutes `BaseChatModel.invoke` through
-            the v2 event protocol, so the inner v1 handler does not see
-            `on_llm_new_token` chunks. The inner stream still yields a
-            finalized message via `on_llm_end`. Use `stream_v2` for
-            the inner graph as well, or call
-            `chat_model.stream(...)` explicitly, to get token-level
-            streaming.
+        Builds a `StreamMux` from the built-in `ValuesTransformer` /
+        `MessagesTransformer`, this graph's compile-time
+        `stream_transformers`, and any additional `transformers=`
+        supplied at the call site. Returns a `GraphRunStream` that the
+        caller drives by iterating any projection — no background
+        thread.
 
         Args:
             input: Graph input.
             config: Optional runnable config forwarded to the graph.
             interrupt_before: Nodes to interrupt before, if any.
             interrupt_after: Nodes to interrupt after, if any.
-            control: Optional run control used to request cooperative drain.
-            transformers: Extra transformer classes or configured factories
-                appended after compile-time `stream_transformers`. Factories
-                are called as `factory(scope)` so they can propagate to
-                subgraph scopes.
+            transformers: Extra transformer instances appended after
+                compile-time `stream_transformers`.
 
         Returns:
             A `GraphRunStream` the caller iterates to drive the run.
         """
-        parent_ns = _resolve_parent_ns(self.config, config)
-        compiled_factories = _normalize_stream_transformer_factories(
-            self.stream_transformers
-        )
-        extra_factories = _normalize_stream_transformer_factories(transformers)
-        mux = StreamMux(
-            factories=[
-                ValuesTransformer,
-                MessagesTransformer,
-                LifecycleTransformer,
-                SubgraphTransformer,
-                *compiled_factories,
-                *extra_factories,
-            ],
-            scope=parent_ns,
-            is_async=False,
-        )
+        from langgraph.stream._mux import StreamMux
+        from langgraph.stream.run_stream import GraphRunStream
+
+        factories = _build_stream_factories(self._stream_transformers, transformers)
+        mux = StreamMux(factories=factories, is_async=False)
+        stream_modes = _collect_stream_modes(mux)
         graph_iter = iter(
             self.stream(
                 input,
-                patch_configurable(config, {CONFIG_KEY_STREAM_MESSAGES_V2: True}),
-                stream_mode=_collect_stream_modes(mux),
+                _merge_v2_messages_flag(config),
+                stream_mode=stream_modes,
                 subgraphs=True,
                 version="v2",
                 interrupt_before=interrupt_before,
                 interrupt_after=interrupt_after,
-                control=control,
             )
         )
         return GraphRunStream(graph_iter, mux)
@@ -3524,8 +3399,7 @@ class Pregel(
         *,
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
-        control: RunControl | None = None,
-        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
+        transformers: Sequence[Any] | None = None,
     ) -> Any:
         """Async counterpart to `stream_v2`.
 
@@ -3533,52 +3407,28 @@ class Pregel(
         concurrently; each subscribed cursor drives the pump when its
         buffer is empty.
 
-        Note:
-            Same nesting limitation as `stream_v2`: nesting v1
-            `astream(stream_mode="messages")` inside a node of an
-            `astream_v2` run drops `on_llm_new_token` chunks because
-            the outer v2 handler reroutes `BaseChatModel.invoke`
-            through the v2 event protocol. Use `astream_v2` for the
-            inner graph as well, or call `chat_model.astream(...)`
-            explicitly, to get token-level streaming.
-
         Args:
             input: Graph input.
             config: Optional runnable config forwarded to the graph.
             interrupt_before: Nodes to interrupt before, if any.
             interrupt_after: Nodes to interrupt after, if any.
-            control: Optional run control used to request cooperative drain.
-            transformers: Extra transformer classes or configured factories
-                appended after compile-time `stream_transformers`. Factories
-                are called as `factory(scope)` so they can propagate to
-                subgraph scopes.
+            transformers: Extra transformer instances appended after
+                compile-time `stream_transformers`.
         """
-        parent_ns = _resolve_parent_ns(self.config, config)
-        compiled_factories = _normalize_stream_transformer_factories(
-            self.stream_transformers
-        )
-        extra_factories = _normalize_stream_transformer_factories(transformers)
-        mux = StreamMux(
-            factories=[
-                ValuesTransformer,
-                MessagesTransformer,
-                LifecycleTransformer,
-                SubgraphTransformer,
-                *compiled_factories,
-                *extra_factories,
-            ],
-            scope=parent_ns,
-            is_async=True,
-        )
+        from langgraph.stream._mux import StreamMux
+        from langgraph.stream.run_stream import AsyncGraphRunStream
+
+        factories = _build_stream_factories(self._stream_transformers, transformers)
+        mux = StreamMux(factories=factories, is_async=True)
+        stream_modes = _collect_stream_modes(mux)
         graph_aiter = self.astream(
             input,
-            patch_configurable(config, {CONFIG_KEY_STREAM_MESSAGES_V2: True}),
-            stream_mode=_collect_stream_modes(mux),
+            _merge_v2_messages_flag(config),
+            stream_mode=stream_modes,
             subgraphs=True,
             version="v2",
             interrupt_before=interrupt_before,
             interrupt_after=interrupt_after,
-            control=control,
         ).__aiter__()
         return AsyncGraphRunStream(graph_aiter, mux)
 
@@ -3595,7 +3445,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v2"],
         **kwargs: Any,
     ) -> GraphOutput[OutputT]: ...
@@ -3613,7 +3462,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v2"],
         **kwargs: Any,
     ) -> list[StreamPart[StateT, OutputT]]: ...
@@ -3631,7 +3479,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v1"] = ...,
         **kwargs: Any,
     ) -> dict[str, Any] | Any: ...
@@ -3648,7 +3495,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v1", "v2"] = "v1",
         **kwargs: Any,
     ) -> dict[str, Any] | Any:
@@ -3673,7 +3519,6 @@ class Pregel(
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            control: Optional run control used to request cooperative drain.
             version: The streaming format version. `"v1"` (default) returns the
                 traditional format, `"v2"` returns `StreamPart` typed dicts when
                 `stream_mode` is not `"values"`.
@@ -3701,7 +3546,6 @@ class Pregel(
                 interrupt_before=interrupt_before,
                 interrupt_after=interrupt_after,
                 durability=durability,
-                control=control,
                 version=version,
                 **kwargs,
             ):
@@ -3725,7 +3569,6 @@ class Pregel(
                 interrupt_before=interrupt_before,
                 interrupt_after=interrupt_after,
                 durability=durability,
-                control=control,
                 **kwargs,
             ):
                 if stream_mode == "values":
@@ -3772,7 +3615,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v2"],
         **kwargs: Any,
     ) -> GraphOutput[OutputT]: ...
@@ -3790,7 +3632,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v2"],
         **kwargs: Any,
     ) -> list[StreamPart[StateT, OutputT]]: ...
@@ -3808,7 +3649,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v1"] = ...,
         **kwargs: Any,
     ) -> dict[str, Any] | Any: ...
@@ -3825,7 +3665,6 @@ class Pregel(
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         durability: Durability | None = None,
-        control: RunControl | None = None,
         version: Literal["v1", "v2"] = "v1",
         **kwargs: Any,
     ) -> dict[str, Any] | Any:
@@ -3850,7 +3689,6 @@ class Pregel(
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            control: Optional run control used to request cooperative drain.
             version: The streaming format version. `"v1"` (default) returns the
                 traditional format, `"v2"` returns `StreamPart` typed dicts when
                 `stream_mode` is not `"values"`.
@@ -3878,7 +3716,6 @@ class Pregel(
                 interrupt_before=interrupt_before,
                 interrupt_after=interrupt_after,
                 durability=durability,
-                control=control,
                 version=version,
                 **kwargs,
             ):
@@ -3902,7 +3739,6 @@ class Pregel(
                 interrupt_before=interrupt_before,
                 interrupt_after=interrupt_after,
                 durability=durability,
-                control=control,
                 **kwargs,
             ):
                 if stream_mode == "values":
@@ -4071,24 +3907,6 @@ def _coerce_checkpoint_values(payload: Any, mapper: Callable[[Any], Any]) -> Non
         payload["values"] = mapper(payload["values"])
 
 
-def _resolve_parent_ns(
-    graph_config: RunnableConfig | None, call_config: RunnableConfig | None
-) -> tuple[str, ...]:
-    """Return the checkpoint namespace the caller is running under.
-
-    `stream_v2` uses this to scope its native projections
-    (`ValuesTransformer`, `MessagesTransformer`) to events emitted at
-    the run's own level. A root call resolves to `()`; a call made
-    from inside a node carries the outer graph's task namespace so the
-    projection still matches its own root-level events.
-    """
-    merged = ensure_config(graph_config, call_config)
-    ns = merged.get(CONF, {}).get(CONFIG_KEY_CHECKPOINT_NS)
-    if not ns:
-        return ()
-    return tuple(ns.split(NS_SEP))
-
-
 def _build_server_info(
     config: RunnableConfig, parent_runtime: Runtime[Any]
 ) -> ServerInfo | None:

libs/langgraph/langgraph/pregel/remote.py

@@ -650,22 +650,46 @@ class RemoteGraph(PregelProtocol):
         """
         updated_stream_modes: list[StreamModeSDK] = []
         req_single = True
+        # `"lifecycle"` is emitted locally by the `StreamLifecycleHandler`
+        # attached inside `Pregel.stream` / `astream`. The remote graph
+        # API has no corresponding mode, so requests for it against a
+        # `RemoteGraph` are silently stripped here and a warning is
+        # logged so the caller isn't left wondering why no lifecycle
+        # events arrive.
+        dropped_lifecycle = False
         # coerce to list, or add default stream mode
         if stream_mode:
             if isinstance(stream_mode, str):
-                updated_stream_modes.append(stream_mode)
+                if stream_mode != "lifecycle":
+                    updated_stream_modes.append(cast(StreamModeSDK, stream_mode))
+                else:
+                    dropped_lifecycle = True
             else:
                 req_single = False
-                updated_stream_modes.extend(stream_mode)
+                for m in stream_mode:
+                    if m == "lifecycle":
+                        dropped_lifecycle = True
+                    else:
+                        updated_stream_modes.append(cast(StreamModeSDK, m))
         else:
-            updated_stream_modes.append(default)
+            updated_stream_modes.append(default)  # type: ignore[arg-type]
         requested_stream_modes = updated_stream_modes.copy()
         # add any from parent graph
         stream: StreamProtocol | None = (
             (config or {}).get(CONF, {}).get(CONFIG_KEY_STREAM)
         )
         if stream:
-            updated_stream_modes.extend(stream.modes)
+            for m in stream.modes:
+                if m == "lifecycle":
+                    dropped_lifecycle = True
+                else:
+                    updated_stream_modes.append(cast(StreamModeSDK, m))
+        if dropped_lifecycle:
+            logger.warning(
+                "Stream mode 'lifecycle' is not supported by RemoteGraph "
+                "and was stripped from the request; no lifecycle events "
+                "will be emitted for this remote run."
+            )
         # map "messages" to "messages-tuple"
         if "messages" in updated_stream_modes:
             updated_stream_modes.remove("messages")

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

@@ -5,41 +5,16 @@ Compile a graph with `transformers=[...]` and call `graph.stream_v2()` /
 graph's raw events into ergonomic per-channel streams.
 """
 
+from langgraph.stream._event_log import EventLog
 from langgraph.stream._types import ProtocolEvent, StreamTransformer
-from langgraph.stream.run_stream import (
-    AsyncGraphRunStream,
-    AsyncSubgraphRunStream,
-    GraphRunStream,
-    SubgraphRunStream,
-)
+from langgraph.stream.run_stream import AsyncGraphRunStream, GraphRunStream
 from langgraph.stream.stream_channel import StreamChannel
-from langgraph.stream.transformers import (
-    CheckpointsTransformer,
-    CustomTransformer,
-    DebugTransformer,
-    LifecyclePayload,
-    LifecycleTransformer,
-    SubgraphStatus,
-    SubgraphTransformer,
-    TasksTransformer,
-    UpdatesTransformer,
-)
 
 __all__ = [
     "AsyncGraphRunStream",
-    "AsyncSubgraphRunStream",
-    "CheckpointsTransformer",
-    "CustomTransformer",
-    "DebugTransformer",
+    "EventLog",
     "GraphRunStream",
-    "LifecyclePayload",
-    "LifecycleTransformer",
     "ProtocolEvent",
     "StreamChannel",
     "StreamTransformer",
-    "SubgraphRunStream",
-    "SubgraphStatus",
-    "SubgraphTransformer",
-    "TasksTransformer",
-    "UpdatesTransformer",
 ]

libs/langgraph/langgraph/stream/_event_log.py

@@ -0,0 +1,306 @@
+from __future__ import annotations
+
+import asyncio
+from collections import deque
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
+from typing import Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class EventLog(Generic[T]):
+    """Single-consumer drainable queue for streaming events.
+
+    Items are popped off the front as the consumer advances — there is
+    no retention beyond what's currently queued. A log 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.
+    The only shape where a log can accumulate meaningfully is
+    concurrent async consumers at unequal rates — a slow consumer's
+    log grows while fast consumers drive the shared pump. That's the
+    documented tradeoff for concurrent consumption; consume at similar
+    rates or use a single consumer if memory matters.
+
+    Lazy-subscribe: `push` is a no-op when no subscriber has registered.
+    Transformers still execute `process()` (so scalar state like
+    `ValuesTransformer._latest` stays current); only the log append is
+    skipped.
+    """
+
+    def __init__(self, maxlen: int | None = None) -> None:
+        """Initialize an empty, unbound log.
+
+        Args:
+            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("EventLog maxlen must be a positive int or None")
+        self._items: deque[T] = deque()
+        self._maxlen: int | None = maxlen
+        self._closed = False
+        self._error: BaseException | None = None
+
+        # Binding state — None means unbound.
+        self._is_async: bool | None = None
+
+        # Flipped on first __iter__ / __aiter__. Pre-subscription
+        # pushes are silent no-ops.
+        self._subscribed = False
+
+        # Pump wiring set by the run stream after bind.
+        self._request_more: Callable[[], bool] | None = None
+        self._arequest_more: Callable[[], Awaitable[bool]] | None = None
+
+    # ------------------------------------------------------------------
+    # Binding
+    # ------------------------------------------------------------------
+
+    def _bind(self, *, is_async: bool) -> None:
+        """Bind this log 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 log has already been bound.
+        """
+        if self._is_async is not None:
+            raise RuntimeError("EventLog is already bound")
+        self._is_async = is_async
+
+    # ------------------------------------------------------------------
+    # Producer API
+    # ------------------------------------------------------------------
+
+    def push(self, item: T) -> None:
+        """Append an item. No-op when no subscriber is registered.
+
+        Non-blocking in both sync and async — matches v1's
+        `put_nowait` producer shape. Memory is bounded by caller pace
+        via the caller-driven pump.
+
+        Raises:
+            RuntimeError: If the log is closed (and subscribed).
+        """
+        if not self._subscribed:
+            return
+        if self._closed:
+            raise RuntimeError("Cannot push to a closed EventLog")
+        self._items.append(item)
+
+    def close(self) -> None:
+        """Mark the log as complete."""
+        self._closed = True
+
+    def fail(self, err: BaseException) -> None:
+        """Mark the log 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 log is unbound or bound to async mode.
+            RuntimeError: If the log already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "EventLog has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if self._is_async:
+            raise TypeError(
+                "This EventLog is bound to async mode — use 'async for' instead."
+            )
+        if self._subscribed:
+            raise RuntimeError(
+                "EventLog 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:
+                yield self._items.popleft()
+            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 log is unbound or bound to sync mode.
+            RuntimeError: If the log already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "EventLog has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if not self._is_async:
+            raise TypeError("This EventLog is bound to sync mode — use 'for' instead.")
+        if self._subscribed:
+            raise RuntimeError(
+                "EventLog 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:
+                yield self._items.popleft()
+            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 log is unbound or bound to async mode.
+            RuntimeError: If the log 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 log is unbound or bound to sync mode.
+            RuntimeError: If the log already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("atee() requires n >= 1")
+        source = self.__aiter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+        error: list[BaseException | None] = [None]
+        lock = asyncio.Lock()
+
+        async def branch(i: int) -> AsyncIterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                    continue
+                if exhausted[0]:
+                    if error[0] is not None:
+                        raise error[0]
+                    return
+                async with lock:
+                    if buf or exhausted[0]:
+                        continue
+                    try:
+                        item = await source.__anext__()
+                    except StopAsyncIteration:
+                        exhausted[0] = True
+                        continue
+                    except Exception as e:
+                        error[0] = e
+                        exhausted[0] = True
+                        continue
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))

libs/langgraph/langgraph/stream/_mux.py

@@ -5,6 +5,7 @@ import time
 from collections.abc import Awaitable, Callable
 from typing import Any
 
+from langgraph.stream._event_log import EventLog
 from langgraph.stream._types import (
     ProtocolEvent,
     StreamTransformer,
@@ -15,11 +16,12 @@ 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=...)`.
+Called once per `StreamMux` (root or mini-mux) with the mux's scope
+— typically a subgraph's namespace or `()` for the root. Standard
+transformer classes (`ValuesTransformer`, `MessagesTransformer`,
+`SubgraphTransformer`) 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=...)`.
 """
 
 
@@ -27,15 +29,14 @@ class StreamMux:
     """Central event dispatcher for the streaming infrastructure.
 
     Owns the main event log and routes events through a transformer
-    pipeline. StreamChannels with a name discovered in transformer
-    projections are auto-wired so that every `push()` also injects a
-    `ProtocolEvent` into the main log. StreamChannels without a name
-    are local-only.
+    pipeline. StreamChannels discovered in transformer projections are
+    auto-wired so that every `push()` also injects a `ProtocolEvent`
+    into the main log.
 
     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.
+    iteration (`handler.astream()`). All EventLog and StreamChannel
+    instances discovered during registration are automatically bound
+    to the matching mode.
 
     Attributes:
         extensions: Merged projection dict across all registered
@@ -52,47 +53,53 @@ class StreamMux:
         is_async: bool = False,
         factories: list[TransformerFactory] | None = None,
         scope: tuple[str, ...] = (),
-        _assign_seq: bool = True,
     ) -> None:
         """Initialize the mux and register transformers in order.
 
         Callers pass either `transformers` (pre-built instances) or
-        `factories` (callables producing fresh instances per mux). Each
-        transformer's `init()` is called, projections are merged into
-        `extensions`, `_native` keys are recorded in `native_keys`, and
-        any StreamChannel instances are bound and (if named) wired.
+        `factories` (callables producing fresh instances per mux). A
+        factory list is preferred — mini-muxes built by `make_child()`
+        inherit the factory list, so transformers propagate naturally
+        into every subgraph's scope. `transformers` is kept for
+        back-compat tests that exercise the mux directly.
+
+        Each transformer's `init()` is called once during registration,
+        projections are merged into `extensions`, `_native` keys are
+        recorded in `native_keys`, and any EventLog / StreamChannel
+        instances are bound and wired.
 
         Args:
-            transformers: Already-built transformer instances. Registered
-                only on this mux — they are NOT cloned into child
-                mini-muxes built by `_make_child`. Use `factories` for
-                transformers that should propagate to nested scopes.
+            transformers: Already-built transformer instances. Mutually
+                exclusive with `factories`.
             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.
+            factories: Zero-or-one-argument callables producing
+                transformers. Called with this mux's `scope`.
             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.
+                is `()`; mini-muxes for subgraphs use the subgraph's
+                namespace tuple.
 
         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.
+            ValueError: If transformers' projection keys collide, or if
+                both `transformers` and `factories` are supplied.
         """
-        self.is_async = is_async
+        if transformers is not None and factories is not None:
+            raise ValueError("Pass either `transformers` or `factories`, not both.")
+
+        self._is_async = is_async
+        self._factories: list[TransformerFactory] = list(factories or ())
         self.scope: tuple[str, ...] = scope
-        self._assign_seq = _assign_seq
-        self._events: StreamChannel[ProtocolEvent] = StreamChannel()
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+        self._events: EventLog[ProtocolEvent] = EventLog()
         self._events._bind(is_async=is_async)
         self._transformers: list[StreamTransformer] = []
         self._channels: list[StreamChannel[Any]] = []
+        self._logs: list[EventLog[Any]] = []
         self._seq = 0
 
         self.extensions: dict[str, Any] = {}
@@ -100,49 +107,60 @@ class StreamMux:
         self._projection_owners: dict[str, str] = {}
         self._transformer_by_key: dict[str, StreamTransformer] = {}
 
-        # Stored only when constructed from factories — used by
-        # `_make_child` to clone the transformer pipeline at a deeper
-        # scope. Pre-built transformers can't be cloned, so a mux
-        # built with `transformers=` rejects child construction.
-        self._factories: list[TransformerFactory] | None = (
-            list(factories) if factories is not None else None
-        )
-        self._pump_fn: Callable[[], bool] | None = None
-        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
-
-        # Factories run first (they propagate to child mini-muxes
-        # via `_make_child`), then any pre-built `transformers=`
-        # instances are registered as root-only — they aren't cloned
-        # for child scopes.
         if factories is not None:
             for factory in factories:
                 self._register(factory(scope))
-        for transformer in transformers or ():
-            self._register(transformer)
+        else:
+            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 make_child(self, scope: tuple[str, ...]) -> StreamMux:
+        """Build a mini-mux with the same factories scoped to `scope`.
 
-    # ------------------------------------------------------------------
-    # Pump wiring + mini-mux nesting
-    # ------------------------------------------------------------------
+        Used by `SubgraphTransformer` to attach a fresh transformer
+        pipeline to each discovered subgraph handle. The child mux
+        inherits the current pump binding (so cursors on its projection
+        logs drive the root pump) and carries the same factory list
+        forward to any grandchild subgraphs.
+
+        Raises:
+            RuntimeError: If the mux was not built from a factory list
+                (i.e., constructed with `transformers=`). Mini-muxes
+                require factories so each scope gets its own fresh
+                transformer instances.
+        """
+        if not self._factories:
+            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,
+        )
+        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 bind_pump(self, fn: Callable[[], bool]) -> None:
-        """Wire the sync pull callback onto every projection in this mux.
+        """Wire the sync pull callback onto every EventLog in the 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)
+        Also propagates to transformers that expose `_bind_pump` so
+        nested handles (e.g., `ChatModelStream` instances produced by
+        `MessagesTransformer`) can drive the graph pump from their
+        projection cursors.
         """
         self._pump_fn = fn
         self._events._request_more = fn
-        for ch in self._channels:
-            ch._request_more = fn
+        for value in self.extensions.values():
+            if isinstance(value, EventLog):
+                value._request_more = fn
+            elif isinstance(value, StreamChannel):
+                value._log._request_more = fn
         for transformer in self._transformers:
             bind = getattr(transformer, "_bind_pump", None)
             if bind is not None:
@@ -152,55 +170,24 @@ class StreamMux:
         """Async counterpart to `bind_pump`."""
         self._apump_fn = fn
         self._events._arequest_more = fn
-        for ch in self._channels:
-            ch._arequest_more = fn
+        for value in self.extensions.values():
+            if isinstance(value, EventLog):
+                value._arequest_more = fn
+            elif isinstance(value, StreamChannel):
+                value._log._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`.
+        processing, binds any EventLog or StreamChannel instances in
+        the projection, and merges the projection into `extensions`.
         """
-        if transformer_requires_async(transformer) and not self.is_async:
+        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 "
@@ -223,56 +210,68 @@ class StreamMux:
                 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._bind_and_wire(projection)
         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:
+        if getattr(transformer, "_native", False):
             self.native_keys.update(projection.keys())
-        transformer._on_register(self)
+        on_register = getattr(transformer, "_on_register", None)
+        if on_register is not None:
+            on_register(self)
+
+    def transformer_by_key(self, key: str) -> StreamTransformer | None:
+        """Return the transformer that owns the projection at `key`, if any."""
+        return self._transformer_by_key.get(key)
 
     def push(self, event: ProtocolEvent) -> None:
         """Route an event through all transformers, then append to the main log.
 
-        Each transformer's `process()` is called in registration order.
+        Each transformer's `process()` is called in registration order
+        — except when the transformer has `scope_exact = True` (the
+        default) and the event's namespace differs from the mux's
+        `scope`, in which case the transformer is skipped. Transformers
+        that need to see cross-scope events opt out by setting
+        `scope_exact = False` (e.g. `SubgraphTransformer`).
+
         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.
+        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 log.
 
         Args:
             event: The protocol event to dispatch.
         """
+        ns = tuple(event["params"]["namespace"])
+        in_scope = ns == self.scope
         keep = True
         for transformer in self._transformers:
+            if transformer.scope_exact and not in_scope:
+                continue
             if not transformer.process(event):
                 keep = False
         if keep:
-            if self._assign_seq:
-                self._seq += 1
-                event["seq"] = self._seq
+            self._seq += 1
+            event["seq"] = self._seq
             self._events.push(event)
 
     def close(self) -> None:
         """Finalize all transformers, close all projections and the main log.
 
-        StreamChannels discovered in transformer projections are
-        auto-closed after `finalize()` runs — transformers don't need
-        to close them manually. If any transformer's `finalize()` raises,
-        the remaining transformers, projections, and the main log are
-        still closed; the first error is re-raised after cleanup
-        completes.
+        EventLogs and 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
@@ -285,9 +284,12 @@ class StreamMux:
             except BaseException as e:
                 if first_error is None:
                     first_error = e
+        for log in self._logs:
+            if not log._closed:
+                log.close()
         for ch in self._channels:
-            if not ch._closed:
-                ch.close()
+            if not ch._log._closed:
+                ch._close()
         self._events.close()
         if first_error is not None:
             raise first_error
@@ -295,10 +297,11 @@ class StreamMux:
     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.
+        EventLogs and 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.
@@ -308,9 +311,12 @@ class StreamMux:
                 transformer.fail(err)
             except BaseException:
                 pass
+        for log in self._logs:
+            if not log._closed:
+                log.fail(err)
         for ch in self._channels:
-            if not ch._closed:
-                ch.fail(err)
+            if not ch._log._closed:
+                ch._fail(err)
         self._events.fail(err)
 
     # ------------------------------------------------------------------
@@ -321,29 +327,32 @@ class StreamMux:
         """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.
+        before appending to the main log — except when the transformer
+        has `scope_exact = True` and the event's namespace differs from
+        `self.scope`, in which case it is skipped. 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.
+        `put_nowait` shape. Memory is bounded by caller pace via the
+        caller-driven pump; see `EventLog` for the full tradeoff story.
 
         Args:
             event: The protocol event to dispatch.
         """
+        ns = tuple(event["params"]["namespace"])
+        in_scope = ns == self.scope
         keep = True
         for transformer in self._transformers:
+            if transformer.scope_exact and not in_scope:
+                continue
             if not await transformer.aprocess(event):
                 keep = False
         if keep:
-            if self._assign_seq:
-                self._seq += 1
-                event["seq"] = self._seq
+            self._seq += 1
+            event["seq"] = self._seq
             self._events.push(event)
 
     async def aclose(self) -> None:
@@ -351,7 +360,7 @@ class StreamMux:
 
         Awaits every task started via `StreamTransformer.schedule()`
         across all transformers, then calls `afinalize()` on each,
-        then auto-closes channels and the main event log.
+        then auto-closes logs, channels, and the main event log.
 
         If any scheduled task raised under `on_error="raise"`, or any
         transformer's `afinalize` raises, the exception propagates.
@@ -383,9 +392,12 @@ class StreamMux:
             except BaseException as e:
                 if first_error is None:
                     first_error = e
+        for log in self._logs:
+            if not log._closed:
+                log.close()
         for ch in self._channels:
-            if not ch._closed:
-                ch.close()
+            if not ch._log._closed:
+                ch._close()
         self._events.close()
         if first_error is not None:
             raise first_error
@@ -395,7 +407,7 @@ class StreamMux:
 
         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.
+        and auto-fails logs, channels, and the main event log.
 
         Args:
             err: The exception that ended the run.
@@ -411,9 +423,12 @@ class StreamMux:
                 await transformer.afail(err)
             except BaseException:
                 pass
+        for log in self._logs:
+            if not log._closed:
+                log.fail(err)
         for ch in self._channels:
-            if not ch._closed:
-                ch.fail(err)
+            if not ch._log._closed:
+                ch._fail(err)
         if not self._events._closed:
             self._events.fail(err)
 
@@ -430,62 +445,43 @@ class StreamMux:
     # Binding and StreamChannel auto-wiring
     # ------------------------------------------------------------------
 
-    def _bind_and_wire(
-        self, projection: dict[str, Any], *, native: bool = False
-    ) -> None:
-        """Bind and optionally wire StreamChannel instances in a projection.
-
-        All StreamChannels are bound and tracked. Channels with a name
-        are additionally wired for protocol auto-forwarding.
-
-        Args:
-            projection: The projection dict returned by a transformer's
-                `init()`.
-            native: True when the owning transformer is `_native`.
-                Named channels owned by a native transformer use the
-                channel name directly as the protocol method;
-                user-defined channels are prefixed with `custom:`.
-        """
+    def _bind_and_wire(self, projection: dict[str, Any]) -> None:
+        """Bind and wire EventLog / StreamChannel instances in a projection."""
         for value in projection.values():
             if isinstance(value, StreamChannel):
-                value._bind(is_async=self.is_async)
+                value._bind(is_async=self._is_async)
                 self._channels.append(value)
-                if value.name is not None:
-                    method = value.name if native else f"custom:{value.name}"
+                channel_name = value.name
 
-                    def _make_forward(method_name: str) -> Callable[[Any], None]:
-                        def _forward(item: Any) -> None:
-                            self._forward(method_name, item)
+                def _make_forward(name: str) -> Callable[[Any], None]:
+                    def _forward(item: Any) -> None:
+                        self._forward(name, item)
 
-                        return _forward
+                    return _forward
 
-                    value._wire(_make_forward(method))
+                value._wire(_make_forward(channel_name))
+            elif isinstance(value, EventLog):
+                value._bind(is_async=self._is_async)
+                self._logs.append(value)
 
-    def _forward(self, method: str, item: Any) -> None:
+    def _forward(self, channel_name: 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.
+        visible in the main event log but are not passed through
+        transformers' `process()` methods.
         """
+        self._seq += 1
         event: ProtocolEvent = {
             "type": "event",
-            "method": method,
+            "seq": self._seq,
+            "method": f"custom:{channel_name}",
             "params": {
                 "namespace": [],
                 "timestamp": int(time.time() * 1000),
                 "data": item,
             },
         }
-        if self._assign_seq:
-            self._seq += 1
-            event["seq"] = self._seq
         self._events.push(event)

libs/langgraph/langgraph/stream/_types.py

@@ -29,8 +29,8 @@ class ProtocolEvent(TypedDict):
     """A protocol event emitted by the streaming infrastructure.
 
     Wraps a raw stream part (values, messages, custom, etc.) in a uniform
-    envelope with a monotonic sequence number assigned by the root StreamMux.
-    Consumers that need a total order across root events should use `seq`, not
+    envelope with a monotonic sequence number assigned by the StreamMux.
+    Consumers that need a total order across events should use `seq`, not
     `params.timestamp` (which is wall-clock and not monotonic).
     """
 
@@ -45,7 +45,8 @@ 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.).
+    build typed derived projections (EventLogs, StreamChannels, promises,
+    etc.).
 
     Set `_native = True` on a transformer to have its projection keys
     exposed as direct attributes on the run stream (in addition to
@@ -54,9 +55,9 @@ class StreamTransformer(ABC):
     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.
+    EventLog and 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:
 
@@ -75,26 +76,34 @@ class StreamTransformer(ABC):
 
     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)`).
+            root mux, a subgraph's namespace tuple inside a mini-mux.
+            Set at construction from the mux's scope (each factory is
+            called as `factory(scope)`). Transformers that only care
+            about events at their own namespace compare against
+            `self.scope`; subgraph-aware transformers can treat it as
+            a parent path.
+        scope_exact: If True (the default), the mux only calls
+            `process` / `aprocess` for events whose namespace equals
+            `self.scope` — user transformers get scope-scoped events
+            for free with no boilerplate. Set False for transformers
+            that need to see events across scopes (e.g.
+            `SubgraphTransformer` forwards deeper events into child
+            mini-muxes).
         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_v2` run requests from the graph.
-            Empty tuple means the transformer consumes only synthetic
-            events (or is purely passive).
+            which modes a `GraphStreamer` 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
+    scope_exact: ClassVar[bool] = True
     required_stream_modes: ClassVar[tuple[str, ...]] = ()
 
     def __init__(self, scope: tuple[str, ...] = ()) -> None:
@@ -102,8 +111,9 @@ class StreamTransformer(ABC):
 
         Args:
             scope: The namespace tuple the owning mux is scoped to.
-                `()` for the root. Factories receive this at
-                construction time (`factory(scope)` in `StreamMux`).
+                `()` for the root, the subgraph's namespace inside a
+                mini-mux. Factories receive this at construction time
+                (`factory(scope)` in `StreamMux`).
         """
         self.scope: tuple[str, ...] = scope
 
@@ -120,14 +130,6 @@ class StreamTransformer(ABC):
         """
         ...
 
-    def _on_register(self, mux: Any) -> None:
-        """Called by `StreamMux._register` after this transformer is wired in.
-
-        Default is a no-op. Override to capture a reference to the
-        owning mux — needed for transformers that build mini-muxes
-        via `mux._make_child(...)` (e.g. `SubgraphTransformer`).
-        """
-
     def process(self, event: ProtocolEvent) -> bool:
         """Handle an event on the sync lane.
 
@@ -169,16 +171,15 @@ class StreamTransformer(ABC):
     def finalize(self) -> None:
         """Called when the run ends normally (sync lane).
 
-        Override to close StreamChannels, resolve promises, or perform
-        other teardown. StreamChannel instances in the projection dict
-        are auto-closed by the mux.
+        Override to close EventLogs, resolve promises, or perform other
+        teardown. StreamChannel instances are auto-closed by the mux.
         """
 
     async def afinalize(self) -> None:
         """Called when the run ends normally (async lane).
 
         By the time this runs, the mux has already awaited every task
-        started via `schedule()`, so StreamChannels can be closed here
+        started via `schedule()`, so EventLogs can be closed here
         without a last-task-wins race.
 
         The default delegates to `finalize`.
@@ -188,9 +189,8 @@ class StreamTransformer(ABC):
     def fail(self, err: BaseException) -> None:
         """Called when the run ends with an error (sync lane).
 
-        Override to fail StreamChannels, reject promises, or perform
-        other teardown. StreamChannel instances in the projection dict
-        are auto-failed by the mux.
+        Override to fail EventLogs, reject promises, or perform other
+        teardown. StreamChannel instances are auto-failed by the mux.
 
         Args:
             err: The exception that ended the run.
@@ -293,8 +293,7 @@ def transformer_requires_async(transformer: StreamTransformer) -> bool:
 
     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.
+    (`aprocess`, `afinalize`, `afail`).
 
     Args:
         transformer: The transformer to inspect.
@@ -304,8 +303,6 @@ def transformer_requires_async(transformer: StreamTransformer) -> bool:
     """
     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):

libs/langgraph/langgraph/stream/run_stream.py

@@ -10,7 +10,7 @@ from langgraph.stream._mux import StreamMux
 from langgraph.stream._types import ProtocolEvent
 
 if TYPE_CHECKING:
-    from langgraph.stream.transformers import SubgraphStatus
+    from langgraph.stream.transformers import ValuesTransformer
 
 
 def _drive_until_done(pump: Callable[[], bool]) -> None:
@@ -25,7 +25,117 @@ async def _adrive_until_done(pump: Callable[[], Awaitable[bool]]) -> None:
         pass
 
 
-class GraphRunStream:
+class BaseRunStream:
+    """Shared shape for any object that wraps a `StreamMux`.
+
+    Root (`GraphRunStream` / `AsyncGraphRunStream`) and scoped
+    (`SubgraphRunStream`) streams both compose a `StreamMux`. The mux
+    owns the projections — `values`, `messages`, `subgraphs`, and any
+    user-registered keys — all exposed via `extensions`. Native
+    projections (`_native = True`) are also bound as direct attributes
+    (`run.values`, `run.messages`, …) for ergonomics.
+
+    Raw iteration (`for event in run` / `async for event in run`) and
+    the `interleave(...)` helper both live here so every subclass
+    behaves consistently. Subclasses only add pump ownership, scope
+    metadata, or sync/async flavor.
+    """
+
+    def __init__(self, mux: StreamMux) -> None:
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        for key in mux.native_keys:
+            setattr(self, key, mux.extensions[key])
+
+    @property
+    def _values_transformer(self) -> ValuesTransformer:
+        """Look up the `ValuesTransformer` backing `output` / `interrupted`.
+
+        Resolved lazily off the mux so subclasses don't have to thread
+        it through their constructors. Raises if no `ValuesTransformer`
+        is registered — `output` / `interrupted` / `interrupts` have
+        nothing to return in that case, so failing loudly is better
+        than returning `None` silently.
+        """
+        from langgraph.stream.transformers import ValuesTransformer
+
+        vt = self._mux.transformer_by_key("values")
+        if not isinstance(vt, ValuesTransformer):
+            raise RuntimeError(
+                "No ValuesTransformer is registered on this mux — "
+                "`output`, `interrupted`, and `interrupts` require one. "
+                "Add it to your GraphStreamer subclass's "
+                "`builtin_factories` or pass it via `transformers=`."
+            )
+        return vt
+
+    def __iter__(self) -> Iterator[ProtocolEvent]:
+        """Sync iteration of protocol events on this mux's main log.
+
+        Raises at the EventLog level if the mux is async-bound.
+        """
+        return iter(self._mux._events)
+
+    def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
+        """Async iteration of protocol events on this mux's main log.
+
+        Raises at the EventLog level if the mux is sync-bound.
+        """
+        return self._mux._events.__aiter__()
+
+    def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
+        """Iterate multiple projections round-robin, yielding ``(name, item)``.
+
+        Each turn advances one projection's cursor; when a cursor's
+        buffer is empty, pulling from it drives the pump once, which
+        fans out to every subscribed projection log. Projections whose
+        items aren't consumed on this turn sit in their own buffers
+        only until the next turn reaches them, bounding memory by the
+        skew between projection rates rather than letting any single
+        log grow to the full run length.
+
+        Projections are exhausted independently; a projection that
+        finishes early drops out of the rotation while others
+        continue. The overall iterator ends once all named projections
+        are done.
+
+        Args:
+            *names: Projection keys to interleave. Must match keys in
+                `extensions`.
+
+        Yields:
+            `(name, item)` tuples in round-robin order across the named
+            projections.
+
+        Raises:
+            KeyError: If a name doesn't match a registered projection.
+
+        Example:
+            ```python
+            for name, item in run.interleave("messages", "values"):
+                if name == "messages":
+                    print("msg:", item)
+                else:
+                    print("val:", item)
+            ```
+        """
+        cursors: dict[str, Iterator[Any]] = {
+            name: iter(self.extensions[name]) for name in names
+        }
+        done: set[str] = set()
+        while len(done) < len(cursors):
+            for name, cursor in cursors.items():
+                if name in done:
+                    continue
+                try:
+                    item = next(cursor)
+                except StopIteration:
+                    done.add(name)
+                    continue
+                yield (name, item)
+
+
+class GraphRunStream(BaseRunStream):
     """Sync run stream with caller-driven pumping.
 
     The caller's iteration on any projection (`values`, `messages`,
@@ -34,84 +144,38 @@ class GraphRunStream:
 
     Projections are single-consumer — iterating `run.values` twice
     raises. Use `projection.tee(n)` if you genuinely need fan-out.
-
-    All transformer projections live in `extensions`. Native transformer
-    projections (those with `_native = True`) are also set as direct
-    attributes on this instance (e.g. `run.values`, `run.messages`).
     """
 
     def __init__(
         self,
-        graph_iter: Iterator[Any] | None,
+        graph_iter: Iterator[Any],
         mux: StreamMux,
-        *,
-        wire_pump: bool = True,
     ) -> None:
         """Initialize the run stream.
 
         Args:
-            graph_iter: Pull-based iterator over the graph's stream,
-                or `None` for nested run streams whose pump is driven
-                by an outer run (e.g. `SubgraphRunStream`).
+            graph_iter: Pull-based iterator over the graph's stream.
             mux: The StreamMux owning projections and the main log.
-            wire_pump: When True (default), bind `_pump_next` as the
-                mux's pump callable. Subclasses that inherit a parent
-                pump via `StreamMux._make_child` should pass False to
-                preserve the parent binding.
+                Must have a `ValuesTransformer` registered under the
+                `"values"` key — `output` / `interrupted` / `interrupts`
+                read from it lazily.
         """
+        super().__init__(mux)
         self._graph_iter = graph_iter
-        self._mux = mux
-        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
         self._exhausted = False
-        self._latest: dict[str, Any] | None = None
-        self._interrupted = False
-        self._interrupts: list[Any] = []
-        self._scope_list: list[str] = list(mux.scope)
-        for key in mux.native_keys:
-            setattr(self, key, mux.extensions[key])
-        if wire_pump:
-            self._wire_request_more(mux)
-
-    def _wire_request_more(self, mux: StreamMux) -> None:
-        """Wire the sync pull callback through the mux.
-
-        Routing through `mux.bind_pump` (rather than walking
-        projections directly here) lets child mini-muxes built by
-        `mux._make_child(...)` inherit the same pump callable, so
-        cursors on a subgraph handle's projections drive the root
-        pump just like cursors on `run.values` do.
-        """
         mux.bind_pump(self._pump_next)
 
-    def _observe_event(self, event: ProtocolEvent) -> None:
-        """Track values-event state for output/interrupted/interrupts."""
-        if event["method"] != "values":
-            return
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return
-        self._latest = params["data"]
-        interrupts = params.get("interrupts", ())
-        if interrupts:
-            self._interrupted = True
-            self._interrupts.extend(interrupts)
-
     def _pump_next(self) -> bool:
         """Pull one event from the graph and push it through the mux.
 
         Returns:
-            True if an event was pulled, False if the graph is exhausted
-            or has raised. Always False when constructed with
-            `graph_iter=None` (the run is driven by an outer pump).
+            True if an event was pulled, False if the graph is
+            exhausted or has raised.
         """
-        if self._exhausted or self._graph_iter is None:
+        if self._exhausted:
             return False
         try:
             part = next(self._graph_iter)
-            event = convert_to_protocol_event(part)
-            self._observe_event(event)
-            self._mux.push(event)
-            return True
         except StopIteration:
             self._mux.close()
             self._exhausted = True
@@ -120,6 +184,8 @@ class GraphRunStream:
             self._mux.fail(e)
             self._exhausted = True
             return False
+        self._mux.push(convert_to_protocol_event(part))
+        return True
 
     def abort(self) -> None:
         """Stop the run early.
@@ -151,22 +217,23 @@ class GraphRunStream:
     def output(self) -> dict[str, Any] | None:
         """Drive the run to completion and return the final state."""
         _drive_until_done(self._pump_next)
-        if (err := self._mux._events._error) is not None:
-            raise err
-        return self._latest
+        vt = self._values_transformer
+        if vt.error is not None:
+            raise vt.error
+        return vt._latest
 
     @property
     def interrupted(self) -> bool:
-        """Drive the run to completion, then return whether it was
-        interrupted.
+        """Drive the run to completion, then return whether it was interrupted.
 
         Raises:
             BaseException: If the run ended with an error.
         """
         _drive_until_done(self._pump_next)
-        if (err := self._mux._events._error) is not None:
-            raise err
-        return self._interrupted
+        vt = self._values_transformer
+        if vt.error is not None:
+            raise vt.error
+        return vt._interrupted
 
     @property
     def interrupts(self) -> list[Any]:
@@ -176,74 +243,22 @@ class GraphRunStream:
             BaseException: If the run ended with an error.
         """
         _drive_until_done(self._pump_next)
-        if (err := self._mux._events._error) is not None:
-            raise err
-        return self._interrupts
-
-    def __iter__(self) -> Iterator[ProtocolEvent]:
-        """Subscribe to the main event log and iterate protocol events."""
-        return iter(self._mux._events)
-
-    def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
-        """Iterate multiple projections round-robin, yielding ``(name, item)``.
-
-        Each turn advances one projection's cursor; when a cursor's buffer
-        is empty, pulling from it drives the pump once, which fans out to
-        every subscribed projection log. Projections whose items aren't
-        consumed on this turn sit in their own buffers only until the next
-        turn reaches them, bounding memory by the skew between projection
-        rates rather than letting any single log grow to the full run
-        length.
-
-        Projections are exhausted independently; a projection that finishes
-        early drops out of the rotation while others continue. The overall
-        iterator ends once all named projections are done.
-
-        Args:
-            *names: Projection keys to interleave. Must match keys in
-                ``extensions``.
-
-        Yields:
-            ``(name, item)`` tuples in round-robin order across the named
-            projections.
-
-        Raises:
-            KeyError: If a name doesn't match a registered projection.
-
-        Example:
-            ```python
-            for name, item in run.interleave("messages", "values"):
-                if name == "messages":
-                    print("msg:", item)
-                else:
-                    print("val:", item)
-            ```
-        """
-        cursors: dict[str, Iterator[Any]] = {
-            name: iter(self.extensions[name]) for name in names
-        }
-        done: set[str] = set()
-        while len(done) < len(cursors):
-            for name, cursor in cursors.items():
-                if name in done:
-                    continue
-                try:
-                    item = next(cursor)
-                except StopIteration:
-                    done.add(name)
-                    continue
-                yield (name, item)
+        vt = self._values_transformer
+        if vt.error is not None:
+            raise vt.error
+        return vt._interrupts
 
 
-class AsyncGraphRunStream:
+class AsyncGraphRunStream(BaseRunStream):
     """Async run stream with caller-driven pumping.
 
     Async iteration on any projection drives the graph forward — there
     is no background task. Concurrent consumers share a single-flight
-    pump via an `asyncio.Lock`, so each awaiting cursor contributes one
-    event per acquisition. Backpressure comes from the logs: when a
-    subscribed log's buffer reaches `maxlen`, `apush` awaits the
-    subscriber to drain, which holds back the pump and paces the graph.
+    pump via an `asyncio.Lock`, so each awaiting cursor contributes
+    one event per acquisition. Backpressure comes from the logs: when
+    a subscribed log's buffer reaches `maxlen`, `apush` awaits the
+    subscriber to drain, which holds back the pump and paces the
+    graph.
 
     Projections are single-consumer — a second `aiter(run.values)`
     raises. Use `projection.tee(n)` for fan-out.
@@ -260,59 +275,23 @@ class AsyncGraphRunStream:
 
     def __init__(
         self,
-        graph_aiter: AsyncIterator[Any] | None,
+        graph_aiter: AsyncIterator[Any],
         mux: StreamMux,
-        *,
-        wire_pump: bool = True,
     ) -> None:
         """Initialize the async run stream.
 
         Args:
-            graph_aiter: Async iterator over the graph's stream, or
-                `None` for nested run streams whose pump is driven by
-                an outer run (e.g. `AsyncSubgraphRunStream`).
+            graph_aiter: Async iterator over the graph's stream.
             mux: The StreamMux owning projections and the main log.
-            wire_pump: When True (default), bind `_apump_next` as the
-                mux's async pump callable. Subclasses that inherit a
-                parent pump via `StreamMux._make_child` should pass
-                False to preserve the parent binding.
+                Must have a `ValuesTransformer` registered under the
+                `"values"` key — `output` / `interrupted` / `interrupts`
+                read from it lazily.
         """
+        super().__init__(mux)
         self._graph_aiter = graph_aiter
-        self._mux = mux
-        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
         self._exhausted = False
-        self._latest: dict[str, Any] | None = None
-        self._interrupted = False
-        self._interrupts: list[Any] = []
-        self._scope_list: list[str] = list(mux.scope)
         self._pump_cond = asyncio.Condition()
         self._pumping = False
-        for key in mux.native_keys:
-            setattr(self, key, mux.extensions[key])
-        if wire_pump:
-            self._wire_arequest_more(mux)
-
-    def _observe_event(self, event: ProtocolEvent) -> None:
-        """Track values-event state for output/interrupted/interrupts."""
-        if event["method"] != "values":
-            return
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return
-        self._latest = params["data"]
-        interrupts = params.get("interrupts", ())
-        if interrupts:
-            self._interrupted = True
-            self._interrupts.extend(interrupts)
-
-    def _wire_arequest_more(self, mux: StreamMux) -> None:
-        """Wire the async pull callback through the mux.
-
-        Mirrors `_wire_request_more`: routing through
-        `mux.bind_apump` lets child mini-muxes inherit the pump
-        callable so cursors on subgraph handles drive the root
-        pump.
-        """
         mux.bind_apump(self._apump_next)
 
     async def _apump_next(self) -> bool:
@@ -336,7 +315,7 @@ class AsyncGraphRunStream:
             False if the graph is exhausted.
         """
         async with self._pump_cond:
-            if self._exhausted or self._graph_aiter is None:
+            if self._exhausted:
                 return False
             if self._pumping:
                 # Another task is pumping; wait for its progress signal.
@@ -347,10 +326,6 @@ class AsyncGraphRunStream:
         try:
             try:
                 part = await self._graph_aiter.__anext__()
-                event = convert_to_protocol_event(part)
-                self._observe_event(event)
-                await self._mux.apush(event)
-                return True
             except StopAsyncIteration:
                 self._exhausted = True
                 await self._mux.aclose()
@@ -359,6 +334,8 @@ class AsyncGraphRunStream:
                 self._exhausted = True
                 await self._mux.afail(e)
                 return False
+            await self._mux.apush(convert_to_protocol_event(part))
+            return True
         finally:
             async with self._pump_cond:
                 self._pumping = False
@@ -408,21 +385,20 @@ class AsyncGraphRunStream:
             BaseException: If the run ended with an error.
         """
         await _adrive_until_done(self._apump_next)
-        if (err := self._mux._events._error) is not None:
+        if (err := self._values_transformer.error) is not None:
             raise err
-        return self._latest
+        return self._values_transformer._latest
 
     async def interrupted(self) -> bool:
-        """Drive the run to completion and return whether it was
-        interrupted.
+        """Drive the run to completion and return whether it was interrupted.
 
         Raises:
             BaseException: If the run ended with an error.
         """
         await _adrive_until_done(self._apump_next)
-        if (err := self._mux._events._error) is not None:
+        if (err := self._values_transformer.error) is not None:
             raise err
-        return self._interrupted
+        return self._values_transformer._interrupted
 
     async def interrupts(self) -> list[Any]:
         """Drive the run to completion and return interrupt payloads.
@@ -431,117 +407,6 @@ class AsyncGraphRunStream:
             BaseException: If the run ended with an error.
         """
         await _adrive_until_done(self._apump_next)
-        if (err := self._mux._events._error) is not None:
+        if (err := self._values_transformer.error) is not None:
             raise err
-        return self._interrupts
-
-    def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
-        """Subscribe to the main event log and iterate protocol events."""
-        return self._mux._events.__aiter__()
-
-
-class _SubgraphRunStreamMixin:
-    """Subgraph metadata + parent-pump delegation shared by both lanes.
-
-    Inherits from `GraphRunStream` (or `AsyncGraphRunStream`) with
-    `graph_iter=None` + `wire_pump=False` — the mini-mux is driven
-    by the parent's pump (inherited via `StreamMux._make_child`), and
-    the handle never pulls upstream itself. Pump-driving methods
-    delegate to the parent pump so `handle.output` and friends drive
-    the root run.
-
-    Subclasses set the parent pump function captured at construction
-    (`_parent_pump_fn` / `_parent_apump_fn`) and override
-    `_pump_next` / `_apump_next` to delegate to it.
-
-    Status is updated in place by `SubgraphTransformer`. Iterate
-    `run.subgraphs` to receive handles as subgraphs spawn, then
-    drill into projections inside the loop body **before** the next
-    pump cycle — same lazy-subscribe constraint as root projections.
-    """
-
-    path: tuple[str, ...]
-    graph_name: str | None
-    trigger_call_id: str | None
-    status: SubgraphStatus
-    error: str | None
-    _seen_terminal: bool
-
-
-class SubgraphRunStream(GraphRunStream, _SubgraphRunStreamMixin):
-    """Sync handle for a discovered subgraph (extends `GraphRunStream`)."""
-
-    def __init__(
-        self,
-        mux: StreamMux,
-        *,
-        path: tuple[str, ...],
-        graph_name: str | None = None,
-        trigger_call_id: str | None = None,
-    ) -> None:
-        # Capture the parent-inherited pump before super().__init__
-        # touches anything; we delegate to it from `_pump_next`.
-        self._parent_pump_fn: Callable[[], bool] | None = mux._pump_fn
-        super().__init__(
-            graph_iter=None,
-            mux=mux,
-            wire_pump=False,
-        )
-        self.path = path
-        self.graph_name = graph_name
-        self.trigger_call_id = trigger_call_id
-        self.status = "started"
-        self.error = None
-        self._seen_terminal = False
-
-    def _pump_next(self) -> bool:
-        """Delegate to the parent's pump.
-
-        Cursors on this handle's projections call here when their
-        buffers empty. Driving the parent fans events into our
-        mini-mux, transparently advancing the whole run.
-        """
-        if (
-            self._exhausted
-            or self._seen_terminal
-            or self._mux._events._closed
-            or self._parent_pump_fn is None
-        ):
-            return False
-        return self._parent_pump_fn()
-
-
-class AsyncSubgraphRunStream(AsyncGraphRunStream, _SubgraphRunStreamMixin):
-    """Async handle for a discovered subgraph (extends `AsyncGraphRunStream`)."""
-
-    def __init__(
-        self,
-        mux: StreamMux,
-        *,
-        path: tuple[str, ...],
-        graph_name: str | None = None,
-        trigger_call_id: str | None = None,
-    ) -> None:
-        self._parent_apump_fn: Callable[[], Awaitable[bool]] | None = mux._apump_fn
-        super().__init__(
-            graph_aiter=None,
-            mux=mux,
-            wire_pump=False,
-        )
-        self.path = path
-        self.graph_name = graph_name
-        self.trigger_call_id = trigger_call_id
-        self.status = "started"
-        self.error = None
-        self._seen_terminal = False
-
-    async def _apump_next(self) -> bool:
-        """Delegate to the parent's async pump."""
-        if (
-            self._exhausted
-            or self._seen_terminal
-            or self._mux._events._closed
-            or self._parent_apump_fn is None
-        ):
-            return False
-        return await self._parent_apump_fn()
+        return self._values_transformer._interrupts

libs/langgraph/langgraph/stream/stream_channel.py

@@ -1,327 +1,109 @@
 from __future__ import annotations
 
-import asyncio
-from collections import deque
-from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
+from collections.abc import AsyncIterator, Callable, Iterator
 from typing import Generic, TypeVar
 
+from langgraph.stream._event_log import EventLog
+
 T = TypeVar("T")
 
 
 class StreamChannel(Generic[T]):
-    """Single-consumer drainable queue for streaming events, with optional
-    protocol auto-forwarding.
+    """A named projection channel with optional protocol auto-forwarding.
 
-    When constructed with a `name`, the StreamMux auto-wires every
-    `push()` to also inject a `ProtocolEvent` into the main event stream
-    using the channel's name as the method. When constructed without a
-    name, the channel is local-only — items are only visible to
-    in-process consumers that iterate the channel directly.
+    Wraps an event log and declares a protocol channel name. When the
+    StreamMux detects a StreamChannel in a transformer's `init()`
+    return value, it automatically wires every `push()` to inject a
+    `ProtocolEvent` into the main event stream using the channel's
+    name as the method.
 
-    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.
+    Auto-forwarded events bypass the transformer pipeline — other
+    transformers' `process()` / `aprocess()` methods do not see
+    `custom:<name>` events produced by a channel push. This prevents a
+    transformer that pushes to its own channel during `process()` from
+    re-triggering itself, but it also means filter- or tap-style
+    transformers cannot observe channel output from peer transformers.
+    Consumers that need that should iterate the main event stream.
 
-    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`.
+    In-process consumers iterate the channel directly (`for item in ch`
+    or `async for item in ch`). Remote SDK clients subscribe via
+    `session.subscribe("custom:<channelName>")`.
 
-    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.
+    Like EventLog, a StreamChannel starts unbound. The mux calls
+    `_bind(is_async)` during registration so the correct iteration
+    protocol is available by the time user code sees it.
 
-    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.
+    Lifecycle (`_close` / `_fail`) is managed by the mux — transformers
+    using only StreamChannels don't need `finalize` or `fail` hooks.
     """
 
-    def __init__(self, name: str | None = None, *, maxlen: int | None = None) -> None:
-        """Initialize the channel.
+    def __init__(self, name: str, *, maxlen: int | None = None) -> None:
+        """Initialize the channel with an empty inner log.
 
         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`.
+            name: The protocol channel name used for auto-forwarded
+                events (`custom:<name>` on the wire).
+            maxlen: Optional retention cap on the inner EventLog. See
+                `EventLog.__init__` for semantics.
         """
-        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[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._log: EventLog[T] = EventLog(maxlen=maxlen)
         self._wire_fn: Callable[[T], None] | None = None
 
-    # ------------------------------------------------------------------
-    # Binding
-    # ------------------------------------------------------------------
-
     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.
+        """Bind the underlying event log to sync or async mode.
 
         Args:
-            is_async: True to enable async iteration, False for sync.
-
-        Raises:
-            RuntimeError: If the channel has already been bound.
+            is_async: True for async iteration, False for sync.
         """
-        if self._is_async is not None:
-            raise RuntimeError("StreamChannel is already bound")
-        self._is_async = is_async
+        self._log._bind(is_async=is_async)
+
+    def push(self, item: T) -> None:
+        """Append an item to the log and auto-forward if wired.
+
+        Args:
+            item: The item to push.
+        """
+        self._log.push(item)
+        if self._wire_fn is not None:
+            self._wire_fn(item)
 
     # ------------------------------------------------------------------
-    # Mux wiring (not called by transformers directly)
+    # Mux lifecycle hooks (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 _close(self) -> None:
+        """Close the underlying log (called by StreamMux on run end)."""
+        self._log.close()
 
-    def push(self, item: T) -> None:
-        """Append an item. Auto-forwards if wired.
-
-        The local buffer append is a no-op when no subscriber is
-        registered, but auto-forwarding always fires so wired events
-        reach the main event log regardless of subscription state.
-
-        Raises:
-            RuntimeError: If the channel is closed (and subscribed).
-        """
-        if self._subscribed:
-            if self._closed:
-                raise RuntimeError("Cannot push to a closed StreamChannel")
-            self._items.append(item)
-        if self._wire_fn is not None:
-            self._wire_fn(item)
-
-    def close(self) -> None:
-        """Mark the channel as complete."""
-        self._closed = True
-
-    def fail(self, err: BaseException) -> None:
-        """Mark the channel as errored.
-
-        Args:
-            err: The exception to surface to the subscriber.
-        """
-        self._error = err
-        self._closed = True
+    def _fail(self, err: BaseException) -> None:
+        """Fail the underlying log (called by StreamMux on run error)."""
+        self._log.fail(err)
 
     # ------------------------------------------------------------------
-    # Sync iteration (caller-driven pump)
+    # Iteration — delegates to the inner event log (multi-cursor)
     # ------------------------------------------------------------------
 
     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:
-                yield self._items.popleft()
-            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)
-    # ------------------------------------------------------------------
+        return iter(self._log)
 
     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:
-                yield self._items.popleft()
-            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
-    # ------------------------------------------------------------------
+        return self._log.__aiter__()
 
     def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
-        """Subscribe and return `n` independent sync iterators.
+        """Fan out the channel into `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.
+        Delegates to the underlying EventLog's `tee()`.
         """
-        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))
+        return self._log.tee(n)
 
     def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
-        """Subscribe and return `n` independent async iterators.
+        """Fan out the channel into `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.
+        Delegates to the underlying EventLog's `atee()`.
         """
-        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))
+        return self._log.atee(n)

libs/langgraph/langgraph/stream/transformers.py

@@ -9,38 +9,43 @@ from langchain_core.language_models.chat_model_stream import (
     ChatModelStream,
 )
 from langchain_core.messages import AIMessageChunk, BaseMessage
-from langchain_protocol.protocol import MessagesData
-from typing_extensions import NotRequired, TypedDict
+from langchain_protocol.protocol import CheckpointRef, LifecycleData, MessagesData
 
-from langgraph.errors import GraphDrained, GraphInterrupt
+from langgraph.errors import GraphInterrupt
+from langgraph.stream._event_log import EventLog
 from langgraph.stream._types import ProtocolEvent, StreamTransformer
-from langgraph.stream.run_stream import AsyncSubgraphRunStream, SubgraphRunStream
-from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.run_stream import BaseRunStream
 
 if TYPE_CHECKING:
     from collections.abc import Awaitable, Callable
 
     from langgraph.stream._mux import StreamMux
 
-_logger = logging.getLogger(__name__)
+
+logger = logging.getLogger(__name__)
+
+
+SubgraphStatus = Literal["started", "running", "completed", "failed", "interrupted"]
+_TERMINAL_STATUSES: frozenset[SubgraphStatus] = frozenset(
+    {"completed", "failed", "interrupted"}
+)
 
 
 class ValuesTransformer(StreamTransformer):
     """Capture values events as a drainable stream of state snapshots.
 
-    Provides the `run.values` projection. `run.output`,
-    `run.interrupted` and `run.interrupts` are tracked directly
-    by the run stream and do not depend on this transformer.
+    Keeps `_latest` / `_interrupted` / `_interrupts` as scalar state
+    regardless of whether the log has a subscriber — so `run.output()`
+    and `run.interrupted` work without forcing the caller to iterate
+    `run.values`. Log pushes are silent no-ops when unsubscribed.
 
     Native transformer — projection keys are exposed as direct
     attributes on the run stream (e.g. `run.values`).
 
-    Only values events at the run's own level are captured; snapshots
-    from deeper subgraphs are left in the main event log but excluded
-    from the projection. "Own level" is defined by `scope`, which
-    `stream_v2` / `astream_v2` populate from the caller's
-    checkpoint namespace so that a nested `stream_v2` call still
-    sees its own root snapshots.
+    `scope` (inherited from `StreamTransformer`) is the namespace the
+    transformer captures values for. `()` matches the root graph;
+    subgraph mini-muxes pass their subgraph's namespace, so each
+    instance sees only its own level.
     """
 
     _native = True
@@ -48,13 +53,10 @@ class ValuesTransformer(StreamTransformer):
 
     def __init__(self, scope: tuple[str, ...] = ()) -> None:
         super().__init__(scope)
-        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._log: EventLog[dict[str, Any]] = EventLog()
         self._latest: dict[str, Any] | None = None
         self._interrupted = False
         self._interrupts: list[Any] = []
-        # Cached as a list once for cheap equality with the protocol
-        # event's `namespace` field, which is `list[str]`.
-        self._scope_list: list[str] = list(scope)
 
     def init(self) -> dict[str, Any]:
         return {"values": self._log}
@@ -68,11 +70,10 @@ class ValuesTransformer(StreamTransformer):
         return self._log._error
 
     def process(self, event: ProtocolEvent) -> bool:
+        # Namespace filtering is handled by the mux via `scope_exact`.
         if event["method"] != "values":
             return True
         params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
         self._latest = params["data"]
         interrupts = params.get("interrupts", ())
         if interrupts:
@@ -82,76 +83,6 @@ class ValuesTransformer(StreamTransformer):
         return True
 
 
-class CustomTransformer(StreamTransformer):
-    """Capture custom events as a drainable stream of arbitrary payloads.
-
-    Nodes emit custom data via `get_stream_writer()`. This transformer
-    surfaces those events on `run.custom` as a `StreamChannel[Any]`,
-    preserving payloads in arrival order.
-
-    Only events at the run's own scope are captured; custom data from
-    deeper subgraphs is available on the respective subgraph handle's
-    `.custom` projection.
-
-    Native transformer — `run.custom` is a direct attribute.
-    """
-
-    _native = True
-    required_stream_modes = ("custom",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: StreamChannel[Any] = StreamChannel()
-        self._scope_list: list[str] = list(scope)
-
-    def init(self) -> dict[str, Any]:
-        return {"custom": self._log}
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "custom":
-            return True
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
-        self._log.push(params["data"])
-        return True
-
-
-class UpdatesTransformer(StreamTransformer):
-    """Capture updates events as a drainable stream of node outputs.
-
-    Surfaces `stream_mode="updates"` data on `run.updates` as a
-    `StreamChannel[dict[str, Any]]`. Each item is a dict mapping a node
-    (or task) name to the update it returned after a step.
-
-    Only events at the run's own scope are captured; updates from deeper
-    subgraphs are available on the respective subgraph handle's
-    `.updates` projection.
-
-    Native transformer — `run.updates` is a direct attribute.
-    """
-
-    _native = True
-    required_stream_modes = ("updates",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
-        self._scope_list: list[str] = list(scope)
-
-    def init(self) -> dict[str, Any]:
-        return {"updates": self._log}
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "updates":
-            return True
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
-        self._log.push(params["data"])
-        return True
-
-
 class MessagesTransformer(StreamTransformer):
     """Capture messages events as ChatModelStream objects.
 
@@ -181,14 +112,10 @@ class MessagesTransformer(StreamTransformer):
     `stream()` method still surface their final `AIMessage` via
     `on_chain_end` when a node returns it as state.
 
-    Only events at the run's own level are projected; tokens from
-    deeper subgraphs are left in the main event log but excluded from
-    `.messages`. "Own level" is defined by `scope`, which
-    `stream_v2` / `astream_v2` populate from the caller's checkpoint
-    namespace so that a `stream_v2` call inside a node still sees its
-    own root chat model streams on `.messages`. Consumers that need
-    subgraph tokens should iterate the raw event stream or register a
-    custom transformer.
+    `scope` (inherited from `StreamTransformer`) is the namespace the
+    transformer captures messages for. `()` matches the root graph;
+    subgraph mini-muxes pass their subgraph's namespace, so each
+    instance sees only its own level.
 
     Native transformer — the `messages` projection is exposed as a
     direct attribute on the run stream.
@@ -199,15 +126,12 @@ class MessagesTransformer(StreamTransformer):
 
     def __init__(self, scope: tuple[str, ...] = ()) -> None:
         super().__init__(scope)
-        self._log: StreamChannel[ChatModelStream] = StreamChannel()
+        self._log: EventLog[ChatModelStream] = EventLog()
         # Correlate protocol events back to a ChatModelStream by run_id
         # (attached to the event's metadata by StreamMessagesHandler).
         self._by_run: dict[str, ChatModelStream] = {}
         self._pump_fn: Callable[[], bool] | None = None
         self._apump_fn: Callable[[], Awaitable[bool]] | None = None
-        # Cached as a list once for cheap equality with the protocol
-        # event's `namespace` field, which is `list[str]`.
-        self._scope_list: list[str] = list(scope)
 
     def init(self) -> dict[str, Any]:
         return {"messages": self._log}
@@ -262,11 +186,10 @@ class MessagesTransformer(StreamTransformer):
         )
 
     def process(self, event: ProtocolEvent) -> bool:
+        # Namespace filtering is handled by the mux via `scope_exact`.
         if event["method"] != "messages":
             return True
         params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
 
         payload, metadata = params["data"]
         node: str | None = metadata.get("langgraph_node")
@@ -297,7 +220,7 @@ class MessagesTransformer(StreamTransformer):
         if event_type == "message-start":
             message_id = event.get("message_id")
             stream = self._make_stream(
-                namespace=[],
+                namespace=list(self.scope),
                 node=node,
                 message_id=str(message_id) if message_id is not None else None,
             )
@@ -311,7 +234,11 @@ class MessagesTransformer(StreamTransformer):
                 del self._by_run[run_id]
 
     def _route_whole_message(self, message: BaseMessage, *, node: str | None) -> None:
-        stream = self._make_stream(namespace=[], node=node, message_id=message.id)
+        stream = self._make_stream(
+            namespace=list(self.scope),
+            node=node,
+            message_id=message.id,
+        )
         for evt in message_to_events(message, message_id=message.id):
             stream.dispatch(evt)
         self._log.push(stream)
@@ -327,602 +254,224 @@ class MessagesTransformer(StreamTransformer):
         self._by_run.clear()
 
 
-SubgraphStatus = Literal["started", "completed", "failed", "interrupted", "drained"]
+class SubgraphRunStream(BaseRunStream):
+    """Scoped view of a single nested subgraph execution.
 
+    Yielded on `run.subgraphs` (or `parent.subgraphs` for grandchildren)
+    when a nested `Pregel` spawns. Wraps a mini-`StreamMux` built with
+    the same transformer factories as the root mux, so `.values`,
+    `.messages`, `.subgraphs` are populated by the standard
+    transformers scoped to this handle's namespace — no duplicated
+    routing logic. The mini-mux borrows the root's pump via
+    `make_child`'s pump inheritance, so any cursor on a subagent
+    projection drives the whole run forward.
 
-def _parse_ns_segment(segment: str) -> tuple[str, str | None]:
-    """Split a namespace segment into `(graph_name, trigger_call_id)`.
+    Lifecycle fields update in place as events arrive:
 
-    Segments are formatted `node_name:task_id` by `prepare_next_tasks`.
-    Returns `(segment, None)` if no `:` is present.
-    """
-    name, sep, task_id = segment.partition(":")
-    return name, task_id if sep else None
+    - `path`: the namespace tuple — stable for the life of the handle.
+    - `graph_name` / `trigger_call_id`: set once from the `started`
+      payload.
+    - `status`: advances `started` → `running` → `completed` /
+      `failed` / `interrupted`.
+    - `error` / `checkpoint`: set on the terminal event when present.
 
-
-class LifecyclePayload(TypedDict, total=False):
-    """Payload of a lifecycle event surfaced on the `lifecycle` channel.
-
-    Auto-forwarded as `lifecycle` protocol events (no `custom:` prefix
-    because `LifecycleTransformer` is a native transformer) so remote
-    SDK clients receive the same data in-process consumers see via
-    `run.lifecycle`.
+    `.output` is a snapshot of the latest values seen at this
+    namespace — it doesn't drive the pump (unlike root's
+    `GraphRunStream.output`), because advancing a subgraph to
+    completion is only meaningful as part of advancing the whole run.
     """
 
-    event: SubgraphStatus
-    namespace: list[str]
-    graph_name: NotRequired[str]
-    trigger_call_id: NotRequired[str]
-    error: NotRequired[str]
-
-
-class _TasksLifecycleBase(StreamTransformer):
-    """Shared bookkeeping for `tasks`-event-driven lifecycle inference.
-
-    Both `LifecycleTransformer` (wire-serializable channel) and
-    `SubgraphTransformer` (in-process navigation handles) discover
-    subgraphs by watching the same `tasks` stream — `started` on the
-    first event at a tracked namespace, terminal status when the
-    parent's `TaskResultPayload` arrives. Centralizing the dispatch
-    + open-set bookkeeping here keeps the inference rules from
-    drifting between the two surfaces.
-
-    Subclasses provide three template-method hooks:
-
-    - `_should_track(ns)` — scope filter (e.g. multi-depth vs
-      direct-children-only).
-    - `_on_started(ns, graph_name, trigger_call_id)` — first sighting
-      action (push payload / build handle / etc.). Called once per
-      discovered namespace.
-    - `_on_terminal(ns, status, error)` — terminal action (push
-      terminal payload / mark handle status). Called once per
-      tracked namespace at result time, or via `finalize` / `fail`
-      sweeps if no parent result arrived.
-
-    Tasks events are suppressed from the main event log (`process`
-    returns False) — they're folded into whichever projection the
-    subclass populates; consumers iterating the raw protocol stream
-    see the higher-level view.
-    """
-
-    required_stream_modes = ("tasks",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._seen: set[tuple[str, ...]] = set()
-        # Maps tracked namespace -> task_id of the parent task whose
-        # `TaskResultPayload` will close it.
-        self._open: dict[tuple[str, ...], str] = {}
-
-    # --- Template-method hooks (subclass overrides) ---
-
-    def _should_track(self, ns: tuple[str, ...]) -> bool:
-        """Scope filter — return True iff `ns` is in this transformer's region."""
-        raise NotImplementedError
-
-    def _on_started(
+    def __init__(
         self,
-        ns: tuple[str, ...],
-        graph_name: str | None,
-        trigger_call_id: str | None,
+        path: tuple[str, ...],
+        mux: StreamMux,
+        *,
+        graph_name: str | None = None,
+        trigger_call_id: str | None = None,
     ) -> None:
-        """Fired once per discovered namespace (first observed task event)."""
-        raise NotImplementedError
+        super().__init__(mux)
+        self.path: tuple[str, ...] = path
+        self.graph_name: str | None = graph_name
+        self.trigger_call_id: str | None = trigger_call_id
+        self.status: SubgraphStatus = "started"
+        self.error: str | None = None
+        self.checkpoint: CheckpointRef | None = None
 
-    def _on_terminal(
-        self,
-        ns: tuple[str, ...],
-        status: SubgraphStatus,
-        error: str | None,
-    ) -> None:
-        """Fired once per tracked namespace when its parent's result arrives,
-        or via finalize/fail safety-net sweeps.
+    @property
+    def output(self) -> dict[str, Any] | None:
+        """Latest values snapshot at this namespace, or `None`.
+
+        Snapshot-only — iterating other projections or the root's
+        `.output` is what drives the pump.
         """
-        raise NotImplementedError
-
-    # --- Dispatch + bookkeeping (shared) ---
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "tasks":
-            return True
-        ns = tuple(event["params"]["namespace"])
-        data = event["params"]["data"]
-        if "result" in data:
-            self._handle_task_result(ns, data)
-        else:
-            self._handle_task_start(ns)
-        # Tasks events are folded into the synthesized projections;
-        # suppress from the main event log so iterators don't double-see
-        # the same information in two shapes.
-        return False
-
-    def _handle_task_start(self, ns: tuple[str, ...]) -> None:
-        if not self._should_track(ns) or ns in self._seen:
-            return
-        self._seen.add(ns)
-        graph_name, trigger_call_id = _parse_ns_segment(ns[-1])
-        self._on_started(ns, graph_name or None, trigger_call_id)
-        if trigger_call_id is not None:
-            self._open[ns] = trigger_call_id
-
-    def _pop_terminal_transitions(
-        self, ns: tuple[str, ...], data: dict[str, Any]
-    ) -> list[tuple[tuple[str, ...], SubgraphStatus, str | None]]:
-        """Return and remove tracked children closed by this task result."""
-        result_id = data.get("id")
-        if not result_id:
-            return []
-        transitions: list[tuple[tuple[str, ...], SubgraphStatus, str | None]] = []
-        for child_ns, parent_task_id in list(self._open.items()):
-            if child_ns[:-1] != ns or parent_task_id != result_id:
-                continue
-            status, error = _terminal_from_result(data)
-            transitions.append((child_ns, status, error))
-            del self._open[child_ns]
-        return transitions
-
-    def _handle_task_result(self, ns: tuple[str, ...], data: dict[str, Any]) -> None:
-        for child_ns, status, error in self._pop_terminal_transitions(ns, data):
-            self._on_terminal(child_ns, status, error)
-
-    def finalize(self) -> None:
-        """Emit `completed` for any tracked namespace still open at run end."""
-        for ns in list(self._open):
-            self._on_terminal(ns, "completed", None)
-        self._open.clear()
-
-    def fail(self, err: BaseException) -> None:
-        """Emit terminal status for any tracked namespace still open."""
-        status, error_str = _status_from_exception(err)
-        for ns in list(self._open):
-            self._on_terminal(ns, status, error_str)
-        self._open.clear()
+        values_t = self._mux.transformer_by_key("values")
+        if isinstance(values_t, ValuesTransformer):
+            return values_t._latest
+        return None
 
 
-def _status_from_exception(err: BaseException) -> tuple[SubgraphStatus, str | None]:
-    """Map a run exception to a subgraph terminal status and error string."""
-    if isinstance(err, GraphDrained):
-        return "drained", None
-    if isinstance(err, GraphInterrupt):
-        return "interrupted", None
-    return "failed", str(err)
+class SubgraphTransformer(StreamTransformer):
+    """Discover subgraphs and route events into per-subgraph mini-muxes.
 
+    Thin state-machine + dispatcher. At its own `scope` (inherited
+    from `StreamTransformer`, determined by the enclosing mux), it
+    watches for `lifecycle` events at exactly one level deeper to
+    discover direct children. Each discovered child gets its own
+    `SubgraphRunStream` backed by a mini-`StreamMux` — built via
+    `parent_mux.make_child(path)`, so the same factory list produces
+    fresh transformer instances at the child's scope.
 
-def _terminal_from_result(
-    payload: dict[str, Any],
-) -> tuple[SubgraphStatus, str | None]:
-    """Map a `TaskResultPayload` to a `(status, error)` pair.
+    Every incoming event that falls under one of the direct children
+    (ns starts with a child's `path`) is forwarded into that child's
+    mini-mux via `push`. The standard transformers in that mini-mux
+    (`ValuesTransformer`, `MessagesTransformer`, and another
+    `SubgraphTransformer` for grandchildren) handle the rest. No
+    duplicated routing or assembly logic.
 
-    Order matters: a result with both `error` and `interrupts` prefers
-    the interrupt classification, since `GraphInterrupt` manifests as
-    a populated `interrupts` list, not as `error`.
-    """
-    if payload.get("interrupts"):
-        return "interrupted", None
-    error = payload.get("error")
-    if error:
-        return "failed", str(error)
-    return "completed", None
+    Lifecycle state for each handle (running / completed / failed /
+    interrupted) is updated in place as events fire. On terminal
+    events, the handle's mini-mux is closed so any subscribed cursors
+    unblock. `finalize` / `fail` handle dangling handles left mid-run.
 
+    Native transformer — `subgraphs` exposes the direct-children log.
 
-class LifecycleTransformer(_TasksLifecycleBase):
-    """Surface subgraph lifecycle as `lifecycle` protocol events.
-
-    Pushes `LifecyclePayload` to a `StreamChannel` named `lifecycle`.
-    The channel is auto-forwarded by the mux so payloads land in the
-    main event log under `method = "lifecycle"` (native transformer —
-    no `custom:` prefix) — visible to remote SDK clients over the
-    wire and to in-process consumers via `run.lifecycle`.
-
-    Tracks subgraphs at every depth strictly below the transformer's
-    scope, so a graph → subgraph → subgraph chain produces lifecycle
-    events for both nested levels in a flat stream.
-
-    Native transformer — projection key `lifecycle` is exposed as
-    `run.lifecycle`.
+    `scope_exact = False`: this transformer sees events at any
+    namespace, because it forwards out-of-scope events to the matching
+    direct-child mini-mux.
     """
 
     _native = True
+    scope_exact = False
+    required_stream_modes = ("lifecycle",)
 
     def __init__(self, scope: tuple[str, ...] = ()) -> None:
         super().__init__(scope)
-        self._channel: StreamChannel[LifecyclePayload] = StreamChannel("lifecycle")
-
-    def init(self) -> dict[str, Any]:
-        return {"lifecycle": self._channel}
-
-    def _should_track(self, ns: tuple[str, ...]) -> bool:
-        depth = len(self.scope)
-        return len(ns) > depth and ns[:depth] == self.scope
-
-    def _on_started(
-        self,
-        ns: tuple[str, ...],
-        graph_name: str | None,
-        trigger_call_id: str | None,
-    ) -> None:
-        if trigger_call_id is None:
-            # Without a task id we can't correlate a parent-result
-            # event back to this namespace — skip the started payload
-            # and rely on finalize/fail to close.
-            return
-        payload: LifecyclePayload = {"event": "started", "namespace": list(ns)}
-        if graph_name:
-            payload["graph_name"] = graph_name
-        payload["trigger_call_id"] = trigger_call_id
-        self._channel.push(payload)
-
-    def _on_terminal(
-        self,
-        ns: tuple[str, ...],
-        status: SubgraphStatus,
-        error: str | None,
-    ) -> None:
-        payload: LifecyclePayload = {"event": status, "namespace": list(ns)}
-        if error is not None:
-            payload["error"] = error
-        self._channel.push(payload)
-
-
-class SubgraphTransformer(_TasksLifecycleBase):
-    """Discover subgraph invocations as in-process navigation handles.
-
-    Per discovered direct-child subgraph, builds a `SubgraphRunStream`
-    (or `AsyncSubgraphRunStream`) wrapping a child mini-mux scoped to
-    the subgraph's namespace. Consumers iterate `run.subgraphs` to
-    receive handles, then drill into `handle.values` / `handle.messages`
-    / `handle.subgraphs` (recursive grandchildren) / `handle.lifecycle`.
-
-    Each mini-mux owns its own scope and uses its own
-    `SubgraphTransformer` to discover its direct children, so
-    grandchildren live on the child handle — never on the root's
-    `subgraphs` log. Forwarding events into the matching child mini-mux
-    is what keeps the child's projections populated.
-
-    Native transformer — `subgraphs` is exposed as `run.subgraphs`.
-    """
-
-    _native = True
-    supports_sync = True
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: StreamChannel[SubgraphRunStream | AsyncSubgraphRunStream] = (
-            StreamChannel()
-        )
-        self._handles: dict[
-            tuple[str, ...], SubgraphRunStream | AsyncSubgraphRunStream
-        ] = {}
+        self._root_log: EventLog[SubgraphRunStream] = EventLog()
+        # Direct children only (namespace = scope + one segment).
+        self._by_ns: dict[tuple[str, ...], SubgraphRunStream] = {}
         self._mux: StreamMux | None = None
 
     def init(self) -> dict[str, Any]:
-        return {"subgraphs": self._log}
+        return {"subgraphs": self._root_log}
 
-    def _on_register(self, mux: Any) -> None:
+    def _on_register(self, mux: StreamMux) -> None:
+        """Capture the enclosing mux so we can build child mini-muxes."""
         self._mux = mux
 
-    def _should_track(self, ns: tuple[str, ...]) -> bool:
-        # Direct children only — grandchildren are picked up by the
-        # child mini-mux's own SubgraphTransformer.
+    def process(self, event: ProtocolEvent) -> bool:
+        ns = tuple(event["params"]["namespace"])
+        method = event["method"]
         depth = len(self.scope)
-        return len(ns) == depth + 1 and ns[:depth] == self.scope
 
-    def _on_started(
-        self,
-        ns: tuple[str, ...],
-        graph_name: str | None,
-        trigger_call_id: str | None,
-    ) -> None:
-        if self._mux is None:
-            return
-        try:
-            child_mux = self._mux._make_child(ns)
-        except RuntimeError:
-            return
-        handle_cls = AsyncSubgraphRunStream if child_mux.is_async else SubgraphRunStream
-        handle = handle_cls(
-            mux=child_mux,
-            path=ns,
-            graph_name=graph_name,
-            trigger_call_id=trigger_call_id,
-        )
-        self._handles[ns] = handle
-        self._log.push(handle)
+        # 1. On `started` for a direct child (ns depth = mine + 1 and
+        #    ns prefix matches mine), register the handle.
+        if method == "lifecycle" and len(ns) == depth + 1 and ns[:-1] == self.scope:
+            data = cast(LifecycleData, event["params"]["data"])
+            if data.get("event") == "started":
+                self._on_started(ns, data)
 
-    def _on_terminal(
-        self,
-        ns: tuple[str, ...],
-        status: SubgraphStatus,
-        error: str | None,
-    ) -> None:
-        handle = self._handles.get(ns)
-        if handle is None or not self._mark_terminal(handle, status, error):
-            return
-        self._close_or_fail_handle(handle, status, error)
+        # 2. Forward the event to the matching direct-child mini-mux
+        #    before the status-change step below so that terminal events
+        #    reach the child's log and grandchild transformers *before*
+        #    the child's mini-mux is closed. Prefix-match: ns must start
+        #    with some child's path.
+        direct_child_ns = ns[: depth + 1] if len(ns) > depth else None
+        if direct_child_ns is not None and direct_child_ns in self._by_ns:
+            self._by_ns[direct_child_ns]._mux.push(event)
 
-    async def _aon_terminal(
-        self,
-        ns: tuple[str, ...],
-        status: SubgraphStatus,
-        error: str | None,
-    ) -> None:
-        handle = self._handles.get(ns)
-        if handle is None or not self._mark_terminal(handle, status, error):
-            return
-        await self._aclose_or_fail_handle(handle, status, error)
+        # 3. Status change for a direct child (ns = child's path, method
+        #    = lifecycle). Update handle fields, close mini-mux on
+        #    terminal.
+        if (
+            method == "lifecycle"
+            and ns in self._by_ns
+            and len(ns) == depth + 1
+            and ns[:-1] == self.scope
+        ):
+            data = cast(LifecycleData, event["params"]["data"])
+            event_type = data.get("event")
+            if event_type in ("running", "completed", "failed", "interrupted"):
+                self._on_status_change(ns, event_type, data)
 
-    def _mark_terminal(
-        self,
-        handle: SubgraphRunStream | AsyncSubgraphRunStream,
-        status: SubgraphStatus,
-        error: str | None,
-    ) -> bool:
-        """Mark a handle terminal once. Returns True on first transition."""
-        if handle._seen_terminal:
-            return False
-        handle.status = status
-        if error is not None and handle.error is None:
-            handle.error = error
-        handle._seen_terminal = True
         return True
 
-    def _close_or_fail_handle(
-        self,
-        handle: SubgraphRunStream | AsyncSubgraphRunStream,
-        status: SubgraphStatus,
-        error: str | None,
-    ) -> None:
-        if handle._mux is None or handle._mux._events._closed:
+    def _on_started(self, ns: tuple[str, ...], data: LifecycleData) -> None:
+        if ns in self._by_ns:
+            # Duplicate started — ignore.
             return
-        if status == "failed":
-            handle._mux.fail(RuntimeError(error or "Subgraph failed"))
-        else:
-            handle._mux.close()
+        # `_on_register` is called by the mux during registration, which
+        # happens before any event can be dispatched — so this should
+        # always be set by the time we process an event.
+        assert self._mux is not None, (
+            "SubgraphTransformer processed an event before _on_register; "
+            "transformer registration ordering is broken."
+        )
+        child_mux = self._mux.make_child(ns)
+        handle = SubgraphRunStream(
+            path=ns,
+            mux=child_mux,
+            graph_name=data.get("graph_name"),
+            trigger_call_id=data.get("trigger_call_id"),
+        )
+        self._by_ns[ns] = handle
+        self._root_log.push(handle)
 
-    async def _aclose_or_fail_handle(
+    def _on_status_change(
         self,
-        handle: SubgraphRunStream | AsyncSubgraphRunStream,
-        status: SubgraphStatus,
-        error: str | None,
+        ns: tuple[str, ...],
+        event_type: SubgraphStatus,
+        data: LifecycleData,
     ) -> None:
-        if handle._mux is None or handle._mux._events._closed:
-            return
-        if status == "failed":
-            await handle._mux.afail(RuntimeError(error or "Subgraph failed"))
-        else:
-            await handle._mux.aclose()
+        handle = self._by_ns[ns]
+        handle.status = event_type
+        err = data.get("error")
+        if err is not None:
+            handle.error = err
+        checkpoint = data.get("checkpoint")
+        if checkpoint is not None:
+            handle.checkpoint = checkpoint
+        if event_type in _TERMINAL_STATUSES:
+            self._close_handle_mux(handle)
 
-    def _handle_for_event(
-        self, event: ProtocolEvent
-    ) -> SubgraphRunStream | AsyncSubgraphRunStream | None:
-        ns = tuple(event["params"]["namespace"])
-        depth = len(self.scope)
-        if len(ns) < depth + 1:
-            return None
-        handle = self._handles.get(ns[: depth + 1])
-        if handle is None or handle._mux is None or handle._mux._events._closed:
-            return None
-        return handle
-
-    def process(self, event: ProtocolEvent) -> bool:
-        # Run tasks bookkeeping first so a `started` handle exists
-        # by the time we forward the event to the child mini-mux.
-        keep = super().process(event)
-        handle = self._handle_for_event(event)
-        if handle is not None:
-            handle._observe_event(event)
-            handle._mux.push(event)
-        return keep
-
-    async def aprocess(self, event: ProtocolEvent) -> bool:
-        # Async counterpart: repeats the tasks bookkeeping here so
-        # child mini-muxes receive events through their async lane.
-        if event["method"] == "tasks":
-            ns = tuple(event["params"]["namespace"])
-            data = event["params"]["data"]
-            if "result" in data:
-                for child_ns, status, error in self._pop_terminal_transitions(ns, data):
-                    await self._aon_terminal(child_ns, status, error)
-            else:
-                self._handle_task_start(ns)
-            keep = False
-        else:
-            keep = True
-        handle = self._handle_for_event(event)
-        if handle is not None:
-            handle._observe_event(event)
-            await handle._mux.apush(event)
-        return keep
-
-    def _complete_open_handles(self) -> BaseException | None:
-        first_error: BaseException | None = None
-        for ns in list(self._open):
+    @staticmethod
+    def _close_handle_mux(handle: SubgraphRunStream) -> None:
+        # Idempotent close — mux.close() runs finalize on its transformers
+        # (which cascades through grandchildren) and closes projection logs.
+        if not handle._mux._events._closed:
             try:
-                self._on_terminal(ns, "completed", None)
-            except BaseException as e:
-                if first_error is None:
-                    first_error = e
-        self._open.clear()
-        for handle in self._handles.values():
-            if self._mark_terminal(handle, "completed", None):
-                try:
-                    self._close_or_fail_handle(handle, "completed", None)
-                except BaseException as e:
-                    if first_error is None:
-                        first_error = e
-        return first_error
-
-    async def _acomplete_open_handles(self) -> BaseException | None:
-        first_error: BaseException | None = None
-        for ns in list(self._open):
-            try:
-                await self._aon_terminal(ns, "completed", None)
-            except BaseException as e:
-                if first_error is None:
-                    first_error = e
-        self._open.clear()
-        for handle in self._handles.values():
-            if self._mark_terminal(handle, "completed", None):
-                try:
-                    await self._aclose_or_fail_handle(handle, "completed", None)
-                except BaseException as e:
-                    if first_error is None:
-                        first_error = e
-        return first_error
+                handle._mux.close()
+            except Exception:
+                logger.warning(
+                    "Error closing subgraph mini-mux at %s; subscribers "
+                    "may not see a clean close.",
+                    handle.path,
+                    exc_info=True,
+                )
 
     def finalize(self) -> None:
-        first_error = self._complete_open_handles()
-        if first_error is not None:
-            raise first_error
-
-    async def afinalize(self) -> None:
-        first_error = await self._acomplete_open_handles()
-        if first_error is not None:
-            raise first_error
+        """Transition any still-open direct children to `completed`."""
+        for handle in self._by_ns.values():
+            if handle.status not in _TERMINAL_STATUSES:
+                handle.status = "completed"
+            self._close_handle_mux(handle)
 
     def fail(self, err: BaseException) -> None:
-        status, error_str = _status_from_exception(err)
-        self._open.clear()
-        for handle in self._handles.values():
-            self._mark_terminal(handle, status, error_str)
-            if handle._mux is not None and not handle._mux._events._closed:
+        """Transition any still-open direct children to `failed` / `interrupted`."""
+        is_interrupt = isinstance(err, GraphInterrupt)
+        terminal: SubgraphStatus = "interrupted" if is_interrupt else "failed"
+        error_str = None if is_interrupt else str(err)
+        for handle in self._by_ns.values():
+            if handle.status not in _TERMINAL_STATUSES:
+                handle.status = terminal
+                if error_str is not None and handle.error is None:
+                    handle.error = error_str
+            if not handle._mux._events._closed:
                 try:
                     handle._mux.fail(err)
                 except Exception:
-                    _logger.warning(
-                        "Error failing subgraph mini-mux at %s; "
-                        "subscribers may not see the terminal error.",
+                    logger.warning(
+                        "Error failing subgraph mini-mux at %s; subscribers "
+                        "may not see the terminal error.",
                         handle.path,
                         exc_info=True,
                     )
-
-    async def afail(self, err: BaseException) -> None:
-        status, error_str = _status_from_exception(err)
-        self._open.clear()
-        for handle in self._handles.values():
-            self._mark_terminal(handle, status, error_str)
-            if handle._mux is not None and not handle._mux._events._closed:
-                try:
-                    await handle._mux.afail(err)
-                except Exception:
-                    _logger.warning(
-                        "Error failing subgraph mini-mux at %s; "
-                        "subscribers may not see the terminal error.",
-                        handle.path,
-                        exc_info=True,
-                    )
-
-
-class CheckpointsTransformer(StreamTransformer):
-    """Capture checkpoint events as a drainable stream.
-
-    Surfaces `stream_mode="checkpoints"` data on `run.checkpoints` as
-    a `StreamChannel[dict[str, Any]]`. Each item is in the same format
-    as returned by `get_state()`.
-
-    Checkpoint events are only emitted when a checkpointer is configured
-    on the graph. When no checkpointer is present, the projection exists
-    but receives no events.
-
-    Only events at the run's own scope are captured; checkpoint data from
-    deeper subgraphs is available on the respective subgraph handle's
-    `.checkpoints` projection.
-
-    Native transformer — `run.checkpoints` is a direct attribute.
-    """
-
-    _native = True
-    required_stream_modes = ("checkpoints",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
-        self._scope_list: list[str] = list(scope)
-
-    def init(self) -> dict[str, Any]:
-        return {"checkpoints": self._log}
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "checkpoints":
-            return True
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
-        self._log.push(params["data"])
-        return True
-
-
-class DebugTransformer(StreamTransformer):
-    """Capture debug events as a drainable stream.
-
-    Surfaces `stream_mode="debug"` data on `run.debug` as a
-    `StreamChannel[dict[str, Any]]`. Each item is a debug event with
-    step-level detail (checkpoint snapshots, task payloads, and
-    task results wrapped with step number and timestamp).
-
-    Only events at the run's own scope are captured; debug data from
-    deeper subgraphs is available on the respective subgraph handle's
-    `.debug` projection.
-
-    Native transformer — `run.debug` is a direct attribute.
-    """
-
-    _native = True
-    required_stream_modes = ("debug",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
-        self._scope_list: list[str] = list(scope)
-
-    def init(self) -> dict[str, Any]:
-        return {"debug": self._log}
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "debug":
-            return True
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
-        self._log.push(params["data"])
-        return True
-
-
-class TasksTransformer(StreamTransformer):
-    """Capture raw task events as a drainable stream.
-
-    Surfaces `stream_mode="tasks"` data on `run.tasks` as a
-    `StreamChannel[dict[str, Any]]`. Each item is a task payload
-    (start or result).
-
-    `LifecycleTransformer` and `SubgraphTransformer` also consume
-    `tasks` events for subgraph discovery and lifecycle tracking.
-    This transformer captures the raw payloads independently for
-    consumers who need task-level detail.
-
-    Only events at the run's own scope are captured; task data from
-    deeper subgraphs is available on the respective subgraph handle's
-    `.tasks` projection.
-
-    Native transformer — `run.tasks` is a direct attribute.
-    """
-
-    _native = True
-    required_stream_modes = ("tasks",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
-        self._scope_list: list[str] = list(scope)
-
-    def init(self) -> dict[str, Any]:
-        return {"tasks": self._log}
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "tasks":
-            return True
-        params = event["params"]
-        if params["namespace"] != self._scope_list:
-            return True
-        self._log.push(params["data"])
-        return True

libs/langgraph/langgraph/types.py

@@ -4,7 +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",
@@ -118,7 +116,15 @@ def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:
 
 
 StreamMode = Literal[
-    "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
+    "values",
+    "updates",
+    "checkpoints",
+    "tasks",
+    "debug",
+    "messages",
+    "custom",
+    "lifecycle",
+    "tools",
 ]
 """How the stream method should emit outputs.
 
@@ -131,6 +137,8 @@ StreamMode = Literal[
 - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
 - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
 - `"debug"`: Emit `"checkpoints"` and `"tasks"` events for debugging purposes.
+- `"lifecycle"`: Emit subgraph lifecycle events (`started`, `running`, `completed`, `failed`, `interrupted`) with payloads matching `LifecycleData`.
+- `"tools"`: Emit tool-call lifecycle events (`tool-started`, `tool-output-delta`, `tool-finished`, `tool-error`) keyed by `tool_call_id`.
 """
 
 StreamWriter = Callable[[Any], None]
@@ -425,83 +433,6 @@ 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 +558,6 @@ class PregelExecutableTask:
     path: tuple[str | int | tuple, ...]
     writers: Sequence[Runnable] = ()
     subgraphs: Sequence[PregelProtocol] = ()
-    timeout: TimeoutPolicy | None = None
 
 
 class StateSnapshot(NamedTuple):
@@ -667,8 +597,6 @@ 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 +626,33 @@ 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 = "1.2.0a1"
+version = "1.1.7a2"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -24,10 +24,10 @@ classifiers = [
     'Programming Language :: Python :: 3.13',
 ]
 dependencies = [
-    "langchain-core>=1.3.2,<2",
-    "langgraph-checkpoint>=4.1.0a1,<5.0.0",
+    "langchain-core==1.3.0a2",
+    "langgraph-checkpoint>=2.1.0,<5.0.0",
     "langgraph-sdk>=0.3.0,<0.4.0",
-    "langgraph-prebuilt>=1.0.12,<1.1.0",
+    "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
@@ -111,543 +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" in saved.checkpoint["channel_values"]
-    assert saved.checkpoint["channel_values"]["messages"] is DELTA_SENTINEL
-
-    state = graph.get_state(config)
-    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
-
-
-# ---------------------------------------------------------------------------
-# 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 saved.checkpoint["channel_values"]["files"] is DELTA_SENTINEL
-    state = graph.get_state(config)
-    assert state.values["files"] == {
-        "/doc_1.txt": "content for turn 1",
-        "/doc_2.txt": "content for turn 2",
-        "/doc_3.txt": "content for turn 3",
-    }
-
-    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

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

libs/langgraph/tests/test_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:
@@ -9425,254 +9397,3 @@ 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

@@ -215,30 +215,6 @@ 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."""
@@ -2110,11 +2086,8 @@ 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"]
@@ -2122,7 +2095,7 @@ 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:
@@ -6125,36 +6098,6 @@ 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):

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 ---
 
 

libs/langgraph/tests/test_stream_data_transformers.py

@@ -1,694 +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_v2.
-"""
-
-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 _drain(transformer: Any) -> list[Any]:
-    return list(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 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 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 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 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 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 list(t._log._items) == []
-
-
-# ---------------------------------------------------------------------------
-# End-to-end: real graphs through stream_v2
-# ---------------------------------------------------------------------------
-
-
-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_v2_custom_projection_opt_in() -> None:
-    """run.custom surfaces get_stream_writer() payloads when opted in."""
-    graph = _make_simple_graph()
-    run = graph.stream_v2(
-        {"value": "hello", "items": []}, 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_v2_custom_and_values_coexist() -> None:
-    """Both run.custom and run.values work in the same run."""
-    graph = _make_simple_graph()
-    run = graph.stream_v2(
-        {"value": "hello", "items": []}, 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_v2_tasks_projection_opt_in() -> None:
-    """run.tasks surfaces raw task events when opted in via transformers=."""
-    graph = _make_simple_graph()
-    run = graph.stream_v2({"value": "x", "items": []}, 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
-
-
-def test_stream_v2_debug_projection_opt_in() -> None:
-    """run.debug surfaces debug events when opted in via transformers=."""
-    graph = _make_simple_graph()
-    run = graph.stream_v2({"value": "x", "items": []}, transformers=[DebugTransformer])
-
-    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_v2_updates_projection_opt_in() -> None:
-    """run.updates surfaces node output dicts when opted in via transformers=."""
-    graph = _make_simple_graph()
-    run = graph.stream_v2(
-        {"value": "x", "items": []}, 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_v2_all_transformers_interleaved() -> None:
-    """All five transformers registered together, consumed via interleave."""
-    graph = _make_simple_graph()
-    run = graph.stream_v2(
-        {"value": "x", "items": []},
-        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_v2_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_v2(
-        {"value": "x", "items": []},
-        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_v2_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_v2(
-        {"value": "x", "items": []},
-        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 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_v2(
-        {"value": "x", "items": []},
-        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_lifecycle_transformer.py

@@ -1,401 +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_v2` 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 _drain_lifecycle(mux: StreamMux) -> list[LifecyclePayload]:
-    """Snapshot the lifecycle channel's buffer."""
-    transformer = mux.transformer_by_key("lifecycle")
-    assert isinstance(transformer, LifecycleTransformer)
-    return list(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 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 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_v2
-# ---------------------------------------------------------------------------
-
-
-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_v2_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_v2({"value": "x", "items": []})
-
-    # 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_v2_with_nested_parent_ns_scopes_lifecycle() -> None:
-    """When `stream_v2` 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_v2
-    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_v2({"value": "x", "items": []}, config=config)
-
-    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

@@ -1,5 +1,10 @@
-"""Tests for MessagesTransformer: protocol event routing, whole-message fallback,
-legacy v1 chunk filtering, and end-to-end via stream_v2 / astream_v2."""
+"""Tests for the MessagesTransformer content-block upgrade (B2).
+
+Verifies that `MessagesTransformer` routes protocol events (emitted by
+`stream_v2` via `on_stream_event`) to `ChatModelStream` objects keyed by
+run_id, and replays whole `AIMessage` payloads via `message_to_events`.
+Legacy v1 `AIMessageChunk` tuples (from `on_llm_new_token`) are ignored.
+"""
 
 from __future__ import annotations
 
@@ -13,14 +18,12 @@ from langchain_core.language_models.chat_model_stream import (
     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._event_log import EventLog
 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)
@@ -38,13 +41,14 @@ def _proto_event(
     node: str = "llm",
 ) -> dict[str, Any]:
     """Build a messages ProtocolEvent carrying a protocol event dict (v2 path)."""
+    metadata: dict[str, Any] = {"langgraph_node": node, "run_id": run_id}
     return {
         "type": "event",
         "method": "messages",
         "params": {
             "namespace": [],
             "timestamp": TS,
-            "data": (event, {"langgraph_node": node, "run_id": run_id}),
+            "data": (event, metadata),
         },
     }
 
@@ -57,17 +61,18 @@ def _v1_chunk(
     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 {}
+    rm: dict[str, Any] = {}
+    if finish:
+        rm["finish_reason"] = "stop"
+    message = AIMessageChunk(content=text, id=msg_id, response_metadata=rm)
+    metadata: dict[str, Any] = {"langgraph_node": node}
     return {
         "type": "event",
         "method": "messages",
         "params": {
             "namespace": [],
             "timestamp": TS,
-            "data": (
-                AIMessageChunk(content=text, id=msg_id, response_metadata=rm),
-                {"langgraph_node": node},
-            ),
+            "data": (message, metadata),
         },
     }
 
@@ -79,43 +84,49 @@ def _whole_msg(
     node: str = "node",
 ) -> dict[str, Any]:
     """Build a messages ProtocolEvent carrying a completed AIMessage."""
+    message = AIMessage(content=text, id=msg_id)
+    metadata: dict[str, Any] = {"langgraph_node": node}
     return {
         "type": "event",
         "method": "messages",
         "params": {
             "namespace": [],
             "timestamp": TS,
-            "data": (AIMessage(content=text, id=msg_id), {"langgraph_node": node}),
+            "data": (message, metadata),
         },
     }
 
 
-def _make_sync_transformer() -> tuple[
-    MessagesTransformer, StreamChannel[ChatModelStream]
-]:
+def _make_sync_transformer() -> tuple[MessagesTransformer, EventLog[ChatModelStream]]:
     t = MessagesTransformer()
-    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    proj = t.init()
+    log: EventLog[ChatModelStream] = proj["messages"]
     log._bind(is_async=False)
-    # Subscribe up front so pushes during process() are retained.
+    # Production subscribes via `iter(log)` from the graph consumer — do that
+    # up front so `push` during `process` isn't a no-op. Tests read buffered
+    # items via `log._items` directly rather than re-iterating.
     log._subscribed = True
     t._bind_pump(lambda: False)
     return t, log
 
 
-def _make_async_transformer() -> tuple[
-    MessagesTransformer, StreamChannel[ChatModelStream]
-]:
+def _make_async_transformer() -> tuple[MessagesTransformer, EventLog[ChatModelStream]]:
     t = MessagesTransformer()
-    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    proj = t.init()
+    log: EventLog[ChatModelStream] = proj["messages"]
     log._bind(is_async=True)
     log._subscribed = True
     return t, log
 
 
+# Standard lifecycle events for one streaming LLM call.
 def _lifecycle(
-    *, text: str = "hello world", message_id: str = "run-1"
+    *,
+    text: str = "hello world",
+    message_id: str = "run-1",
 ) -> list[dict[str, Any]]:
-    """Produce a valid protocol event lifecycle: start, delta, finish."""
+    """Produce a valid protocol event lifecycle: start, delta, finish, end."""
+    # Split text into two deltas to exercise delta accumulation.
     half = len(text) // 2
     first, second = text[:half], text[half:]
     return [
@@ -144,23 +155,8 @@ def _lifecycle(
     ]
 
 
-def _simple_graph():
-    def call_model(state: MessagesState) -> dict[str, Any]:
-        model = GenericFakeChatModel(messages=iter(["hello world"]))
-        stream = model.stream_v2(state["messages"])
-        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
+# Primary path: protocol event routing
 # ---------------------------------------------------------------------------
 
 
@@ -173,10 +169,12 @@ class TestProtocolEventRouting:
                 run_id="run-1",
             )
         )
+        # Stream is in the log immediately.
         log.close()
-        (stream,) = list(log._items)
-        assert isinstance(stream, ChatModelStream)
-        assert stream.message_id == "run-1"
+        streams = list(log._items)
+        assert len(streams) == 1
+        assert isinstance(streams[0], ChatModelStream)
+        assert streams[0].message_id == "run-1"
 
     def test_full_lifecycle_yields_done_stream(self) -> None:
         t, log = _make_sync_transformer()
@@ -185,7 +183,7 @@ class TestProtocolEventRouting:
         log.close()
         (stream,) = list(log._items)
         assert stream.done
-        assert stream.output.text == "hello world"
+        assert stream.output.content == "hello world"
 
     def test_message_finish_cleans_up_routing(self) -> None:
         t, log = _make_sync_transformer()
@@ -194,6 +192,7 @@ class TestProtocolEventRouting:
         assert t._by_run == {}
 
     def test_events_without_prior_start_are_ignored(self) -> None:
+        """Orphan delta events (no preceding message-start) are dropped silently."""
         t, log = _make_sync_transformer()
         t.process(
             _proto_event(
@@ -209,7 +208,9 @@ class TestProtocolEventRouting:
         assert list(log._items) == []
 
     def test_concurrent_streams_routed_by_run_id(self) -> None:
+        """Two interleaved LLM calls each produce their own stream."""
         t, log = _make_sync_transformer()
+        # Interleave events from two different run_ids.
         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):
@@ -219,8 +220,8 @@ class TestProtocolEventRouting:
         streams = list(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"
+        assert by_id["run-a"].output.content == "aaaa"
+        assert by_id["run-b"].output.content == "bbbb"
 
     def test_text_deltas_accumulated_on_stream(self) -> None:
         t, log = _make_sync_transformer()
@@ -228,10 +229,11 @@ class TestProtocolEventRouting:
             t.process(_proto_event(evt))
         log.close()
         (stream,) = list(log._items)
-        assert "".join(stream._text_proj._deltas) == "abcdef"
+        deltas = list(stream._text_proj._deltas)
+        assert "".join(deltas) == "abcdef"
 
     def test_stream_pushed_on_message_start_not_finish(self) -> None:
-        # Consumer can see the stream before message-finish arrives.
+        """Consumer can see the stream before it finishes."""
         t, log = _make_sync_transformer()
         t.process(
             _proto_event(
@@ -239,6 +241,8 @@ class TestProtocolEventRouting:
                 run_id="run-1",
             )
         )
+        # The log has the stream immediately — even though message-finish
+        # hasn't arrived yet.
         assert len(log._items) == 1
 
     def test_node_metadata_set_on_stream(self) -> None:
@@ -250,12 +254,12 @@ class TestProtocolEventRouting:
                 node="my_llm",
             )
         )
-        (stream,) = list(log._items)
+        (stream,) = [*log._items]
         assert stream.node == "my_llm"
 
 
 # ---------------------------------------------------------------------------
-# Whole-message fallback
+# Non-streaming (whole AIMessage) fallback
 # ---------------------------------------------------------------------------
 
 
@@ -266,14 +270,15 @@ class TestWholeMessageFallback:
         log.close()
         (stream,) = list(log._items)
         assert stream.done
-        assert stream.output.text == "the full answer"
+        assert stream.output.content == "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,) = list(log._items)
-        assert [e["event"] for e in stream._events] == [
+        event_types = [e["event"] for e in stream._events]
+        assert event_types == [
             "message-start",
             "content-block-start",
             "content-block-delta",
@@ -283,27 +288,45 @@ class TestWholeMessageFallback:
 
 
 # ---------------------------------------------------------------------------
-# Filtering
+# Legacy v1 chunks are ignored (users must migrate to stream_v2)
+# ---------------------------------------------------------------------------
+
+
+class TestLegacyChunksIgnored:
+    def test_aimessage_chunk_tuple_is_dropped(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_v1_chunk("hello"))
+        t.process(_v1_chunk(" world", finish=True))
+        log.close()
+        assert list(log._items) == []
+
+
+# ---------------------------------------------------------------------------
+# Filtering behaviors
 # ---------------------------------------------------------------------------
 
 
 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
-        )
+        values_event = {
+            "type": "event",
+            "method": "values",
+            "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
+        }
+        assert t.process(values_event) is True
 
     def test_subgraph_namespace_dropped(self) -> None:
-        t, log = _make_sync_transformer()
-        t.process(
+        """Root MessagesTransformer (via the mux) ignores non-root events."""
+        from langgraph.stream._mux import StreamMux
+
+        mux = StreamMux([MessagesTransformer()], is_async=False)
+        t = mux.transformer_by_key("messages")
+        assert isinstance(t, MessagesTransformer)
+        t._log._subscribed = True
+        t._bind_pump(lambda: False)
+
+        mux.push(
             {
                 "type": "event",
                 "method": "messages",
@@ -317,21 +340,12 @@ class TestFiltering:
                 },
             }
         )
-        log.close()
-        assert list(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_v2.
-        t, log = _make_sync_transformer()
-        t.process(_v1_chunk("hello"))
-        t.process(_v1_chunk(" world", finish=True))
-        log.close()
-        assert list(log._items) == []
+        t._log.close()
+        assert list(t._log._items) == []
 
 
 # ---------------------------------------------------------------------------
-# Lifecycle: fail / finalize
+# Lifecycle: finalize / fail
 # ---------------------------------------------------------------------------
 
 
@@ -340,7 +354,8 @@ class TestLifecycle:
         t, log = _make_sync_transformer()
         t.process(
             _proto_event(
-                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+                {"event": "message-start", "message_id": "run-1"},
+                run_id="run-1",
             )
         )
         streams = list(log._items)
@@ -353,7 +368,8 @@ class TestLifecycle:
         t, _ = _make_sync_transformer()
         t.process(
             _proto_event(
-                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+                {"event": "message-start", "message_id": "run-1"},
+                run_id="run-1",
             )
         )
         assert "run-1" in t._by_run
@@ -362,7 +378,7 @@ class TestLifecycle:
 
 
 # ---------------------------------------------------------------------------
-# Async mode
+# Async mode (AsyncChatModelStream)
 # ---------------------------------------------------------------------------
 
 
@@ -371,24 +387,30 @@ class TestAsyncMode:
         t, log = _make_async_transformer()
         for evt in _lifecycle(text="async stream"):
             t.process(_proto_event(evt))
-        assert isinstance(list(log._items)[0], AsyncChatModelStream)
+        streams = list(log._items)
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
 
     @pytest.mark.anyio
-    async def test_text_projection_yields_deltas(self) -> None:
+    async def test_async_text_projection_yields_deltas(self) -> None:
         t, log = _make_async_transformer()
         for evt in _lifecycle(text="hello world"):
             t.process(_proto_event(evt))
         (stream,) = list(log._items)
         assert isinstance(stream, AsyncChatModelStream)
-        assert "".join([d async for d in stream.text]) == "hello world"
+        collected = []
+        async for delta in stream.text:
+            collected.append(delta)
+        assert "".join(collected) == "hello world"
 
     @pytest.mark.anyio
-    async def test_output_awaitable(self) -> None:
+    async def test_async_output_awaitable(self) -> None:
         t, log = _make_async_transformer()
         for evt in _lifecycle(text="async"):
             t.process(_proto_event(evt))
         (stream,) = list(log._items)
-        assert (await stream.output).text == "async"
+        msg = await stream.output
+        assert msg.content == "async"
 
 
 # ---------------------------------------------------------------------------
@@ -404,7 +426,10 @@ class TestWireRequestMore:
 
         assert messages_t._pump_fn is None
         run = GraphRunStream(iter([]), mux)
+        # After wire, the transformer's pump callback is set.
         assert messages_t._pump_fn is not None
+        # And calling it invokes GraphRunStream._pump_next (drains an empty
+        # graph_iter, returns False).
         assert messages_t._pump_fn() is False
         assert run._exhausted
 
@@ -412,14 +437,17 @@ class TestWireRequestMore:
         values_t = ValuesTransformer()
         messages_t = MessagesTransformer()
         mux = StreamMux([values_t, messages_t], is_async=False)
-        GraphRunStream(iter([]), mux)
 
-        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        GraphRunStream(iter([]), mux)
+        log: EventLog[ChatModelStream] = mux.extensions["messages"]
         log._subscribed = True
+
         for evt in _lifecycle():
             messages_t.process(_proto_event(evt))
 
         (stream,) = list(log._items)
+        # Pump was threaded through: the stream's _request_more points at
+        # the same callable the transformer was bound with.
         assert stream._request_more is messages_t._pump_fn
 
 
@@ -429,55 +457,73 @@ class TestWireRequestMore:
 
 
 class TestViaMux:
-    def _make_mux(
-        self,
-    ) -> tuple[MessagesTransformer, StreamMux, StreamChannel[ChatModelStream]]:
+    def test_streaming_via_mux(self) -> None:
         t = MessagesTransformer()
         v = ValuesTransformer()
         mux = StreamMux([v, t], is_async=False)
         t._bind_pump(lambda: False)
-        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log: EventLog[ChatModelStream] = mux.extensions["messages"]
+        # Simulate a consumer subscribing (as `run.messages` iteration would).
         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,) = list(log._items)
-        assert stream.output.text == "mux stream"
+        assert stream.output.content == "mux stream"
 
     def test_whole_message_via_mux(self) -> None:
-        t, mux, log = self._make_mux()
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=False)
+        t._bind_pump(lambda: False)
+        log: EventLog[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+
         mux.push(_whole_msg("result"))
         mux.close()
+
         (stream,) = list(log._items)
-        assert stream.output.text == "result"
+        assert stream.output.content == "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: EventLog[ChatModelStream] = mux.extensions["messages"]
         log._subscribed = True
 
         for evt in _lifecycle(text="async mux"):
             await mux.apush(_proto_event(evt))
 
-        (stream,) = list(log._items)
-        assert (await stream.output).text == "async mux"
+        streams = list(log._items)
+        assert len(streams) == 1
+        msg = await streams[0].output
+        assert msg.content == "async mux"
         await mux.aclose()
 
 
 # ---------------------------------------------------------------------------
-# End-to-end: graph → stream_v2 → run.messages (node calls stream_v2)
+# End-to-end: full graph → stream_v2 → run.messages
 # ---------------------------------------------------------------------------
 
 
 class TestEndToEnd:
-    """stream_v2 path: node calls model.stream_v2() explicitly."""
+    """Prove the full pipeline works when a node calls `model.stream_v2()`.
+
+    These tests exercise the path that the new messages projection is
+    designed for: a user node invokes `stream_v2` on a chat model,
+    `on_stream_event` fires on `StreamMessagesHandler`, the handler
+    forwards to the mux, and the transformer routes events into a
+    `ChatModelStream` exposed on `run.messages`.
+
+    Nothing in Pregel calls `stream_v2` automatically yet; the planned
+    `graph.stream_v2()` API (B4) and the `create_react_agent`
+    integration (C2) will wire that up. Until then, populating the
+    messages projection is opt-in at the node level.
+    """
 
     def test_node_calling_stream_v2_populates_messages(self) -> None:
         model = GenericFakeChatModel(messages=iter(["hello world"]))
@@ -495,9 +541,11 @@ class TestEndToEnd:
         )
 
         run = graph.stream_v2({"messages": "hi"})
-        (stream,) = list(run.messages)
-        assert isinstance(stream, ChatModelStream)
-        assert stream.output.text == "hello world"
+        streams = list(run.messages)
+
+        assert len(streams) == 1
+        assert isinstance(streams[0], ChatModelStream)
+        assert streams[0].output.content == "hello world"
 
     def test_node_stream_v2_text_deltas_iterate(self) -> None:
         """Consumer can iterate `.text` on the streamed message in real time."""
@@ -516,11 +564,14 @@ class TestEndToEnd:
         )
 
         run = graph.stream_v2({"messages": "go"})
+
+        # Pull the stream handle out, then iterate its text deltas.
         (stream,) = list(run.messages)
-        assert "".join(stream.text) == "streamed answer"
+        text = "".join(stream.text)
+        assert text == "streamed answer"
 
     def test_non_llm_message_returned_from_node(self) -> None:
-        """Whole-message fallback: node returns a finalized AIMessage directly."""
+        """Node returns a finalized AIMessage directly — whole-message fallback."""
 
         def return_message(state: MessagesState) -> dict[str, Any]:
             return {"messages": AIMessage(content="hardcoded", id="msg-abc")}
@@ -534,8 +585,10 @@ class TestEndToEnd:
         )
 
         run = graph.stream_v2({"messages": "hi"})
-        (stream,) = list(run.messages)
-        assert stream.output.text == "hardcoded"
+        streams = list(run.messages)
+
+        assert len(streams) == 1
+        assert streams[0].output.content == "hardcoded"
 
     @pytest.mark.anyio
     async def test_async_node_calling_astream_v2(self) -> None:
@@ -543,7 +596,8 @@ class TestEndToEnd:
 
         async def call_model(state: MessagesState) -> dict[str, Any]:
             stream = await model.astream_v2(state["messages"])
-            return {"messages": await stream}
+            msg = await stream
+            return {"messages": msg}
 
         graph = (
             StateGraph(MessagesState)
@@ -554,21 +608,33 @@ class TestEndToEnd:
         )
 
         run = await graph.astream_v2({"messages": "hi"})
-        streams = [s async for s in run.messages]
+
+        streams = []
+        async for stream in run.messages:
+            streams.append(stream)
+
         assert len(streams) == 1
         assert isinstance(streams[0], AsyncChatModelStream)
-        assert (await streams[0].output).text == "async answer"
+        msg = await streams[0].output
+        assert msg.content == "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."""
+        """Iterate `stream.text` inside `async for stream in run.messages`.
+
+        The inner `stream.text` cursor drives the shared graph pump via
+        `AsyncProjection._arequest_more`, wired by
+        `MessagesTransformer._bind_apump` and
+        `AsyncGraphRunStream._wire_arequest_more`.
+        """
         import asyncio
 
         model = GenericFakeChatModel(messages=iter(["hello world"]))
 
         async def call_model(state: MessagesState) -> dict[str, Any]:
             stream = await model.astream_v2(state["messages"])
-            return {"messages": await stream}
+            msg = await stream
+            return {"messages": msg}
 
         graph = (
             StateGraph(MessagesState)
@@ -580,31 +646,37 @@ class TestEndToEnd:
 
         run = await graph.astream_v2({"messages": "hi"})
 
-        async def consume() -> list[str]:
+        async def consume_nested() -> 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_v2 → run.messages (node calls invoke)
-# ---------------------------------------------------------------------------
+        deltas = await asyncio.wait_for(consume_nested(), timeout=2.0)
+        assert "".join(deltas) == "hello world"
 
 
 class TestEndToEndV2Invoke:
-    """Auto-routing path: stream_v2 injects CONFIG_KEY_STREAM_MESSAGES_V2,
-    causing BaseChatModel to drive the v2 protocol event generator even for
-    model.invoke()."""
+    """Nodes call `model.invoke()`; `stream_v2` routes through v2.
+
+    Exercises the auto-routing path added in
+    `feat(core): route invoke through v2 event path for
+    _V2StreamingCallbackHandler`: `stream_v2` injects
+    `CONFIG_KEY_STREAM_MESSAGES_V2` into the config, pregel attaches
+    `StreamMessagesHandlerV2`, `BaseChatModel._should_stream_v2` sees the
+    v2 marker and drives the protocol event generator, and
+    `on_stream_event` forwards each event onto the messages channel.
+    """
+
+    def test_invoke_with_v2_marker_populates_messages(self) -> None:
+        """Node calling `model.invoke()` produces one ChatModelStream with v2 events."""
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
 
-    def _graph(self, model):
         def call_model(state: MessagesState) -> dict[str, Any]:
             return {"messages": model.invoke(state["messages"])}
 
-        return (
+        graph = (
             StateGraph(MessagesState)
             .add_node("call_model", call_model)
             .add_edge(START, "call_model")
@@ -612,19 +684,33 @@ class TestEndToEndV2Invoke:
             .compile()
         )
 
-    def test_invoke_populates_messages(self) -> None:
-        run = self._graph(
-            GenericFakeChatModel(messages=iter(["hello world"]))
-        ).stream_v2({"messages": "hi"})
-        (stream,) = list(run.messages)
-        assert isinstance(stream, ChatModelStream)
-        assert stream.output.text == "hello world"
+        run = graph.stream_v2({"messages": "hi"})
+        streams = list(run.messages)
 
-    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_v2({"messages": "go"})
+        assert len(streams) == 1, (
+            "Expected exactly one ChatModelStream — the streamed invoke and "
+            "the node's return of the same AIMessage must dedupe."
+        )
+        stream = streams[0]
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.content == "hello world"
+
+    def test_invoke_v2_emits_protocol_events(self) -> None:
+        """Iterating the stream yields the full v2 lifecycle (not v1 chunks)."""
+        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_v2({"messages": "go"})
         (stream,) = list(run.messages)
 
         events = list(stream)
@@ -640,16 +726,31 @@ class TestEndToEndV2Invoke:
             assert isinstance(event, dict)
             assert "event" in event
         # Typed projection still assembles the final text.
-        assert stream.output.text == "streamed answer"
+        assert stream.output.content == "streamed answer"
 
-    def test_invoke_text_deltas_iterate(self) -> None:
-        run = self._graph(
-            GenericFakeChatModel(messages=iter(["delta streaming works"]))
-        ).stream_v2({"messages": "hi"})
+    def test_invoke_text_deltas_iterate_live(self) -> None:
+        """`.text` projection yields deltas in order."""
+        model = GenericFakeChatModel(messages=iter(["delta streaming works"]))
+
+        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_v2({"messages": "hi"})
         (stream,) = list(run.messages)
-        assert "".join(stream.text) == "delta streaming works"
 
-    def test_invoke_two_nodes_two_streams(self) -> None:
+        assembled = "".join(stream.text)
+        assert assembled == "delta streaming works"
+
+    def test_invoke_dedupe_survives_multi_node_graph(self) -> None:
+        """Two model-invoking nodes produce exactly two streams, each once."""
         model_a = GenericFakeChatModel(messages=iter(["alpha"]))
         model_b = GenericFakeChatModel(messages=iter(["beta"]))
 
@@ -669,12 +770,18 @@ class TestEndToEndV2Invoke:
             .compile()
         )
 
-        streams = list(graph.stream_v2({"messages": "hi"}).messages)
+        run = graph.stream_v2({"messages": "hi"})
+        streams = list(run.messages)
+
         assert len(streams) == 2
-        assert {s.output.text for s in streams} == {"alpha", "beta"}
+        contents = {s.output.content for s in streams}
+        assert contents == {"alpha", "beta"}
 
     def test_invoke_plus_constructed_message_two_streams(self) -> None:
-        """Live-streamed node + constructed-message node → two ChatModelStreams."""
+        """A v2-streamed node + a node that returns a constructed AIMessage
+        produces two ChatModelStreams — one from the live event lifecycle,
+        one synthesized from the constructed message via `message_to_events`.
+        """
         model = GenericFakeChatModel(messages=iter(["live stream"]))
 
         def streaming_node(state: MessagesState) -> dict[str, Any]:
@@ -695,15 +802,17 @@ class TestEndToEndV2Invoke:
 
         run = graph.stream_v2({"messages": "hi"})
         streams = list(run.messages)
+
         assert len(streams) == 2
         assert streams[0].node == "streaming_node"
-        assert streams[0].output.text == "live stream"
+        assert streams[0].output.content == "live stream"
         assert streams[1].node == "constructed_node"
-        assert streams[1].output.text == "hardcoded"
+        assert streams[1].output.content == "hardcoded"
         assert streams[1].message_id == "constructed-1"
 
     @pytest.mark.anyio
-    async def test_ainvoke_populates_messages(self) -> None:
+    async def test_ainvoke_with_v2_marker_populates_messages(self) -> None:
+        """Async mirror: `model.ainvoke()` + `astream_v2`."""
         model = GenericFakeChatModel(messages=iter(["async invoke"]))
 
         async def call_model(state: MessagesState) -> dict[str, Any]:
@@ -718,21 +827,24 @@ class TestEndToEndV2Invoke:
         )
 
         run = await graph.astream_v2({"messages": "hi"})
-        streams = [s async for s in run.messages]
+
+        streams = []
+        async for stream in run.messages:
+            streams.append(stream)
+
         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
-# ---------------------------------------------------------------------------
+        msg = await streams[0].output
+        assert msg.content == "async invoke"
 
 
 class TestDirectMessagesModeStaysV1:
+    """Regression guard: direct `graph.stream(stream_mode="messages")`
+    (no `stream_v2`) must keep the v1 `(AIMessageChunk, metadata)`
+    tuple shape. The v2 flag is only injected by `stream_v2` / `astream_v2`.
+    """
+
     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_v2 / astream_v2."""
         model = GenericFakeChatModel(messages=iter(["legacy path"]))
 
         def call_model(state: MessagesState) -> dict[str, Any]:
@@ -747,82 +859,28 @@ class TestDirectMessagesModeStaysV1:
         )
 
         parts = list(graph.stream({"messages": "hi"}, stream_mode="messages"))
+        # Should have at least one streamed chunk; each part is
+        # (AIMessageChunk, metadata) — not a v2 event dict.
         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_v2(self) -> None:
-        """An outer `stream_v2()` 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",
-                )
+        for part in parts:
+            payload, _metadata = part
+            assert isinstance(payload, AIMessageChunk), (
+                "direct graph.stream(stream_mode='messages') leaked v2 "
+                "event dicts — stream_v2 flag bled through."
             )
-            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()
+        assembled = "".join(
+            p[0].content for p in parts if isinstance(p[0].content, str)
         )
-
-        result = outer.stream_v2({}).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
-# ---------------------------------------------------------------------------
+        assert assembled == "legacy path"
 
 
 class TestStreamMessagesHandlerV2Unit:
+    """Unit tests on the handler class itself."""
+
     def test_on_llm_new_token_is_noop(self) -> None:
-        """v2 handler must not emit v1 chunks even when on_llm_new_token fires."""
+        """v2 handler must not emit v1 chunks even if `on_llm_new_token` fires
+        (e.g. from a node calling `model.stream()` directly on a v2-flagged run).
+        """
         from uuid import uuid4
 
         from langchain_core.outputs import ChatGenerationChunk
@@ -832,6 +890,9 @@ class TestStreamMessagesHandlerV2Unit:
         emitted: list[Any] = []
         handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
         run_id = uuid4()
+        # Register a fake run so `self.metadata.get(run_id)` would succeed for
+        # other callbacks — this makes sure the no-op is unconditional, not a
+        # side effect of missing metadata.
         handler.metadata[run_id] = ((), {"langgraph_node": "x"})
 
         handler.on_llm_new_token(
@@ -840,37 +901,7 @@ class TestStreamMessagesHandlerV2Unit:
             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,
+        assert emitted == [], (
+            "StreamMessagesHandlerV2.on_llm_new_token must not push to the "
+            "messages stream — it's the v2 marker's guarantee."
         )
-        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_v2.py

@@ -19,11 +19,9 @@ from typing_extensions import TypedDict, assert_type
 
 from langgraph._internal._constants import INTERRUPT
 from langgraph.constants import END, START
-from langgraph.errors import GraphDrained
 from langgraph.func import entrypoint
 from langgraph.graph import StateGraph
 from langgraph.graph.message import MessagesState
-from langgraph.runtime import RunControl
 from langgraph.types import (
     CheckpointPayload,
     CheckpointStreamPart,
@@ -231,32 +229,6 @@ class TestV2Stream:
         for c in chunks:
             _assert_stream_part_shape(c)
 
-    def test_stream_v2_accepts_control_for_drain(self) -> None:
-        class DrainState(TypedDict, total=False):
-            value: str
-            skipped: str
-
-        control = RunControl()
-
-        def first_node(state: DrainState) -> dict[str, str]:
-            control.request_drain("sigterm")
-            return {"value": "done"}
-
-        def second_node(state: DrainState) -> dict[str, str]:
-            return {"skipped": "nope"}
-
-        builder = StateGraph(DrainState)
-        builder.add_node("first", first_node)
-        builder.add_node("second", second_node)
-        builder.add_edge(START, "first")
-        builder.add_edge("first", "second")
-        builder.add_edge("second", END)
-        graph = builder.compile()
-
-        run = graph.stream_v2({}, control=control)
-        with pytest.raises(GraphDrained, match="sigterm"):
-            list(run.values)
-
     def test_subgraphs_ns(self) -> None:
         outer = _make_subgraph()
         chunks = list(

libs/langgraph/tests/test_stream_v2_e2e.py

@@ -1,792 +0,0 @@
-"""End-to-end tests exercising all stream_v2 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_v2 / astream_v2 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_v2 and verify values + lifecycle."""
-        graph = _make_nested_graph()
-        run = graph.stream_v2({"value": "x", "items": []})
-
-        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_v2({"value": "x", "items": []})
-
-        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_v2({"value": "x", "items": []})
-        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_v2({"value": "x", "items": []})
-        snapshots = list(run1.values)
-        final_via_values = snapshots[-1]
-
-        run2 = _make_nested_graph().stream_v2({"value": "x", "items": []})
-        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_v2({"value": "x", "items": []}) 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_v2({"value": "x", "items": []})
-        _ = 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_v2({"messages": "hi"})
-        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_v2({"messages": "go"})
-        (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_v2({"messages": "hi"})
-        (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_v2({"messages": ["hi"], "done": False})
-        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_v2({"messages": ["hi"], "done": False})
-
-        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_v2(
-            {"value": "x", "items": []},
-            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_v2({"value": "x", "items": []})
-        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_v2(
-            {"value": "x", "items": []},
-            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_v2(
-            {"value": "x", "items": []},
-            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_v2({"value": "x", "items": []}, config)
-
-        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_v2({"value": "x", "items": []}, config)
-
-        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_v2({"value": "x", "items": []})
-
-        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_v2({"value": "x", "items": []})
-
-        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_v2({"value": "x", "items": []})
-
-        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_v2({"value": "x", "items": []})
-
-        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_v2({"value": "x", "items": []})
-        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_v2({"value": "x", "items": []})
-        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_v2({"messages": "hi"})
-        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_v2({"value": "x", "items": []}, config)
-
-        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_v2({"value": "x", "items": []})
-        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_v2({"value": "x", "items": []})
-        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_v2({"value": "x", "items": []})
-        _ = 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_v2(
-            {"value": "x", "items": []},
-            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_v2({"value": "x", "items": []})
-
-        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_v2(
-            {"value": "x", "items": []},
-            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_v2({"items": []})
-        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_v2({"value": "x", "items": []})
-        handle_paths: list[tuple[str, ...]] = []
-        for handle in run1.subgraphs:
-            list(handle.values)
-            handle_paths.append(handle.path)
-
-        run2 = _make_nested_graph().stream_v2({"value": "x", "items": []})
-        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_v2(
-            {"messages": "hi"},
-            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_time_travel.py

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

libs/langgraph/tests/test_time_travel_async.py

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

libs/langgraph/tests/test_tool_stream_handler.py

@@ -1,4 +1,4 @@
-"""Tests for StreamToolCallHandler and ToolRuntime.emit_output_delta.
+"""Tests for StreamToolCallHandler and emit_tool_output_delta.
 
 These tests exercise the langgraph-core piece in isolation — the prebuilt
 `ToolCallTransformer` has its own test file. Here we feed real graphs
@@ -13,13 +13,13 @@ from typing import Annotated, Any
 import pytest
 from langchain_core.messages import AIMessage
 from langchain_core.tools import tool
-from langgraph.prebuilt import ToolNode, ToolRuntime
+from langgraph.prebuilt import ToolNode
 from typing_extensions import TypedDict
 
+from langgraph.config import emit_tool_output_delta
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
 from langgraph.graph.message import add_messages
-from langgraph.pregel._tools import _tool_call_writer
 
 
 class _State(TypedDict):
@@ -99,12 +99,12 @@ class TestSyncGraphSyncTool:
         # ToolNode wraps the return in a ToolMessage.
         assert events[1][1]["tool_call_id"] == "tc1"
 
-    def test_emit_output_delta_produces_delta_events(self) -> None:
+    def test_emit_tool_output_delta_produces_delta_events(self) -> None:
         @tool
-        def streaming_echo(text: str, runtime: ToolRuntime) -> str:
+        def streaming_echo(text: str) -> str:
             """stream chunks."""
             for chunk in ("a", "b", "c"):
-                runtime.emit_output_delta(chunk)
+                emit_tool_output_delta(chunk)
             return text
 
         graph = _build_graph(
@@ -146,10 +146,10 @@ class TestSyncGraphSyncTool:
         assert kinds == ["tool-started", "tool-error"]
         assert events[1][1]["message"] == "nope"
 
-    def test_writer_unset_outside_tool(self) -> None:
-        # Outside any tool body the ContextVar that ToolRuntime reads
-        # is unset — emitting from there would be a no-op.
-        assert _tool_call_writer.get() is None
+    def test_emit_outside_tool_is_noop(self) -> None:
+        # Called at import time (outside any tool body) — must not raise.
+        emit_tool_output_delta("ignored")
+        emit_tool_output_delta({"any": "payload"})
 
     def test_no_events_without_tools_mode(self) -> None:
         @tool
@@ -177,9 +177,9 @@ class TestAsyncGraphAsyncTool:
     @pytest.mark.anyio
     async def test_async_tool_produces_events(self) -> None:
         @tool
-        async def aecho(text: str, runtime: ToolRuntime) -> str:
+        async def aecho(text: str) -> str:
             """async echo."""
-            runtime.emit_output_delta(text)
+            emit_tool_output_delta(text)
             return f"got:{text}"
 
         graph = _build_graph(_caller_async("aecho", {"text": "hi"}), [aecho])
@@ -200,10 +200,10 @@ class TestAsyncGraphAsyncTool:
 class TestConcurrentToolCalls:
     def test_parallel_tool_calls_do_not_bleed(self) -> None:
         @tool
-        def streamer(marker: str, runtime: ToolRuntime) -> str:
+        def streamer(marker: str) -> str:
             """emits marker twice."""
-            runtime.emit_output_delta(f"{marker}-1")
-            runtime.emit_output_delta(f"{marker}-2")
+            emit_tool_output_delta(f"{marker}-1")
+            emit_tool_output_delta(f"{marker}-2")
             return marker
 
         def caller(state: _State) -> dict:

libs/langgraph/uv.lock

@@ -1348,11 +1348,10 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.2"
+version = "1.3.0a2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
-    { name = "langchain-protocol" },
     { name = "langsmith" },
     { name = "packaging" },
     { name = "pydantic" },
@@ -1361,26 +1360,14 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a8/03/7219502e8ca728d65eb44d7a3eb60239230742a70dbfc9241b9bfd61c4ab/langchain_core-1.3.2.tar.gz", hash = "sha256:fd7a50b2f28ba561fd9d7f5d2760bc9e06cf00cdf820a3ccafe88a94ffa8d5b7", size = 911813, upload-time = "2026-04-24T15:49:23.699Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7d/d5/8fa4431007cbb7cfed7590f4d6a5dea3ad724f4174d248f6642ef5ce7d05/langchain_core-1.3.2-py3-none-any.whl", hash = "sha256:d44a66127f9f8db735bdfd0ab9661bccb47a97113cfd3f2d89c74864422b7274", size = 542390, upload-time = "2026-04-24T15:49:21.991Z" },
-]
-
-[[package]]
-name = "langchain-protocol"
-version = "0.0.12"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/5c/51/1157009b6f94e6e58be58fa8b620187d657909a8b36a6bf5b0c52a2711f6/langchain_protocol-0.0.12.tar.gz", hash = "sha256:5e14c434290a705c9510fdb1a83ecf7561a5e6e0dfd053930ade80dba069269f", size = 6408, upload-time = "2026-04-25T01:05:01.489Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/95/82/3431e3061c917439589fa88a6b23c9bc0e154cba0f05d2e895a68c76ff74/langchain_protocol-0.0.12-py3-none-any.whl", hash = "sha256:402b61f42d4139692528cf37226c367bb6efc8ff8165b29380accb0abfece7b2", size = 6639, upload-time = "2026-04-25T01:05:00.487Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
 ]
 
 [[package]]
 name = "langgraph"
-version = "1.2.0a1"
+version = "1.1.7a2"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1452,7 +1439,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.3.2,<2" },
+    { name = "langchain-core", specifier = "==1.3.0a2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "../prebuilt" },
     { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1561,7 +1548,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.1.0a1"
+version = "4.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -1609,7 +1596,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.1.0a1"
+version = "3.0.5"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -1719,7 +1706,7 @@ inmem = [
 requires-dist = [
     { name = "click", specifier = ">=8.1.7" },
     { name = "httpx", specifier = ">=0.24.0" },
-    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.9.0" },
+    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
     { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
     { name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
     { name = "pathspec", specifier = ">=0.11.0" },
@@ -1755,7 +1742,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.12"
+version = "1.0.9"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -1764,7 +1751,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.3.1" },
+    { name = "langchain-core", specifier = ">=1.0.0" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
 ]
 
@@ -1839,20 +1826,20 @@ requires-dist = [
 dev = [
     { name = "codespell" },
     { name = "langgraph", editable = "." },
-    { name = "mypy", specifier = "==1.20.2" },
+    { name = "mypy", specifier = "==1.19.1" },
     { name = "pydantic", specifier = ">=2.12.4" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watch" },
-    { name = "ruff", specifier = "==0.15.12" },
+    { name = "ruff", specifier = "==0.15.6" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
 lint = [
     { name = "codespell" },
-    { name = "mypy", specifier = "==1.20.2" },
-    { name = "ruff", specifier = "==0.15.12" },
+    { name = "mypy", specifier = "==1.19.1" },
+    { name = "ruff", specifier = "==0.15.6" },
     { name = "starlette" },
     { name = "ty", specifier = "==0.0.23" },
 ]
@@ -1865,7 +1852,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.31"
+version = "0.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -1875,12 +1862,11 @@ dependencies = [
     { name = "requests" },
     { name = "requests-toolbelt" },
     { name = "uuid-utils" },
-    { 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/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
 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/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
 ]
 
 [package.optional-dependencies]
@@ -2153,7 +2139,7 @@ wheels = [
 
 [[package]]
 name = "nbconvert"
-version = "7.17.1"
+version = "7.17.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -2171,9 +2157,9 @@ dependencies = [
     { name = "pygments" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/01/b1/708e53fe2e429c103c6e6e159106bcf0357ac41aa4c28772bd8402339051/nbconvert-7.17.1.tar.gz", hash = "sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2", size = 865311, upload-time = "2026-04-08T00:44:14.914Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/38/47/81f886b699450d0569f7bc551df2b1673d18df7ff25cc0c21ca36ed8a5ff/nbconvert-7.17.0.tar.gz", hash = "sha256:1b2696f1b5be12309f6c7d707c24af604b87dfaf6d950794c7b07acab96dda78", size = 862855, upload-time = "2026-01-29T16:37:48.478Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/67/f8/bb0a9d5f46819c821dc1f004aa2cc29b1d91453297dbf5ff20470f00f193/nbconvert-7.17.1-py3-none-any.whl", hash = "sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8", size = 261927, upload-time = "2026-04-08T00:44:12.845Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/4b/8d5f796a792f8a25f6925a96032f098789f448571eb92011df1ae59e8ea8/nbconvert-7.17.0-py3-none-any.whl", hash = "sha256:4f99a63b337b9a23504347afdab24a11faa7d86b405e5c8f9881cd313336d518", size = 261510, upload-time = "2026-01-29T16:37:46.322Z" },
 ]
 
 [[package]]
@@ -3031,11 +3017,11 @@ wheels = [
 
 [[package]]
 name = "python-dotenv"
-version = "1.2.2"
+version = "1.2.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135, upload-time = "2026-03-01T16:00:26.196Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f0/26/19cadc79a718c5edbec86fd4919a6b6d3f681039a2f6d66d14be94e75fb9/python_dotenv-1.2.1.tar.gz", hash = "sha256:42667e897e16ab0d66954af0e60a9caa94f0fd4ecf3aaf6d2d260eec1aa36ad6", size = 44221, upload-time = "2025-10-26T15:12:10.434Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101, upload-time = "2026-03-01T16:00:25.09Z" },
+    { url = "https://files.pythonhosted.org/packages/14/1b/a298b06749107c305e1fe0f814c6c74aea7b2f1e10989cb30f544a1b3253/python_dotenv-1.2.1-py3-none-any.whl", hash = "sha256:b81ee9561e9ca4004139c6cbba3a238c32b03e4894671e181b671e8cb8425d61", size = 21230, upload-time = "2025-10-26T15:12:09.109Z" },
 ]
 
 [[package]]

libs/prebuilt/langgraph/prebuilt/__init__.py

@@ -1,5 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
 
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
 from langgraph.prebuilt._tool_call_transformer import ToolCallTransformer
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import (
@@ -14,6 +15,7 @@ from langgraph.prebuilt.tool_validator import ValidationNode
 __all__ = [
     "create_react_agent",
     "ToolNode",
+    "ToolCallStream",
     "ToolCallTransformer",
     "tools_condition",
     "ValidationNode",

libs/prebuilt/langgraph/prebuilt/_tool_call_stream.py

@@ -11,7 +11,7 @@ from __future__ import annotations
 from collections.abc import AsyncIterator, Iterator
 from typing import Any
 
-from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream._event_log import EventLog
 
 
 class ToolCallStream:
@@ -21,7 +21,7 @@ class ToolCallStream:
     are populated as events arrive:
 
     - `tool_call_id`, `tool_name`, `input`: stable from the start event.
-    - `output_deltas`: a `StreamChannel` of delta chunks. Iterate (sync or
+    - `output_deltas`: an `EventLog` of delta chunks. Iterate (sync or
       async) to consume partial output in arrival order.
     - `output`: terminal payload from `tool-finished`, or `None` if the
       call failed or is still in flight.
@@ -51,14 +51,14 @@ class ToolCallStream:
         self.tool_call_id = tool_call_id
         self.tool_name = tool_name
         self.input = input
-        self._output_deltas: StreamChannel[Any] = StreamChannel()
+        self._output_deltas: EventLog[Any] = EventLog()
         self.output: Any = None
         self.error: str | None = None
         self.completed = False
 
     @property
-    def output_deltas(self) -> StreamChannel[Any]:
-        """The channel of streamed `tool-output-delta` payloads.
+    def output_deltas(self) -> EventLog[Any]:
+        """The EventLog of streamed `tool-output-delta` payloads.
 
         Iterate (sync or async depending on how the run was started)
         to consume partial output in arrival order. The log closes when

libs/prebuilt/langgraph/prebuilt/_tool_call_transformer.py

@@ -5,8 +5,8 @@ from __future__ import annotations
 from collections.abc import Awaitable, Callable
 from typing import Any
 
+from langgraph.stream._event_log import EventLog
 from langgraph.stream._types import ProtocolEvent, StreamTransformer
-from langgraph.stream.stream_channel import StreamChannel
 
 from langgraph.prebuilt._tool_call_stream import ToolCallStream
 
@@ -21,12 +21,11 @@ class ToolCallTransformer(StreamTransformer):
     Native transformer — the `tool_calls` projection is exposed as a
     direct attribute on the run stream.
 
-    A nameless `StreamChannel[ToolCallStream]` is used (no protocol
-    auto-forwarding) because the live handles are not serializable and
-    should not be injected into the main event log. Wire consumers
-    subscribe to the `tools` channel instead, where the raw protocol
-    events flow through untouched by this transformer (`process`
-    returns `True`).
+    `EventLog[ToolCallStream]` is used (not `StreamChannel`) because the
+    live handles are not serializable and should not be auto-forwarded
+    onto the main event log. Wire consumers subscribe to the `tools`
+    channel instead, where the raw protocol events flow through
+    untouched by this transformer (`process` returns `True`).
 
     Registered explicitly by users at compile time via
     `builder.compile(transformers=[ToolCallTransformer])` — not a
@@ -38,7 +37,7 @@ class ToolCallTransformer(StreamTransformer):
 
     def __init__(self, scope: tuple[str, ...] = ()) -> None:
         super().__init__(scope)
-        self._log: StreamChannel[ToolCallStream] = StreamChannel()
+        self._log: EventLog[ToolCallStream] = EventLog()
         self._active: dict[str, ToolCallStream] = {}
         self._is_async = False
         self._pump_fn: Callable[[], bool] | None = None

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -82,11 +82,9 @@ from langchain_core.tools.base import (
     _is_injected_arg_type,
     get_all_basemodel_annotations,
 )
-from langgraph._internal._constants import CONF, CONFIG_KEY_READ
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
-from langgraph.pregel._tools import _tool_call_writer
 from langgraph.runtime import ExecutionInfo, ServerInfo  # noqa: TC002
 from langgraph.store.base import BaseStore  # noqa: TC002
 from langgraph.types import Command, Send, StreamWriter
@@ -616,7 +614,6 @@ class _InjectedArgs:
     store: str | None
     runtime: str | None
     all_injected_keys: set[str]
-    _optional_state_args: set[str]
 
 
 class ToolNode(RunnableCallable):
@@ -802,7 +799,7 @@ class ToolNode(RunnableCallable):
         # Construct ToolRuntime instances at the top level for each tool call
         tool_runtimes = []
         for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input, cfg)
+            state = self._extract_state(input)
             tool_runtime = ToolRuntime(
                 state=state,
                 tool_call_id=call["id"],
@@ -810,7 +807,6 @@ class ToolNode(RunnableCallable):
                 context=runtime.context,
                 store=runtime.store,
                 stream_writer=runtime.stream_writer,
-                tools=list(self.tools_by_name.values()),
                 execution_info=runtime.execution_info,
                 server_info=runtime.server_info,
             )
@@ -837,7 +833,7 @@ class ToolNode(RunnableCallable):
         # Construct ToolRuntime instances at the top level for each tool call
         tool_runtimes = []
         for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input, cfg)
+            state = self._extract_state(input)
             tool_runtime = ToolRuntime(
                 state=state,
                 tool_call_id=call["id"],
@@ -845,7 +841,6 @@ class ToolNode(RunnableCallable):
                 context=runtime.context,
                 store=runtime.store,
                 stream_writer=runtime.stream_writer,
-                tools=list(self.tools_by_name.values()),
                 execution_info=runtime.execution_info,
                 server_info=runtime.server_info,
             )
@@ -861,30 +856,14 @@ class ToolNode(RunnableCallable):
 
     def _combine_tool_outputs(
         self,
-        outputs: list[ToolMessage | Command | list[ToolMessage | Command]],
+        outputs: list[ToolMessage | Command],
         input_type: Literal["list", "dict", "tool_calls"],
     ) -> list[Command | list[ToolMessage] | dict[str, list[ToolMessage]]]:
-        # Flatten list entries from tools that returned multiple items
-        flat_outputs: list[ToolMessage | Command]
-        if any(isinstance(output, list) for output in outputs):
-            flat_outputs = []
-            for output in outputs:
-                if isinstance(output, list):
-                    flat_outputs.extend(output)
-                else:
-                    flat_outputs.append(output)
-        else:
-            flat_outputs = cast("list[ToolMessage | Command]", outputs)
-
         # preserve existing behavior for non-command tool outputs for backwards
         # compatibility
-        if not any(isinstance(output, Command) for output in flat_outputs):
+        if not any(isinstance(output, Command) for output in outputs):
             # TypedDict, pydantic, dataclass, etc. should all be able to load from dict
-            return (
-                flat_outputs
-                if input_type == "list"
-                else {self._messages_key: flat_outputs}
-            )
+            return outputs if input_type == "list" else {self._messages_key: outputs}
 
         # LangGraph will automatically handle list of Command and non-command node
         # updates
@@ -894,7 +873,7 @@ class ToolNode(RunnableCallable):
 
         # combine all parent commands with goto into a single parent command
         parent_command: Command | None = None
-        for output in flat_outputs:
+        for output in outputs:
             if isinstance(output, Command):
                 if (
                     output.graph is Command.PARENT
@@ -924,7 +903,7 @@ class ToolNode(RunnableCallable):
         request: ToolCallRequest,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
-    ) -> ToolMessage | Command | list[Command | ToolMessage]:
+    ) -> ToolMessage | Command:
         """Execute tool call with configured error handling.
 
         Args:
@@ -933,7 +912,7 @@ class ToolNode(RunnableCallable):
             config: Runnable configuration.
 
         Returns:
-            ToolMessage, Command, or list of Command/ToolMessage.
+            ToolMessage or Command.
 
         Raises:
             Exception: If tool fails and handle_tool_errors is False.
@@ -965,11 +944,6 @@ class ToolNode(RunnableCallable):
                     call["name"], exc, call["args"], filtered_errors
                 ) from exc
 
-            # Inside try so validation errors route through _handle_tool_errors
-            return self._normalize_tool_response(
-                response, request.tool_call, input_type
-            )
-
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios,
         # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation
@@ -1011,12 +985,23 @@ class ToolNode(RunnableCallable):
                 status="error",
             )
 
+        # Process successful response
+        if isinstance(response, Command):
+            # Validate Command before returning to handler
+            return self._validate_tool_command(response, request.tool_call, input_type)
+        if isinstance(response, ToolMessage):
+            response.content = cast("str | list", msg_content_output(response.content))
+            return response
+
+        msg = f"Tool {call['name']} returned unexpected type: {type(response)}"
+        raise TypeError(msg)
+
     def _run_one(
         self,
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         tool_runtime: ToolRuntime,
-    ) -> ToolMessage | Command | list[Command | ToolMessage]:
+    ) -> ToolMessage | Command:
         """Execute single tool call with wrap_tool_call wrapper if configured.
 
         Args:
@@ -1071,7 +1056,7 @@ class ToolNode(RunnableCallable):
         request: ToolCallRequest,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
-    ) -> ToolMessage | Command | list[Command | ToolMessage]:
+    ) -> ToolMessage | Command:
         """Execute tool call asynchronously with configured error handling.
 
         Args:
@@ -1080,7 +1065,7 @@ class ToolNode(RunnableCallable):
             config: Runnable configuration.
 
         Returns:
-            ToolMessage, Command, or list of Command/ToolMessage.
+            ToolMessage or Command.
 
         Raises:
             Exception: If tool fails and handle_tool_errors is False.
@@ -1112,11 +1097,6 @@ class ToolNode(RunnableCallable):
                     call["name"], exc, call["args"], filtered_errors
                 ) from exc
 
-            # Inside try so validation errors route through _handle_tool_errors
-            return self._normalize_tool_response(
-                response, request.tool_call, input_type
-            )
-
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios,
         # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation
@@ -1158,12 +1138,23 @@ class ToolNode(RunnableCallable):
                 status="error",
             )
 
+        # Process successful response
+        if isinstance(response, Command):
+            # Validate Command before returning to handler
+            return self._validate_tool_command(response, request.tool_call, input_type)
+        if isinstance(response, ToolMessage):
+            response.content = cast("str | list", msg_content_output(response.content))
+            return response
+
+        msg = f"Tool {call['name']} returned unexpected type: {type(response)}"
+        raise TypeError(msg)
+
     async def _arun_one(
         self,
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         tool_runtime: ToolRuntime,
-    ) -> ToolMessage | Command | list[Command | ToolMessage]:
+    ) -> ToolMessage | Command:
         """Execute single tool call asynchronously with awrap_tool_call wrapper if configured.
 
         Args:
@@ -1279,37 +1270,18 @@ class ToolNode(RunnableCallable):
         return None
 
     def _extract_state(
-        self,
-        input: list[AnyMessage] | dict[str, Any] | BaseModel,
-        config: RunnableConfig,
+        self, input: list[AnyMessage] | dict[str, Any] | BaseModel
     ) -> list[AnyMessage] | dict[str, Any] | BaseModel:
-        """Extract state from input.
+        """Extract state from input, handling ToolCallWithContext if present.
 
-        Three input shapes:
+        Args:
+            input: The input which may be raw state or ToolCallWithContext.
 
-        - `ToolCallWithContext` dict — legacy Send payload carrying an inlined
-          state snapshot; return `input["state"]`.
-        - list of `ToolCall` dicts — new Send payload with no inlined state;
-          hydrate state from channels via `CONFIG_KEY_READ`.
-        - regular graph state (dict/list/BaseModel) — return `input` as-is.
+        Returns:
+            The actual state to pass to wrap_tool_call wrappers.
         """
         if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
             return input["state"]
-        if (
-            isinstance(input, list)
-            and input
-            and isinstance(input[-1], dict)
-            and input[-1].get("type") == "tool_call"
-        ):
-            read = config.get(CONF, {}).get(CONFIG_KEY_READ)
-            if read is None:
-                return {}
-            # Pregel installs CONFIG_KEY_READ as
-            # `functools.partial(local_read, scratchpad, channels, managed, task)`.
-            # Match the previous inlined-state contract by reading channels only;
-            # managed values have their own injection path (`ToolRuntime.context`).
-            channels = read.args[1]
-            return cast("dict[str, Any]", read(list(channels), True))
         return input
 
     def _inject_tool_args(
@@ -1361,7 +1333,7 @@ class ToolNode(RunnableCallable):
             return tool_call
 
         tool_call_copy: ToolCall = copy(tool_call)
-        injected_args: dict[str, Any] = {}
+        injected_args = {}
 
         # Inject state
         if injected.state:
@@ -1389,20 +1361,14 @@ class ToolNode(RunnableCallable):
             # Extract state values
             if isinstance(state, dict):
                 for tool_arg, state_field in injected.state.items():
-                    if not state_field:
-                        injected_args[tool_arg] = state
-                    elif state_field in state:
-                        injected_args[tool_arg] = state[state_field]
-                    elif tool_arg not in injected._optional_state_args:
-                        raise KeyError(state_field)
+                    injected_args[tool_arg] = (
+                        state[state_field] if state_field else state
+                    )
             else:
                 for tool_arg, state_field in injected.state.items():
-                    if not state_field:
-                        injected_args[tool_arg] = state
-                    elif hasattr(state, state_field):
-                        injected_args[tool_arg] = getattr(state, state_field)
-                    elif tool_arg not in injected._optional_state_args:
-                        raise AttributeError(state_field)
+                    injected_args[tool_arg] = (
+                        getattr(state, state_field) if state_field else state
+                    )
 
         # Inject store
         if injected.store:
@@ -1429,84 +1395,11 @@ class ToolNode(RunnableCallable):
         tool_call_copy["args"] = {**stripped_args, **injected_args}
         return tool_call_copy
 
-    def _normalize_tool_response(
-        self,
-        response: Any,
-        tool_call: ToolCall,
-        input_type: Literal["list", "dict", "tool_calls"],
-    ) -> ToolMessage | Command | list[Command | ToolMessage]:
-        """Validate and normalize a tool's raw return value."""
-        if isinstance(response, Command):
-            return self._validate_tool_command(response, tool_call, input_type)
-        if isinstance(response, ToolMessage):
-            response.content = cast("str | list", msg_content_output(response.content))
-            return response
-        if isinstance(response, list):
-            if all(isinstance(r, (Command, ToolMessage)) for r in response):
-                return self._validate_tool_command_list(response, tool_call, input_type)
-            msg = (
-                f"Tool {tool_call['name']} returned a list with invalid element "
-                "types: expected all Command or ToolMessage"
-            )
-            raise TypeError(msg)
-        msg = f"Tool {tool_call['name']} returned unexpected type: {type(response)}"
-        raise TypeError(msg)
-
-    def _validate_tool_command_list(
-        self,
-        response: list[Command | ToolMessage],
-        tool_call: ToolCall,
-        input_type: Literal["list", "dict", "tool_calls"],
-    ) -> list[Command | ToolMessage]:
-        """Validate a list of Command/ToolMessage returned by a single tool call.
-
-        Requires exactly one terminating ToolMessage (matching the outer tool_call_id)
-        across the list — either as a top-level element or nested in a
-        Command.update["messages"].
-        """
-        expected_id = tool_call["id"]
-
-        terminator_count = 0
-        for item in response:
-            if isinstance(item, ToolMessage):
-                if item.tool_call_id == expected_id:
-                    terminator_count += 1
-            elif isinstance(item, Command) and isinstance(item.update, dict):
-                for msg in item.update.get(self._messages_key, []):
-                    if isinstance(msg, ToolMessage) and msg.tool_call_id == expected_id:
-                        terminator_count += 1
-
-        if terminator_count != 1:
-            msg = (
-                f"Tool {tool_call['name']} returned a list with "
-                f"{terminator_count} messages bound to tool_call_id "
-                f"{expected_id!r}; expected exactly one terminating ToolMessage."
-            )
-            raise ValueError(msg)
-
-        # Per-Command normalization still runs, but the list-level count above
-        # already guarantees exactly one terminator, so individual Commands may
-        # lack one.
-        validated: list[Command | ToolMessage] = []
-        for item in response:
-            if isinstance(item, Command):
-                validated.append(
-                    self._validate_tool_command(
-                        item, tool_call, input_type, require_terminator=False
-                    )
-                )
-            else:
-                item.content = cast("str | list", msg_content_output(item.content))
-                validated.append(item)
-        return validated
-
     def _validate_tool_command(
         self,
         command: Command,
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
-        *,
-        require_terminator: bool = True,
     ) -> Command:
         if isinstance(command.update, dict):
             # input type is dict when ToolNode is invoked with a dict input
@@ -1556,11 +1449,7 @@ class ToolNode(RunnableCallable):
 
         # validate that we always have a ToolMessage matching the tool call in
         # Command.update if command is sent to the CURRENT graph
-        if (
-            require_terminator
-            and updated_command.graph is None
-            and not has_matching_tool_message
-        ):
+        if updated_command.graph is None and not has_matching_tool_message:
             example_update = (
                 '`Command(update={"messages": '
                 '[ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`'
@@ -1680,7 +1569,6 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
     - `context`: Runtime context (shared with `Runtime`)
     - `store`: `BaseStore` instance for persistent storage (shared with `Runtime`)
     - `stream_writer`: `StreamWriter` for streaming output (shared with `Runtime`)
-    - `tools`: List of all available `BaseTool` instances
 
     No `Annotated` wrapper is needed - just use `runtime: ToolRuntime`
     as a parameter.
@@ -1723,32 +1611,11 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
     context: ContextT
     config: RunnableConfig
     stream_writer: StreamWriter
-    tools: list[BaseTool]
     tool_call_id: str | None
     store: BaseStore | None
     execution_info: ExecutionInfo | None = None
     server_info: ServerInfo | None = None
 
-    def emit_output_delta(self, delta: Any) -> None:
-        """Stream a partial output chunk on the `tools` stream channel.
-
-        Reads the per-tool-call writer that `StreamToolCallHandler`
-        installs on a ContextVar at `on_tool_start` and forwards `delta`
-        through it. Silent no-op when the graph was not run with
-        `"tools"` in `stream_mode` (no writer is set), so tool authors
-        can leave `emit_output_delta` calls in place without gating
-        them on stream mode.
-
-        Args:
-            delta: Partial output chunk. Any JSON-serializable value;
-                surfaced as-is on the `tools` channel's
-                `tool-output-delta` payload under `"delta"`.
-        """
-        writer = _tool_call_writer.get()
-        if writer is None:
-            return
-        writer(delta)
-
 
 class InjectedState(InjectedToolArg):
     """Annotation for injecting graph state into tool arguments.
@@ -1992,7 +1859,6 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
     store_arg: str | None = None
     runtime_arg: str | None = None
     all_injected_keys: set[str] = set()
-    _optional_state_args: set[str] = set()
 
     for name, type_ in all_annotations.items():
         # Track all InjectedToolArg-annotated params (including custom subclasses)
@@ -2007,9 +1873,6 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
         if state_inj := _get_injection_from_type(type_, InjectedState):
             if isinstance(state_inj, InjectedState) and state_inj.field:
                 state_args[name] = state_inj.field
-                field_info = full_schema.model_fields.get(name)
-                if field_info and not field_info.is_required():
-                    _optional_state_args.add(name)
             else:
                 state_args[name] = None
 
@@ -2026,5 +1889,4 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
         store=store_arg,
         runtime=runtime_arg,
         all_injected_keys=all_injected_keys,
-        _optional_state_args=_optional_state_args,
     )

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "1.0.12"
+version = "1.0.9"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -25,12 +25,12 @@ classifiers = [
 ]
 dependencies = [
     "langgraph-checkpoint>=2.1.0,<5.0.0",
-    "langchain-core>=1.3.1",
+    "langchain-core>=1.0.0",
 ]
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/prebuilt"
-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/prebuilt/tests/test_injected_state_not_required.py

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