bump langchain-core to cb/chat-model-updates branch

refactor(langgraph,prebuilt): merge EventLog into StreamChannel with optional name (#7637 )
feat(langgraph): add streaming transformer infrastructure and tests (#7519 )
2026-08-27 01:52:25 +02:00 · 2026-04-29 12:54:30 -04:00 · 2026-04-28 18:43:51 -04:00 · 2026-04-28 20:29:21 +00:00 · 2026-04-28 01:20:26 -07:00 · 2026-04-27 13:11:43 -04:00
64 changed files with 10046 additions and 2203 deletions
@@ -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" != "1.0.1" ]; then
-            echo "LANGCHAIN_OPENAI_VERSION != 1.0.1; $LANGCHAIN_OPENAI_VERSION"
+          if [ "$LANGCHAIN_OPENAI_VERSION" != "1.1.14" ]; then
+            echo "LANGCHAIN_OPENAI_VERSION != 1.1.14; $LANGCHAIN_OPENAI_VERSION"
            exit 1
          fi
          LANGCHAIN_ANTHROPIC_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-anthropic'); print(v);")
@@ -100,4 +100,3 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
-.worktrees/
@@ -4,7 +4,7 @@ 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 (
@@ -32,7 +32,7 @@ Conn = _internal.Conn  # For backward compatibility
 class PostgresSaver(BasePostgresSaver):
    """Checkpointer that stores checkpoints in a Postgres database."""

-    lock: threading.RLock
+    lock: threading.Lock

    def __init__(
        self,
@@ -48,7 +48,7 @@ class PostgresSaver(BasePostgresSaver):

        self.conn = conn
        self.pipe = pipe
-        self.lock = threading.RLock()
+        self.lock = threading.Lock()
        self.supports_pipeline = Capabilities().has_pipeline()

    @classmethod
@@ -442,22 +442,6 @@ class PostgresSaver(BasePostgresSaver):
            including its configuration, metadata, parent checkpoint (if any),
            and pending writes.
        """
-        from langgraph.checkpoint.base import DeltaChannelSentinel
-
-        channel_values = self._load_blobs(value["channel_values"])
-        if any(isinstance(v, DeltaChannelSentinel) for v in channel_values.values()):
-            cp_config = cast(
-                RunnableConfig,
-                {
-                    "configurable": {
-                        "thread_id": value["thread_id"],
-                        "checkpoint_ns": value["checkpoint_ns"],
-                        "checkpoint_id": value["checkpoint_id"],
-                    }
-                },
-            )
-            with self._cursor() as cur:
-                self._resolve_delta_channels(cp_config, channel_values, cur)
        return CheckpointTuple(
            {
                "configurable": {
@@ -470,7 +454,7 @@ class PostgresSaver(BasePostgresSaver):
                **value["checkpoint"],
                "channel_values": {
                    **(value["checkpoint"].get("channel_values") or {}),
-                    **channel_values,
+                    **self._load_blobs(value["channel_values"]),
                },
            },
            value["metadata"],
@@ -13,7 +13,6 @@ from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
-    DeltaChannelSentinel,
    get_checkpoint_id,
    get_serializable_checkpoint_metadata,
 )
@@ -392,58 +391,6 @@ class AsyncPostgresSaver(BasePostgresSaver):
                async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                    yield cur

-    async def _aget_channel_writes_cur(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-        cur: Any,
-    ) -> list[Any]:
-        """Async version of _get_channel_writes_cur — see sync version for rationale."""
-        await cur.execute(
-            "SELECT checkpoint_id, parent_checkpoint_id FROM checkpoints "
-            "WHERE thread_id = %s AND checkpoint_ns = %s",
-            (thread_id, checkpoint_ns),
-        )
-        parent_map: dict[str, str | None] = {
-            row["checkpoint_id"]: row["parent_checkpoint_id"]
-            for row in await cur.fetchall()
-        }
-        ancestor_ids: list[str] = []
-        cid: str | None = parent_map.get(checkpoint_id)
-        while cid is not None:
-            ancestor_ids.append(cid)
-            cid = parent_map.get(cid)
-        if not ancestor_ids:
-            return []
-        await cur.execute(
-            "SELECT checkpoint_id, type, blob FROM checkpoint_writes "
-            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
-            "  AND checkpoint_id = ANY(%s) "
-            "ORDER BY task_id, idx",
-            (thread_id, checkpoint_ns, channel, ancestor_ids),
-        )
-        writes_by_cp: dict[str, list[tuple[str, bytes]]] = defaultdict(list)
-        for row in await cur.fetchall():
-            writes_by_cp[row["checkpoint_id"]].append((row["type"], row["blob"]))
-        result = []
-        for cid in reversed(ancestor_ids):
-            for type_tag, blob in writes_by_cp.get(cid, []):
-                result.append(self.serde.loads_typed((type_tag, blob)))
-        return result
-
-    async def aget_channel_writes(
-        self, config: RunnableConfig, channel: str
-    ) -> list[Any]:
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        checkpoint_id = config["configurable"]["checkpoint_id"]
-        async with self._cursor() as cur:
-            return await self._aget_channel_writes_cur(
-                thread_id, checkpoint_ns, checkpoint_id, channel, cur
-            )
-
    async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.
@@ -456,31 +403,11 @@ class AsyncPostgresSaver(BasePostgresSaver):
            including its configuration, metadata, parent checkpoint (if any),
            and pending writes.
        """
-        thread_id = value["thread_id"]
-        checkpoint_ns = value["checkpoint_ns"]
-        checkpoint_id = value["checkpoint_id"]
-        blob_values = value["channel_values"]
-
-        channel_values: dict[str, Any] = {}
-        if blob_values:
-            channel_values = self._load_blobs(blob_values)
-            delta_channels = [
-                ch
-                for ch, v in channel_values.items()
-                if isinstance(v, DeltaChannelSentinel)
-            ]
-            if delta_channels:
-                async with self._cursor() as cur:
-                    for channel in delta_channels:
-                        channel_values[channel] = await self._aget_channel_writes_cur(
-                            thread_id, checkpoint_ns, checkpoint_id, channel, cur
-                        )
-
        return CheckpointTuple(
            {
                "configurable": {
-                    "thread_id": thread_id,
-                    "checkpoint_ns": checkpoint_ns,
+                    "thread_id": value["thread_id"],
+                    "checkpoint_ns": value["checkpoint_ns"],
                    "checkpoint_id": value["checkpoint_id"],
                }
            },
@@ -488,15 +415,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
                **value["checkpoint"],
                "channel_values": {
                    **(value["checkpoint"].get("channel_values") or {}),
-                    **channel_values,
+                    **self._load_blobs(value["channel_values"]),
                },
            },
            value["metadata"],
            (
                {
                    "configurable": {
-                        "thread_id": thread_id,
-                        "checkpoint_ns": checkpoint_ns,
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
                        "checkpoint_id": value["parent_checkpoint_id"],
                    }
                }
@@ -2,7 +2,6 @@ from __future__ import annotations

 import random
 import warnings
-from collections import defaultdict
 from collections.abc import Sequence
 from importlib.metadata import version as get_version
 from typing import Any, cast
@@ -12,7 +11,6 @@ from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
    ChannelVersions,
-    DeltaChannelSentinel,
    get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
@@ -187,78 +185,15 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
        )

    def _load_blobs(
-        self,
-        blob_values: Any,
+        self, blob_values: list[tuple[bytes, bytes, bytes]]
    ) -> dict[str, Any]:
        if not blob_values:
            return {}
-        result: dict[str, Any] = {}
-        for k, t, v in blob_values:
-            type_tag = t.decode()
-            if type_tag != "empty":
-                result[k.decode()] = self.serde.loads_typed((type_tag, v))
-        return result
-
-    def _resolve_delta_channels(
-        self,
-        config: RunnableConfig,
-        channel_values: dict[str, Any],
-        cur: Any,
-    ) -> None:
-        for channel, value in list(channel_values.items()):
-            if isinstance(value, DeltaChannelSentinel):
-                channel_values[channel] = self._get_channel_writes_cur(
-                    config["configurable"]["thread_id"],
-                    config["configurable"].get("checkpoint_ns", ""),
-                    config["configurable"]["checkpoint_id"],
-                    channel,
-                    cur,
-                )
-
-    def _get_channel_writes_cur(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        channel: str,
-        cur: Any,
-    ) -> list[Any]:
-        """Fetch writes for `channel` across the checkpoint ancestor chain, oldest→newest.
-
-        Two queries:
-        1. Fetch all (checkpoint_id, parent_checkpoint_id) for the thread — cheap, IDs only.
-        2. Walk the ancestor chain in Python, then fetch writes with a plain ANY() filter.
-        """
-        cur.execute(
-            "SELECT checkpoint_id, parent_checkpoint_id FROM checkpoints "
-            "WHERE thread_id = %s AND checkpoint_ns = %s",
-            (thread_id, checkpoint_ns),
-        )
-        parent_map: dict[str, str | None] = {
-            row["checkpoint_id"]: row["parent_checkpoint_id"] for row in cur.fetchall()
+        return {
+            k.decode(): self.serde.loads_typed((t.decode(), v))
+            for k, t, v in blob_values
+            if t.decode() != "empty"
        }
-        ancestor_ids: list[str] = []
-        cid: str | None = parent_map.get(checkpoint_id)
-        while cid is not None:
-            ancestor_ids.append(cid)
-            cid = parent_map.get(cid)
-        if not ancestor_ids:
-            return []
-        cur.execute(
-            "SELECT checkpoint_id, type, blob FROM checkpoint_writes "
-            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
-            "  AND checkpoint_id = ANY(%s) "
-            "ORDER BY task_id, idx",
-            (thread_id, checkpoint_ns, channel, ancestor_ids),
-        )
-        writes_by_cp: dict[str, list[tuple[str, bytes]]] = defaultdict(list)
-        for row in cur.fetchall():
-            writes_by_cp[row["checkpoint_id"]].append((row["type"], row["blob"]))
-        result = []
-        for cid in reversed(ancestor_ids):
-            for type_tag, blob in writes_by_cp.get(cid, []):
-                result.append(self.serde.loads_typed((type_tag, blob)))
-        return result

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

        checkpoint = await saver.aget_tuple(config)
        assert checkpoint.checkpoint["channel_values"] == {}
-
-
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
-async def test_delta_channel_chain_reconstruction(saver_name: str) -> None:
-    """AsyncPostgresSaver reconstructs DeltaChannel chain via point-lookup traversal."""
-    pytest.importorskip(
-        "langgraph.channels.delta", reason="langgraph core not installed"
-    )
-
-    from typing import Annotated
-
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
-    from typing_extensions import TypedDict
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    def respond(state: State) -> dict:
-        n = len(state["messages"])
-        return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-
-    async with _saver(saver_name) as saver:
-        graph = builder.compile(checkpointer=saver)
-        config = {"configurable": {"thread_id": "diff-channel-test-1"}}
-
-        await graph.ainvoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
-        await graph.ainvoke(
-            {"messages": [HumanMessage(content="there", id="h2")]}, config
-        )
-
-        state = await graph.aget_state(config)
-        msgs = state.values["messages"]
-        assert len(msgs) == 4, f"expected 4, got {len(msgs)}: {msgs}"
-        assert msgs[0].content == "hi"
-        assert msgs[1].content == "reply-1"
-        assert msgs[2].content == "there"
-        assert msgs[3].content == "reply-3"
@@ -259,7 +259,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.0.3"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -268,7 +268,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.0.3"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -1,14 +1,11 @@
 from __future__ import annotations

 import copy
-import dataclasses
 import logging
-import threading
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
 from typing import (  # noqa: UP035
    Any,
    Generic,
-    List,
    Literal,
    NamedTuple,
    TypedDict,
@@ -31,21 +28,6 @@ from langgraph.checkpoint.serde.types import (

 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
-
-
-@dataclasses.dataclass
-class DeltaChannelSentinel:
-    """Marker stored in checkpoint_blobs for a DeltaChannel field.
-
-    No data is stored here — the actual per-step writes live in checkpoint_writes
-    and are replayed through the reducer at load time.
-    """
-
-    pass
-
-
-_DELTA_RECONSTRUCTION: threading.local = threading.local()
-
 logger = logging.getLogger(__name__)


@@ -475,56 +457,6 @@ class BaseCheckpointSaver(Generic[V]):
        """
        raise NotImplementedError

-    def get_channel_writes(self, config: RunnableConfig, channel: str) -> List[Any]:  # noqa: UP006
-        """Collect all writes for `channel` across this checkpoint's ancestry, oldest→newest.
-
-        Default implementation walks the full thread history via `list()`. Savers can
-        override with a more efficient query (InMemorySaver and PostgresSaver do this).
-        """
-        # Guard against re-entrant calls: when list() triggers reconstruction which
-        # calls list() again, the inner call returns tuples with DeltaChannelSentinel
-        # in channel_values (which get_channel_writes ignores — it only reads
-        # pending_writes). This breaks the recursion safely.
-        if getattr(_DELTA_RECONSTRUCTION, "active", False):
-            return []
-        _DELTA_RECONSTRUCTION.active = True
-        try:
-            result: list[Any] = []
-            target_id = config["configurable"].get("checkpoint_id")
-            for tup in self.list(config):
-                if tup.config["configurable"].get("checkpoint_id") == target_id:
-                    continue  # skip the checkpoint itself; we want its ancestors' writes
-                if tup.pending_writes:
-                    for _, ch, value in tup.pending_writes:
-                        if ch == channel:
-                            result.append(value)
-            result.reverse()  # list() yields newest→oldest; we want oldest→newest
-            return result
-        finally:
-            _DELTA_RECONSTRUCTION.active = False
-
-    async def aget_channel_writes(
-        self, config: RunnableConfig, channel: str
-    ) -> List[Any]:  # noqa: UP006
-        """Async version of get_channel_writes."""
-        if getattr(_DELTA_RECONSTRUCTION, "active", False):
-            return []
-        _DELTA_RECONSTRUCTION.active = True
-        try:
-            result: list[Any] = []
-            target_id = config["configurable"].get("checkpoint_id")
-            async for tup in self.alist(config):
-                if tup.config["configurable"].get("checkpoint_id") == target_id:
-                    continue
-                if tup.pending_writes:
-                    for _, ch, value in tup.pending_writes:
-                        if ch == channel:
-                            result.append(value)
-            result.reverse()
-            return result
-        finally:
-            _DELTA_RECONSTRUCTION.active = False
-
    def get_next_version(self, current: V | None, channel: None) -> V:
        """Generate the next version ID for a channel.

@@ -9,7 +9,7 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any, cast
+from typing import Any

 from langchain_core.runnables import RunnableConfig

@@ -20,7 +20,6 @@ from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
-    DeltaChannelSentinel,
    SerializerProtocol,
    get_checkpoint_id,
    get_checkpoint_metadata,
@@ -122,60 +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 _resolve_delta_channels(
-        self,
-        config: RunnableConfig,
-        channel_values: dict[str, Any],
-    ) -> None:
-        """Replace DeltaChannelSentinel entries with reconstructed write lists."""
-        for channel, value in list(channel_values.items()):
-            if isinstance(value, DeltaChannelSentinel):
-                channel_values[channel] = self.get_channel_writes(config, channel)
-
-    def get_channel_writes(self, config: RunnableConfig, channel: str) -> list[Any]:
-        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 collecting checkpoint IDs.
-        chain: list[str] = []
-        current: str | None = checkpoint_id
-        while current is not None:
-            entry = ns_storage.get(current)
-            if entry is None:
-                break
-            chain.append(current)
-            _, _, parent = entry
-            current = parent
-        # Collect writes oldest→newest.
-        result: list[Any] = []
-        for cp_id in reversed(chain):
-            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
-            for (_task_id, _idx), (_, ch, serialized, _) in sorted(step_writes.items()):
-                if ch == channel:
-                    result.append(self.serde.loads_typed(serialized))
-        return result
-
-    async def aget_channel_writes(
-        self, config: RunnableConfig, channel: str
-    ) -> list[Any]:
-        return self.get_channel_writes(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.
@@ -198,17 +153,13 @@ class InMemorySaver(
                checkpoint, metadata, parent_checkpoint_id = saved
                writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
-                channel_values = self._load_blobs(
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint_["channel_versions"],
-                )
-                self._resolve_delta_channels(config, channel_values)
                return CheckpointTuple(
                    config=config,
                    checkpoint={
                        **checkpoint_,
-                        "channel_values": channel_values,
+                        "channel_values": self._load_blobs(
+                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
+                        ),
                    },
                    metadata=self.serde.loads_typed(metadata),
                    pending_writes=[
@@ -232,27 +183,19 @@ class InMemorySaver(
                checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
                writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                checkpoint_ = self.serde.loads_typed(checkpoint)
-                resolved_config = cast(
-                    RunnableConfig,
-                    {
+                return CheckpointTuple(
+                    config={
                        "configurable": {
                            "thread_id": thread_id,
                            "checkpoint_ns": checkpoint_ns,
                            "checkpoint_id": checkpoint_id,
                        }
                    },
-                )
-                channel_values = self._load_blobs(
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint_["channel_versions"],
-                )
-                self._resolve_delta_channels(resolved_config, channel_values)
-                return CheckpointTuple(
-                    config=resolved_config,
                    checkpoint={
                        **checkpoint_,
-                        "channel_values": channel_values,
+                        "channel_values": self._load_blobs(
+                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
+                        ),
                    },
                    metadata=self.serde.loads_typed(metadata),
                    pending_writes=[
@@ -347,28 +290,21 @@ class InMemorySaver(

                    checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)

-                    list_config = cast(
-                        RunnableConfig,
-                        {
+                    yield CheckpointTuple(
+                        config={
                            "configurable": {
                                "thread_id": thread_id,
                                "checkpoint_ns": checkpoint_ns,
                                "checkpoint_id": checkpoint_id,
                            }
                        },
-                    )
-                    channel_values = self._load_blobs(
-                        thread_id,
-                        checkpoint_ns,
-                        checkpoint_["channel_versions"],
-                    )
-                    self._resolve_delta_channels(list_config, channel_values)
-
-                    yield CheckpointTuple(
-                        config=list_config,
                        checkpoint={
                            **checkpoint_,
-                            "channel_values": channel_values,
+                            "channel_values": self._load_blobs(
+                                thread_id,
+                                checkpoint_ns,
+                                checkpoint_["channel_versions"],
+                            ),
                        },
                        metadata=metadata,
                        parent_config=(
@@ -55,6 +55,19 @@ _warned_unregistered_types: set[tuple[str, str]] = set()
 _warned_blocked_types: set[tuple[str, str]] = set()


+def _is_safe_json_type(id_list: list[str]) -> bool:
+    """Return True if an lc=2 id refers to a type in SAFE_MSGPACK_TYPES.
+
+    Safe types bypass the ``allowed_json_modules`` gate so that old "json" format
+    checkpoints (written before the msgpack migration) can be resumed without
+    requiring users to configure an explicit allowlist.
+    """
+    if len(id_list) < 2:
+        return False
+    module_name = ".".join(id_list[:-1])
+    return (module_name, id_list[-1]) in _lg_msgpack.SAFE_MSGPACK_TYPES
+
+
 def _warn_once(
    seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
 ) -> None:
@@ -64,14 +77,6 @@ def _warn_once(
    logger.warning(msg, *args)


-def _get_delta_sentinel_cls() -> type:
-    from langgraph.checkpoint.base import (
-        DeltaChannelSentinel,
-    )  # lazy import avoids circular dep
-
-    return DeltaChannelSentinel
-
-
 class JsonPlusSerializer(SerializerProtocol):
    """Serializer that uses ormsgpack, with optional fallbacks.

@@ -172,19 +177,23 @@ class JsonPlusSerializer(SerializerProtocol):
        return out

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

        return LC_REVIVER(value)

@@ -232,6 +241,13 @@ class JsonPlusSerializer(SerializerProtocol):
            method_display = "<init>"

        dotted = ".".join(needed)
+        # Safe types (the same set already allowed for msgpack deserialization) are
+        # permitted without an explicit allowlist — they are known-safe LangGraph and
+        # LangChain types.  This restores backwards-compat for old "json" checkpoints
+        # that pre-date the msgpack migration without reopening the broader security gate.
+        if _is_safe_json_type(list(needed)):
+            return
+
        if not self._allowed_json_modules:
            raise InvalidModuleError(
                f"Refused to deserialize JSON constructor: {dotted} (method: {method_display}). "
@@ -264,8 +280,6 @@ class JsonPlusSerializer(SerializerProtocol):
            return "bytes", obj
        elif isinstance(obj, bytearray):
            return "bytearray", obj
-        elif isinstance(obj, _get_delta_sentinel_cls()):
-            return "delta", b""
        else:
            try:
                return "msgpack", _msgpack_enc(obj)
@@ -288,10 +302,6 @@ class JsonPlusSerializer(SerializerProtocol):
            return ormsgpack.unpackb(
                data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
            )
-        elif type_ == "delta":
-            from langgraph.checkpoint.base import DeltaChannelSentinel
-
-            return DeltaChannelSentinel()
        elif self.pickle_fallback and type_ == "pickle":
            return pickle.loads(data_)
        else:
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.0.3"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"
@@ -45,6 +45,9 @@ dev = [
  "pycryptodome>=3.23.0",
 ]

+[tool.uv.sources]
+langchain-core = { git = "https://github.com/langchain-ai/langchain", branch = "cb/chat-model-updates", subdirectory = "libs/core" }
+
 [tool.hatch.build.targets.wheel]
 include = ["langgraph"]

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


+def test_lc2_json_safe_type_revives_without_allowlist() -> None:
+    """Old 'json' blobs with lc=2 for safe types must revive without an explicit allowlist.
+
+    Regression test for: https://github.com/langchain-ai/langgraph/issues/7498
+    Threads checkpointed before v1.0.1 (pre-msgpack) stored messages as lc=2 JSON
+    constructor dicts. Resuming those threads must reconstruct proper BaseMessage objects
+    rather than returning raw dicts that cause MESSAGE_COERCION_FAILURE in add_messages.
+    """
+    from langchain_core.messages import AIMessage
+
+    serde = JsonPlusSerializer()  # default: _allowed_json_modules=None
+
+    human_blob = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["langchain_core", "messages", "human", "HumanMessage"],
+        "kwargs": {"content": "hello", "type": "human"},
+    }
+    ai_blob = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["langchain_core", "messages", "ai", "AIMessage"],
+        "kwargs": {"content": "hi there", "type": "ai"},
+    }
+    result = serde.loads_typed(("json", json.dumps([human_blob, ai_blob]).encode()))
+
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage), (
+        f"Expected HumanMessage, got {type(result[0])}: {result[0]!r}\n"
+        "lc=2 JSON blobs for safe types must deserialize without an explicit allowlist"
+    )
+    assert result[0].content == "hello"
+    assert isinstance(result[1], AIMessage)
+    assert result[1].content == "hi there"
+
+
+def test_lc2_json_unknown_type_stays_blocked_without_allowlist() -> None:
+    """lc=2 JSON blobs for types NOT in SAFE_MSGPACK_TYPES still require an allowlist."""
+    serde = JsonPlusSerializer()
+    load = {
+        "lc": 2,
+        "type": "constructor",
+        "id": ["pprint", "pprint"],
+        "kwargs": {"object": "HELLO"},
+    }
+    # No allowlist configured → raw dict returned (not raised, not reconstructed)
+    result = serde.loads_typed(("json", json.dumps(load).encode()))
+    assert isinstance(result, dict), "Unknown lc=2 type must stay as raw dict"
+    assert result.get("lc") == 2
+
+
 def test_deserde_invalid_module() -> None:
    serde = JsonPlusSerializer()
    load = {
@@ -997,15 +1048,3 @@ 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_channel_sentinel_serde_round_trip() -> None:
-    from langgraph.checkpoint.base import DeltaChannelSentinel
-    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
-    serde = JsonPlusSerializer()
-    original = DeltaChannelSentinel()
-    type_tag, blob = serde.dumps_typed(original)
-    assert type_tag == "delta"
-    loaded = serde.loads_typed((type_tag, blob))
-    assert isinstance(loaded, DeltaChannelSentinel)
@@ -320,74 +320,3 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
    assert direct is not None
    expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
    assert direct.checkpoint["channel_values"]["foo"] == expected
-
-
-class TestInMemorySaverDeltaChannel:
-    def test_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
-        """_load_blobs returns DeltaChannelSentinel for delta channels (reconstruction deferred)."""
-        from langgraph.checkpoint.base import (
-            DeltaChannelSentinel,
-            empty_checkpoint,
-        )
-
-        saver = InMemorySaver()
-        serde = JsonPlusSerializer()
-
-        thread_id, ns, channel = "t1", "", "messages"
-        v1 = "00000000000000000000000000000001.0000000000000000"
-
-        sentinel = DeltaChannelSentinel()
-        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(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 isinstance(result[channel], DeltaChannelSentinel)
-
-    def test_get_channel_writes_collects_writes(self) -> None:
-        """get_channel_writes collects per-step writes oldest→newest."""
-        from langgraph.checkpoint.base import empty_checkpoint
-
-        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"),
-        }
-        # cp1 has a write for channel
-        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
-            "task1",
-            channel,
-            serde.dumps_typed({"content": "hi"}),
-            "",
-        )
-        # cp2 has a write for channel
-        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
-            "task2",
-            channel,
-            serde.dumps_typed({"content": "bye"}),
-            "",
-        )
-
-        config: RunnableConfig = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": ns,
-                "checkpoint_id": "cp2",
-            }
-        }
-        result = saver.get_channel_writes(config, channel)
-        assert result == [{"content": "hi"}, {"content": "bye"}]
@@ -267,10 +267,11 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.2.28"
-source = { registry = "https://pypi.org/simple" }
+version = "1.3.2"
+source = { git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates#aee50839376e379891c99fcbe6d5264f66dedc68" }
 dependencies = [
    { name = "jsonpatch" },
+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -279,14 +280,22 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f8/a4/317a1a3ac1df33a64adb3670bf88bbe3b3d5baa274db6863a979db472897/langchain_core-1.2.28.tar.gz", hash = "sha256:271a3d8bd618f795fdeba112b0753980457fc90537c46a0c11998516a74dc2cb", size = 846119, upload-time = "2026-04-08T18:19:34.867Z" }
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/bf/efb5e2ed832e4d6d45590e25a9e5191986b291b543bc6a807b48bee070b0/langchain_protocol-0.0.14.tar.gz", hash = "sha256:bc1e8553122e6ede310280462d5813023a172ff2785ccbbdec54d43f3a15e5f2", size = 5862, upload-time = "2026-04-29T16:40:18.657Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a8/92/32f785f077c7e898da97064f113c73fbd9ad55d1e2169cf3a391b183dedb/langchain_core-1.2.28-py3-none-any.whl", hash = "sha256:80764232581eaf8057bcefa71dbf8adc1f6a28d257ebd8b95ba9b8b452e8c6ac", size = 508727, upload-time = "2026-04-08T18:19:32.823Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/e9/06c47ecb2aff08f83dfa30058da3bf86be64862c19569043ed5331bbeecd/langchain_protocol-0.0.14-py3-none-any.whl", hash = "sha256:ffc35089779bd8ca217015180cef5e660fc3b074efdaa0f2e95df73583f1a047", size = 6984, upload-time = "2026-04-29T16:40:17.841Z" },
 ]

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.0.3"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -330,7 +339,7 @@ test = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=0.2.38" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "ormsgpack", specifier = ">=1.12.0" },
 ]

@@ -1 +1 @@
-__version__ = "0.4.23"
+__version__ = "0.4.24"
@@ -0,0 +1,124 @@
+"""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,
+    )
@@ -9,35 +9,12 @@ 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."""
@@ -10,7 +10,13 @@ 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


@@ -440,16 +446,32 @@ def _container_root_for_uv_lock_package(


 def _uv_lock_package_copy_items(
-    package: UvLockPackage, plan: UvLockPlan
+    package: UvLockPackage,
+    plan: UvLockPlan,
+    ignore_spec: pathspec.PathSpec,
 ) -> 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,
@@ -461,18 +483,32 @@ 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))
            )
@@ -956,10 +992,13 @@ 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):
+        for source, destination in _uv_lock_package_copy_items(
+            package, plan, ignore_spec
+        ):
            docker_plan.add_raw(copy_from_project_root(source, destination.as_posix()))
        docker_plan.add_instruction(
            "WORKDIR", plan.container_roots[package.root].as_posix()
@@ -99,6 +99,13 @@ 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__/")
@@ -4,6 +4,7 @@ import os
 import pathlib
 import tempfile
 import textwrap
+from unittest.mock import patch

 import click
 import pytest
@@ -1855,6 +1856,364 @@ 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)
@@ -66,6 +66,9 @@ CONFIG_KEY_RUNTIME = sys.intern("__pregel_runtime")
 # holds a `Runtime` instance with context, store, stream writer, etc.
 CONFIG_KEY_RESUME_MAP = sys.intern("__pregel_resume_map")
 # holds a mapping of task ns -> resume value for resuming tasks
+CONFIG_KEY_STREAM_MESSAGES_V2 = sys.intern("__pregel_stream_messages_v2")
+# when True, attach StreamMessagesHandlerV2 so content-block (v2) events
+# flow through stream_mode="messages"; set by StreamingHandler only.

 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")
@@ -107,6 +110,7 @@ RESERVED = {
    CONFIG_KEY_CHECKPOINT_ID,
    CONFIG_KEY_CHECKPOINT_NS,
    CONFIG_KEY_RESUME_MAP,
+    CONFIG_KEY_STREAM_MESSAGES_V2,
    # other constants
    PUSH,
    PULL,
@@ -1,7 +1,6 @@
 from langgraph.channels.any_value import AnyValue
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
-from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -21,7 +20,6 @@ __all__ = (
    "UntrackedValue",
    "EphemeralValue",
    "BinaryOperatorAggregate",
-    "DeltaChannel",
    "NamedBarrierValue",
    "NamedBarrierValueAfterFinish",
    # topics
@@ -119,12 +119,3 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
        Returns `True` if the channel was updated, `False` otherwise.
        """
        return False
-
-    def after_checkpoint(self, version: Any, checkpoint_id: str | None = None) -> None:
-        """Called after checkpoint() with the assigned version, and after
-        from_checkpoint() with the current channel version.
-
-        No-op by default. Override in channels that track their own version
-        for incremental checkpointing (e.g. DeltaChannel).
-        """
-        pass
@@ -1,137 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Callable, Sequence
-from typing import Any, Generic
-
-from langgraph.checkpoint.base import DeltaChannelSentinel
-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
-from langgraph.errors import EmptyChannelError
-
-__all__ = ("DeltaChannel",)
-
-
-class DeltaChannel(
-    Generic[Value], BaseChannel[list[Value], Value, DeltaChannelSentinel]
-):
-    """A channel that stores only a sentinel in checkpoints; per-step writes are
-    stored in checkpoint_writes and replayed through the operator at load time.
-
-    Use with append-style reducers (e.g. `add_messages`) on long-running threads
-    to eliminate O(N²) blob growth — storage is O(N) using the writes table that
-    every checkpointer already maintains.
-
-    Works with all checkpointers. Savers with dedicated implementations
-    (InMemorySaver, PostgresSaver) reconstruct in one pass; others fall back to
-    walking the checkpoint list.
-
-    Usage::
-
-        class State(TypedDict):
-            messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
-            # Dict-type reducer (type inferred from the Annotated outer type):
-            files: Annotated[dict, DeltaChannel(merge_files)]
-    """
-
-    __slots__ = ("value", "operator")
-
-    def __init__(
-        self,
-        operator: Callable[[list[Value], Any], list[Value]],
-    ) -> None:
-        super().__init__(list)
-        self.operator = operator
-        self.value: list[Value] = []
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, DeltaChannel):
-            return False
-        if (
-            self.operator.__name__ != "<lambda>"
-            and other.operator.__name__ != "<lambda>"
-        ):
-            return self.operator is other.operator
-        return True
-
-    @property
-    def ValueType(self) -> Any:
-        return list[self.typ]  # type: ignore[name-defined]
-
-    @property
-    def UpdateType(self) -> Any:
-        return self.typ | list[self.typ]  # type: ignore[name-defined]
-
-    def copy(self) -> Self:
-        new = DeltaChannel(self.operator)
-        new.typ = self.typ
-        new.key = self.key
-        new.value = self.value if self.value is MISSING else self.value.copy()
-        return new
-
-    def from_checkpoint(self, checkpoint: Any) -> Self:
-        new = DeltaChannel(self.operator)
-        new.typ = self.typ
-        new.key = self.key
-        if checkpoint is MISSING:
-            try:
-                new.value = new.typ()
-            except Exception:
-                new.value = []
-        elif isinstance(checkpoint, list):
-            # Flat list of write values (oldest→newest) from get_channel_writes.
-            try:
-                value: Any = new.typ()
-            except Exception:
-                value = []
-            for write in checkpoint:
-                value = new.operator(value, write)
-            new.value = value
-        else:
-            # Backward compat: plain accumulated value (e.g. from a migrated thread).
-            try:
-                new.value = list(checkpoint)
-            except Exception:
-                new.value = []
-        return new
-
-    def update(self, values: Sequence[Any]) -> bool:
-        if not values:
-            return False
-        seen_overwrite = False
-        for value in values:
-            is_overwrite, overwrite_value = _get_overwrite(value)
-            if is_overwrite:
-                if seen_overwrite:
-                    from langgraph.errors import (
-                        ErrorCode,
-                        InvalidUpdateError,
-                        create_error_message,
-                    )
-
-                    msg = create_error_message(
-                        message="Can receive only one Overwrite value per super-step.",
-                        error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
-                    )
-                    raise InvalidUpdateError(msg)
-                self.value = (
-                    list(overwrite_value) if overwrite_value is not None else self.typ()
-                )
-                seen_overwrite = True
-            elif not seen_overwrite:
-                base = self.typ() if self.value is MISSING else self.value
-                self.value = self.operator(base, value)
-        return True
-
-    def get(self) -> list[Value]:
-        if self.value is MISSING:
-            raise EmptyChannelError()
-        return self.value
-
-    def is_available(self) -> bool:
-        return self.value is not MISSING
-
-    def checkpoint(self) -> DeltaChannelSentinel:
-        return DeltaChannelSentinel()
@@ -1,6 +1,5 @@
 from __future__ import annotations

-import collections.abc
 import inspect
 import logging
 import typing
@@ -48,8 +47,7 @@ from langgraph._internal._pydantic import create_model
 from langgraph._internal._runnable import coerce_to_runnable
 from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs
 from langgraph.channels.base import BaseChannel
-from langgraph.channels.binop import BinaryOperatorAggregate, _strip_extras
-from langgraph.channels.delta import DeltaChannel
+from langgraph.channels.binop import BinaryOperatorAggregate
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -1047,6 +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,
    ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
        """Compiles the `StateGraph` into a `CompiledStateGraph` object.

@@ -1079,12 +1078,18 @@ 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.

        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]] = [
@@ -1162,6 +1167,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            store=store,
            cache=cache,
            name=name or "LangGraph",
+            stream_transformers=transformers,
        )
        compiled._serde_allowlist = serde_allowlist

@@ -1670,18 +1676,6 @@ 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__"):
-                    outer = _strip_extras(typ.__origin__)
-                    if outer in (
-                        collections.abc.Sequence,
-                        collections.abc.MutableSequence,
-                    ):
-                        outer = list
-                    item.typ = outer
-                    try:
-                        item.value = outer()
-                    except Exception:
-                        item.value = []
                return item
            elif isclass(item) and issubclass(item, BaseChannel):
                # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]
@@ -1,6 +1,5 @@
 from __future__ import annotations

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

@@ -13,8 +12,6 @@ from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec

 LATEST_VERSION = 4

-logger = logging.getLogger(__name__)
-

 def empty_checkpoint() -> Checkpoint:
    return Checkpoint(
@@ -70,12 +67,13 @@ def channels_from_checkpoint(
            channel_specs[k] = v
        else:
            managed_specs[k] = v
-    channels: dict[str, BaseChannel] = {}
-    for k, v in channel_specs.items():
-        ch = v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
-        ch.after_checkpoint(checkpoint["channel_versions"].get(k), checkpoint.get("id"))
-        channels[k] = ch
-    return channels, managed_specs
+    return (
+        {
+            k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
+            for k, v in channel_specs.items()
+        },
+        managed_specs,
+    )


 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -891,12 +891,6 @@ class PregelLoop:
            id=self.checkpoint["id"] if exiting else None,
            updated_channels=self.updated_channels,
        )
-        if do_checkpoint and self.channels:
-            for k, ch in self.channels.items():
-                ch.after_checkpoint(
-                    self.checkpoint["channel_versions"].get(k),
-                    self.checkpoint.get("id"),
-                )
        # sanitize TASK channel in the checkpoint before saving (durability=="exit")
        if TASKS in self.checkpoint["channel_values"] and any(
            isinstance(channel, UntrackedValue) for channel in self.channels.values()
@@ -24,6 +24,11 @@ try:
 except ImportError:
    _StreamingCallbackHandler = object  # type: ignore

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

@@ -248,3 +253,126 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
        **kwargs: Any,
    ) -> Any:
        self.metadata.pop(run_id, None)
+
+
+class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler):
+    """v2 variant of `StreamMessagesHandler`.
+
+    Declaring `_V2StreamingCallbackHandler` as a base flips
+    `BaseChatModel.invoke` to route through `_stream_chat_model_events`
+    (firing `on_stream_event`) instead of `_stream` (firing
+    `on_llm_new_token`). Inherits `on_stream_event` from the parent,
+    which forwards protocol events onto the messages stream channel.
+
+    Pregel attaches this class instead of the v1 handler only when
+    `StreamingHandler` opts in via the internal
+    `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
+    `graph.stream(stream_mode="messages")` callers keep the v1
+    AIMessageChunk shape.
+    """
+
+    def on_llm_new_token(
+        self,
+        token: str,
+        *,
+        chunk: ChatGenerationChunk | None = None,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Intentional no-op — v1 chunks are not used on v2-flagged runs.
+
+        The v2 marker already steers `invoke` to the event generator, so
+        `on_llm_new_token` should not fire under normal routing. This
+        override stays a pass-through (no call to `super()`) to make
+        the intent explicit and to guard against any caller (e.g. a
+        node that calls `model.stream()` directly, which still fires
+        the v1 callback) leaking AIMessageChunks onto a v2-flagged
+        messages stream.
+        """
+        # Intentionally empty: v2 handler does not forward v1 chunks.
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        super().__init__(stream, subgraphs, parent_ns=parent_ns)
+        self._streamed_run_ids: set[UUID] = set()
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        if meta := self.metadata.get(run_id):
+            if response.generations and response.generations[0]:
+                gen = response.generations[0][0]
+                if isinstance(gen, ChatGeneration):
+                    if run_id in self._streamed_run_ids:
+                        if gen.message.id is None:
+                            gen.message.id = str(uuid4())
+                        self.seen.add(gen.message.id)
+                    else:
+                        self._emit(meta, gen.message, dedupe=True)
+        self._streamed_run_ids.discard(run_id)
+        self.metadata.pop(run_id, None)
+
+    def on_llm_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._streamed_run_ids.discard(run_id)
+        super().on_llm_error(
+            error,
+            run_id=run_id,
+            parent_run_id=parent_run_id,
+            **kwargs,
+        )
+
+    def on_stream_event(
+        self,
+        event: dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Forward a protocol event from `stream_v2` as a messages stream part.
+
+        Fires once per `MessagesData` event (`message-start`, per-block
+        `content-block-*`, `message-finish`). The transformer layer
+        correlates events back to a single `ChatModelStream` via
+        `metadata["run_id"]` — attached here so the v1
+        `stream_mode="messages"` output (which emits
+        `(AIMessageChunk, metadata)` via `on_llm_new_token`) keeps its
+        original metadata shape.
+
+        Lives on the v2 handler rather than the v1 base: content-block
+        events are a v2-only concept, and forwarding them only when the
+        v2 handler is attached keeps the message channel's shape
+        predictable for v1 callers.
+        """
+        if meta := self.metadata.get(run_id):
+            # Record message_id on message-start so on_chain_end's
+            # dedupe skips the finalized AIMessage the node returns
+            # (otherwise the messages projection double-counts: once
+            # from streaming, once from the chain output).
+            if event.get("event") == "message-start":
+                self._streamed_run_ids.add(run_id)
+                msg_id = event.get("message_id")
+                if msg_id:
+                    self.seen.add(msg_id)
+            v2_meta = {**meta[1], "run_id": str(run_id)}
+            self.stream((meta[0], "messages", (event, v2_meta)))
@@ -0,0 +1,268 @@
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Callable, Iterator
+from contextvars import ContextVar, Token
+from typing import Any, TypeVar, cast
+from uuid import UUID
+
+from langchain_core.callbacks import BaseCallbackHandler
+
+from langgraph._internal._constants import NS_SEP
+from langgraph.constants import TAG_NOSTREAM
+from langgraph.pregel.protocol import StreamChunk
+
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = object  # type: ignore[assignment,misc]
+
+
+T = TypeVar("T")
+
+ToolCallWriter = Callable[[Any], None]
+"""A closure bound to a single tool call that emits `tool-output-delta` events."""
+
+_tool_call_writer: ContextVar[ToolCallWriter | None] = ContextVar(
+    "langgraph_tool_call_writer", default=None
+)
+"""ContextVar holding the writer for the currently-executing tool call.
+
+Set by `StreamToolCallHandler.on_tool_start` and reset on end/error.
+Read by `ToolRuntime.emit_output_delta` (in `langgraph.prebuilt`).
+"""
+
+
+class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
+    """Callback handler that emits tool-call lifecycle events on the stream.
+
+    Fires on LangChain's `on_tool_*` callbacks and pushes to the `tools`
+    stream mode. Emits `tool-started` / `tool-output-delta` /
+    `tool-finished` / `tool-error` payloads keyed by `tool_call_id`.
+
+    While a tool is executing, this handler sets `_tool_call_writer` to a
+    closure bound to that call's namespace and `tool_call_id`.
+    `ToolRuntime.emit_output_delta` reads that ContextVar so tool bodies
+    can stream partial output without threading the writer through their
+    own signature.
+
+    Attached by `Pregel.stream` / `astream` when `"tools"` is in
+    `stream_modes`. `run_inline = True` keeps event ordering
+    deterministic.
+    """
+
+    run_inline = True
+
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        """Configure the handler to stream tool-call events.
+
+        Args:
+            stream: Callable that accepts a `StreamChunk` tuple
+                `(namespace, mode, payload)` and enqueues it.
+            subgraphs: Whether to emit events from tools called inside
+                nested subgraphs. When False, only tools at the
+                handler's own scope (`parent_ns`) emit.
+            parent_ns: Namespace where the handler was attached.
+                Mirrors the `StreamMessagesHandler` escape hatch:
+                tools whose containing namespace equals `parent_ns`
+                still emit even with `subgraphs=False`, so a node that
+                explicitly streams a subgraph with `stream_mode="tools"`
+                sees its own tools.
+        """
+        self.stream = stream
+        self.subgraphs = subgraphs
+        self.parent_ns = parent_ns
+        # run_id → (namespace, tool_call_id, ContextVar token)
+        # `on_tool_end` does not receive `tool_call_id` in kwargs, so
+        # we correlate by `run_id` which is present on every callback.
+        self._run_to_call: dict[
+            UUID, tuple[tuple[str, ...], str, Token[ToolCallWriter | None]]
+        ] = {}
+
+    def _ns_for_emit(
+        self,
+        metadata: dict[str, Any] | None,
+        tags: list[str] | None,
+    ) -> tuple[str, ...] | None:
+        """Resolve the namespace this tool call should emit at, or `None` to skip.
+
+        Mirrors `StreamMessagesHandler.on_chat_model_start`'s namespace
+        derivation: parses `langgraph_checkpoint_ns` (which ends with
+        the `node_name:task_id` of the calling node), drops that
+        trailing segment, and returns the containing subgraph's own
+        namespace. Returns `None` when the call should be silently
+        suppressed:
+
+        - `metadata` is missing — handler is attached to a context
+          without Pregel routing info.
+        - `TAG_NOSTREAM` is in `tags` — caller explicitly opted out.
+        - Tool runs in a subgraph (`len(ns) > 0`) and the handler was
+          attached with `subgraphs=False` and a different `parent_ns`
+          than the call's containing subgraph.
+        """
+        if not metadata:
+            return None
+        if tags and TAG_NOSTREAM in tags:
+            return None
+        nskey = metadata.get("langgraph_checkpoint_ns")
+        if not nskey:
+            ns: tuple[str, ...] = ()
+        else:
+            ns = tuple(cast(str, nskey).split(NS_SEP))[:-1]
+        if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
+            return None
+        return ns
+
+    def _start(
+        self,
+        serialized: dict[str, Any] | None,
+        input_str: str,
+        *,
+        run_id: UUID,
+        metadata: dict[str, Any] | None,
+        tags: list[str] | None,
+        inputs: dict[str, Any] | None,
+        kwargs: dict[str, Any],
+    ) -> None:
+        ns = self._ns_for_emit(metadata, tags)
+        if ns is None:
+            return
+        tool_call_id = cast("str | None", kwargs.get("tool_call_id")) or str(run_id)
+        tool_name = (
+            (serialized or {}).get("name")
+            or cast("str | None", kwargs.get("name"))
+            or ""
+        )
+
+        def writer(delta: Any) -> None:
+            self.stream(
+                (
+                    ns,
+                    "tools",
+                    {
+                        "event": "tool-output-delta",
+                        "tool_call_id": tool_call_id,
+                        "delta": delta,
+                    },
+                )
+            )
+
+        token = _tool_call_writer.set(writer)
+        self._run_to_call[run_id] = (ns, tool_call_id, token)
+
+        payload: dict[str, Any] = {
+            "event": "tool-started",
+            "tool_call_id": tool_call_id,
+            "tool_name": tool_name,
+        }
+        if inputs is not None:
+            payload["input"] = inputs
+        self.stream((ns, "tools", payload))
+
+    def _end(self, output: Any, *, run_id: UUID) -> None:
+        info = self._run_to_call.pop(run_id, None)
+        if info is None:
+            return
+        ns, tool_call_id, token = info
+        self._reset_writer(token)
+        self.stream(
+            (
+                ns,
+                "tools",
+                {
+                    "event": "tool-finished",
+                    "tool_call_id": tool_call_id,
+                    "output": output,
+                },
+            )
+        )
+
+    def _error(self, error: BaseException, *, run_id: UUID) -> None:
+        info = self._run_to_call.pop(run_id, None)
+        if info is None:
+            return
+        ns, tool_call_id, token = info
+        self._reset_writer(token)
+        self.stream(
+            (
+                ns,
+                "tools",
+                {
+                    "event": "tool-error",
+                    "tool_call_id": tool_call_id,
+                    "message": str(error),
+                },
+            )
+        )
+
+    def tap_output_aiter(
+        self, run_id: UUID, output: AsyncIterator[T]
+    ) -> AsyncIterator[T]:
+        """Pass-through — required by the `_StreamingCallbackHandler` protocol."""
+        return output
+
+    def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
+        """Pass-through — sync counterpart to `tap_output_aiter`."""
+        return output
+
+    @staticmethod
+    def _reset_writer(token: Token[ToolCallWriter | None]) -> None:
+        # Token is invalid if `on_tool_end` runs in a different context
+        # than `on_tool_start` (e.g. langchain may hand off to a thread
+        # worker without copying the context). Swallow that case; the
+        # ContextVar lifetime is bounded by the enclosing task anyway.
+        try:
+            _tool_call_writer.reset(token)
+        except ValueError:
+            pass
+
+    # ------------------------------------------------------------------
+    # Sync callbacks
+    # ------------------------------------------------------------------
+
+    def on_tool_start(
+        self,
+        serialized: dict[str, Any],
+        input_str: str,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
+        metadata: dict[str, Any] | None = None,
+        inputs: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._start(
+            serialized,
+            input_str,
+            run_id=run_id,
+            metadata=metadata,
+            tags=tags,
+            inputs=inputs,
+            kwargs=kwargs,
+        )
+
+    def on_tool_end(
+        self,
+        output: Any,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._end(output, run_id=run_id)
+
+    def on_tool_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._error(error, run_id=run_id)
@@ -1,11 +1,9 @@
 from __future__ import annotations

 import ast
-import functools
 import inspect
 import re
 import textwrap
-import types
 from collections.abc import Callable
 from typing import Any

@@ -66,74 +64,46 @@ def find_subgraph_pregel(candidate: Runnable) -> PregelProtocol | None:
    return None


-@functools.lru_cache(maxsize=256)
-def _get_nonlocal_names(code: types.CodeType) -> frozenset[str]:
-    """Return the set of nonlocal variable names referenced by a function.
-
-    Cached by code object so the expensive source fetch + AST parse only
-    happens once per unique function definition across repeated graph compiles.
-
-    Args:
-        code: The code object of the function to analyse.
-
-    Returns:
-        Frozenset of variable names that the function reads from its enclosing
-        scope (free variables and globals referenced in function bodies).
-    """
-    try:
-        source = inspect.getsource(code)
-        tree = ast.parse(textwrap.dedent(source))
-        visitor = FunctionNonLocals()
-        visitor.visit(tree)
-        return frozenset(visitor.nonlocals)
-    except (SyntaxError, TypeError, OSError, SystemError):
-        return frozenset()
-
-
 def get_function_nonlocals(func: Callable) -> list[Any]:
    """Get the nonlocal variables accessed by a function.

-    The expensive source-parsing step is cached by code object; only the
-    cheap closure-variable lookup runs on every call.
-
    Args:
        func: The function to check.

    Returns:
        List[Any]: The nonlocal variables accessed by the function.
    """
-    actual_func = (
-        func.__wrapped__
-        if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
-        else func
-    )
-    # Fast path: no free variables means nothing to scan.
-    if not actual_func.__code__.co_freevars:
-        return []
-
-    nonlocal_names = _get_nonlocal_names(actual_func.__code__)
-    if not nonlocal_names:
-        return []
-
-    closure = inspect.getclosurevars(actual_func)
-    candidates = {**closure.globals, **closure.nonlocals}
-    values: list[Any] = []
-    for k, v in candidates.items():
-        if k in nonlocal_names:
-            values.append(v)
-        for kk in nonlocal_names:
-            if "." in kk and kk.startswith(k):
-                vv = v
-                for part in kk.split(".")[1:]:
-                    if vv is None:
-                        break
-                    else:
-                        try:
-                            vv = getattr(vv, part)
-                        except AttributeError:
+    try:
+        code = inspect.getsource(func)
+        tree = ast.parse(textwrap.dedent(code))
+        visitor = FunctionNonLocals()
+        visitor.visit(tree)
+        values: list[Any] = []
+        closure = (
+            inspect.getclosurevars(func.__wrapped__)
+            if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
+            else inspect.getclosurevars(func)
+        )
+        candidates = {**closure.globals, **closure.nonlocals}
+        for k, v in candidates.items():
+            if k in visitor.nonlocals:
+                values.append(v)
+            for kk in visitor.nonlocals:
+                if "." in kk and kk.startswith(k):
+                    vv = v
+                    for part in kk.split(".")[1:]:
+                        if vv is None:
                            break
-                else:
-                    values.append(vv)
+                        else:
+                            try:
+                                vv = getattr(vv, part)
+                            except AttributeError:
+                                break
+                    else:
+                        values.append(vv)
+    except (SyntaxError, TypeError, OSError, SystemError):
+        return []
+
    return values


@@ -73,6 +73,7 @@ from langgraph._internal._constants import (
    CONFIG_KEY_RUNTIME,
    CONFIG_KEY_SEND,
    CONFIG_KEY_STREAM,
+    CONFIG_KEY_STREAM_MESSAGES_V2,
    CONFIG_KEY_TASK_ID,
    CONFIG_KEY_THREAD_ID,
    ERROR,
@@ -133,10 +134,14 @@ from langgraph.pregel._loop import (
    AsyncPregelLoop,
    SyncPregelLoop,
 )
-from langgraph.pregel._messages import StreamMessagesHandler
+from langgraph.pregel._messages import (
+    StreamMessagesHandler,
+    StreamMessagesHandlerV2,
+)
 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
 from langgraph.pregel._validate import validate_graph, validate_keys
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
@@ -148,6 +153,15 @@ from langgraph.runtime import (
    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,
@@ -340,6 +354,58 @@ class NodeBuilder:
        )


+def _collect_stream_modes(mux: Any) -> list[StreamMode]:
+    """Return the union of `required_stream_modes` across registered transformers.
+
+    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.
+    """
+    modes: set[StreamMode] = set()
+    for transformer in mux._transformers:
+        modes.update(
+            cast(
+                "tuple[StreamMode, ...]",
+                getattr(transformer, "required_stream_modes", ()),
+            )
+        )
+    return 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.
+
+    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.
+    """
+    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__}."
+            )
+
+        def factory(scope: tuple[str, ...], _spec: Callable[..., Any] = spec) -> Any:
+            return _spec(scope)
+
+        factories.append(factory)
+    return factories
+
+
 class Pregel(
    PregelProtocol[StateT, ContextT, InputT, OutputT],
    Generic[StateT, ContextT, InputT, OutputT],
@@ -671,6 +737,7 @@ 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,
        **deprecated_kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
        if (
@@ -717,6 +784,9 @@ 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(
+            stream_transformers or ()
+        )
        self._serde_allowlist: set[tuple[str, ...]] | None = None
        if auto_validate:
            self.validate()
@@ -1049,14 +1119,13 @@ class Pregel(

        step = saved.metadata.get("step", -1) + 1
        stop = step + 2
-        checkpoint = saved.checkpoint
        channels, managed = channels_from_checkpoint(
            self.channels,
-            checkpoint,
+            saved.checkpoint,
        )
        # tasks for this checkpoint
        next_tasks = prepare_next_tasks(
-            checkpoint,
+            saved.checkpoint,
            saved.pending_writes or [],
            self.nodes,
            channels,
@@ -1169,14 +1238,13 @@ class Pregel(

        step = saved.metadata.get("step", -1) + 1
        stop = step + 2
-        checkpoint = saved.checkpoint
        channels, managed = channels_from_checkpoint(
            self.channels,
-            checkpoint,
+            saved.checkpoint,
        )
        # tasks for this checkpoint
        next_tasks = prepare_next_tasks(
-            checkpoint,
+            saved.checkpoint,
            saved.pending_writes or [],
            self.nodes,
            channels,
@@ -1522,8 +1590,9 @@ class Pregel(
            saved = checkpointer.get_tuple(config)
            if saved is not None:
                self._migrate_checkpoint(saved.checkpoint)
-            base_checkpoint = saved.checkpoint if saved else empty_checkpoint()
-            checkpoint = copy_checkpoint(base_checkpoint) if saved else base_checkpoint
+            checkpoint = (
+                copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
+            )
            checkpoint_previous_versions = (
                saved.checkpoint["channel_versions"].copy() if saved else {}
            )
@@ -1967,8 +2036,9 @@ class Pregel(
            saved = await checkpointer.aget_tuple(config)
            if saved is not None:
                self._migrate_checkpoint(saved.checkpoint)
-            base_checkpoint = saved.checkpoint if saved else empty_checkpoint()
-            checkpoint = copy_checkpoint(base_checkpoint) if saved else base_checkpoint
+            checkpoint = (
+                copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
+            )
            checkpoint_previous_versions = (
                saved.checkpoint["channel_versions"].copy() if saved else {}
            )
@@ -2582,19 +2652,7 @@ class Pregel(
        stream = SyncQueue()

        config = ensure_config(self.config, config)
-        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,
-        )
+        run_manager = None
        try:
            # assign defaults
            (
@@ -2615,6 +2673,36 @@ 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.",
@@ -2626,14 +2714,33 @@ 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
+                    else StreamMessagesHandler
+                )
                run_manager.inheritable_handlers.append(
-                    StreamMessagesHandler(
+                    messages_handler_cls(
                        stream.put,
                        subgraphs,
                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
                    )
                )

+            # 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,
+                    )
+                )
+
            # set up custom stream mode
            if "custom" in stream_modes:

@@ -2804,7 +2911,8 @@ class Pregel(
            # set final channel values as run output
            run_manager.on_chain_end(loop.output)
        except BaseException as e:
-            run_manager.on_chain_error(e)
+            if run_manager is not None:
+                run_manager.on_chain_error(e)
            raise

    @overload
@@ -2944,33 +3052,7 @@ class Pregel(
        )

        config = ensure_config(self.config, config)
-        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
-        )
+        run_manager = None
        try:
            # assign defaults
            (
@@ -2991,6 +3073,50 @@ 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.",
@@ -3003,14 +3129,33 @@ 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
+                    else StreamMessagesHandler
+                )
                run_manager.inheritable_handlers.append(
-                    StreamMessagesHandler(
+                    messages_handler_cls(
                        stream_put,
                        subgraphs,
                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
                    )
                )

+            # 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,
+                    )
+                )
+
            # set up custom stream mode
            def stream_writer(c: Any) -> None:
                aioloop.call_soon_threadsafe(
@@ -3234,9 +3379,150 @@ class Pregel(
            # set final channel values as run output
            await run_manager.on_chain_end(loop.output)
        except BaseException as e:
-            await asyncio.shield(run_manager.on_chain_error(e))
+            if run_manager is not None:
+                await asyncio.shield(run_manager.on_chain_error(e))
            raise

+    def stream_v2(
+        self,
+        input: InputT | Command | None,
+        config: RunnableConfig | None = None,
+        *,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
+    ) -> Any:
+        """Start a sync v2 streaming run driven by transformer projections.
+
+        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.
+
+        Note:
+            Nesting v1 `stream(stream_mode="messages")` inside a node
+            of a `stream_v2` run is not fully supported. The outer v2
+            messages handler is inheritable, so it sits in the inner
+            chat model's callback chain; `BaseChatModel.invoke` then
+            routes through the v2 event protocol and the inner v1
+            messages handler does not see `on_llm_new_token` chunks.
+            The inner stream still yields a finalized message via
+            `on_llm_end`, but token-by-token output is lost. Use
+            `stream_v2` for the inner graph as well, or call
+            `chat_model.stream(...)` explicitly inside the node, 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.
+            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.
+
+        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,
+        )
+        values_t = cast(ValuesTransformer, mux.transformer_by_key("values"))
+        graph_iter = iter(
+            self.stream(
+                input,
+                patch_configurable(config, {CONFIG_KEY_STREAM_MESSAGES_V2: True}),
+                stream_mode=_collect_stream_modes(mux),
+                subgraphs=True,
+                version="v2",
+                interrupt_before=interrupt_before,
+                interrupt_after=interrupt_after,
+            )
+        )
+        return GraphRunStream(graph_iter, mux, values_t)
+
+    async def astream_v2(
+        self,
+        input: InputT | Command | None,
+        config: RunnableConfig | None = None,
+        *,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        transformers: Sequence[Callable[[tuple[str, ...]], Any]] | None = None,
+    ) -> Any:
+        """Async counterpart to `stream_v2`.
+
+        Returns an `AsyncGraphRunStream` whose projections can be awaited
+        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. The inner stream still
+            yields a finalized message at end-of-call. Use
+            `astream_v2` for the inner graph as well, or call
+            `chat_model.astream(...)` explicitly inside the node, 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.
+            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.
+        """
+        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,
+        )
+        values_t = cast(ValuesTransformer, mux.transformer_by_key("values"))
+        graph_aiter = self.astream(
+            input,
+            patch_configurable(config, {CONFIG_KEY_STREAM_MESSAGES_V2: True}),
+            stream_mode=_collect_stream_modes(mux),
+            subgraphs=True,
+            version="v2",
+            interrupt_before=interrupt_before,
+            interrupt_after=interrupt_after,
+        ).__aiter__()
+        return AsyncGraphRunStream(graph_aiter, mux, values_t)
+
    @overload
    def invoke(
        self,
@@ -3712,6 +3998,24 @@ 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:
@@ -0,0 +1,35 @@
+"""Streaming infrastructure for LangGraph.
+
+Compile a graph with `transformers=[...]` and call `graph.stream_v2()` /
+`graph.astream_v2()` to drive a transformer pipeline that projects the
+graph's raw events into ergonomic per-channel streams.
+"""
+
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    AsyncSubgraphRunStream,
+    GraphRunStream,
+    SubgraphRunStream,
+)
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    LifecyclePayload,
+    LifecycleTransformer,
+    SubgraphStatus,
+    SubgraphTransformer,
+)
+
+__all__ = [
+    "AsyncGraphRunStream",
+    "AsyncSubgraphRunStream",
+    "GraphRunStream",
+    "LifecyclePayload",
+    "LifecycleTransformer",
+    "ProtocolEvent",
+    "StreamChannel",
+    "StreamTransformer",
+    "SubgraphRunStream",
+    "SubgraphStatus",
+    "SubgraphTransformer",
+]
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+import time
+from typing import Any, cast
+
+from langgraph.stream._types import ProtocolEvent, _ProtocolEventParams
+from langgraph.types import StreamPart
+
+
+def convert_to_protocol_event(part: StreamPart) -> ProtocolEvent:
+    """Convert a v2 StreamPart to a ProtocolEvent.
+
+    Args:
+        part: A stream part with keys `type`, `ns`, `data`, and
+            optionally `interrupts` (present on values events).
+
+    Returns:
+        The equivalent ProtocolEvent.
+    """
+    part_dict = cast(dict[str, Any], part)
+    params: _ProtocolEventParams = {
+        "namespace": list(part_dict["ns"]),
+        "timestamp": int(time.time() * 1000),
+        "data": part_dict["data"],
+    }
+    if "interrupts" in part_dict:
+        params["interrupts"] = part_dict["interrupts"]
+    return {
+        "type": "event",
+        "method": part_dict["type"],
+        "params": params,
+    }
@@ -0,0 +1,491 @@
+from __future__ import annotations
+
+import asyncio
+import time
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langgraph.stream._types import (
+    ProtocolEvent,
+    StreamTransformer,
+    transformer_requires_async,
+)
+from langgraph.stream.stream_channel import StreamChannel
+
+TransformerFactory = Callable[["tuple[str, ...]"], StreamTransformer]
+"""Factory that builds a scoped transformer for a mux.
+
+Called once per `StreamMux` with the mux's scope (typically `()` for
+the root). Standard transformer classes accept a single positional
+scope argument, so the class itself is a valid factory. User
+transformers can close over their config:
+`lambda scope: MyTransformer(scope, foo=...)`.
+"""
+
+
+class StreamMux:
+    """Central event dispatcher for the streaming infrastructure.
+
+    Owns the main event log and routes events through a transformer
+    pipeline. StreamChannels with a name discovered in transformer
+    projections are auto-wired so that every `push()` also injects a
+    `ProtocolEvent` into the main log. StreamChannels without a name
+    are local-only.
+
+    Pass `is_async=True` when the mux will be consumed via async
+    iteration (`handler.astream()`). All StreamChannel instances
+    discovered during registration are automatically bound to the
+    matching mode.
+
+    Attributes:
+        extensions: Merged projection dict across all registered
+            transformers. Treat as read-only — mutations won't be
+            reflected back in individual transformers' state.
+        native_keys: Projection keys contributed by transformers with
+            `_native = True`.
+    """
+
+    def __init__(
+        self,
+        transformers: list[StreamTransformer] | None = None,
+        *,
+        is_async: bool = False,
+        factories: list[TransformerFactory] | None = None,
+        scope: tuple[str, ...] = (),
+        _assign_seq: bool = True,
+    ) -> None:
+        """Initialize the mux and register transformers in order.
+
+        Callers pass either `transformers` (pre-built instances) or
+        `factories` (callables producing fresh instances per mux). Each
+        transformer's `init()` is called, projections are merged into
+        `extensions`, `_native` keys are recorded in `native_keys`, and
+        any StreamChannel instances are bound and (if named) wired.
+
+        Args:
+            transformers: Already-built transformer instances. Registered
+                only on this mux — they are NOT cloned into child
+                mini-muxes built by `_make_child`. Use `factories` for
+                transformers that should propagate to nested scopes.
+            is_async: True for async dispatch (`apush` / `aclose` /
+                `afail`), False for the sync path.
+            factories: One-argument callables `(scope) -> StreamTransformer`.
+                Called once with this mux's `scope` here, and cloned
+                again per child scope by `_make_child` so each
+                sub-mux gets fresh instances.
+            scope: The namespace the mux operates within. The root mux
+                is `()`.
+            _assign_seq: Internal flag for child muxes. Root muxes assign
+                monotonic `seq` numbers when appending to their main event
+                log; child muxes share forwarded event objects and must not
+                mutate their envelopes.
+
+        Raises:
+            RuntimeError: If any transformer requires an async run but
+                the mux is in sync mode.
+            TypeError: If a transformer's `init()` doesn't return a dict.
+            ValueError: If transformers' projection keys collide.
+        """
+        self.is_async = is_async
+        self.scope: tuple[str, ...] = scope
+        self._assign_seq = _assign_seq
+        self._events: StreamChannel[ProtocolEvent] = StreamChannel()
+        self._events._bind(is_async=is_async)
+        self._transformers: list[StreamTransformer] = []
+        self._channels: list[StreamChannel[Any]] = []
+        self._seq = 0
+
+        self.extensions: dict[str, Any] = {}
+        self.native_keys: set[str] = set()
+        self._projection_owners: dict[str, str] = {}
+        self._transformer_by_key: dict[str, StreamTransformer] = {}
+
+        # Stored only when constructed from factories — used by
+        # `_make_child` to clone the transformer pipeline at a deeper
+        # scope. Pre-built transformers can't be cloned, so a mux
+        # built with `transformers=` rejects child construction.
+        self._factories: list[TransformerFactory] | None = (
+            list(factories) if factories is not None else None
+        )
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+        # Factories run first (they propagate to child mini-muxes
+        # via `_make_child`), then any pre-built `transformers=`
+        # instances are registered as root-only — they aren't cloned
+        # for child scopes.
+        if factories is not None:
+            for factory in factories:
+                self._register(factory(scope))
+        for transformer in transformers or ():
+            self._register(transformer)
+
+    def transformer_by_key(self, key: str) -> StreamTransformer | None:
+        """Return the transformer that contributed `key` to the projection."""
+        return self._transformer_by_key.get(key)
+
+    # ------------------------------------------------------------------
+    # Pump wiring + mini-mux nesting
+    # ------------------------------------------------------------------
+
+    def bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback onto every projection in this mux.
+
+        Records the pump on the mux so child mini-muxes built by
+        `_make_child` can inherit it. Propagates to:
+        - the main event log (`self._events`)
+        - every projection StreamChannel in `extensions`
+        - any registered transformer that exposes `_bind_pump` (e.g.
+          `MessagesTransformer` so `ChatModelStream` instances drive the
+          shared pump from their cursors)
+        """
+        self._pump_fn = fn
+        self._events._request_more = fn
+        for ch in self._channels:
+            ch._request_more = fn
+        for transformer in self._transformers:
+            bind = getattr(transformer, "_bind_pump", None)
+            if bind is not None:
+                bind(fn)
+
+    def bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Async counterpart to `bind_pump`."""
+        self._apump_fn = fn
+        self._events._arequest_more = fn
+        for ch in self._channels:
+            ch._arequest_more = fn
+        for transformer in self._transformers:
+            abind = getattr(transformer, "_bind_apump", None)
+            if abind is not None:
+                abind(fn)
+
+    def _make_child(self, scope: tuple[str, ...]) -> StreamMux:
+        """Build a mini-mux with the same factories scoped to `scope`.
+
+        Used by `SubgraphTransformer` to attach a fresh transformer
+        pipeline to each discovered subgraph handle. The child mux
+        inherits the current pump bindings (so cursors on its
+        projection logs drive the root pump), carries the same factory
+        list forward to any grandchild subgraphs, and does not assign
+        `seq` numbers so forwarded events can be shared without
+        mutating their envelope.
+
+        Raises:
+            RuntimeError: If the mux was not constructed with
+                `factories=`. Mini-muxes require factories so each scope
+                gets its own fresh transformer instances.
+        """
+        if self._factories is None:
+            raise RuntimeError(
+                "StreamMux._make_child requires the mux to be constructed "
+                "with `factories=`; pre-built transformers can't be "
+                "cloned to a new scope."
+            )
+        child = StreamMux(
+            factories=self._factories,
+            is_async=self.is_async,
+            scope=scope,
+            _assign_seq=False,
+        )
+        if self._pump_fn is not None:
+            child.bind_pump(self._pump_fn)
+        if self._apump_fn is not None:
+            child.bind_apump(self._apump_fn)
+        return child
+
+    def _register(self, transformer: StreamTransformer) -> None:
+        """Register a single transformer.
+
+        Calls `transformer.init()`, stores the transformer for event
+        processing, binds any StreamChannel instances in the projection,
+        and merges the projection into `extensions`.
+        """
+        if transformer_requires_async(transformer) and not self.is_async:
+            raise RuntimeError(
+                f"{type(transformer).__name__} requires an async run — "
+                "it overrides aprocess/afinalize/afail or sets "
+                "requires_async=True. Use astream(), not stream()."
+            )
+        projection = transformer.init()
+        if not isinstance(projection, dict):
+            raise TypeError(
+                f"StreamTransformer.init() must return a dict, "
+                f"got {type(projection).__name__}"
+            )
+        conflicts = set(projection) & set(self.extensions)
+        if conflicts:
+            attributions = ", ".join(
+                f"{key!r} (owned by {self._projection_owners[key]})"
+                for key in sorted(conflicts)
+            )
+            raise ValueError(
+                f"Transformer {type(transformer).__name__} returned "
+                f"projection keys that conflict with already-registered "
+                f"keys: {attributions}"
+            )
+        is_native = bool(getattr(transformer, "_native", False))
+        self._transformers.append(transformer)
+        self._bind_and_wire(projection, native=is_native)
+        self.extensions.update(projection)
+        owner_name = type(transformer).__name__
+        for key in projection:
+            self._projection_owners[key] = owner_name
+            self._transformer_by_key[key] = transformer
+        if is_native:
+            self.native_keys.update(projection.keys())
+        transformer._on_register(self)
+
+    def push(self, event: ProtocolEvent) -> None:
+        """Route an event through all transformers, then append to the main log.
+
+        Each transformer's `process()` is called in registration order.
+        If any transformer returns False, the event is suppressed from
+        the main log, but transformers that already saw it keep their
+        side effects.
+
+        On the root mux, `seq` is assigned right before an event enters
+        the main log, not before the transformer pipeline runs. This
+        ensures that events auto-forwarded from StreamChannels during
+        `process()` get earlier seq numbers than the original event,
+        preserving monotonic ordering in the root log. Child muxes do
+        not assign `seq`, so subgraph forwarding can share event objects
+        without mutating their envelopes.
+
+        Args:
+            event: The protocol event to dispatch.
+        """
+        keep = True
+        for transformer in self._transformers:
+            if not transformer.process(event):
+                keep = False
+        if keep:
+            if self._assign_seq:
+                self._seq += 1
+                event["seq"] = self._seq
+            self._events.push(event)
+
+    def close(self) -> None:
+        """Finalize all transformers, close all projections and the main log.
+
+        StreamChannels discovered in transformer projections are
+        auto-closed after `finalize()` runs — transformers don't need
+        to close them manually. If any transformer's `finalize()` raises,
+        the remaining transformers, projections, and the main log are
+        still closed; the first error is re-raised after cleanup
+        completes.
+
+        Raises:
+            BaseException: The first error raised by a transformer's
+                `finalize()`, re-raised after cleanup finishes.
+        """
+        first_error: BaseException | None = None
+        for transformer in self._transformers:
+            try:
+                transformer.finalize()
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        for ch in self._channels:
+            if not ch._closed:
+                ch.close()
+        self._events.close()
+        if first_error is not None:
+            raise first_error
+
+    def fail(self, err: BaseException) -> None:
+        """Fail all transformers, projections, and the main log.
+
+        StreamChannels discovered in transformer projections are
+        auto-failed — transformers don't need to fail them manually.
+        If any transformer's `fail()` raises, the remaining
+        transformers, projections, and the main log are still failed.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        for transformer in self._transformers:
+            try:
+                transformer.fail(err)
+            except BaseException:
+                pass
+        for ch in self._channels:
+            if not ch._closed:
+                ch.fail(err)
+        self._events.fail(err)
+
+    # ------------------------------------------------------------------
+    # Async dispatch
+    # ------------------------------------------------------------------
+
+    async def apush(self, event: ProtocolEvent) -> None:
+        """Dispatch an event on the async lane.
+
+        Awaits each transformer's `aprocess` in registration order
+        before appending to the main log. A slow `aprocess` serializes
+        the pipeline by design — that's the guarantee that lets a later
+        transformer (or a synchronous consumer) see the result of the
+        async work. For decoupled work, use `schedule()` from inside
+        `process` / `aprocess` instead.
+
+        The main log append is a non-blocking `push` — matching v1's
+        `put_nowait` shape. The root mux assigns `seq`; child muxes do
+        not, so forwarded subgraph events can be shared without copying.
+        Memory is bounded by caller pace via the caller-driven pump; see
+        `StreamChannel` for the full tradeoff story.
+
+        Args:
+            event: The protocol event to dispatch.
+        """
+        keep = True
+        for transformer in self._transformers:
+            if not await transformer.aprocess(event):
+                keep = False
+        if keep:
+            if self._assign_seq:
+                self._seq += 1
+                event["seq"] = self._seq
+            self._events.push(event)
+
+    async def aclose(self) -> None:
+        """Finalize on the async lane.
+
+        Awaits every task started via `StreamTransformer.schedule()`
+        across all transformers, then calls `afinalize()` on each,
+        then auto-closes channels and the main event log.
+
+        If any scheduled task raised under `on_error="raise"`, or any
+        transformer's `afinalize` raises, the exception propagates.
+        The caller (the pump) handles it by routing into `afail`.
+
+        Raises:
+            BaseException: The first scheduled-task or `afinalize`
+                error, re-raised after cleanup.
+        """
+        pending = self._collect_scheduled_tasks()
+        if pending:
+            results = await asyncio.gather(*pending, return_exceptions=True)
+            first_err = next(
+                (
+                    r
+                    for r in results
+                    if isinstance(r, BaseException)
+                    and not isinstance(r, asyncio.CancelledError)
+                ),
+                None,
+            )
+            if first_err is not None:
+                raise first_err
+
+        first_error: BaseException | None = None
+        for transformer in self._transformers:
+            try:
+                await transformer.afinalize()
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        for ch in self._channels:
+            if not ch._closed:
+                ch.close()
+        self._events.close()
+        if first_error is not None:
+            raise first_error
+
+    async def afail(self, err: BaseException) -> None:
+        """Fail on the async lane.
+
+        Cancels every scheduled task across all transformers, awaits
+        them to completion, then runs each transformer's `afail` hook
+        and auto-fails channels and the main event log.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        pending = self._collect_scheduled_tasks()
+        for task in pending:
+            task.cancel()
+        if pending:
+            await asyncio.gather(*pending, return_exceptions=True)
+
+        for transformer in self._transformers:
+            try:
+                await transformer.afail(err)
+            except BaseException:
+                pass
+        for ch in self._channels:
+            if not ch._closed:
+                ch.fail(err)
+        if not self._events._closed:
+            self._events.fail(err)
+
+    def _collect_scheduled_tasks(self) -> list[asyncio.Task[Any]]:
+        """Return a snapshot of in-flight tasks scheduled via transformers."""
+        return [
+            task
+            for transformer in self._transformers
+            for task in getattr(transformer, "_stream_scheduled_tasks", ())
+            if not task.done()
+        ]
+
+    # ------------------------------------------------------------------
+    # Binding and StreamChannel auto-wiring
+    # ------------------------------------------------------------------
+
+    def _bind_and_wire(
+        self, projection: dict[str, Any], *, native: bool = False
+    ) -> None:
+        """Bind and optionally wire StreamChannel instances in a projection.
+
+        All StreamChannels are bound and tracked. Channels with a name
+        are additionally wired for protocol auto-forwarding.
+
+        Args:
+            projection: The projection dict returned by a transformer's
+                `init()`.
+            native: True when the owning transformer is `_native`.
+                Named channels owned by a native transformer use the
+                channel name directly as the protocol method;
+                user-defined channels are prefixed with `custom:`.
+        """
+        for value in projection.values():
+            if isinstance(value, StreamChannel):
+                value._bind(is_async=self.is_async)
+                self._channels.append(value)
+                if value.name is not None:
+                    method = value.name if native else f"custom:{value.name}"
+
+                    def _make_forward(method_name: str) -> Callable[[Any], None]:
+                        def _forward(item: Any) -> None:
+                            self._forward(method_name, item)
+
+                        return _forward
+
+                    value._wire(_make_forward(method))
+
+    def _forward(self, method: str, item: Any) -> None:
+        """Inject a ProtocolEvent for a StreamChannel push.
+
+        Forwarded events bypass the transformer pipeline to avoid
+        infinite recursion (a transformer that pushes to a channel
+        during `process()` would re-trigger itself). These events are
+        visible in this mux's main event log but are not passed through
+        transformers' `process()` methods. Only the root mux assigns
+        `seq` to forwarded channel events.
+
+        Args:
+            method: The full protocol method (already with or without
+                the `custom:` prefix; resolved by `_bind_and_wire`).
+            item: The payload pushed onto the channel.
+        """
+        event: ProtocolEvent = {
+            "type": "event",
+            "method": method,
+            "params": {
+                "namespace": [],
+                "timestamp": int(time.time() * 1000),
+                "data": item,
+            },
+        }
+        if self._assign_seq:
+            self._seq += 1
+            event["seq"] = self._seq
+        self._events.push(event)
@@ -0,0 +1,313 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Coroutine
+from typing import Any, ClassVar, Literal
+
+from typing_extensions import NotRequired, TypedDict
+
+_logger = logging.getLogger(__name__)
+
+
+class _ProtocolEventParams(TypedDict):
+    """Parameters for a protocol event.
+
+    `timestamp` is wall-clock milliseconds since the epoch and can go
+    backwards across NTP adjustments — use `ProtocolEvent.seq` for
+    ordering.
+    """
+
+    namespace: list[str]
+    timestamp: int
+    data: Any
+    interrupts: NotRequired[tuple[Any, ...]]
+
+
+class ProtocolEvent(TypedDict):
+    """A protocol event emitted by the streaming infrastructure.
+
+    Wraps a raw stream part (values, messages, custom, etc.) in a uniform
+    envelope with a monotonic sequence number assigned by the root StreamMux.
+    Consumers that need a total order across root events should use `seq`, not
+    `params.timestamp` (which is wall-clock and not monotonic).
+    """
+
+    type: Literal["event"]
+    eventId: NotRequired[str]
+    seq: NotRequired[int]
+    method: str  # StreamMode value: "values", "messages", "custom", etc.
+    params: _ProtocolEventParams
+
+
+class StreamTransformer(ABC):
+    """Extension point for custom stream projections.
+
+    Transformers observe protocol events flowing through the StreamMux and
+    build typed derived projections (StreamChannels, promises, etc.).
+
+    Set `_native = True` on a transformer to have its projection keys
+    exposed as direct attributes on the run stream (in addition to
+    appearing in `run.extensions`).
+
+    Subclasses must implement `init` and override at least one of
+    `process` / `aprocess`. The `finalize` / `afinalize` and `fail` /
+    `afail` hooks are optional — the default implementations are no-ops.
+    StreamChannel instances in the projection dict are auto-closed /
+    auto-failed by the mux, so most transformers don't need `finalize`
+    or `fail` at all.
+
+    Transformers that need async work pick the async lane by:
+
+    1. Overriding `aprocess` (and optionally `afinalize` / `afail`), or
+    2. Calling `self.schedule(coro)` from inside a sync `process`, or
+    3. Setting `requires_async = True` explicitly.
+
+    The mux detects these cases at registration and raises if they're
+    used under sync `stream()` — they only work under `astream()`.
+
+    Use `aprocess` when the pump must wait for async work before the
+    next transformer sees the event (e.g. PII redaction that mutates
+    `event` in place). Use `schedule()` for decoupled async work whose
+    result lands on an independent projection (e.g. async moderation
+    scoring, cost lookup, external tracing).
+
+    Attributes:
+        scope: Namespace the transformer operates within — `()` for the
+            root mux. Set at construction from the mux's scope (each
+            factory is called as `factory(scope)`).
+        requires_async: Explicit opt-in for transformers that need a
+            running event loop but don't override any async method (for
+            example, transformers that call `schedule()` from a sync
+            `process`). The mux also auto-detects the async lane when
+            `aprocess`, `afinalize`, or `afail` is overridden.
+        supports_sync: Set True only for transformers that override
+            async-lane hooks while still fully supporting the sync lane.
+            Such transformers may be registered under `stream()`.
+        required_stream_modes: Stream modes the graph must emit for
+            this transformer to have anything to process. Computed as
+            the union across all registered transformers to determine
+            which modes a `stream_v2` run requests from the graph.
+            Empty tuple means the transformer consumes only synthetic
+            events (or is purely passive).
+    """
+
+    requires_async: ClassVar[bool] = False
+    supports_sync: ClassVar[bool] = False
+    required_stream_modes: ClassVar[tuple[str, ...]] = ()
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        """Initialize the transformer with its mux's scope.
+
+        Args:
+            scope: The namespace tuple the owning mux is scoped to.
+                `()` for the root. Factories receive this at
+                construction time (`factory(scope)` in `StreamMux`).
+        """
+        self.scope: tuple[str, ...] = scope
+
+    @abstractmethod
+    def init(self) -> dict[str, Any]:
+        """Return the projection dict.
+
+        Keys become entries in `run.extensions`. If the transformer has
+        `_native = True`, keys are also set as direct attributes on the
+        run stream.
+
+        StreamChannel instances in the return value are automatically
+        wired by the StreamMux for protocol event auto-forwarding.
+        """
+        ...
+
+    def _on_register(self, mux: Any) -> None:
+        """Called by `StreamMux._register` after this transformer is wired in.
+
+        Default is a no-op. Override to capture a reference to the
+        owning mux — needed for transformers that build mini-muxes
+        via `mux._make_child(...)` (e.g. `SubgraphTransformer`).
+        """
+
+    def process(self, event: ProtocolEvent) -> bool:
+        """Handle an event on the sync lane.
+
+        Called for every event before it is appended to the main event
+        log. Subclasses must override either `process` or `aprocess`.
+        The default raises so a missing override fails loudly rather
+        than silently passing every event through.
+
+        Args:
+            event: The protocol event to observe.
+
+        Returns:
+            True to keep the event in the main log, False to suppress it.
+        """
+        raise NotImplementedError(
+            f"{type(self).__name__} must override process() or aprocess()"
+        )
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        """Handle an event on the async lane.
+
+        The mux awaits this before dispatching to the next transformer,
+        so a slow `aprocess` serializes the pipeline. Use it only when
+        a later transformer — or a consumer reading the event
+        synchronously — must see the result of the async work (e.g.
+        PII redaction that mutates `event` in place).
+
+        The default delegates to `process`, so purely-sync transformers
+        run unchanged under `astream()`.
+
+        Args:
+            event: The protocol event to observe.
+
+        Returns:
+            True to keep the event in the main log, False to suppress it.
+        """
+        return self.process(event)
+
+    def finalize(self) -> None:
+        """Called when the run ends normally (sync lane).
+
+        Override to close StreamChannels, resolve promises, or perform
+        other teardown. StreamChannel instances in the projection dict
+        are auto-closed by the mux.
+        """
+
+    async def afinalize(self) -> None:
+        """Called when the run ends normally (async lane).
+
+        By the time this runs, the mux has already awaited every task
+        started via `schedule()`, so StreamChannels can be closed here
+        without a last-task-wins race.
+
+        The default delegates to `finalize`.
+        """
+        self.finalize()
+
+    def fail(self, err: BaseException) -> None:
+        """Called when the run ends with an error (sync lane).
+
+        Override to fail StreamChannels, reject promises, or perform
+        other teardown. StreamChannel instances in the projection dict
+        are auto-failed by the mux.
+
+        Args:
+            err: The exception that ended the run.
+        """
+
+    async def afail(self, err: BaseException) -> None:
+        """Called when the run ends with an error (async lane).
+
+        The mux cancels and awaits every task started via `schedule()`
+        before calling this, so cleanup doesn't race with in-flight work.
+
+        The default delegates to `fail`.
+
+        Args:
+            err: The exception that ended the run.
+        """
+        self.fail(err)
+
+    # ------------------------------------------------------------------
+    # Scheduled async work
+    # ------------------------------------------------------------------
+
+    def schedule(
+        self,
+        coro: Coroutine[Any, Any, Any],
+        *,
+        on_error: Literal["log", "raise"] = "log",
+    ) -> asyncio.Task[Any]:
+        """Schedule a coroutine tied to this transformer's lifecycle.
+
+        The mux holds the task reference, awaits all scheduled tasks
+        during `aclose()` before calling `afinalize()`, and cancels
+        them on `afail()`. Authors don't need to track tasks or
+        implement the last-task-closes-the-log dance.
+
+        Requires a running event loop — call only under `astream()`.
+        Set `requires_async = True` on the class so registration under
+        sync `stream()` fails fast with a clear message.
+
+        Args:
+            coro: The coroutine to run. Its lifecycle is owned by the
+                mux from this point on.
+            on_error: `"log"` (default) catches and logs any exception
+                the coroutine raises, so a single failure doesn't tear
+                down the run. `"raise"` lets the exception propagate
+                when the mux joins pendings, converting the close path
+                into the fail path.
+
+        Returns:
+            The asyncio Task. Authors rarely need to await it directly
+            — consumers read results from whatever projection the
+            coroutine pushes into.
+
+        Raises:
+            RuntimeError: If called without a running event loop (i.e.
+                under sync `stream()` rather than `astream()`).
+        """
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            raise RuntimeError(
+                f"{type(self).__name__}.schedule() requires a running "
+                "event loop; this transformer must run under astream(), "
+                "not stream(). Set requires_async=True on the class so "
+                "this fails at registration rather than at first event."
+            ) from None
+
+        wrapped = self._wrap_scheduled(coro) if on_error == "log" else coro
+        task = asyncio.create_task(wrapped)
+        tasks = self._scheduled_task_set()
+        tasks.add(task)
+        task.add_done_callback(tasks.discard)
+        return task
+
+    @staticmethod
+    async def _wrap_scheduled(coro: Coroutine[Any, Any, Any]) -> Any:
+        try:
+            return await coro
+        except asyncio.CancelledError:
+            raise
+        except BaseException:
+            _logger.exception("Scheduled StreamTransformer task failed")
+
+    def _scheduled_task_set(self) -> set[asyncio.Task[Any]]:
+        """Return the lazily-allocated task set.
+
+        Avoids requiring subclasses to call `super().__init__()`.
+        """
+        tasks: set[asyncio.Task[Any]] | None = getattr(
+            self, "_stream_scheduled_tasks", None
+        )
+        if tasks is None:
+            tasks = set()
+            self._stream_scheduled_tasks = tasks
+        return tasks
+
+
+def transformer_requires_async(transformer: StreamTransformer) -> bool:
+    """Return True if the transformer needs a running event loop.
+
+    A transformer requires async if it explicitly opts in
+    (`requires_async = True`) or overrides any of the async-lane methods
+    (`aprocess`, `afinalize`, `afail`) without also declaring that it
+    supports the sync lane.
+
+    Args:
+        transformer: The transformer to inspect.
+
+    Returns:
+        True if the transformer cannot run under sync `stream()`.
+    """
+    if transformer.requires_async:
+        return True
+    if transformer.supports_sync:
+        return False
+    cls = type(transformer)
+    for name in ("aprocess", "afinalize", "afail"):
+        if getattr(cls, name) is not getattr(StreamTransformer, name):
+            return True
+    return False
@@ -0,0 +1,521 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping
+from types import MappingProxyType, TracebackType
+from typing import TYPE_CHECKING, Any
+
+from langgraph.stream._convert import convert_to_protocol_event
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+
+if TYPE_CHECKING:
+    from langgraph.stream.transformers import SubgraphStatus, ValuesTransformer
+
+
+def _drive_until_done(pump: Callable[[], bool]) -> None:
+    """Call the sync pump until it returns False."""
+    while pump():
+        pass
+
+
+async def _adrive_until_done(pump: Callable[[], Awaitable[bool]]) -> None:
+    """Call the async pump until it returns False."""
+    while await pump():
+        pass
+
+
+class GraphRunStream:
+    """Sync run stream with caller-driven pumping.
+
+    The caller's iteration on any projection (`values`, `messages`,
+    raw events, or `output`) drives the graph forward. No background
+    thread is used — the caller's `for` loop is the pump.
+
+    Projections are single-consumer — iterating `run.values` twice
+    raises. Use `projection.tee(n)` if you genuinely need fan-out.
+
+    All transformer projections live in `extensions`. Native transformer
+    projections (those with `_native = True`) are also set as direct
+    attributes on this instance (e.g. `run.values`, `run.messages`).
+    """
+
+    def __init__(
+        self,
+        graph_iter: Iterator[Any] | None,
+        mux: StreamMux,
+        values_transformer: ValuesTransformer,
+        *,
+        wire_pump: bool = True,
+    ) -> None:
+        """Initialize the run stream.
+
+        Args:
+            graph_iter: Pull-based iterator over the graph's stream,
+                or `None` for nested run streams whose pump is driven
+                by an outer run (e.g. `SubgraphRunStream`).
+            mux: The StreamMux owning projections and the main log.
+            values_transformer: The built-in values transformer
+                providing `output` / `interrupted` / `interrupts`.
+            wire_pump: When True (default), bind `_pump_next` as the
+                mux's pump callable. Subclasses that inherit a parent
+                pump via `StreamMux._make_child` should pass False to
+                preserve the parent binding.
+        """
+        self._graph_iter = graph_iter
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        self._values_transformer = values_transformer
+        self._exhausted = False
+        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 _pump_next(self) -> bool:
+        """Pull one event from the graph and push it through the mux.
+
+        Returns:
+            True if an event was pulled, False if the graph is exhausted
+            or has raised. Always False when constructed with
+            `graph_iter=None` (the run is driven by an outer pump).
+        """
+        if self._exhausted or self._graph_iter is None:
+            return False
+        try:
+            part = next(self._graph_iter)
+            self._mux.push(convert_to_protocol_event(part))
+            return True
+        except StopIteration:
+            self._mux.close()
+            self._exhausted = True
+            return False
+        except Exception as e:
+            self._mux.fail(e)
+            self._exhausted = True
+            return False
+
+    def abort(self) -> None:
+        """Stop the run early.
+
+        Closes the mux and marks the stream exhausted. The graph
+        iterator is dropped; any in-flight nodes see the closure on
+        their next yield point. Idempotent.
+        """
+        if self._exhausted:
+            return
+        self._exhausted = True
+        try:
+            self._mux.close()
+        except Exception:
+            pass
+
+    def __enter__(self) -> GraphRunStream:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        self.abort()
+
+    @property
+    def output(self) -> dict[str, Any] | None:
+        """Drive the run to completion and return the final state."""
+        _drive_until_done(self._pump_next)
+        if (err := self._values_transformer.error) is not None:
+            raise err
+        return self._values_transformer._latest
+
+    @property
+    def interrupted(self) -> bool:
+        """Drive the run to completion, then return whether it was
+        interrupted.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        _drive_until_done(self._pump_next)
+        if (err := self._values_transformer.error) is not None:
+            raise err
+        return self._values_transformer._interrupted
+
+    @property
+    def interrupts(self) -> list[Any]:
+        """Drive the run to completion, then return interrupt payloads.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        _drive_until_done(self._pump_next)
+        if (err := self._values_transformer.error) is not None:
+            raise err
+        return self._values_transformer._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)
+
+
+class AsyncGraphRunStream:
+    """Async run stream with caller-driven pumping.
+
+    Async iteration on any projection drives the graph forward — there
+    is no background task. Concurrent consumers share a single-flight
+    pump via an `asyncio.Lock`, so each awaiting cursor contributes one
+    event per acquisition. Backpressure comes from the logs: when a
+    subscribed log's buffer reaches `maxlen`, `apush` awaits the
+    subscriber to drain, which holds back the pump and paces the graph.
+
+    Projections are single-consumer — a second `aiter(run.values)`
+    raises. Use `projection.tee(n)` for fan-out.
+
+    Use as an async context manager to guarantee clean shutdown on
+    early exit:
+
+    ```python
+    async with await handler.astream(input) as run:
+        async for msg in run.messages:
+            ...
+    ```
+    """
+
+    def __init__(
+        self,
+        graph_aiter: AsyncIterator[Any] | None,
+        mux: StreamMux,
+        values_transformer: ValuesTransformer,
+        *,
+        wire_pump: bool = True,
+    ) -> None:
+        """Initialize the async run stream.
+
+        Args:
+            graph_aiter: Async iterator over the graph's stream, or
+                `None` for nested run streams whose pump is driven by
+                an outer run (e.g. `AsyncSubgraphRunStream`).
+            mux: The StreamMux owning projections and the main log.
+            values_transformer: The built-in values transformer
+                providing `output` / `interrupted` / `interrupts`.
+            wire_pump: When True (default), bind `_apump_next` as the
+                mux's async pump callable. Subclasses that inherit a
+                parent pump via `StreamMux._make_child` should pass
+                False to preserve the parent binding.
+        """
+        self._graph_aiter = graph_aiter
+        self._mux = mux
+        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
+        self._values_transformer = values_transformer
+        self._exhausted = False
+        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 _wire_arequest_more(self, mux: StreamMux) -> None:
+        """Wire the async pull callback through the mux.
+
+        Mirrors `_wire_request_more`: routing through
+        `mux.bind_apump` lets child mini-muxes inherit the pump
+        callable so cursors on subgraph handles drive the root
+        pump.
+        """
+        mux.bind_apump(self._apump_next)
+
+    async def _apump_next(self) -> bool:
+        """Drive one pump step, or wait for the active pumper to drive one.
+
+        "Take-a-number" semantics: at most one task at a time calls
+        `graph_aiter.__anext__()` (asyncio iterators can't be advanced
+        concurrently). Other callers wait on a Condition that the
+        active pumper notifies after each step. This lets a "passive"
+        consumer — one whose projection's buffer is being filled by the
+        active pumper's push — wake up as soon as its data lands,
+        instead of queueing on the pump and only observing its data one
+        graph event late.
+
+        `except Exception` is intentional — `CancelledError` and other
+        `BaseException` subclasses propagate, matching asyncio's
+        cancellation contract.
+
+        Returns:
+            True if a pump step completed (by this task or another),
+            False if the graph is exhausted.
+        """
+        async with self._pump_cond:
+            if self._exhausted or self._graph_aiter is None:
+                return False
+            if self._pumping:
+                # Another task is pumping; wait for its progress signal.
+                await self._pump_cond.wait()
+                return not self._exhausted
+            self._pumping = True
+
+        try:
+            try:
+                part = await self._graph_aiter.__anext__()
+                await self._mux.apush(convert_to_protocol_event(part))
+                return True
+            except StopAsyncIteration:
+                self._exhausted = True
+                await self._mux.aclose()
+                return False
+            except Exception as e:
+                self._exhausted = True
+                await self._mux.afail(e)
+                return False
+        finally:
+            async with self._pump_cond:
+                self._pumping = False
+                self._pump_cond.notify_all()
+
+    async def abort(self) -> None:
+        """Stop the run early.
+
+        Marks the stream exhausted, wakes any pump-waiters, and closes
+        the mux. Any `apush` blocked on backpressure wakes and returns
+        without appending. Idempotent.
+        """
+        async with self._pump_cond:
+            if self._exhausted:
+                return
+            self._exhausted = True
+            self._pump_cond.notify_all()
+        try:
+            await self._mux.aclose()
+        except Exception:
+            pass
+
+    async def __aenter__(self) -> AsyncGraphRunStream:
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        await self.abort()
+
+    async def output(self) -> dict[str, Any] | None:
+        """Drive the run to completion and return the final state.
+
+        Methods (not properties) on the async lane so `run.output`
+        without `await` raises at type-check time instead of silently
+        yielding a coroutine object.
+
+        Example:
+            ```python
+            output = await run.output()
+            ```
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._values_transformer.error) is not None:
+            raise err
+        return self._values_transformer._latest
+
+    async def interrupted(self) -> bool:
+        """Drive the run to completion and return whether it was
+        interrupted.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._values_transformer.error) is not None:
+            raise err
+        return self._values_transformer._interrupted
+
+    async def interrupts(self) -> list[Any]:
+        """Drive the run to completion and return interrupt payloads.
+
+        Raises:
+            BaseException: If the run ended with an error.
+        """
+        await _adrive_until_done(self._apump_next)
+        if (err := self._values_transformer.error) is not None:
+            raise err
+        return self._values_transformer._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,
+        values_transformer: ValuesTransformer,
+        *,
+        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,
+            values_transformer=values_transformer,
+            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,
+        values_transformer: ValuesTransformer,
+        *,
+        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,
+            values_transformer=values_transformer,
+            wire_pump=False,
+        )
+        self.path = path
+        self.graph_name = graph_name
+        self.trigger_call_id = trigger_call_id
+        self.status = "started"
+        self.error = None
+        self._seen_terminal = False
+
+    async def _apump_next(self) -> bool:
+        """Delegate to the parent's async pump."""
+        if (
+            self._exhausted
+            or self._seen_terminal
+            or self._mux._events._closed
+            or self._parent_apump_fn is None
+        ):
+            return False
+        return await self._parent_apump_fn()
@@ -0,0 +1,327 @@
+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 StreamChannel(Generic[T]):
+    """Single-consumer drainable queue for streaming events, with optional
+    protocol auto-forwarding.
+
+    When constructed with a `name`, the StreamMux auto-wires every
+    `push()` to also inject a `ProtocolEvent` into the main event stream
+    using the channel's name as the method. When constructed without a
+    name, the channel is local-only — items are only visible to
+    in-process consumers that iterate the channel directly.
+
+    Items are popped off the front as the consumer advances — there is
+    no retention beyond what's currently queued. A channel accepts
+    exactly one subscriber; a second `__iter__` / `__aiter__` call
+    raises. Use `tee(n)` / `atee(n)` for fan-out.
+
+    Starts unbound — neither `__iter__` nor `__aiter__` is available
+    until the StreamMux calls `_bind(is_async)`. After binding, only
+    the matching iteration protocol works; the other raises `TypeError`.
+
+    Pump wiring (set by the run stream, not by `_bind`):
+        - `_request_more`: sync pump callable, returns True if a new
+          event was produced.
+        - `_arequest_more`: async pump coroutine factory, same contract.
+
+    Memory is bounded by caller pace: both sync and async use caller-
+    driven pumps, so each cursor advance produces at most one event.
+
+    Lazy-subscribe: `push` appends to the local buffer only when a
+    subscriber has registered. Auto-forward via `_wire_fn` always fires
+    regardless of subscription state.
+
+    Lifecycle (`close` / `fail`) is managed by the mux — transformers
+    don't need to close their channels manually.
+    """
+
+    def __init__(self, name: str | None = None, *, maxlen: int | None = None) -> None:
+        """Initialize the channel.
+
+        Args:
+            name: Optional protocol channel name. When set, the
+                StreamMux wires every `push()` to also inject a
+                `ProtocolEvent` into the main event stream. Surfaced
+                on the wire as `custom:<name>` for user-defined
+                transformers, or as `<name>` for channels owned by a
+                native transformer (`_native = True`). When `None`,
+                the channel is local-only.
+            maxlen: Accepted for forward compatibility; currently
+                unused. The caller-driven pump bounds memory naturally
+                for single-consumer use.
+
+        Raises:
+            ValueError: If `maxlen` is not a positive integer or `None`.
+        """
+        if maxlen is not None and maxlen <= 0:
+            raise ValueError("StreamChannel maxlen must be a positive int or None")
+        self.name = name
+        self._items: deque[T] = deque()
+        self._maxlen: int | None = maxlen
+        self._closed = False
+        self._error: BaseException | None = None
+
+        self._is_async: bool | None = None
+
+        self._subscribed = False
+
+        self._request_more: Callable[[], bool] | None = None
+        self._arequest_more: Callable[[], Awaitable[bool]] | None = None
+
+        self._wire_fn: Callable[[T], None] | None = None
+
+    # ------------------------------------------------------------------
+    # 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.
+
+        Args:
+            is_async: True to enable async iteration, False for sync.
+
+        Raises:
+            RuntimeError: If the channel has already been bound.
+        """
+        if self._is_async is not None:
+            raise RuntimeError("StreamChannel is already bound")
+        self._is_async = is_async
+
+    # ------------------------------------------------------------------
+    # Mux wiring (not called by transformers directly)
+    # ------------------------------------------------------------------
+
+    def _wire(self, fn: Callable[[T], None]) -> None:
+        """Install the auto-forward callback (called by StreamMux)."""
+        self._wire_fn = fn
+
+    # ------------------------------------------------------------------
+    # Producer API
+    # ------------------------------------------------------------------
+
+    def push(self, item: T) -> None:
+        """Append an item. Auto-forwards if wired.
+
+        The local buffer append is a no-op when no subscriber is
+        registered, but auto-forwarding always fires so wired events
+        reach the main event log regardless of subscription state.
+
+        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
+
+    # ------------------------------------------------------------------
+    # Sync iteration (caller-driven pump)
+    # ------------------------------------------------------------------
+
+    def __iter__(self) -> Iterator[T]:
+        """Subscribe and return a sync cursor. Can be called only once.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to async mode.
+            RuntimeError: If the channel already has a subscriber.
+        """
+        if self._is_async is None:
+            raise TypeError(
+                "StreamChannel has not been bound yet. "
+                "Register the transformer with a StreamMux first."
+            )
+        if self._is_async:
+            raise TypeError(
+                "This StreamChannel is bound to async mode — use 'async for' instead."
+            )
+        if self._subscribed:
+            raise RuntimeError(
+                "StreamChannel already has a subscriber; use .tee(n) for fan-out."
+            )
+        self._subscribed = True
+        return self._sync_cursor()
+
+    def _sync_cursor(self) -> Iterator[T]:
+        while True:
+            if self._items:
+                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 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
+    # ------------------------------------------------------------------
+
+    def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
+        """Subscribe and return `n` independent sync iterators.
+
+        Each branch has its own buffer; items pulled from the
+        underlying cursor are copied into every branch. Branches are
+        naturally bounded by caller pace since the sync pump is
+        caller-driven.
+
+        Args:
+            n: Number of branches to create. Must be >= 1.
+
+        Returns:
+            A tuple of `n` iterators over the same underlying stream.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to async mode.
+            RuntimeError: If the channel already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("tee() requires n >= 1")
+        source = self.__iter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+
+        def branch(i: int) -> Iterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                elif exhausted[0]:
+                    return
+                else:
+                    try:
+                        item = next(source)
+                    except StopIteration:
+                        exhausted[0] = True
+                        return
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))
+
+    def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
+        """Subscribe and return `n` independent async iterators.
+
+        Caller-driven fan-out: each branch's `__anext__` either pops
+        from its own buffer or, under a shared `asyncio.Lock`, pulls
+        one item from the underlying cursor and distributes it to
+        every branch's buffer.
+
+        Args:
+            n: Number of branches to create. Must be >= 1.
+
+        Returns:
+            A tuple of `n` async iterators over the same underlying
+            stream.
+
+        Raises:
+            TypeError: If the channel is unbound or bound to sync mode.
+            RuntimeError: If the channel already has a subscriber.
+            ValueError: If `n` < 1.
+        """
+        if n < 1:
+            raise ValueError("atee() requires n >= 1")
+        source = self.__aiter__()
+        buffers: list[deque[T]] = [deque() for _ in range(n)]
+        exhausted = [False]
+        error: list[BaseException | None] = [None]
+        lock = asyncio.Lock()
+
+        async def branch(i: int) -> AsyncIterator[T]:
+            buf = buffers[i]
+            while True:
+                if buf:
+                    yield buf.popleft()
+                    continue
+                if exhausted[0]:
+                    if error[0] is not None:
+                        raise error[0]
+                    return
+                async with lock:
+                    if buf or exhausted[0]:
+                        continue
+                    try:
+                        item = await source.__anext__()
+                    except StopAsyncIteration:
+                        exhausted[0] = True
+                        continue
+                    except Exception as e:
+                        error[0] = e
+                        exhausted[0] = True
+                        continue
+                    for b in buffers:
+                        b.append(item)
+
+        return tuple(branch(i) for i in range(n))
@@ -0,0 +1,749 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any, Literal, cast
+
+from langchain_core.language_models._compat_bridge import message_to_events
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessageChunk, BaseMessage
+from langchain_protocol.protocol import MessagesData
+from typing_extensions import NotRequired, TypedDict
+
+from langgraph.errors import GraphInterrupt
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import AsyncSubgraphRunStream, SubgraphRunStream
+from langgraph.stream.stream_channel import StreamChannel
+
+if TYPE_CHECKING:
+    from collections.abc import Awaitable, Callable
+
+    from langgraph.stream._mux import StreamMux
+
+_logger = logging.getLogger(__name__)
+
+
+class ValuesTransformer(StreamTransformer):
+    """Capture values events as a drainable stream of state snapshots.
+
+    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.
+    """
+
+    _native = True
+    required_stream_modes = ("values",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[dict[str, Any]] = StreamChannel()
+        self._latest: dict[str, Any] | None = None
+        self._interrupted = False
+        self._interrupts: list[Any] = []
+        # Cached as a list once for cheap equality with the protocol
+        # event's `namespace` field, which is `list[str]`.
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"values": self._log}
+
+    @property
+    def error(self) -> BaseException | None:
+        """The error that ended the run, or `None` if it succeeded.
+
+        Set by the mux when it auto-fails the projection log.
+        """
+        return self._log._error
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "values":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+        self._latest = params["data"]
+        interrupts = params.get("interrupts", ())
+        if interrupts:
+            self._interrupted = True
+            self._interrupts.extend(interrupts)
+        self._log.push(params["data"])
+        return True
+
+
+class MessagesTransformer(StreamTransformer):
+    """Capture messages events as ChatModelStream objects.
+
+    The messages projection yields one `ChatModelStream` (or
+    `AsyncChatModelStream`) per LLM call. Consumers iterate
+    `run.messages` to get stream handles, then use each handle's typed
+    projections (`.text`, `.reasoning`, `.tool_calls`, `.usage`,
+    `.output`) for per-message content.
+
+    Two input shapes are handled (via `params["data"] = (payload,
+    metadata)` from `StreamMessagesHandler`):
+
+    1. Protocol event (dict with `"event"` key) — emitted by
+       `stream_v2()` / `astream_v2()` via the `on_stream_event`
+       callback. Routed to an existing `ChatModelStream` by
+       `metadata["run_id"]`. A `message-start` event creates a new
+       stream; `message-finish` closes it.
+    2. Whole `AIMessage` — emitted from `on_chain_end` when a node
+       returns a finalized message. Replayed as a synthetic protocol
+       event lifecycle via `message_to_events`, then the
+       already-complete stream is pushed to the log.
+
+    V1 `AIMessageChunk` tuples (from `on_llm_new_token`) are not
+    streamed into this projection: chat models that want to populate
+    `run.messages` with content-block streaming must use
+    `stream_v2()` / `astream_v2()`. Models called via the legacy
+    `stream()` method still surface their final `AIMessage` via
+    `on_chain_end` when a node returns it as state.
+
+    Only events at the run's own level are projected; tokens from
+    deeper subgraphs are left in the main event log but excluded from
+    `.messages`. "Own level" is defined by `scope`, which
+    `stream_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.
+
+    Native transformer — the `messages` projection is exposed as a
+    direct attribute on the run stream.
+    """
+
+    _native = True
+    required_stream_modes = ("messages",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[ChatModelStream] = StreamChannel()
+        # Correlate protocol events back to a ChatModelStream by run_id
+        # (attached to the event's metadata by StreamMessagesHandler).
+        self._by_run: dict[str, ChatModelStream] = {}
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+        # Cached as a list once for cheap equality with the protocol
+        # event's `namespace` field, which is `list[str]`.
+        self._scope_list: list[str] = list(scope)
+
+    def init(self) -> dict[str, Any]:
+        return {"messages": self._log}
+
+    def _bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback. Called by GraphRunStream._wire_request_more."""
+        self._pump_fn = fn
+
+    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Wire the async pull callback.
+
+        Called by `AsyncGraphRunStream._wire_arequest_more` so each
+        `AsyncChatModelStream` this transformer creates can drive the
+        shared graph pump from its projection cursors.
+        """
+        self._apump_fn = fn
+
+    def _make_stream(
+        self,
+        *,
+        namespace: list[str],
+        node: str | None,
+        message_id: str | None,
+    ) -> ChatModelStream:
+        """Create a ChatModelStream (sync) or AsyncChatModelStream (async).
+
+        Wires whichever pump is bound. Prefers the async pump so nested
+        iteration under `AsyncGraphRunStream` drives the graph forward
+        without a background task. The unwired fallback (no pump bound)
+        is used by unit tests that dispatch events manually.
+        """
+        if self._apump_fn is not None:
+            astream = AsyncChatModelStream(
+                namespace=namespace,
+                node=node,
+                message_id=message_id,
+            )
+            astream.set_arequest_more(self._apump_fn)
+            return astream
+        if self._pump_fn is not None:
+            stream: ChatModelStream = ChatModelStream(
+                namespace=namespace,
+                node=node,
+                message_id=message_id,
+            )
+            stream.set_request_more(self._pump_fn)
+            return stream
+        return AsyncChatModelStream(
+            namespace=namespace,
+            node=node,
+            message_id=message_id,
+        )
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "messages":
+            return True
+        params = event["params"]
+        if params["namespace"] != self._scope_list:
+            return True
+
+        payload, metadata = params["data"]
+        node: str | None = metadata.get("langgraph_node")
+        run_id = str(metadata.get("run_id", "")) if metadata else ""
+
+        if isinstance(payload, dict) and "event" in payload:
+            self._route_protocol_event(
+                cast("MessagesData", payload), run_id=run_id, node=node
+            )
+        elif isinstance(payload, BaseMessage) and not isinstance(
+            payload, AIMessageChunk
+        ):
+            self._route_whole_message(payload, node=node)
+        # Legacy AIMessageChunk tuples (from on_llm_new_token) are ignored;
+        # v1 streaming callers must switch to stream_v2() to populate this
+        # projection.
+
+        return True
+
+    def _route_protocol_event(
+        self,
+        event: MessagesData,
+        *,
+        run_id: str,
+        node: str | None,
+    ) -> None:
+        event_type = event.get("event")
+        if event_type == "message-start":
+            message_id = event.get("message_id")
+            stream = self._make_stream(
+                namespace=[],
+                node=node,
+                message_id=str(message_id) if message_id is not None else None,
+            )
+            self._by_run[run_id] = stream
+            self._log.push(stream)
+            stream.dispatch(event)
+        elif run_id in self._by_run:
+            stream = self._by_run[run_id]
+            stream.dispatch(event)
+            if event_type == "message-finish":
+                del self._by_run[run_id]
+
+    def _route_whole_message(self, message: BaseMessage, *, node: str | None) -> None:
+        stream = self._make_stream(namespace=[], node=node, message_id=message.id)
+        for evt in message_to_events(message, message_id=message.id):
+            stream.dispatch(evt)
+        self._log.push(stream)
+
+    def finalize(self) -> None:
+        """Clear any routing state — streams close themselves via `message-finish`."""
+        self._by_run.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Propagate run error to any streams still open when the graph fails."""
+        for stream in list(self._by_run.values()):
+            stream.fail(err)
+        self._by_run.clear()
+
+
+SubgraphStatus = Literal["started", "completed", "failed", "interrupted"]
+
+
+def _parse_ns_segment(segment: str) -> tuple[str, str | None]:
+    """Split a namespace segment into `(graph_name, trigger_call_id)`.
+
+    Segments are formatted `node_name:task_id` by `prepare_next_tasks`.
+    Returns `(segment, None)` if no `:` is present.
+    """
+    name, sep, task_id = segment.partition(":")
+    return name, task_id if sep else None
+
+
+class LifecyclePayload(TypedDict, total=False):
+    """Payload of a lifecycle event surfaced on the `lifecycle` channel.
+
+    Auto-forwarded as `lifecycle` protocol events (no `custom:` prefix
+    because `LifecycleTransformer` is a native transformer) so remote
+    SDK clients receive the same data in-process consumers see via
+    `run.lifecycle`.
+    """
+
+    event: SubgraphStatus
+    namespace: list[str]
+    graph_name: NotRequired[str]
+    trigger_call_id: NotRequired[str]
+    error: NotRequired[str]
+
+
+class _TasksLifecycleBase(StreamTransformer):
+    """Shared bookkeeping for `tasks`-event-driven lifecycle inference.
+
+    Both `LifecycleTransformer` (wire-serializable channel) and
+    `SubgraphTransformer` (in-process navigation handles) discover
+    subgraphs by watching the same `tasks` stream — `started` on the
+    first event at a tracked namespace, terminal status when the
+    parent's `TaskResultPayload` arrives. Centralizing the dispatch
+    + open-set bookkeeping here keeps the inference rules from
+    drifting between the two surfaces.
+
+    Subclasses provide three template-method hooks:
+
+    - `_should_track(ns)` — scope filter (e.g. multi-depth vs
+      direct-children-only).
+    - `_on_started(ns, graph_name, trigger_call_id)` — first sighting
+      action (push payload / build handle / etc.). Called once per
+      discovered namespace.
+    - `_on_terminal(ns, status, error)` — terminal action (push
+      terminal payload / mark handle status). Called once per
+      tracked namespace at result time, or via `finalize` / `fail`
+      sweeps if no parent result arrived.
+
+    Tasks events are suppressed from the main event log (`process`
+    returns False) — they're folded into whichever projection the
+    subclass populates; consumers iterating the raw protocol stream
+    see the higher-level view.
+    """
+
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._seen: set[tuple[str, ...]] = set()
+        # Maps tracked namespace -> task_id of the parent task whose
+        # `TaskResultPayload` will close it.
+        self._open: dict[tuple[str, ...], str] = {}
+
+    # --- Template-method hooks (subclass overrides) ---
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        """Scope filter — return True iff `ns` is in this transformer's region."""
+        raise NotImplementedError
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        """Fired once per discovered namespace (first observed task event)."""
+        raise NotImplementedError
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        """Fired once per tracked namespace when its parent's result arrives,
+        or via finalize/fail safety-net sweeps.
+        """
+        raise NotImplementedError
+
+    # --- Dispatch + bookkeeping (shared) ---
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if event["method"] != "tasks":
+            return True
+        ns = tuple(event["params"]["namespace"])
+        data = event["params"]["data"]
+        if "result" in data:
+            self._handle_task_result(ns, data)
+        else:
+            self._handle_task_start(ns)
+        # Tasks events are folded into the synthesized projections;
+        # suppress from the main event log so iterators don't double-see
+        # the same information in two shapes.
+        return False
+
+    def _handle_task_start(self, ns: tuple[str, ...]) -> None:
+        if not self._should_track(ns) or ns in self._seen:
+            return
+        self._seen.add(ns)
+        graph_name, trigger_call_id = _parse_ns_segment(ns[-1])
+        self._on_started(ns, graph_name or None, trigger_call_id)
+        if trigger_call_id is not None:
+            self._open[ns] = trigger_call_id
+
+    def _pop_terminal_transitions(
+        self, ns: tuple[str, ...], data: dict[str, Any]
+    ) -> list[tuple[tuple[str, ...], SubgraphStatus, str | None]]:
+        """Return and remove tracked children closed by this task result."""
+        result_id = data.get("id")
+        if not result_id:
+            return []
+        transitions: list[tuple[tuple[str, ...], SubgraphStatus, str | None]] = []
+        for child_ns, parent_task_id in list(self._open.items()):
+            if child_ns[:-1] != ns or parent_task_id != result_id:
+                continue
+            status, error = _terminal_from_result(data)
+            transitions.append((child_ns, status, error))
+            del self._open[child_ns]
+        return transitions
+
+    def _handle_task_result(self, ns: tuple[str, ...], data: dict[str, Any]) -> None:
+        for child_ns, status, error in self._pop_terminal_transitions(ns, data):
+            self._on_terminal(child_ns, status, error)
+
+    def finalize(self) -> None:
+        """Emit `completed` for any tracked namespace still open at run end."""
+        for ns in list(self._open):
+            self._on_terminal(ns, "completed", None)
+        self._open.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Emit `failed` / `interrupted` for any tracked namespace still open."""
+        is_interrupt = isinstance(err, GraphInterrupt)
+        status: SubgraphStatus = "interrupted" if is_interrupt else "failed"
+        error_str = None if is_interrupt else str(err)
+        for ns in list(self._open):
+            self._on_terminal(ns, status, error_str)
+        self._open.clear()
+
+
+def _status_from_exception(err: BaseException) -> tuple[SubgraphStatus, str | None]:
+    """Map a run exception to a subgraph terminal status and error string."""
+    if isinstance(err, GraphInterrupt):
+        return "interrupted", None
+    return "failed", str(err)
+
+
+def _terminal_from_result(
+    payload: dict[str, Any],
+) -> tuple[SubgraphStatus, str | None]:
+    """Map a `TaskResultPayload` to a `(status, error)` pair.
+
+    Order matters: a result with both `error` and `interrupts` prefers
+    the interrupt classification, since `GraphInterrupt` manifests as
+    a populated `interrupts` list, not as `error`.
+    """
+    if payload.get("interrupts"):
+        return "interrupted", None
+    error = payload.get("error")
+    if error:
+        return "failed", str(error)
+    return "completed", None
+
+
+class LifecycleTransformer(_TasksLifecycleBase):
+    """Surface subgraph lifecycle as `lifecycle` protocol events.
+
+    Pushes `LifecyclePayload` to a `StreamChannel` named `lifecycle`.
+    The channel is auto-forwarded by the mux so payloads land in the
+    main event log under `method = "lifecycle"` (native transformer —
+    no `custom:` prefix) — visible to remote SDK clients over the
+    wire and to in-process consumers via `run.lifecycle`.
+
+    Tracks subgraphs at every depth strictly below the transformer's
+    scope, so a graph → subgraph → subgraph chain produces lifecycle
+    events for both nested levels in a flat stream.
+
+    Native transformer — projection key `lifecycle` is exposed as
+    `run.lifecycle`.
+    """
+
+    _native = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._channel: StreamChannel[LifecyclePayload] = StreamChannel("lifecycle")
+
+    def init(self) -> dict[str, Any]:
+        return {"lifecycle": self._channel}
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        depth = len(self.scope)
+        return len(ns) > depth and ns[:depth] == self.scope
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        if trigger_call_id is None:
+            # Without a task id we can't correlate a parent-result
+            # event back to this namespace — skip the started payload
+            # and rely on finalize/fail to close.
+            return
+        payload: LifecyclePayload = {"event": "started", "namespace": list(ns)}
+        if graph_name:
+            payload["graph_name"] = graph_name
+        payload["trigger_call_id"] = trigger_call_id
+        self._channel.push(payload)
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        payload: LifecyclePayload = {"event": status, "namespace": list(ns)}
+        if error is not None:
+            payload["error"] = error
+        self._channel.push(payload)
+
+
+class SubgraphTransformer(_TasksLifecycleBase):
+    """Discover subgraph invocations as in-process navigation handles.
+
+    Per discovered direct-child subgraph, builds a `SubgraphRunStream`
+    (or `AsyncSubgraphRunStream`) wrapping a child mini-mux scoped to
+    the subgraph's namespace. Consumers iterate `run.subgraphs` to
+    receive handles, then drill into `handle.values` / `handle.messages`
+    / `handle.subgraphs` (recursive grandchildren) / `handle.lifecycle`.
+
+    Each mini-mux owns its own scope and uses its own
+    `SubgraphTransformer` to discover its direct children, so
+    grandchildren live on the child handle — never on the root's
+    `subgraphs` log. Forwarding events into the matching child mini-mux
+    is what keeps the child's projections populated.
+
+    Native transformer — `subgraphs` is exposed as `run.subgraphs`.
+    """
+
+    _native = True
+    supports_sync = True
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[SubgraphRunStream | AsyncSubgraphRunStream] = (
+            StreamChannel()
+        )
+        self._handles: dict[
+            tuple[str, ...], SubgraphRunStream | AsyncSubgraphRunStream
+        ] = {}
+        self._mux: StreamMux | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"subgraphs": self._log}
+
+    def _on_register(self, mux: Any) -> None:
+        self._mux = mux
+
+    def _should_track(self, ns: tuple[str, ...]) -> bool:
+        # Direct children only — grandchildren are picked up by the
+        # child mini-mux's own SubgraphTransformer.
+        depth = len(self.scope)
+        return len(ns) == depth + 1 and ns[:depth] == self.scope
+
+    def _on_started(
+        self,
+        ns: tuple[str, ...],
+        graph_name: str | None,
+        trigger_call_id: str | None,
+    ) -> None:
+        if self._mux is None:
+            return
+        try:
+            child_mux = self._mux._make_child(ns)
+        except RuntimeError:
+            # Mux wasn't built from factories — no mini-mux navigation
+            # available. Skip; LifecycleTransformer still tracks the
+            # subgraph via the flat event stream.
+            return
+        values_t = child_mux.transformer_by_key("values")
+        if not isinstance(values_t, ValuesTransformer):
+            return
+        handle_cls = AsyncSubgraphRunStream if child_mux.is_async else SubgraphRunStream
+        handle = handle_cls(
+            mux=child_mux,
+            values_transformer=values_t,
+            path=ns,
+            graph_name=graph_name,
+            trigger_call_id=trigger_call_id,
+        )
+        self._handles[ns] = handle
+        self._log.push(handle)
+
+    def _on_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        handle = self._handles.get(ns)
+        if handle is None or not self._mark_terminal(handle, status, error):
+            return
+        self._close_or_fail_handle(handle, status, error)
+
+    async def _aon_terminal(
+        self,
+        ns: tuple[str, ...],
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        handle = self._handles.get(ns)
+        if handle is None or not self._mark_terminal(handle, status, error):
+            return
+        await self._aclose_or_fail_handle(handle, status, error)
+
+    def _mark_terminal(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> bool:
+        """Mark a handle terminal once. Returns True on first transition."""
+        if handle._seen_terminal:
+            return False
+        handle.status = status
+        if error is not None and handle.error is None:
+            handle.error = error
+        handle._seen_terminal = True
+        return True
+
+    def _close_or_fail_handle(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        if handle._mux is None or handle._mux._events._closed:
+            return
+        if status == "failed":
+            handle._mux.fail(RuntimeError(error or "Subgraph failed"))
+        else:
+            handle._mux.close()
+
+    async def _aclose_or_fail_handle(
+        self,
+        handle: SubgraphRunStream | AsyncSubgraphRunStream,
+        status: SubgraphStatus,
+        error: str | None,
+    ) -> None:
+        if handle._mux is None or handle._mux._events._closed:
+            return
+        if status == "failed":
+            await handle._mux.afail(RuntimeError(error or "Subgraph failed"))
+        else:
+            await handle._mux.aclose()
+
+    def _child_mux_for_event(self, event: ProtocolEvent) -> StreamMux | 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._mux
+
+    def process(self, event: ProtocolEvent) -> bool:
+        # Discover / update terminal status before forwarding so a
+        # `started` handle exists by the time the child mini-mux sees
+        # its own first event.
+        keep = super().process(event)
+        child_mux = self._child_mux_for_event(event)
+        if child_mux is not None:
+            child_mux.push(event)
+        return keep
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        # Async counterpart to `process`: repeat the tasks bookkeeping
+        # here instead of delegating to `process`, 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
+        child_mux = self._child_mux_for_event(event)
+        if child_mux is not None:
+            await child_mux.apush(event)
+        return keep
+
+    def _complete_open_handles(self) -> BaseException | None:
+        first_error: BaseException | None = None
+        for ns in list(self._open):
+            try:
+                self._on_terminal(ns, "completed", None)
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        self._open.clear()
+        for handle in self._handles.values():
+            if self._mark_terminal(handle, "completed", None):
+                try:
+                    self._close_or_fail_handle(handle, "completed", None)
+                except BaseException as e:
+                    if first_error is None:
+                        first_error = e
+        return first_error
+
+    async def _acomplete_open_handles(self) -> BaseException | None:
+        first_error: BaseException | None = None
+        for ns in list(self._open):
+            try:
+                await self._aon_terminal(ns, "completed", None)
+            except BaseException as e:
+                if first_error is None:
+                    first_error = e
+        self._open.clear()
+        for handle in self._handles.values():
+            if self._mark_terminal(handle, "completed", None):
+                try:
+                    await self._aclose_or_fail_handle(handle, "completed", None)
+                except BaseException as e:
+                    if first_error is None:
+                        first_error = e
+        return first_error
+
+    def finalize(self) -> None:
+        first_error = self._complete_open_handles()
+        if first_error is not None:
+            raise first_error
+
+    async def afinalize(self) -> None:
+        first_error = await self._acomplete_open_handles()
+        if first_error is not None:
+            raise first_error
+
+    def fail(self, err: BaseException) -> None:
+        status, error_str = _status_from_exception(err)
+        self._open.clear()
+        for handle in self._handles.values():
+            self._mark_terminal(handle, status, error_str)
+            if handle._mux is not None and not handle._mux._events._closed:
+                try:
+                    handle._mux.fail(err)
+                except Exception:
+                    _logger.warning(
+                        "Error failing subgraph mini-mux at %s; "
+                        "subscribers may not see the terminal error.",
+                        handle.path,
+                        exc_info=True,
+                    )
+
+    async def afail(self, err: BaseException) -> None:
+        status, error_str = _status_from_exception(err)
+        self._open.clear()
+        for handle in self._handles.values():
+            self._mark_terminal(handle, status, error_str)
+            if handle._mux is not None and not handle._mux._events._closed:
+                try:
+                    await handle._mux.afail(err)
+                except Exception:
+                    _logger.warning(
+                        "Error failing subgraph mini-mux at %s; "
+                        "subscribers may not see the terminal error.",
+                        handle.path,
+                        exc_info=True,
+                    )
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph"
-version = "1.1.9"
+version = "1.1.10"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -24,10 +24,10 @@ classifiers = [
    'Programming Language :: Python :: 3.13',
 ]
 dependencies = [
-    "langchain-core>=1.3.0,<2",
+    "langchain-core>=1.3.2,<2",
    "langgraph-checkpoint>=2.1.0,<5.0.0",
    "langgraph-sdk>=0.3.0,<0.4.0",
-    "langgraph-prebuilt>=1.0.9,<1.1.0",
+    "langgraph-prebuilt>=1.0.12,<1.1.0",
    "xxhash>=3.5.0",
    "pydantic>=2.7.4",
 ]
@@ -81,6 +81,7 @@ dev = [


 [tool.uv.sources]
+langchain-core = { git = "https://github.com/langchain-ai/langchain", branch = "cb/chat-model-updates", subdirectory = "libs/core" }
 langgraph-prebuilt = { path = "../prebuilt", editable = true }
 langgraph-checkpoint = { path = "../checkpoint", editable = true }
 langgraph-checkpoint-sqlite = { path = "../checkpoint-sqlite", editable = true }
@@ -117,291 +117,3 @@ def test_untracked_value() -> None:
    new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
    with pytest.raises(EmptyChannelError):
        new_channel.get()
-
-
-def test_delta_channel_basic_two_steps() -> None:
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.base import DeltaChannelSentinel
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
-
-    # Step 1: one message added
-    ch.update([HumanMessage(content="hi", id="h1")])
-    d1 = ch.checkpoint()
-    assert isinstance(d1, DeltaChannelSentinel)
-
-    # Step 2: another message
-    ch.update([AIMessage(content="hello", id="a1")])
-    d2 = ch.checkpoint()
-    assert isinstance(d2, DeltaChannelSentinel)
-
-    # Full accumulated value is preserved in memory
-    assert len(ch.get()) == 2
-    assert ch.get()[0].content == "hi"
-    assert ch.get()[1].content == "hello"
-
-
-def test_delta_channel_from_checkpoint_writes_list() -> None:
-    """from_checkpoint with a flat list of individual writes replays them through the operator."""
-    from langchain_core.messages import AIMessage, HumanMessage
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    spec = DeltaChannel(add_messages)
-    # Each element is one write value (as stored in checkpoint_writes)
-    writes = [
-        HumanMessage(content="hi", id="h1"),
-        AIMessage(content="hello", id="a1"),
-        HumanMessage(content="bye", id="h2"),
-    ]
-    ch = spec.from_checkpoint(writes)
-    msgs = ch.get()
-    assert len(msgs) == 3
-    assert msgs[0].content == "hi"
-    assert msgs[1].content == "hello"
-    assert msgs[2].content == "bye"
-
-
-def test_delta_channel_from_checkpoint_backwards_compat() -> None:
-    from langchain_core.messages import HumanMessage
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    # Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
-    spec = DeltaChannel(add_messages)
-    old_value = [HumanMessage(content="old", id="h1")]
-    ch = spec.from_checkpoint(old_value)
-    assert ch.get() == old_value
-
-
-def test_delta_channel_overwrite() -> None:
-    from langchain_core.messages import HumanMessage
-    from langgraph.checkpoint.base import DeltaChannelSentinel
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-    from langgraph.types import Overwrite
-
-    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
-    ch.update([HumanMessage(content="old", id="h1")])
-
-    ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
-    d = ch.checkpoint()
-    assert isinstance(d, DeltaChannelSentinel)
-    # After overwrite, value is reset to only the new message
-    assert len(ch.get()) == 1
-    assert ch.get()[0].content == "new"
-
-
-def test_delta_channel_remove_message_and_replay() -> None:
-    """RemoveMessage must round-trip correctly when writes are replayed."""
-    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    spec = DeltaChannel(add_messages)
-    ch = spec.from_checkpoint(MISSING)
-
-    # Step 1: add two messages
-    ch.update([HumanMessage(content="hi", id="h1")])
-    ch.update([AIMessage(content="hello", id="a1")])
-    assert ch.get() == [
-        HumanMessage(content="hi", id="h1"),
-        AIMessage(content="hello", id="a1"),
-    ]
-
-    # Step 2: remove the AI message
-    ch.update([RemoveMessage(id="a1")])
-    assert ch.get() == [HumanMessage(content="hi", id="h1")]
-
-    # Replay the writes list from scratch — must reproduce the post-remove state
-    writes = [
-        HumanMessage(content="hi", id="h1"),
-        AIMessage(content="hello", id="a1"),
-        RemoveMessage(id="a1"),
-    ]
-    ch2 = spec.from_checkpoint(writes)
-    assert ch2.get() == [HumanMessage(content="hi", id="h1")]
-
-
-def test_delta_channel_update_by_id_and_replay() -> None:
-    """Updating a message by ID must round-trip correctly through writes replay."""
-    from langchain_core.messages import HumanMessage
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    spec = DeltaChannel(add_messages)
-    ch = spec.from_checkpoint(MISSING)
-
-    # Step 1: add a message
-    ch.update([HumanMessage(content="original", id="h1")])
-
-    # Step 2: update the same message by ID
-    ch.update([HumanMessage(content="updated", id="h1")])
-    assert ch.get() == [HumanMessage(content="updated", id="h1")]
-
-    # Replay writes — must produce the updated message, not the original
-    writes = [
-        HumanMessage(content="original", id="h1"),
-        HumanMessage(content="updated", id="h1"),
-    ]
-    ch2 = spec.from_checkpoint(writes)
-    assert len(ch2.get()) == 1
-    assert ch2.get()[0].content == "updated"
-
-
-def test_delta_channel_checkpoint_returns_sentinel() -> None:
-    """checkpoint() always returns DeltaChannelSentinel regardless of state."""
-    from langgraph.checkpoint.base import DeltaChannelSentinel
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.message import add_messages
-
-    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
-    assert isinstance(ch.checkpoint(), DeltaChannelSentinel)
-
-    from langchain_core.messages import HumanMessage
-
-    ch.update([HumanMessage(content="hi", id="h1")])
-    assert isinstance(ch.checkpoint(), DeltaChannelSentinel)
-
-
-def test_delta_channel_inmemory_saver_assembles_writes() -> None:
-    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
-    from typing import Annotated
-
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-    from typing_extensions import TypedDict
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    n = {"v": 0}
-
-    def respond(state: State) -> dict:
-        n["v"] += 1
-        return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-    saver = InMemorySaver()
-    graph = builder.compile(checkpointer=saver)
-    config = {"configurable": {"thread_id": "t1"}}
-
-    graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
-    graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
-
-    # get_tuple must return a resolved list (not DeltaChannelSentinel)
-    from langgraph.checkpoint.base import DeltaChannelSentinel
-
-    saved = saver.get_tuple(config)
-    assert saved is not None
-    assert "messages" in saved.checkpoint["channel_values"]
-    assert not isinstance(
-        saved.checkpoint["channel_values"]["messages"], DeltaChannelSentinel
-    )
-    assert isinstance(saved.checkpoint["channel_values"]["messages"], list)
-
-    state = graph.get_state(config)
-    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
-
-
-def _delta_channel_with_type(operator, typ):
-    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
-    from typing import Annotated
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph.state import _get_channel
-
-    return _get_channel("_test", Annotated[typ, DeltaChannel(operator)])
-
-
-def test_delta_channel_dict_reducer_fresh_channel() -> None:
-    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
-
-    def merge_dicts(left: dict, right: dict) -> dict:
-        return {**left, **right}
-
-    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
-    # Should be available (not raise EmptyChannelError) and start empty
-    assert ch.is_available()
-    assert ch.get() == {}
-
-
-def test_delta_channel_dict_reducer_basic_updates() -> None:
-    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
-    from langgraph.checkpoint.base import DeltaChannelSentinel
-
-    def merge_dicts(left: dict, right: dict) -> dict:
-        return {**left, **right}
-
-    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
-
-    ch.update([{"a": 1}])
-    d1 = ch.checkpoint()
-    assert isinstance(d1, DeltaChannelSentinel)
-
-    ch.update([{"b": 2}])
-    d2 = ch.checkpoint()
-    assert isinstance(d2, DeltaChannelSentinel)
-
-    assert ch.get() == {"a": 1, "b": 2}
-
-
-def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
-    """from_checkpoint with a writes list replays correctly through a dict merge reducer."""
-
-    def merge_dicts(left: dict, right: dict) -> dict:
-        return {**left, **right}
-
-    spec = _delta_channel_with_type(merge_dicts, dict)
-    # Each element is one write value (oldest→newest)
-    writes = [{"a": 1}, {"b": 2}, {"c": 3}]
-    ch = spec.from_checkpoint(writes)
-    assert ch.get() == {"a": 1, "b": 2, "c": 3}
-
-
-def test_delta_channel_dict_reducer_with_deletions() -> None:
-    """Dict reducer that treats None values as deletions works end-to-end (deepagents pattern)."""
-
-    def merge_files(left: dict | None, right: dict) -> dict:
-        if left is None:
-            return {k: v for k, v in right.items() if v is not None}
-        result = {**left}
-        for k, v in right.items():
-            if v is None:
-                result.pop(k, None)
-            else:
-                result[k] = v
-        return result
-
-    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
-
-    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
-
-    # Delete file1, add file3
-    ch.update([{"file1.py": None, "file3.py": "content3"}])
-
-    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
-
-    # Confirm writes reconstruction produces the same result
-    writes = [
-        {"file1.py": "content1", "file2.py": "content2"},
-        {"file1.py": None, "file3.py": "content3"},
-    ]
-    spec = _delta_channel_with_type(merge_files, dict)
-    ch2 = spec.from_checkpoint(writes)
-    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
@@ -1,372 +0,0 @@
-"""Benchmark: DeltaChannel vs BinaryOperatorAggregate storage and time.
-
-Run directly:  python tests/test_delta_channel_benchmark.py
-Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
-
-Simulates realistic multi-turn conversations with paragraph-length messages
-(~100 tokens each) scaling up to 1M-token-equivalent histories.
-
-Token estimates: 1 token ≈ 4 chars; each turn ≈ 200 tokens (human + AI).
-A 1M-token conversation ≈ 5,000 turns of realistic messages.
-
-DeltaChannel stores only a zero-byte sentinel in checkpoint_blobs; the actual
-write data lives in checkpoint_writes (already stored there). Reconstruction
-walks the parent chain and replays writes through the operator — O(N) total
-storage vs O(N²) for plain add_messages.
-"""
-
-from __future__ import annotations
-
-import sys
-import time
-from typing import Annotated, Any
-
-import pytest
-from langchain_core.messages import AIMessage, HumanMessage
-from langgraph.checkpoint.memory import MemorySaver
-from typing_extensions import TypedDict
-
-from langgraph.channels.delta import DeltaChannel
-from langgraph.graph import END, StateGraph
-from langgraph.graph.message import add_messages
-
-try:
-    from langgraph.checkpoint.sqlite import SqliteSaver
-
-    _SQLITE_AVAILABLE = True
-except ImportError:
-    _SQLITE_AVAILABLE = False
-
-try:
-    from langgraph.checkpoint.postgres import PostgresSaver
-
-    _POSTGRES_AVAILABLE = True
-    _POSTGRES_URI = (
-        "postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable"
-    )
-except ImportError:
-    _POSTGRES_AVAILABLE = False
-
-# ---------------------------------------------------------------------------
-# Realistic message payload (~100 tokens / ~400 chars each)
-# ---------------------------------------------------------------------------
-
-_HUMAN_TEMPLATE = (
-    "I need help understanding the implications of {topic} on our system architecture. "
-    "Specifically, I'm concerned about how this interacts with our existing {concern} "
-    "and whether we need to refactor the {component} layer before proceeding."
-)
-
-_AI_TEMPLATE = (
-    "Great question about {topic}. The key insight here is that {concern} introduces "
-    "a subtle ordering dependency that most teams overlook until they hit it in production. "
-    "For your {component} layer specifically, I'd recommend starting with a careful audit "
-    "of the interface boundaries before making any structural changes. This will give you "
-    "a clear picture of the blast radius and let you sequence the migration safely."
-)
-
-_TOPICS = [
-    "distributed tracing",
-    "eventual consistency",
-    "schema migration",
-    "backpressure handling",
-    "idempotency guarantees",
-    "cache invalidation",
-    "connection pooling",
-    "rate limiting",
-    "circuit breaking",
-    "observability pipelines",
-]
-
-_CONCERNS = [
-    "concurrency model",
-    "retry semantics",
-    "state management",
-    "error propagation",
-    "latency budget",
-]
-
-_COMPONENTS = [
-    "persistence",
-    "routing",
-    "ingestion",
-    "aggregation",
-    "serialization",
-]
-
-
-def _human_content(i: int) -> str:
-    return _HUMAN_TEMPLATE.format(
-        topic=_TOPICS[i % len(_TOPICS)],
-        concern=_CONCERNS[i % len(_CONCERNS)],
-        component=_COMPONENTS[i % len(_COMPONENTS)],
-    )
-
-
-def _ai_content(i: int) -> str:
-    return _AI_TEMPLATE.format(
-        topic=_TOPICS[i % len(_TOPICS)],
-        concern=_CONCERNS[i % len(_CONCERNS)],
-        component=_COMPONENTS[i % len(_COMPONENTS)],
-    )
-
-
-# ---------------------------------------------------------------------------
-# State definitions
-# ---------------------------------------------------------------------------
-
-
-class BinaryState(TypedDict):
-    messages: Annotated[list, add_messages]
-
-
-class DeltaState(TypedDict):
-    messages: Annotated[list, DeltaChannel(add_messages)]
-
-
-# ---------------------------------------------------------------------------
-# Graph factory
-# ---------------------------------------------------------------------------
-
-
-def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
-    def human_node(state: Any) -> dict:
-        return {}
-
-    def ai_node(state: Any) -> dict:
-        i = len(state["messages"]) // 2
-        return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
-
-    g = StateGraph(state_cls)
-    g.add_node("human", human_node)
-    g.add_node("ai", ai_node)
-    g.add_edge("human", "ai")
-    g.add_edge("ai", END)
-    g.set_entry_point("human")
-    return g.compile(checkpointer=checkpointer or MemorySaver())
-
-
-# ---------------------------------------------------------------------------
-# Measurement helpers
-# ---------------------------------------------------------------------------
-
-
-def _total_blob_bytes(saver: MemorySaver) -> int:
-    total = 0
-    for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
-        if blob is not None:
-            total += len(blob)
-    return total
-
-
-def _run_turns(
-    n_turns: int,
-    state_cls: type,
-    checkpointer: Any = None,
-) -> tuple[float, float, int]:
-    """Run n_turns conversation turns.
-
-    Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
-    blob_bytes is -1 for savers without in-memory blob stores (e.g. SQLite).
-    Read latency is measured as the time to invoke the graph with no new
-    messages after the full history is built — this forces state rehydration.
-    """
-    graph = _make_graph(state_cls, checkpointer)
-    config = {"configurable": {"thread_id": "bench"}}
-
-    t0 = time.perf_counter()
-    for i in range(n_turns):
-        graph.invoke(
-            {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
-            config,
-        )
-    write_elapsed = time.perf_counter() - t0
-
-    # Measure read/rehydration: get_state forces the channel to rebuild
-    t1 = time.perf_counter()
-    for _ in range(5):
-        graph.get_state(config)
-    read_elapsed = (time.perf_counter() - t1) / 5
-
-    if isinstance(graph.checkpointer, MemorySaver):
-        blob_bytes = _total_blob_bytes(graph.checkpointer)
-    else:
-        blob_bytes = -1
-    return write_elapsed, read_elapsed, blob_bytes
-
-
-def _fmt_bytes(n: int) -> str:
-    if n >= 1_000_000:
-        return f"{n / 1_000_000:.1f} MB"
-    if n >= 1_000:
-        return f"{n / 1_000:.1f} KB"
-    return f"{n} B"
-
-
-def _approx_tokens(n_turns: int) -> str:
-    # ~100 tokens human + ~100 tokens AI per turn
-    tokens = n_turns * 200
-    if tokens >= 1_000_000:
-        return f"~{tokens / 1_000_000:.1f}M tok"
-    if tokens >= 1_000:
-        return f"~{tokens / 1_000:.0f}K tok"
-    return f"~{tokens} tok"
-
-
-# ---------------------------------------------------------------------------
-# Benchmark matrix
-# ---------------------------------------------------------------------------
-
-# Turn counts chosen to demonstrate O(N²) vs O(N) storage growth without running too long.
-# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
-TURN_COUNTS = [10, 25, 50, 100, 500]
-
-
-def _checkpointer_factories() -> list[tuple[str, Any]]:
-    """Return (label, context_manager_or_none) pairs for available checkpointers."""
-    return [("InMemory", None)]
-
-
-def run_benchmark() -> None:
-    print()
-    print(
-        "DeltaChannel vs add_messages (BinaryOperatorAggregate) — checkpoint storage & latency"
-    )
-    print("Simulating realistic multi-turn conversations up to ~1M-token histories")
-    print("(5,000 turns × ~200 tokens/turn ≈ 1M tokens — Claude's full context window)")
-    print()
-
-    checkpointers: list[tuple[str, Any]] = [("InMemory", None)]
-    if _POSTGRES_AVAILABLE:
-        try:
-            import psycopg
-
-            psycopg.connect(_POSTGRES_URI).close()
-            checkpointers.append(("Postgres (recursive CTE)", "postgres"))
-        except Exception:
-            pass
-
-    for cp_label, cp_hint in checkpointers:
-        print(f"--- Checkpointer: {cp_label} ---")
-        _run_benchmark_for_checkpointer(cp_hint)
-
-
-def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
-    import contextlib
-    import tempfile
-
-    @contextlib.contextmanager
-    def _make_saver():
-        if cp_hint is None:
-            yield None
-        elif cp_hint == "postgres":
-            with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
-                saver.setup()
-                with saver._cursor() as cur:
-                    cur.execute("DELETE FROM checkpoints WHERE thread_id = 'bench'")
-                    cur.execute(
-                        "DELETE FROM checkpoint_blobs WHERE thread_id = 'bench'"
-                    )
-                    cur.execute(
-                        "DELETE FROM checkpoint_writes WHERE thread_id = 'bench'"
-                    )
-                yield saver
-        else:
-            with tempfile.NamedTemporaryFile(suffix=".db") as f:
-                with SqliteSaver.from_conn_string(f.name) as saver:
-                    yield saver
-
-    rows = []
-    for turns in TURN_COUNTS:
-        with _make_saver() as saver:
-            b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
-        with _make_saver() as saver:
-            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
-        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt))
-
-    # ── Table 1: Storage ─────────────────────────────────────────────────────
-    W = 70
-    print("Storage (checkpoint blob bytes)")
-    print("=" * W)
-    print(
-        f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}  {'savings':>8}"
-    )
-    print("-" * W)
-    storage_results = []
-    for turns, b_bytes, d_bytes, b_rt, d_rt in rows:
-        if b_bytes < 0:
-            print(
-                f"{turns:>6}  {_approx_tokens(turns):>10}  {'n/a':>12}  {'n/a':>12}  {'n/a':>8}"
-            )
-        else:
-            ratio = b_bytes / d_bytes if d_bytes else float("inf")
-            storage_results.append((turns, b_bytes, d_bytes, ratio))
-            print(
-                f"{turns:>6}  {_approx_tokens(turns):>10}  "
-                f"{_fmt_bytes(b_bytes):>12}  {_fmt_bytes(d_bytes):>12}  "
-                f"{ratio:>7.0f}x"
-            )
-    print("=" * W)
-    print()
-
-    # ── Table 2: Read latency ─────────────────────────────────────────────────
-    print("Read latency (avg of 5 get_state calls)")
-    print("=" * W)
-    print(f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}")
-    print("-" * W)
-    for turns, b_bytes, d_bytes, b_rt, d_rt in rows:
-        print(
-            f"{turns:>6}  {_approx_tokens(turns):>10}  "
-            f"{b_rt * 1000:>10.1f}ms  {d_rt * 1000:>10.1f}ms"
-        )
-    print("=" * W)
-    print()
-
-    if storage_results:
-        turns, b_bytes, d_bytes, ratio = storage_results[-1]
-        b_rt = rows[-1][-2]
-        d_rt = rows[-1][-1]
-        print(
-            f"At {turns} turns: {_fmt_bytes(b_bytes)} → {_fmt_bytes(d_bytes)} ({ratio:.0f}x less storage); "
-            f"read {b_rt * 1000:.1f}ms → {d_rt * 1000:.1f}ms"
-        )
-        print()
-
-    print("Legend:")
-    print("  add_msgs = Annotated[list, add_messages]  — O(N²) storage")
-    print(
-        "  delta    = DeltaChannel(add_messages)     — O(N) storage, full chain replay"
-    )
-    print()
-
-
-# ---------------------------------------------------------------------------
-# Pytest entry point
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.skip(
-    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
-)
-def test_delta_channel_benchmark(capsys: Any) -> None:
-    """Storage grows O(N²) for add_messages, O(N) for DeltaChannel."""
-    with capsys.disabled():
-        run_benchmark()
-
-    # Correctness assertion: DeltaChannel must use less storage at scale.
-    for turns in [25, 50]:
-        _, _, b_bytes = _run_turns(turns, BinaryState)
-        _, _, d_bytes = _run_turns(turns, DeltaState)
-        assert d_bytes < b_bytes, (
-            f"DeltaChannel should use less storage at {turns} turns, "
-            f"got delta={d_bytes} binary={b_bytes}"
-        )
-
-
-# ---------------------------------------------------------------------------
-# Script entry point
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    run_benchmark()
-    sys.exit(0)
@@ -9400,190 +9400,3 @@ def test_fork_does_not_apply_pending_writes(

    # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
    assert result == {"value": 121}
-
-
-async def test_delta_channel_end_to_end_inmemory() -> None:
-    """Full graph run: DeltaChannel accumulates correctly across multiple turns."""
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    def respond(state: State) -> dict:
-        n = len(state["messages"])
-        return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-    graph = builder.compile(checkpointer=InMemorySaver())
-
-    config = {"configurable": {"thread_id": "diff-test-1"}}
-
-    # Turn 1
-    graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
-    # Turn 2
-    graph.invoke({"messages": [HumanMessage(content="world", id="h2")]}, config)
-    # Turn 3
-    graph.invoke({"messages": [HumanMessage(content="bye", id="h3")]}, config)
-
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # 3 human + 3 AI = 6 total
-    assert len(msgs) == 6, f"expected 6 messages, got {len(msgs)}: {msgs}"
-    assert msgs[0].content == "hello"
-    assert msgs[2].content == "world"
-    assert msgs[4].content == "bye"
-    assert msgs[1].content == "reply-1"
-    assert msgs[3].content == "reply-3"
-    assert msgs[5].content == "reply-5"
-
-
-async def test_delta_channel_time_travel() -> None:
-    """Time-travel back to turn-1 checkpoint and resume; continuation must not include turn-2 deltas."""
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    counter = {"n": 0}
-
-    def respond(state: State) -> dict:
-        counter["n"] += 1
-        return {
-            "messages": [
-                AIMessage(content=f"ai-{counter['n']}", id=f"ai-{counter['n']}")
-            ]
-        }
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_edge(START, "respond")
-    saver = InMemorySaver()
-    graph = builder.compile(checkpointer=saver)
-
-    config = {"configurable": {"thread_id": "diff-time-travel"}}
-
-    # Run 2 turns: h1→ai-1, h2→ai-2
-    graph.invoke({"messages": [HumanMessage(content="h1", id="h1")]}, config)
-    graph.invoke({"messages": [HumanMessage(content="h2", id="h2")]}, config)
-
-    # Find the checkpoint after turn 1 (2 messages: h1 + ai-1)
-    history = list(graph.get_state_history(config))
-    after_turn1 = next(h for h in history if len(h.values.get("messages", [])) == 2)
-
-    assert len(after_turn1.values["messages"]) == 2
-    assert after_turn1.values["messages"][0].content == "h1"
-    assert after_turn1.values["messages"][1].content == "ai-1"
-
-    # Resume from turn-1 checkpoint: inject h3, expect 3 messages total (h1, ai-1, ai-N)
-    # NOT 5 messages (turn-2 deltas must not bleed into the resumed run)
-    result = graph.invoke(
-        {"messages": [HumanMessage(content="h3", id="h3")]},
-        after_turn1.config,
-    )
-    msgs = result["messages"]
-    # Should be: h1, ai-1, h3, ai-N — 4 messages total
-    assert len(msgs) == 4, (
-        f"expected 4 messages after time-travel resume, got {len(msgs)}: {msgs}"
-    )
-    assert msgs[0].content == "h1"
-    assert msgs[1].content == "ai-1"
-    assert msgs[2].content == "h3"
-
-
-async def test_delta_channel_remove_message_end_to_end() -> None:
-    """RemoveMessage inside a DeltaChannel graph must persist and reload correctly."""
-    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    def respond(state: State) -> dict:
-        return {"messages": [AIMessage(content="reply", id="ai-1")]}
-
-    def delete_first(state: State) -> dict:
-        # removes the first message
-        return {"messages": [RemoveMessage(id=state["messages"][0].id)]}
-
-    builder = StateGraph(State)
-    builder.add_node("respond", respond)
-    builder.add_node("delete_first", delete_first)
-    builder.add_edge(START, "respond")
-    builder.add_edge("respond", "delete_first")
-    graph = builder.compile(checkpointer=InMemorySaver())
-
-    config = {"configurable": {"thread_id": "diff-remove-test"}}
-    graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
-
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # h1 was removed, only ai-1 should remain
-    assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
-    assert msgs[0].id == "ai-1"
-
-    # A subsequent turn must reconstruct from the checkpoint correctly
-    graph.invoke({"messages": [HumanMessage(content="again", id="h2")]}, config)
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    # ai-1 + h2 + ai-1(second reply, same id overwrites) + h2 removed
-    # more simply: after second run we expect ai-1 updated + h2 remaining minus deleted h2
-    # just assert h1 is still gone
-    assert all(m.id != "h1" for m in msgs), (
-        "h1 should still be absent after second turn"
-    )
-
-
-async def test_delta_channel_update_by_id_end_to_end() -> None:
-    """Updating a message by ID via DeltaChannel must persist and reload correctly."""
-    from langchain_core.messages import HumanMessage
-    from langgraph.checkpoint.memory import InMemorySaver
-
-    from langgraph.channels.delta import DeltaChannel
-    from langgraph.graph import START, StateGraph
-    from langgraph.graph.message import add_messages
-
-    class State(TypedDict):
-        messages: Annotated[list, DeltaChannel(add_messages)]
-
-    def update_msg(state: State) -> dict:
-        # re-send h1 with updated content
-        return {"messages": [HumanMessage(content="updated", id="h1")]}
-
-    builder = StateGraph(State)
-    builder.add_node("update_msg", update_msg)
-    builder.add_edge(START, "update_msg")
-    graph = builder.compile(checkpointer=InMemorySaver())
-
-    config = {"configurable": {"thread_id": "diff-update-id-test"}}
-    graph.invoke({"messages": [HumanMessage(content="original", id="h1")]}, config)
-
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
-    assert msgs[0].content == "updated"
-    assert msgs[0].id == "h1"
-
-    # Second turn: verify the updated state is the base for further accumulation
-    graph.invoke({"messages": [HumanMessage(content="new", id="h2")]}, config)
-    state = graph.get_state(config)
-    msgs = state.values["messages"]
-    ids = [m.id for m in msgs]
-    assert "h1" in ids  # h1 persists (updated, not duplicated)
-    assert "h2" in ids
-    assert ids.count("h1") == 1, "h1 must not be duplicated"
@@ -0,0 +1,401 @@
+"""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',)"
+        )
@@ -0,0 +1,876 @@
+"""Tests for MessagesTransformer: protocol event routing, whole-message fallback,
+legacy v1 chunk filtering, and end-to-end via stream_v2 / astream_v2."""
+
+from __future__ import annotations
+
+import time
+from typing import Any
+
+import pytest
+from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.language_models.chat_model_stream import (
+    AsyncChatModelStream,
+    ChatModelStream,
+)
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.runnables import RunnableConfig
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import MessagesState, StateGraph
+from langgraph.stream._mux import StreamMux
+from langgraph.stream.run_stream import GraphRunStream
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import MessagesTransformer, ValuesTransformer
+
+TS = int(time.time() * 1000)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _proto_event(
+    event: dict[str, Any],
+    *,
+    run_id: str = "run-1",
+    node: str = "llm",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a protocol event dict (v2 path)."""
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (event, {"langgraph_node": node, "run_id": run_id}),
+        },
+    }
+
+
+def _v1_chunk(
+    text: str,
+    msg_id: str = "msg-1",
+    *,
+    finish: bool = False,
+    node: str = "llm",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a v1 AIMessageChunk tuple."""
+    rm: dict[str, Any] = {"finish_reason": "stop"} if finish else {}
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (
+                AIMessageChunk(content=text, id=msg_id, response_metadata=rm),
+                {"langgraph_node": node},
+            ),
+        },
+    }
+
+
+def _whole_msg(
+    text: str,
+    msg_id: str = "msg-10",
+    *,
+    node: str = "node",
+) -> dict[str, Any]:
+    """Build a messages ProtocolEvent carrying a completed AIMessage."""
+    return {
+        "type": "event",
+        "method": "messages",
+        "params": {
+            "namespace": [],
+            "timestamp": TS,
+            "data": (AIMessage(content=text, id=msg_id), {"langgraph_node": node}),
+        },
+    }
+
+
+def _make_sync_transformer() -> tuple[
+    MessagesTransformer, StreamChannel[ChatModelStream]
+]:
+    t = MessagesTransformer()
+    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    log._bind(is_async=False)
+    # Subscribe up front so pushes during process() are retained.
+    log._subscribed = True
+    t._bind_pump(lambda: False)
+    return t, log
+
+
+def _make_async_transformer() -> tuple[
+    MessagesTransformer, StreamChannel[ChatModelStream]
+]:
+    t = MessagesTransformer()
+    log: StreamChannel[ChatModelStream] = t.init()["messages"]
+    log._bind(is_async=True)
+    log._subscribed = True
+    return t, log
+
+
+def _lifecycle(
+    *, text: str = "hello world", message_id: str = "run-1"
+) -> list[dict[str, Any]]:
+    """Produce a valid protocol event lifecycle: start, delta, finish."""
+    half = len(text) // 2
+    first, second = text[:half], text[half:]
+    return [
+        {"event": "message-start", "role": "ai", "message_id": message_id},
+        {
+            "event": "content-block-start",
+            "index": 0,
+            "content_block": {"type": "text", "text": ""},
+        },
+        {
+            "event": "content-block-delta",
+            "index": 0,
+            "content_block": {"type": "text", "text": first},
+        },
+        {
+            "event": "content-block-delta",
+            "index": 0,
+            "content_block": {"type": "text", "text": second},
+        },
+        {
+            "event": "content-block-finish",
+            "index": 0,
+            "content_block": {"type": "text", "text": text},
+        },
+        {"event": "message-finish", "reason": "stop"},
+    ]
+
+
+def _simple_graph():
+    def call_model(state: MessagesState) -> dict[str, Any]:
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+        stream = model.stream_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
+# ---------------------------------------------------------------------------
+
+
+class TestProtocolEventRouting:
+    def test_message_start_creates_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+            )
+        )
+        log.close()
+        (stream,) = list(log._items)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.message_id == "run-1"
+
+    def test_full_lifecycle_yields_done_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle(text="hello world"):
+            t.process(_proto_event(evt, run_id="run-1"))
+        log.close()
+        (stream,) = list(log._items)
+        assert stream.done
+        assert stream.output.text == "hello world"
+
+    def test_message_finish_cleans_up_routing(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle():
+            t.process(_proto_event(evt, run_id="run-1"))
+        assert t._by_run == {}
+
+    def test_events_without_prior_start_are_ignored(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {
+                    "event": "content-block-delta",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": "orphan"},
+                },
+                run_id="unknown",
+            )
+        )
+        log.close()
+        assert list(log._items) == []
+
+    def test_concurrent_streams_routed_by_run_id(self) -> None:
+        t, log = _make_sync_transformer()
+        life_a = _lifecycle(text="aaaa", message_id="run-a")
+        life_b = _lifecycle(text="bbbb", message_id="run-b")
+        for a, b in zip(life_a, life_b):
+            t.process(_proto_event(a, run_id="run-a"))
+            t.process(_proto_event(b, run_id="run-b"))
+        log.close()
+        streams = 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"
+
+    def test_text_deltas_accumulated_on_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        for evt in _lifecycle(text="abcdef"):
+            t.process(_proto_event(evt))
+        log.close()
+        (stream,) = list(log._items)
+        assert "".join(stream._text_proj._deltas) == "abcdef"
+
+    def test_stream_pushed_on_message_start_not_finish(self) -> None:
+        # Consumer can see the stream before message-finish arrives.
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+            )
+        )
+        assert len(log._items) == 1
+
+    def test_node_metadata_set_on_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "role": "ai", "message_id": "run-1"},
+                run_id="run-1",
+                node="my_llm",
+            )
+        )
+        (stream,) = list(log._items)
+        assert stream.node == "my_llm"
+
+
+# ---------------------------------------------------------------------------
+# Whole-message fallback
+# ---------------------------------------------------------------------------
+
+
+class TestWholeMessageFallback:
+    def test_whole_ai_message_produces_complete_stream(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_whole_msg("the full answer"))
+        log.close()
+        (stream,) = list(log._items)
+        assert stream.done
+        assert stream.output.text == "the full answer"
+
+    def test_whole_message_has_full_lifecycle(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(_whole_msg("full"))
+        log.close()
+        (stream,) = list(log._items)
+        assert [e["event"] for e in stream._events] == [
+            "message-start",
+            "content-block-start",
+            "content-block-delta",
+            "content-block-finish",
+            "message-finish",
+        ]
+
+
+# ---------------------------------------------------------------------------
+# Filtering
+# ---------------------------------------------------------------------------
+
+
+class TestFiltering:
+    def test_non_messages_events_pass_through(self) -> None:
+        t, _ = _make_sync_transformer()
+        assert (
+            t.process(
+                {
+                    "type": "event",
+                    "method": "values",
+                    "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
+                }
+            )
+            is True
+        )
+
+    def test_subgraph_namespace_dropped(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            {
+                "type": "event",
+                "method": "messages",
+                "params": {
+                    "namespace": ["subgraph"],
+                    "timestamp": TS,
+                    "data": (
+                        {"event": "message-start", "message_id": "run-x"},
+                        {"run_id": "run-x"},
+                    ),
+                },
+            }
+        )
+        log.close()
+        assert 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) == []
+
+
+# ---------------------------------------------------------------------------
+# Lifecycle: fail / finalize
+# ---------------------------------------------------------------------------
+
+
+class TestLifecycle:
+    def test_fail_propagates_to_open_streams(self) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+            )
+        )
+        streams = list(log._items)
+        err = RuntimeError("graph died")
+        t.fail(err)
+        assert t._by_run == {}
+        assert streams[0]._error is err
+
+    def test_finalize_clears_routing_state(self) -> None:
+        t, _ = _make_sync_transformer()
+        t.process(
+            _proto_event(
+                {"event": "message-start", "message_id": "run-1"}, run_id="run-1"
+            )
+        )
+        assert "run-1" in t._by_run
+        t.finalize()
+        assert t._by_run == {}
+
+
+# ---------------------------------------------------------------------------
+# Async mode
+# ---------------------------------------------------------------------------
+
+
+class TestAsyncMode:
+    def test_async_mode_creates_async_stream(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="async stream"):
+            t.process(_proto_event(evt))
+        assert isinstance(list(log._items)[0], AsyncChatModelStream)
+
+    @pytest.mark.anyio
+    async def test_text_projection_yields_deltas(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="hello world"):
+            t.process(_proto_event(evt))
+        (stream,) = list(log._items)
+        assert isinstance(stream, AsyncChatModelStream)
+        assert "".join([d async for d in stream.text]) == "hello world"
+
+    @pytest.mark.anyio
+    async def test_output_awaitable(self) -> None:
+        t, log = _make_async_transformer()
+        for evt in _lifecycle(text="async"):
+            t.process(_proto_event(evt))
+        (stream,) = list(log._items)
+        assert (await stream.output).text == "async"
+
+
+# ---------------------------------------------------------------------------
+# GraphRunStream integration
+# ---------------------------------------------------------------------------
+
+
+class TestWireRequestMore:
+    def test_bind_pump_called_on_wire(self) -> None:
+        values_t = ValuesTransformer()
+        messages_t = MessagesTransformer()
+        mux = StreamMux([values_t, messages_t], is_async=False)
+
+        assert messages_t._pump_fn is None
+        run = GraphRunStream(iter([]), mux, values_t)
+        assert messages_t._pump_fn is not None
+        assert messages_t._pump_fn() is False
+        assert run._exhausted
+
+    def test_created_streams_have_request_more(self) -> None:
+        values_t = ValuesTransformer()
+        messages_t = MessagesTransformer()
+        mux = StreamMux([values_t, messages_t], is_async=False)
+        GraphRunStream(iter([]), mux, values_t)
+
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+        for evt in _lifecycle():
+            messages_t.process(_proto_event(evt))
+
+        (stream,) = list(log._items)
+        assert stream._request_more is messages_t._pump_fn
+
+
+# ---------------------------------------------------------------------------
+# End-to-end via StreamMux
+# ---------------------------------------------------------------------------
+
+
+class TestViaMux:
+    def _make_mux(
+        self,
+    ) -> tuple[MessagesTransformer, StreamMux, StreamChannel[ChatModelStream]]:
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=False)
+        t._bind_pump(lambda: False)
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+        return t, mux, log
+
+    def test_streaming_via_mux(self) -> None:
+        t, mux, log = self._make_mux()
+        for evt in _lifecycle(text="mux stream"):
+            mux.push(_proto_event(evt))
+        mux.close()
+        (stream,) = list(log._items)
+        assert stream.output.text == "mux stream"
+
+    def test_whole_message_via_mux(self) -> None:
+        t, mux, log = self._make_mux()
+        mux.push(_whole_msg("result"))
+        mux.close()
+        (stream,) = list(log._items)
+        assert stream.output.text == "result"
+
+    @pytest.mark.anyio
+    async def test_async_streaming_via_mux(self) -> None:
+        t = MessagesTransformer()
+        v = ValuesTransformer()
+        mux = StreamMux([v, t], is_async=True)
+        log: StreamChannel[ChatModelStream] = mux.extensions["messages"]
+        log._subscribed = True
+
+        for evt in _lifecycle(text="async mux"):
+            await mux.apush(_proto_event(evt))
+
+        (stream,) = list(log._items)
+        assert (await stream.output).text == "async mux"
+        await mux.aclose()
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: graph → stream_v2 → run.messages (node calls stream_v2)
+# ---------------------------------------------------------------------------
+
+
+class TestEndToEnd:
+    """stream_v2 path: node calls model.stream_v2() explicitly."""
+
+    def test_node_calling_stream_v2_populates_messages(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = model.stream_v2(state["messages"])
+            return {"messages": stream.output}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_v2({"messages": "hi"})
+        (stream,) = list(run.messages)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.text == "hello world"
+
+    def test_node_stream_v2_text_deltas_iterate(self) -> None:
+        """Consumer can iterate `.text` on the streamed message in real time."""
+        model = GenericFakeChatModel(messages=iter(["streamed answer"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = model.stream_v2(state["messages"])
+            return {"messages": stream.output}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = graph.stream_v2({"messages": "go"})
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "streamed answer"
+
+    def test_non_llm_message_returned_from_node(self) -> None:
+        """Whole-message fallback: node returns a finalized AIMessage directly."""
+
+        def return_message(state: MessagesState) -> dict[str, Any]:
+            return {"messages": AIMessage(content="hardcoded", id="msg-abc")}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("return_message", return_message)
+            .add_edge(START, "return_message")
+            .add_edge("return_message", END)
+            .compile()
+        )
+
+        run = graph.stream_v2({"messages": "hi"})
+        (stream,) = list(run.messages)
+        assert stream.output.text == "hardcoded"
+
+    @pytest.mark.anyio
+    async def test_async_node_calling_astream_v2(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["async answer"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = await model.astream_v2(state["messages"])
+            return {"messages": await stream}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_v2({"messages": "hi"})
+        streams = [s async for s in run.messages]
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
+        assert (await streams[0].output).text == "async answer"
+
+    @pytest.mark.anyio
+    async def test_nested_async_iteration_yields_text_deltas(self) -> None:
+        """Inner stream.text drives the shared graph pump via the async pump binding."""
+        import asyncio
+
+        model = GenericFakeChatModel(messages=iter(["hello world"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            stream = await model.astream_v2(state["messages"])
+            return {"messages": await stream}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_v2({"messages": "hi"})
+
+        async def consume() -> list[str]:
+            collected: list[str] = []
+            async for stream in run.messages:
+                async for delta in stream.text:
+                    collected.append(delta)
+            return collected
+
+        assert "".join(await asyncio.wait_for(consume(), timeout=2.0)) == "hello world"
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: graph → stream_v2 → run.messages (node calls invoke)
+# ---------------------------------------------------------------------------
+
+
+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()."""
+
+    def _graph(self, model):
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        return (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+    def test_invoke_populates_messages(self) -> None:
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["hello world"]))
+        ).stream_v2({"messages": "hi"})
+        (stream,) = list(run.messages)
+        assert isinstance(stream, ChatModelStream)
+        assert stream.output.text == "hello world"
+
+    def test_invoke_emits_protocol_events(self) -> None:
+        """Iterating the stream yields the full v2 lifecycle, not v1 chunks."""
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["streamed answer"]))
+        ).stream_v2({"messages": "go"})
+        (stream,) = list(run.messages)
+
+        events = list(stream)
+        event_types = [e.get("event") for e in events]
+        assert "message-start" in event_types
+        assert "content-block-start" in event_types
+        assert "content-block-delta" in event_types
+        assert "content-block-finish" in event_types
+        assert "message-finish" in event_types
+        # Sanity: every event is a dict carrying an "event" key — not an
+        # AIMessageChunk tuple from the v1 path.
+        for event in events:
+            assert isinstance(event, dict)
+            assert "event" in event
+        # Typed projection still assembles the final text.
+        assert stream.output.text == "streamed answer"
+
+    def test_invoke_text_deltas_iterate(self) -> None:
+        run = self._graph(
+            GenericFakeChatModel(messages=iter(["delta streaming works"]))
+        ).stream_v2({"messages": "hi"})
+        (stream,) = list(run.messages)
+        assert "".join(stream.text) == "delta streaming works"
+
+    def test_invoke_two_nodes_two_streams(self) -> None:
+        model_a = GenericFakeChatModel(messages=iter(["alpha"]))
+        model_b = GenericFakeChatModel(messages=iter(["beta"]))
+
+        def node_a(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model_a.invoke(state["messages"])}
+
+        def node_b(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model_b.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("node_a", node_a)
+            .add_node("node_b", node_b)
+            .add_edge(START, "node_a")
+            .add_edge("node_a", "node_b")
+            .add_edge("node_b", END)
+            .compile()
+        )
+
+        streams = list(graph.stream_v2({"messages": "hi"}).messages)
+        assert len(streams) == 2
+        assert {s.output.text for s in streams} == {"alpha", "beta"}
+
+    def test_invoke_plus_constructed_message_two_streams(self) -> None:
+        """Live-streamed node + constructed-message node → two ChatModelStreams."""
+        model = GenericFakeChatModel(messages=iter(["live stream"]))
+
+        def streaming_node(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        def constructed_node(state: MessagesState) -> dict[str, Any]:
+            return {"messages": [AIMessage(content="hardcoded", id="constructed-1")]}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("streaming_node", streaming_node)
+            .add_node("constructed_node", constructed_node)
+            .add_edge(START, "streaming_node")
+            .add_edge("streaming_node", "constructed_node")
+            .add_edge("constructed_node", END)
+            .compile()
+        )
+
+        run = graph.stream_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[1].node == "constructed_node"
+        assert streams[1].output.text == "hardcoded"
+        assert streams[1].message_id == "constructed-1"
+
+    @pytest.mark.anyio
+    async def test_ainvoke_populates_messages(self) -> None:
+        model = GenericFakeChatModel(messages=iter(["async invoke"]))
+
+        async def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": await model.ainvoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        run = await graph.astream_v2({"messages": "hi"})
+        streams = [s async for s in run.messages]
+        assert len(streams) == 1
+        assert isinstance(streams[0], AsyncChatModelStream)
+        assert (await streams[0].output).text == "async invoke"
+
+
+# ---------------------------------------------------------------------------
+# Regression: direct stream_mode="messages" must stay v1
+# ---------------------------------------------------------------------------
+
+
+class TestDirectMessagesModeStaysV1:
+    def test_direct_graph_stream_messages_yields_ai_message_chunks(self) -> None:
+        """graph.stream(stream_mode="messages") must not leak v2 event dicts —
+        the v2 flag is only injected by stream_v2 / astream_v2."""
+        model = GenericFakeChatModel(messages=iter(["legacy path"]))
+
+        def call_model(state: MessagesState) -> dict[str, Any]:
+            return {"messages": model.invoke(state["messages"])}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("call_model", call_model)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", END)
+            .compile()
+        )
+
+        parts = list(graph.stream({"messages": "hi"}, stream_mode="messages"))
+        assert parts, "expected stream_mode='messages' to emit tuples"
+        for payload, _metadata in parts:
+            assert isinstance(payload, AIMessageChunk)
+        assert (
+            "".join(p[0].content for p in parts if isinstance(p[0].content, str))
+            == "legacy path"
+        )
+
+    def test_nested_graph_stream_messages_stays_v1_under_outer_stream_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",
+                )
+            )
+            assert parts
+            payloads = [payload for payload, _metadata in parts]
+            return {
+                "saw_only_chunks": all(
+                    isinstance(payload, AIMessageChunk) for payload in payloads
+                ),
+                "first_payload_type": type(payloads[0]).__name__,
+                "text": "".join(
+                    payload.content
+                    for payload in payloads
+                    if isinstance(payload, AIMessageChunk)
+                    and isinstance(payload.content, str)
+                ),
+            }
+
+        outer = (
+            StateGraph(OuterState)
+            .add_node("call_subgraph", call_subgraph)
+            .add_edge(START, "call_subgraph")
+            .add_edge("call_subgraph", END)
+            .compile()
+        )
+
+        result = outer.stream_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
+# ---------------------------------------------------------------------------
+
+
+class TestStreamMessagesHandlerV2Unit:
+    def test_on_llm_new_token_is_noop(self) -> None:
+        """v2 handler must not emit v1 chunks even when on_llm_new_token fires."""
+        from uuid import uuid4
+
+        from langchain_core.outputs import ChatGenerationChunk
+
+        from langgraph.pregel._messages import StreamMessagesHandlerV2
+
+        emitted: list[Any] = []
+        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
+        run_id = uuid4()
+        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
+
+        handler.on_llm_new_token(
+            "hello",
+            chunk=ChatGenerationChunk(message=AIMessageChunk(content="hello")),
+            run_id=run_id,
+        )
+
+        assert emitted == []
+
+    def test_on_llm_end_dedupes_when_final_message_id_differs(self) -> None:
+        """A streamed v2 message should not be emitted again from the final
+        AIMessage fallback when its final id does not match `message-start`."""
+        from uuid import uuid4
+
+        from langchain_core.outputs import ChatGeneration, LLMResult
+
+        from langgraph.pregel._messages import StreamMessagesHandlerV2
+
+        emitted: list[Any] = []
+        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
+        run_id = uuid4()
+        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
+
+        handler.on_stream_event(
+            {"event": "message-start", "message_id": "stream-msg-1"},
+            run_id=run_id,
+        )
+        handler.on_llm_end(
+            LLMResult(
+                generations=[
+                    [
+                        ChatGeneration(
+                            message=AIMessage(content="hello", id="final-msg-1")
+                        )
+                    ]
+                ]
+            ),
+            run_id=run_id,
+        )
+
+        assert len(emitted) == 1
@@ -0,0 +1,863 @@
+"""Tests for SubgraphTransformer.
+
+Subscribes to `tasks` events and produces in-process `SubgraphRunStream`
+handles backed by mini-muxes (built via `StreamMux._make_child`). The
+synthetic-event tests isolate the inference / mini-mux wiring; the
+real-graph tests exercise the end-to-end navigation path through
+`stream_v2`.
+"""
+
+from __future__ import annotations
+
+import operator
+import time
+from collections.abc import AsyncIterator
+from functools import partial
+from typing import Annotated, Any
+
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.errors import GraphInterrupt
+from langgraph.graph import StateGraph
+from langgraph.pregel.main import _normalize_stream_transformer_factories
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.run_stream import (
+    AsyncGraphRunStream,
+    AsyncSubgraphRunStream,
+    GraphRunStream,
+    SubgraphRunStream,
+)
+from langgraph.stream.transformers import (
+    LifecycleTransformer,
+    MessagesTransformer,
+    SubgraphTransformer,
+    ValuesTransformer,
+)
+
+TS = int(time.time() * 1000)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _tasks_start(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "input": None,
+                "triggers": [],
+            },
+        },
+    }
+
+
+def _tasks_result(
+    namespace: list[str],
+    *,
+    task_id: str,
+    name: str,
+    error: str | None = None,
+    interrupts: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    return {
+        "type": "event",
+        "method": "tasks",
+        "params": {
+            "namespace": namespace,
+            "timestamp": TS,
+            "data": {
+                "id": task_id,
+                "name": name,
+                "error": error,
+                "interrupts": interrupts or [],
+                "result": {},
+            },
+        },
+    }
+
+
+def _native_factories() -> list[Any]:
+    """Mirror the factory list `Pregel.stream_v2` registers."""
+    return [
+        ValuesTransformer,
+        MessagesTransformer,
+        LifecycleTransformer,
+        SubgraphTransformer,
+    ]
+
+
+def _stream_part(
+    method: str,
+    namespace: tuple[str, ...],
+    data: Any,
+) -> dict[str, Any]:
+    return {"type": method, "ns": namespace, "data": data}
+
+
+async def _astream_parts(*parts: dict[str, Any]) -> AsyncIterator[dict[str, Any]]:
+    for part in parts:
+        yield part
+
+
+def _arm(mux: StreamMux) -> None:
+    """Pre-subscribe every projection in the mux so synthetic pushes accumulate.
+
+    Real consumer code subscribes by iterating the projection; tests
+    inspect `_items` directly, so the lazy-subscribe gate has to be
+    flipped manually before any synthetic events are pushed.
+    """
+    mux._events._subscribed = True
+    for value in mux.extensions.values():
+        if hasattr(value, "_subscribed"):
+            value._subscribed = True
+
+
+def _arm_recursive(mux: StreamMux) -> None:
+    """Arm `mux` and every mini-mux currently held by SubgraphTransformer handles.
+
+    Mini-muxes are created during `mux.push(...)` when a new direct
+    child is discovered. Tests must call this after each push that
+    might have created a new mini-mux so subsequent pushes' projection
+    side effects accumulate (rather than dropping silently against an
+    unsubscribed log).
+    """
+    _arm(mux)
+    for handle in _subgraph_transformer(mux)._handles.values():
+        if handle._mux is not None:
+            _arm_recursive(handle._mux)
+
+
+def _build_root_mux(*, scope: tuple[str, ...] = ()) -> StreamMux:
+    mux = StreamMux(
+        factories=_native_factories(),
+        scope=scope,
+        is_async=False,
+    )
+    _arm(mux)
+    return mux
+
+
+def _subgraph_transformer(mux: StreamMux) -> SubgraphTransformer:
+    transformer = mux.transformer_by_key("subgraphs")
+    assert isinstance(transformer, SubgraphTransformer)
+    return transformer
+
+
+def _drain_subgraphs(mux: StreamMux) -> list[SubgraphRunStream]:
+    return list(_subgraph_transformer(mux)._log._items)
+
+
+def _child_mux(handle: SubgraphRunStream | AsyncSubgraphRunStream) -> StreamMux:
+    assert handle._mux is not None
+    return handle._mux
+
+
+def _event_items(mux: StreamMux) -> list[ProtocolEvent]:
+    return list(mux._events._items)
+
+
+def _lifecycle_payloads(mux: StreamMux) -> list[dict[str, Any]]:
+    lifecycle_t = mux.transformer_by_key("lifecycle")
+    assert isinstance(lifecycle_t, LifecycleTransformer)
+    return list(lifecycle_t._channel._items)
+
+
+# ---------------------------------------------------------------------------
+# Synthetic-event tests
+# ---------------------------------------------------------------------------
+
+
+def test_handle_created_on_first_direct_child_task() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.path == ("agent:abc",)
+    assert handle.graph_name == "agent"
+    assert handle.trigger_call_id == "abc"
+    assert handle.status == "started"
+    _child_mux(handle)  # mini-mux backed
+
+
+def test_handle_status_completes_on_parent_result() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "completed"
+    assert handle.error is None
+
+
+def test_handle_status_failed_with_error() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "failed"
+    assert handle.error == "boom"
+
+
+def test_handle_status_interrupted() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(
+        _tasks_result(
+            [],
+            task_id="abc",
+            name="agent",
+            interrupts=[{"value": "pause"}],
+        )
+    )
+
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "interrupted"
+
+
+def test_grandchild_discovered_via_child_mini_mux() -> None:
+    """Each mini-mux owns its own scope; grandchildren live on the child handle."""
+    mux = _build_root_mux()
+    # Direct child started — creates the mini-mux.
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    # Pre-subscribe the freshly-created mini-mux so subsequent
+    # forwarded events land on its projections (consumer would
+    # subscribe naturally by iterating handle.subgraphs, but the
+    # test inspects `_items` directly).
+    _arm_recursive(mux)
+    # Grandchild's first task event flows down into the child mini-mux.
+    mux.push(_tasks_start(["agent:abc", "tool:def"], task_id="t2", name="deep"))
+
+    [child_handle] = _drain_subgraphs(mux)
+    assert child_handle.path == ("agent:abc",)
+    # The grandchild appears on the CHILD'S subgraphs projection.
+    grandchildren = list(child_handle.subgraphs._items)
+    assert len(grandchildren) == 1
+    assert grandchildren[0].path == ("agent:abc", "tool:def")
+
+
+def test_finalize_completes_open_handles() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.close()
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "completed"
+
+
+def test_fail_marks_open_handles_interrupted_for_graph_interrupt() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(GraphInterrupt())
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "interrupted"
+
+
+def test_fail_marks_open_handles_failed_for_other_errors() -> None:
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    mux.fail(RuntimeError("boom"))
+    [handle] = _drain_subgraphs(mux)
+    assert handle.status == "failed"
+    assert handle.error == "boom"
+
+
+def test_child_mux_requires_factories() -> None:
+    """A mux constructed only from `transformers=` can't clone factories."""
+    transformer = SubgraphTransformer()
+    mux = StreamMux(transformers=[transformer], is_async=False)
+    with pytest.raises(RuntimeError, match="factories"):
+        mux._make_child(("anything",))
+
+
+def test_subgraph_and_lifecycle_agree_on_terminal_status() -> None:
+    """Both transformers consume the same tasks signal — no drift."""
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent", error="boom"))
+
+    [handle] = _drain_subgraphs(mux)
+    payloads = _lifecycle_payloads(mux)
+    assert handle.status == "failed"
+    assert payloads[-1]["event"] == "failed"
+    assert handle.error == payloads[-1]["error"]
+
+
+def test_required_stream_modes_declared() -> None:
+    assert SubgraphTransformer.required_stream_modes == ("tasks",)
+
+
+def test_tasks_events_suppressed_from_main_log() -> None:
+    """Tasks events are folded into discovery and don't appear on the main log."""
+    mux = _build_root_mux()
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    mux.push(_tasks_result([], task_id="abc", name="agent"))
+
+    methods = [evt["method"] for evt in _event_items(mux)]
+    assert "tasks" not in methods
+
+
+class _ChildEventObserver(StreamTransformer):
+    """Records child-scope event identity without mutating it."""
+
+    records: list[tuple[tuple[str, ...], int, int, bool]] = []
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if self.scope and event["method"] == "values":
+            self.records.append(
+                (
+                    self.scope,
+                    id(event),
+                    id(event["params"]["data"]),
+                    "seq" in event,
+                )
+            )
+        return True
+
+
+def test_child_forwarding_reuses_event_without_assigning_seq() -> None:
+    _ChildEventObserver.records = []
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildEventObserver,
+        ],
+        is_async=False,
+    )
+    _arm(mux)
+    mux.push(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+    data = {"x": 1}
+    event: ProtocolEvent = {
+        "type": "event",
+        "method": "values",
+        "params": {
+            "namespace": ["agent:abc"],
+            "timestamp": TS,
+            "data": data,
+        },
+    }
+    mux.push(event)
+
+    assert _ChildEventObserver.records == [(("agent:abc",), id(event), id(data), False)]
+    [root_event] = [evt for evt in _event_items(mux) if evt["method"] == "values"]
+    assert root_event is event
+    assert "seq" in root_event
+
+
+class _AsyncProbeTransformer(StreamTransformer):
+    """Async-only transformer used to verify mini-mux async dispatch."""
+
+    required_stream_modes = ("tasks",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self.seen: list[tuple[str, ...]] = []
+        self.finalized = False
+        self.failed: BaseException | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"async_probe": self}
+
+    async def aprocess(self, event: ProtocolEvent) -> bool:
+        self.seen.append(tuple(event["params"]["namespace"]))
+        return True
+
+    async def afinalize(self) -> None:
+        self.finalized = True
+
+    async def afail(self, err: BaseException) -> None:
+        self.failed = err
+
+
+@pytest.mark.anyio
+async def test_async_child_mini_mux_uses_async_lane() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _AsyncProbeTransformer,
+        ],
+        is_async=True,
+    )
+    await mux.apush(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    handle = _subgraph_transformer(mux)._handles[("agent:abc",)]
+    assert isinstance(handle, AsyncSubgraphRunStream)
+    probe = _child_mux(handle).transformer_by_key("async_probe")
+    assert isinstance(probe, _AsyncProbeTransformer)
+    assert probe.seen == [("agent:abc",)]
+
+    await mux.apush(_tasks_result([], task_id="abc", name="agent"))
+    assert probe.finalized is True
+
+
+@pytest.mark.anyio
+async def test_async_child_mini_mux_fail_uses_async_lane() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _AsyncProbeTransformer,
+        ],
+        is_async=True,
+    )
+    await mux.apush(_tasks_start(["agent:abc"], task_id="t1", name="tool"))
+
+    handle = _subgraph_transformer(mux)._handles[("agent:abc",)]
+    probe = _child_mux(handle).transformer_by_key("async_probe")
+    assert isinstance(probe, _AsyncProbeTransformer)
+
+    err = RuntimeError("boom")
+    await mux.afail(err)
+    assert probe.failed is err
+
+
+class _StandardCtorTransformer(StreamTransformer):
+    """Transformer class that inherits the standard scoped constructor."""
+
+    def init(self) -> dict[str, Any]:
+        return {"standard_ctor": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ScopedTransformer(StreamTransformer):
+    """Transformer class that uses the inherited scoped construction."""
+
+    def init(self) -> dict[str, Any]:
+        return {"scoped": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ConfigurableFactoryTransformer(StreamTransformer):
+    """Transformer built by a configured per-scope factory."""
+
+    def __init__(self, scope: tuple[str, ...] = (), *, label: str) -> None:
+        super().__init__(scope)
+        self.label = label
+
+    def init(self) -> dict[str, Any]:
+        return {"configurable": self}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+
+class _ChildExploder(StreamTransformer):
+    """Raise from child mini-muxes to verify errors propagate upstream."""
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        if self.scope and event["method"] == "values":
+            raise RuntimeError("child boom")
+        return True
+
+
+class _ChildFinalizeExploder(StreamTransformer):
+    """Raise from child mini-mux finalization."""
+
+    supports_sync = True
+
+    def init(self) -> dict[str, Any]:
+        return {}
+
+    def process(self, event: ProtocolEvent) -> bool:
+        return True
+
+    def finalize(self) -> None:
+        if self.scope:
+            raise RuntimeError("child finalize boom")
+
+    async def afinalize(self) -> None:
+        if self.scope:
+            raise RuntimeError("child afinalize boom")
+
+
+def test_normalize_transformer_factories_supports_scoped_classes() -> None:
+    factories = _normalize_stream_transformer_factories(
+        [_StandardCtorTransformer, _ScopedTransformer]
+    )
+
+    standard_ctor = factories[0](("child",))
+    scoped = factories[1](("child",))
+    assert isinstance(standard_ctor, _StandardCtorTransformer)
+    assert standard_ctor.scope == ("child",)
+    assert isinstance(scoped, _ScopedTransformer)
+    assert scoped.scope == ("child",)
+
+
+def test_normalize_transformer_factories_supports_configured_factories() -> None:
+    factories = _normalize_stream_transformer_factories(
+        [partial(_ConfigurableFactoryTransformer, label="configured")]
+    )
+
+    built = factories[0](("child",))
+    assert isinstance(built, _ConfigurableFactoryTransformer)
+    assert built.label == "configured"
+    assert built.scope == ("child",)
+
+
+def test_normalize_transformer_factories_rejects_instances() -> None:
+    with pytest.raises(TypeError, match="pre-built instance"):
+        _normalize_stream_transformer_factories([_StandardCtorTransformer()])
+
+
+def test_child_forwarding_errors_fail_sync_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildExploder,
+        ],
+        is_async=False,
+    )
+    values_t = mux.transformer_by_key("values")
+    assert isinstance(values_t, ValuesTransformer)
+    run = GraphRunStream(
+        iter(
+            [
+                _stream_part(
+                    "tasks",
+                    ("agent:abc",),
+                    {
+                        "id": "t1",
+                        "name": "tool",
+                        "input": None,
+                        "triggers": [],
+                    },
+                ),
+                _stream_part("values", ("agent:abc",), {"x": 1}),
+            ]
+        ),
+        mux,
+        values_t,
+    )
+
+    handle = next(iter(run.subgraphs))
+    assert handle.path == ("agent:abc",)
+    with pytest.raises(RuntimeError, match="child boom"):
+        _ = run.output
+    assert run._mux._events._error is not None
+
+
+@pytest.mark.anyio
+async def test_child_forwarding_errors_fail_async_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildExploder,
+        ],
+        is_async=True,
+    )
+    values_t = mux.transformer_by_key("values")
+    assert isinstance(values_t, ValuesTransformer)
+    run = AsyncGraphRunStream(
+        _astream_parts(
+            _stream_part(
+                "tasks",
+                ("agent:abc",),
+                {
+                    "id": "t1",
+                    "name": "tool",
+                    "input": None,
+                    "triggers": [],
+                },
+            ),
+            _stream_part("values", ("agent:abc",), {"x": 1}),
+        ),
+        mux,
+        values_t,
+    )
+
+    handle = await run.subgraphs.__aiter__().__anext__()
+    assert handle.path == ("agent:abc",)
+    with pytest.raises(RuntimeError, match="child boom"):
+        await run.output()
+    assert run._mux._events._error is not None
+
+
+def test_child_finalize_errors_propagate_to_sync_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildFinalizeExploder,
+        ],
+        is_async=False,
+    )
+    values_t = mux.transformer_by_key("values")
+    assert isinstance(values_t, ValuesTransformer)
+    run = GraphRunStream(
+        iter(
+            [
+                _stream_part(
+                    "tasks",
+                    ("agent:abc",),
+                    {
+                        "id": "t1",
+                        "name": "tool",
+                        "input": None,
+                        "triggers": [],
+                    },
+                )
+            ]
+        ),
+        mux,
+        values_t,
+    )
+
+    with pytest.raises(RuntimeError, match="child finalize boom"):
+        _ = run.output
+
+
+@pytest.mark.anyio
+async def test_child_finalize_errors_propagate_to_async_run() -> None:
+    mux = StreamMux(
+        factories=[
+            ValuesTransformer,
+            MessagesTransformer,
+            LifecycleTransformer,
+            SubgraphTransformer,
+            _ChildFinalizeExploder,
+        ],
+        is_async=True,
+    )
+    values_t = mux.transformer_by_key("values")
+    assert isinstance(values_t, ValuesTransformer)
+    run = AsyncGraphRunStream(
+        _astream_parts(
+            _stream_part(
+                "tasks",
+                ("agent:abc",),
+                {
+                    "id": "t1",
+                    "name": "tool",
+                    "input": None,
+                    "triggers": [],
+                },
+            )
+        ),
+        mux,
+        values_t,
+    )
+
+    with pytest.raises(RuntimeError, match="child afinalize boom"):
+        await run.output()
+
+
+# ---------------------------------------------------------------------------
+# End-to-end real-graph tests
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    value: str
+    items: Annotated[list[str], operator.add]
+
+
+def _passthrough(state: _State) -> dict[str, Any]:
+    return {"value": state["value"] + "!", "items": ["x"]}
+
+
+def _make_two_level_nested() -> Any:
+    """outer → middle → inner. Three Pregel instances, two nesting levels."""
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("inner_node", _passthrough)
+    inner_b.add_edge(START, "inner_node")
+    inner_b.add_edge("inner_node", END)
+    inner = inner_b.compile()
+
+    middle_b: StateGraph = StateGraph(_State, input_schema=_State)
+    middle_b.add_node("inner", inner)
+    middle_b.add_edge(START, "inner")
+    middle_b.add_edge("inner", END)
+    middle = middle_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("middle", middle)
+    outer_b.add_edge(START, "middle")
+    outer_b.add_edge("middle", END)
+    return outer_b.compile()
+
+
+def _item_node(item: str):
+    def node(state: _State) -> dict[str, Any]:
+        return {"items": [item]}
+
+    return node
+
+
+def _make_two_sibling_subgraphs() -> Any:
+    """outer → one → two, where both nodes are compiled subgraphs."""
+    one_b: StateGraph = StateGraph(_State, input_schema=_State)
+    one_b.add_node("add_one", _item_node("one"))
+    one_b.add_edge(START, "add_one")
+    one_b.add_edge("add_one", END)
+    one = one_b.compile()
+
+    two_b: StateGraph = StateGraph(_State, input_schema=_State)
+    two_b.add_node("add_two", _item_node("two"))
+    two_b.add_edge(START, "add_two")
+    two_b.add_edge("add_two", END)
+    two = two_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("one", one)
+    outer_b.add_node("two", two)
+    outer_b.add_edge(START, "one")
+    outer_b.add_edge("one", "two")
+    outer_b.add_edge("two", END)
+    return outer_b.compile()
+
+
+def _failing_node(state: _State) -> dict[str, Any]:
+    raise ValueError("child boom")
+
+
+def _make_failing_nested() -> Any:
+    inner_b: StateGraph = StateGraph(_State, input_schema=_State)
+    inner_b.add_node("fail", _failing_node)
+    inner_b.add_edge(START, "fail")
+    inner_b.add_edge("fail", END)
+    inner = inner_b.compile()
+
+    outer_b: StateGraph = StateGraph(_State, input_schema=_State)
+    outer_b.add_node("inner", inner)
+    outer_b.add_edge(START, "inner")
+    outer_b.add_edge("inner", END)
+    return outer_b.compile()
+
+
+def test_stream_v2_real_graph_yields_subgraph_handles() -> None:
+    """Iterating `run.subgraphs` yields handles for direct-child subgraphs."""
+    graph = _make_two_level_nested()
+    run = graph.stream_v2({"value": "x", "items": []})
+
+    handle_paths: list[tuple[str, ...]] = []
+    final_status: dict[tuple[str, ...], str] = {}
+    for handle in run.subgraphs:
+        # Drill into the handle's projections inside the loop body so
+        # the mini-mux is subscribed before the next pump cycle.
+        list(handle.values)
+        handle_paths.append(handle.path)
+        final_status[handle.path] = handle.status
+
+    assert len(handle_paths) == 1
+    assert handle_paths[0][0].startswith("middle:")
+    assert final_status[handle_paths[0]] == "completed"
+
+
+def test_stream_v2_grandchild_visible_on_child_handle() -> None:
+    """Drilling into `handle.subgraphs` surfaces nested grandchildren."""
+    graph = _make_two_level_nested()
+    run = graph.stream_v2({"value": "x", "items": []})
+
+    grandchild_paths: list[tuple[str, ...]] = []
+    middle_path: tuple[str, ...] | None = None
+    for middle_handle in run.subgraphs:
+        # Subscribe to grandchildren before the next pump cycle.
+        for inner_handle in middle_handle.subgraphs:
+            # Subscribe to inner.values so its mini-mux drains.
+            list(inner_handle.values)
+            grandchild_paths.append(inner_handle.path)
+        middle_path = middle_handle.path
+
+    assert middle_path is not None
+    assert len(grandchild_paths) == 1
+    inner_path = grandchild_paths[0]
+    assert inner_path[1].startswith("inner:")
+    assert inner_path[: len(middle_path)] == middle_path
+
+
+def test_subgraph_output_stops_at_own_terminal_without_draining_siblings() -> None:
+    """A handle's `output` must not pump past its terminal event.
+
+    If it over-pumps the root run, the second sibling handle is yielded
+    only after it has already completed, so subscribing to `values`
+    inside the loop body misses its events.
+    """
+    graph = _make_two_sibling_subgraphs()
+    run = graph.stream_v2({"value": "x", "items": []})
+
+    paths: list[tuple[str, ...]] = []
+    second_values: list[dict[str, Any]] = []
+    for handle in run.subgraphs:
+        paths.append(handle.path)
+        if handle.graph_name == "one":
+            assert handle.output is not None
+            assert handle.status == "completed"
+        elif handle.graph_name == "two":
+            second_values = list(handle.values)
+
+    assert [path[0].split(":", 1)[0] for path in paths] == ["one", "two"]
+    assert second_values
+    assert second_values[-1]["items"] == ["one", "two"]
+
+
+def test_aborted_subgraph_handle_does_not_fail_parent_forwarding() -> None:
+    graph = _make_two_sibling_subgraphs()
+    run = graph.stream_v2({"value": "x", "items": []})
+
+    seen: list[str | None] = []
+    for handle in run.subgraphs:
+        seen.append(handle.graph_name)
+        if handle.graph_name == "one":
+            # Subscribe before aborting to ensure forwarding into the
+            # closed mini-mux would have raised without the closed check.
+            iter(handle.values)
+            handle.abort()
+        elif handle.graph_name == "two":
+            assert list(handle.values)
+
+    assert seen == ["one", "two"]
+
+
+def test_failed_subgraph_output_raises_terminal_error() -> None:
+    graph = _make_failing_nested()
+    run = graph.stream_v2({"value": "x", "items": []})
+
+    handle = next(iter(run.subgraphs))
+    with pytest.raises(RuntimeError, match="child boom"):
+        _ = handle.output
+    assert handle.status == "failed"
+    assert handle.error == "child boom"
@@ -0,0 +1,792 @@
+"""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"
@@ -1161,9 +1161,7 @@ def test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id(
    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"
-    ]
+    head_checkpoint_id = graph.get_state(config).config["configurable"]["checkpoint_id"]
    called.clear()
    resume_config = {
        "configurable": {
@@ -0,0 +1,290 @@
+"""Tests for StreamToolCallHandler and ToolRuntime.emit_output_delta.
+
+These tests exercise the langgraph-core piece in isolation — the prebuilt
+`ToolCallTransformer` has its own test file. Here we feed real graphs
+through `Pregel.stream(stream_mode=["tools", ...])` and inspect the raw
+`(ns, mode, payload)` tuples on the `tools` channel.
+"""
+
+from __future__ import annotations
+
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode, ToolRuntime
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.pregel._tools import _tool_call_writer
+
+
+class _State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def _caller_sync(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
+    def caller(state: _State) -> dict:
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
+                )
+            ]
+        }
+
+    return caller
+
+
+def _caller_async(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
+    async def caller(state: _State) -> dict:
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
+                )
+            ]
+        }
+
+    return caller
+
+
+def _build_graph(caller, tools) -> Any:
+    sg = StateGraph(_State)
+    sg.add_node("caller", caller)
+    sg.add_node("tools", ToolNode(tools))
+    sg.add_edge(START, "caller")
+    sg.add_edge("caller", "tools")
+    sg.add_edge("tools", END)
+    return sg.compile()
+
+
+def _tool_events(stream) -> list[tuple[tuple[str, ...], dict]]:
+    """Collect `(ns, payload)` for every `tools`-mode chunk."""
+    out: list[tuple[tuple[str, ...], dict]] = []
+    for ns, mode, payload in stream:
+        if mode == "tools":
+            out.append((tuple(ns), payload))
+    return out
+
+
+class TestSyncGraphSyncTool:
+    def test_started_finished_cycle(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return f"echoed:{text}"
+
+        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        assert [p["event"] for _, p in events] == [
+            "tool-started",
+            "tool-finished",
+        ]
+        assert events[0][1]["tool_call_id"] == "tc1"
+        assert events[0][1]["tool_name"] == "echo"
+        assert events[0][1]["input"] == {"text": "hi"}
+        # ToolNode wraps the return in a ToolMessage.
+        assert events[1][1]["tool_call_id"] == "tc1"
+
+    def test_emit_output_delta_produces_delta_events(self) -> None:
+        @tool
+        def streaming_echo(text: str, runtime: ToolRuntime) -> str:
+            """stream chunks."""
+            for chunk in ("a", "b", "c"):
+                runtime.emit_output_delta(chunk)
+            return text
+
+        graph = _build_graph(
+            _caller_sync("streaming_echo", {"text": "x"}), [streaming_echo]
+        )
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        deltas = [p["delta"] for _, p in events if p["event"] == "tool-output-delta"]
+        assert deltas == ["a", "b", "c"]
+        # The deltas must be bracketed by started and finished.
+        ordered = [p["event"] for _, p in events]
+        assert ordered[0] == "tool-started"
+        assert ordered[-1] == "tool-finished"
+
+    def test_tool_error_event(self) -> None:
+        @tool
+        def boom() -> str:
+            """raises."""
+            raise ValueError("nope")
+
+        graph = _build_graph(_caller_sync("boom", {}), [boom])
+        events: list[tuple[tuple[str, ...], dict]] = []
+        with pytest.raises(ValueError, match="nope"):
+            for ns, mode, payload in graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            ):
+                if mode == "tools":
+                    events.append((tuple(ns), payload))
+
+        kinds = [p["event"] for _, p in events]
+        assert kinds == ["tool-started", "tool-error"]
+        assert events[1][1]["message"] == "nope"
+
+    def test_writer_unset_outside_tool(self) -> None:
+        # Outside any tool body the ContextVar that ToolRuntime reads
+        # is unset — emitting from there would be a no-op.
+        assert _tool_call_writer.get() is None
+
+    def test_no_events_without_tools_mode(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return text
+
+        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
+        # No "tools" in stream_mode — handler is not attached and zero
+        # `tools`-method events fire.
+        chunks = list(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["values"],
+                subgraphs=True,
+            )
+        )
+        assert all(
+            not (isinstance(c, tuple) and len(c) == 3 and c[1] == "tools")
+            for c in chunks
+        )
+
+
+class TestAsyncGraphAsyncTool:
+    @pytest.mark.anyio
+    async def test_async_tool_produces_events(self) -> None:
+        @tool
+        async def aecho(text: str, runtime: ToolRuntime) -> str:
+            """async echo."""
+            runtime.emit_output_delta(text)
+            return f"got:{text}"
+
+        graph = _build_graph(_caller_async("aecho", {"text": "hi"}), [aecho])
+        events: list[tuple[tuple[str, ...], dict]] = []
+        async for ns, mode, payload in graph.astream(
+            {"messages": []},
+            stream_mode=["tools"],
+            subgraphs=True,
+        ):
+            if mode == "tools":
+                events.append((tuple(ns), payload))
+
+        kinds = [p["event"] for _, p in events]
+        assert kinds == ["tool-started", "tool-output-delta", "tool-finished"]
+        assert events[1][1]["delta"] == "hi"
+
+
+class TestConcurrentToolCalls:
+    def test_parallel_tool_calls_do_not_bleed(self) -> None:
+        @tool
+        def streamer(marker: str, runtime: ToolRuntime) -> str:
+            """emits marker twice."""
+            runtime.emit_output_delta(f"{marker}-1")
+            runtime.emit_output_delta(f"{marker}-2")
+            return marker
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "streamer", "args": {"marker": "A"}, "id": "a"},
+                            {"name": "streamer", "args": {"marker": "B"}, "id": "b"},
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [streamer])
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        # Group deltas by tool_call_id.
+        by_id: dict[str, list[str]] = {}
+        for _, p in events:
+            if p["event"] == "tool-output-delta":
+                by_id.setdefault(p["tool_call_id"], []).append(p["delta"])
+        assert by_id["a"] == ["A-1", "A-2"]
+        assert by_id["b"] == ["B-1", "B-2"]
+
+
+class TestSubgraphNamespacePropagation:
+    def test_tool_inside_subgraph_emits_with_subgraph_ns(self) -> None:
+        @tool
+        def inner_tool(text: str) -> str:
+            """inner tool."""
+            return text
+
+        def sub_caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {
+                                "name": "inner_tool",
+                                "args": {"text": "x"},
+                                "id": "tc1",
+                            }
+                        ],
+                    )
+                ]
+            }
+
+        inner = StateGraph(_State)
+        inner.add_node("sub_caller", sub_caller)
+        inner.add_node("sub_tools", ToolNode([inner_tool]))
+        inner.add_edge(START, "sub_caller")
+        inner.add_edge("sub_caller", "sub_tools")
+        inner.add_edge("sub_tools", END)
+        inner_graph = inner.compile()
+
+        outer = StateGraph(_State)
+        outer.add_node("sub", inner_graph)
+        outer.add_edge(START, "sub")
+        outer.add_edge("sub", END)
+        graph = outer.compile()
+
+        events = _tool_events(
+            graph.stream(
+                {"messages": []},
+                stream_mode=["tools"],
+                subgraphs=True,
+            )
+        )
+
+        # All `tools` events should carry a non-empty namespace rooted
+        # at the `sub` node.
+        assert events, "expected at least one tools event"
+        for ns, _ in events:
+            assert ns  # non-empty
+            assert ns[0].startswith("sub:")
@@ -427,49 +427,3 @@ def test_callback_manager_copies_configurable_ids_to_tracing_metadata() -> None:
        "thread_id": "th-123",
        "user_id": "uid-1",
    }
-
-
-def test_get_nonlocal_names_cached_by_code_object() -> None:
-    """_get_nonlocal_names caches by code object so repeated calls are cheap."""
-    from langgraph.pregel._utils import _get_nonlocal_names
-
-    x = 1
-
-    def my_func() -> int:
-        return x
-
-    result1 = _get_nonlocal_names(my_func.__code__)
-    result2 = _get_nonlocal_names(my_func.__code__)
-
-    # Same frozenset instance returned (cache hit)
-    assert result1 is result2
-    assert "x" in result1
-
-
-def test_get_function_nonlocals_fast_path_no_freevars() -> None:
-    """Functions with no free variables return [] without AST parsing."""
-    from langgraph.pregel._utils import _get_nonlocal_names, get_function_nonlocals
-
-    cache_info_before = _get_nonlocal_names.cache_info()
-
-    def pure_func(a: int, b: int) -> int:
-        return a + b
-
-    result = get_function_nonlocals(pure_func)
-
-    # Should have returned early without touching the cache
-    assert result == []
-    assert _get_nonlocal_names.cache_info().misses == cache_info_before.misses
-
-
-def test_get_function_nonlocals_returns_closure_values() -> None:
-    """get_function_nonlocals correctly extracts values from closures."""
-    from langgraph.pregel._utils import get_function_nonlocals
-
-    sentinel = object()
-
-    def my_func() -> object:
-        return sentinel
-
-    result = get_function_nonlocals(my_func)
-    assert sentinel in result
@@ -1348,10 +1348,11 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.0"
-source = { registry = "https://pypi.org/simple" }
+version = "1.3.2"
+source = { git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates#aee50839376e379891c99fcbe6d5264f66dedc68" }
 dependencies = [
    { name = "jsonpatch" },
+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -1360,14 +1361,22 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/bf/efb5e2ed832e4d6d45590e25a9e5191986b291b543bc6a807b48bee070b0/langchain_protocol-0.0.14.tar.gz", hash = "sha256:bc1e8553122e6ede310280462d5813023a172ff2785ccbbdec54d43f3a15e5f2", size = 5862, upload-time = "2026-04-29T16:40:18.657Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/e9/06c47ecb2aff08f83dfa30058da3bf86be64862c19569043ed5331bbeecd/langchain_protocol-0.0.14-py3-none-any.whl", hash = "sha256:ffc35089779bd8ca217015180cef5e660fc3b074efdaa0f2e95df73583f1a047", size = 6984, upload-time = "2026-04-29T16:40:17.841Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.9"
+version = "1.1.10"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -1439,7 +1448,7 @@ test = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.3.0,<2" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-prebuilt", editable = "../prebuilt" },
    { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1451,7 +1460,7 @@ requires-dist = [
 dev = [
    { name = "httpx" },
    { name = "jupyter" },
-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-checkpoint-postgres", editable = "../checkpoint-postgres" },
    { name = "langgraph-checkpoint-sqlite", editable = "../checkpoint-sqlite" },
@@ -1484,7 +1493,7 @@ lint = [
 ]
 test = [
    { name = "httpx" },
-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-checkpoint-postgres", editable = "../checkpoint-postgres" },
    { name = "langgraph-checkpoint-sqlite", editable = "../checkpoint-sqlite" },
@@ -1548,7 +1557,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.2"
+version = "4.0.3"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -1557,7 +1566,7 @@ dependencies = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=0.2.38" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "ormsgpack", specifier = ">=1.12.0" },
 ]

@@ -1742,7 +1751,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.10"
+version = "1.0.12"
 source = { editable = "../prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -1751,14 +1760,14 @@ dependencies = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
 ]

 [package.metadata.requires-dev]
 dev = [
    { name = "codespell" },
-    { name = "langchain-core" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "langgraph", editable = "." },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-checkpoint-postgres", editable = "../checkpoint-postgres" },
@@ -1778,7 +1787,7 @@ lint = [
    { name = "ruff" },
 ]
 test = [
-    { name = "langchain-core" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
    { name = "langgraph", editable = "." },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-checkpoint-postgres", editable = "../checkpoint-postgres" },
@@ -1826,20 +1835,20 @@ requires-dist = [
 dev = [
    { name = "codespell" },
    { name = "langgraph", editable = "." },
-    { name = "mypy", specifier = "==1.19.1" },
+    { name = "mypy", specifier = "==1.20.2" },
    { name = "pydantic", specifier = ">=2.12.4" },
    { name = "pytest" },
    { name = "pytest-asyncio" },
    { name = "pytest-mock" },
    { name = "pytest-watch" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "ruff", specifier = "==0.15.12" },
    { name = "starlette" },
    { name = "ty", specifier = "==0.0.23" },
 ]
 lint = [
    { name = "codespell" },
-    { name = "mypy", specifier = "==1.19.1" },
-    { name = "ruff", specifier = "==0.15.6" },
+    { name = "mypy", specifier = "==1.20.2" },
+    { name = "ruff", specifier = "==0.15.12" },
    { name = "starlette" },
    { name = "ty", specifier = "==0.0.23" },
 ]
@@ -2140,7 +2149,7 @@ wheels = [

 [[package]]
 name = "nbconvert"
-version = "7.17.0"
+version = "7.17.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "beautifulsoup4" },
@@ -2158,9 +2167,9 @@ dependencies = [
    { name = "pygments" },
    { name = "traitlets" },
 ]
-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" }
+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" }
 wheels = [
-    { 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" },
+    { 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" },
 ]

 [[package]]
@@ -3018,11 +3027,11 @@ wheels = [

 [[package]]
 name = "python-dotenv"
-version = "1.2.1"
+version = "1.2.2"
 source = { registry = "https://pypi.org/simple" }
-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" }
+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" }
 wheels = [
-    { 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" },
+    { 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" },
 ]

 [[package]]
@@ -1,5 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""

+from langgraph.prebuilt._tool_call_transformer import ToolCallTransformer
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import (
    InjectedState,
@@ -13,6 +14,7 @@ from langgraph.prebuilt.tool_validator import ValidationNode
 __all__ = [
    "create_react_agent",
    "ToolNode",
+    "ToolCallTransformer",
    "tools_condition",
    "ValidationNode",
    "InjectedState",
@@ -0,0 +1,117 @@
+"""In-process handle for a single tool call's streaming execution.
+
+Mirrors the shape of `ChatModelStream` from langchain-core but simpler —
+a tool has one output channel, no content-block multiplexing. Populated
+by `ToolCallTransformer` as `tool-started` / `tool-output-delta` /
+`tool-finished` / `tool-error` events flow in on the `tools` channel.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Iterator
+from typing import Any
+
+from langgraph.stream.stream_channel import StreamChannel
+
+
+class ToolCallStream:
+    """Scoped view of a single tool call's lifecycle.
+
+    Yielded on `run.tool_calls` once per `tool-started` event. Fields
+    are populated as events arrive:
+
+    - `tool_call_id`, `tool_name`, `input`: stable from the start event.
+    - `output_deltas`: a `StreamChannel` of delta chunks. Iterate (sync or
+      async) to consume partial output in arrival order.
+    - `output`: terminal payload from `tool-finished`, or `None` if the
+      call failed or is still in flight.
+    - `error`: terminal error string from `tool-error`, or `None` if the
+      call succeeded or is still in flight.
+    - `completed`: True once a terminal event (`tool-finished` or
+      `tool-error`) has been observed.
+
+    `ToolCallStream` is not meant to be constructed by end users — it's
+    produced by `ToolCallTransformer` as events flow through the mux.
+    """
+
+    def __init__(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        input: dict[str, Any] | None = None,
+    ) -> None:
+        """Initialize a fresh handle for a tool call.
+
+        Args:
+            tool_call_id: The `tool_call_id` from the AIMessage.
+            tool_name: The tool's name.
+            input: The tool's input arguments (as reported by
+                `on_tool_start`), or `None` if none were captured.
+        """
+        self.tool_call_id = tool_call_id
+        self.tool_name = tool_name
+        self.input = input
+        self._output_deltas: StreamChannel[Any] = StreamChannel()
+        self.output: Any = None
+        self.error: str | None = None
+        self.completed = False
+
+    @property
+    def output_deltas(self) -> StreamChannel[Any]:
+        """The channel of streamed `tool-output-delta` payloads.
+
+        Iterate (sync or async depending on how the run was started)
+        to consume partial output in arrival order. The log closes when
+        the tool finishes or errors.
+        """
+        return self._output_deltas
+
+    def _bind(self, *, is_async: bool) -> None:
+        """Bind the deltas log to sync or async iteration.
+
+        Called by `ToolCallTransformer` when constructing this handle so
+        the log matches the enclosing mux's mode.
+        """
+        self._output_deltas._bind(is_async=is_async)
+
+    def _push_delta(self, delta: Any) -> None:
+        self._output_deltas.push(delta)
+
+    def _finish(self, output: Any) -> None:
+        self.output = output
+        self.completed = True
+        self._output_deltas.close()
+
+    def _fail(self, message: str) -> None:
+        self.error = message
+        self.completed = True
+        self._output_deltas.close()
+
+    def __iter__(self) -> Iterator[Any]:
+        """Iterate delta chunks synchronously.
+
+        Equivalent to `iter(self.output_deltas)`. Raises `TypeError` if
+        the underlying log is bound to async mode.
+        """
+        return iter(self._output_deltas)
+
+    def __aiter__(self) -> AsyncIterator[Any]:
+        """Iterate delta chunks asynchronously.
+
+        Equivalent to `aiter(self.output_deltas)`. Raises `TypeError`
+        if the underlying log is bound to sync mode.
+        """
+        return self._output_deltas.__aiter__()
+
+    def __repr__(self) -> str:
+        status = (
+            "completed"
+            if self.completed and self.error is None
+            else "failed"
+            if self.completed
+            else "running"
+        )
+        return (
+            f"ToolCallStream(tool_call_id={self.tool_call_id!r}, "
+            f"tool_name={self.tool_name!r}, status={status})"
+        )
@@ -0,0 +1,129 @@
+"""Transformer that projects `tools` channel events into `ToolCallStream`s."""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langgraph.stream._types import ProtocolEvent, StreamTransformer
+from langgraph.stream.stream_channel import StreamChannel
+
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
+
+
+class ToolCallTransformer(StreamTransformer):
+    """Project `tools` channel events into `ToolCallStream` handles.
+
+    Each `tool-started` event spawns a `ToolCallStream`, pushed onto
+    `run.tool_calls`. Subsequent `tool-output-delta` events append to
+    that stream's deltas log; `tool-finished` and `tool-error` close it.
+
+    Native transformer — the `tool_calls` projection is exposed as a
+    direct attribute on the run stream.
+
+    A nameless `StreamChannel[ToolCallStream]` is used (no protocol
+    auto-forwarding) because the live handles are not serializable and
+    should not be injected into the main event log. Wire consumers
+    subscribe to the `tools` channel instead, where the raw protocol
+    events flow through untouched by this transformer (`process`
+    returns `True`).
+
+    Registered explicitly by users at compile time via
+    `builder.compile(transformers=[ToolCallTransformer])` — not a
+    default built-in, so the `tools` channel is user-opt-in.
+    """
+
+    _native = True
+    required_stream_modes = ("tools",)
+
+    def __init__(self, scope: tuple[str, ...] = ()) -> None:
+        super().__init__(scope)
+        self._log: StreamChannel[ToolCallStream] = StreamChannel()
+        self._active: dict[str, ToolCallStream] = {}
+        self._is_async = False
+        self._pump_fn: Callable[[], bool] | None = None
+        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
+
+    def init(self) -> dict[str, Any]:
+        return {"tool_calls": self._log}
+
+    def _bind_pump(self, fn: Callable[[], bool]) -> None:
+        """Wire the sync pull callback onto this transformer.
+
+        Called by `StreamMux.bind_pump`. Stored so each new
+        `ToolCallStream` created by `process` can wire its deltas log
+        for pump-driven iteration.
+        """
+        self._pump_fn = fn
+        self._is_async = False
+
+    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
+        """Async counterpart to `_bind_pump`."""
+        self._apump_fn = fn
+        self._is_async = True
+
+    def _new_stream(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        tool_input: dict[str, Any] | None,
+    ) -> ToolCallStream:
+        stream = ToolCallStream(tool_call_id, tool_name, tool_input)
+        stream._bind(is_async=self._is_async)
+        if self._apump_fn is not None:
+            stream._output_deltas._arequest_more = self._apump_fn
+        if self._pump_fn is not None:
+            stream._output_deltas._request_more = self._pump_fn
+        return stream
+
+    def process(self, event: ProtocolEvent) -> bool:
+        # Namespace filtering is handled by the mux via `scope_exact`.
+        if event["method"] != "tools":
+            return True
+
+        data = event["params"]["data"]
+        tool_call_id = data.get("tool_call_id")
+        if tool_call_id is None:
+            return True
+        event_type = data.get("event")
+
+        stream: ToolCallStream | None
+        if event_type == "tool-started":
+            stream = self._new_stream(
+                tool_call_id,
+                data.get("tool_name", ""),
+                data.get("input"),
+            )
+            self._active[tool_call_id] = stream
+            self._log.push(stream)
+        elif event_type == "tool-output-delta":
+            stream = self._active.get(tool_call_id)
+            if stream is not None:
+                stream._push_delta(data.get("delta"))
+        elif event_type == "tool-finished":
+            stream = self._active.pop(tool_call_id, None)
+            if stream is not None:
+                stream._finish(data.get("output"))
+        elif event_type == "tool-error":
+            stream = self._active.pop(tool_call_id, None)
+            if stream is not None:
+                stream._fail(data.get("message", ""))
+
+        # Pass-through — wire consumers subscribe to the `tools` channel
+        # directly and reconstruct handles client-side.
+        return True
+
+    def finalize(self) -> None:
+        """Close any still-active tool streams left open at run end."""
+        for stream in self._active.values():
+            if not stream.completed:
+                stream._finish(None)
+        self._active.clear()
+
+    def fail(self, err: BaseException) -> None:
+        """Fail any still-active tool streams when the run errors."""
+        message = str(err)
+        for stream in self._active.values():
+            if not stream.completed:
+                stream._fail(message)
+        self._active.clear()
@@ -82,9 +82,11 @@ from langchain_core.tools.base import (
    _is_injected_arg_type,
    get_all_basemodel_annotations,
 )
+from langgraph._internal._constants import CONF, CONFIG_KEY_READ
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.pregel._tools import _tool_call_writer
 from langgraph.runtime import ExecutionInfo, ServerInfo  # noqa: TC002
 from langgraph.store.base import BaseStore  # noqa: TC002
 from langgraph.types import Command, Send, StreamWriter
@@ -800,7 +802,7 @@ class ToolNode(RunnableCallable):
        # Construct ToolRuntime instances at the top level for each tool call
        tool_runtimes = []
        for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input)
+            state = self._extract_state(input, cfg)
            tool_runtime = ToolRuntime(
                state=state,
                tool_call_id=call["id"],
@@ -835,7 +837,7 @@ class ToolNode(RunnableCallable):
        # Construct ToolRuntime instances at the top level for each tool call
        tool_runtimes = []
        for call, cfg in zip(tool_calls, config_list, strict=False):
-            state = self._extract_state(input)
+            state = self._extract_state(input, cfg)
            tool_runtime = ToolRuntime(
                state=state,
                tool_call_id=call["id"],
@@ -859,14 +861,30 @@ class ToolNode(RunnableCallable):

    def _combine_tool_outputs(
        self,
-        outputs: list[ToolMessage | Command],
+        outputs: list[ToolMessage | Command | 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 outputs):
+        if not any(isinstance(output, Command) for output in flat_outputs):
            # TypedDict, pydantic, dataclass, etc. should all be able to load from dict
-            return outputs if input_type == "list" else {self._messages_key: outputs}
+            return (
+                flat_outputs
+                if input_type == "list"
+                else {self._messages_key: flat_outputs}
+            )

        # LangGraph will automatically handle list of Command and non-command node
        # updates
@@ -876,7 +894,7 @@ class ToolNode(RunnableCallable):

        # combine all parent commands with goto into a single parent command
        parent_command: Command | None = None
-        for output in outputs:
+        for output in flat_outputs:
            if isinstance(output, Command):
                if (
                    output.graph is Command.PARENT
@@ -906,7 +924,7 @@ class ToolNode(RunnableCallable):
        request: ToolCallRequest,
        input_type: Literal["list", "dict", "tool_calls"],
        config: RunnableConfig,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
        """Execute tool call with configured error handling.

        Args:
@@ -915,7 +933,7 @@ class ToolNode(RunnableCallable):
            config: Runnable configuration.

        Returns:
-            ToolMessage or Command.
+            ToolMessage, Command, or list of Command/ToolMessage.

        Raises:
            Exception: If tool fails and handle_tool_errors is False.
@@ -947,6 +965,11 @@ 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
@@ -988,23 +1011,12 @@ 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:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
        """Execute single tool call with wrap_tool_call wrapper if configured.

        Args:
@@ -1059,7 +1071,7 @@ class ToolNode(RunnableCallable):
        request: ToolCallRequest,
        input_type: Literal["list", "dict", "tool_calls"],
        config: RunnableConfig,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
        """Execute tool call asynchronously with configured error handling.

        Args:
@@ -1068,7 +1080,7 @@ class ToolNode(RunnableCallable):
            config: Runnable configuration.

        Returns:
-            ToolMessage or Command.
+            ToolMessage, Command, or list of Command/ToolMessage.

        Raises:
            Exception: If tool fails and handle_tool_errors is False.
@@ -1100,6 +1112,11 @@ 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
@@ -1141,23 +1158,12 @@ 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:
+    ) -> ToolMessage | Command | list[Command | ToolMessage]:
        """Execute single tool call asynchronously with awrap_tool_call wrapper if configured.

        Args:
@@ -1273,18 +1279,37 @@ class ToolNode(RunnableCallable):
        return None

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

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

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

    def _inject_tool_args(
@@ -1404,11 +1429,84 @@ 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
@@ -1458,7 +1556,11 @@ 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 updated_command.graph is None and not has_matching_tool_message:
+        if (
+            require_terminator
+            and updated_command.graph is None
+            and not has_matching_tool_message
+        ):
            example_update = (
                '`Command(update={"messages": '
                '[ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`'
@@ -1627,6 +1729,26 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
    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.
@@ -1842,17 +1964,9 @@ def _get_injection_from_type(
    return None


-# Cache keyed by tool object identity. Stores (tool, result) to keep a strong
-# reference that prevents GC from reusing the id for a different object.
-_INJECTED_ARGS_CACHE: dict[int, tuple[BaseTool, _InjectedArgs]] = {}
-
-
 def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
    """Extract all injected arguments from tool in a single pass.

-    Results are cached by tool identity so the expensive type-hint and schema
-    inspection only runs once per unique tool object across ToolNode instances.
-
    This function analyzes both the tool's input schema and function signature
    to identify all arguments that should be injected (state, store, runtime).

@@ -1862,11 +1976,6 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
    Returns:
        _InjectedArgs structure containing all detected injections.
    """
-    tool_id = id(tool)
-    entry = _INJECTED_ARGS_CACHE.get(tool_id)
-    if entry is not None and entry[0] is tool:
-        return entry[1]
-
    # Get annotations from both schema and function signature
    full_schema = tool.get_input_schema()
    schema_annotations = get_all_basemodel_annotations(full_schema)
@@ -1912,12 +2021,10 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
        if _get_injection_from_type(type_, ToolRuntime):
            runtime_arg = name

-    result = _InjectedArgs(
+    return _InjectedArgs(
        state=state_args,
        store=store_arg,
        runtime=runtime_arg,
        all_injected_keys=all_injected_keys,
        _optional_state_args=_optional_state_args,
    )
-    _INJECTED_ARGS_CACHE[tool_id] = (tool, result)
-    return result
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-prebuilt"
-version = "1.0.10"
+version = "1.0.12"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -25,7 +25,7 @@ classifiers = [
 ]
 dependencies = [
    "langgraph-checkpoint>=2.1.0,<5.0.0",
-    "langchain-core>=1.0.0",
+    "langchain-core>=1.3.1",
 ]

 [project.urls]
@@ -62,6 +62,7 @@ dev = [
 default-groups = ['dev']

 [tool.uv.sources]
+langchain-core = { git = "https://github.com/langchain-ai/langchain", branch = "cb/chat-model-updates", subdirectory = "libs/core" }
 langgraph = { path = "../langgraph", editable = true }
 langgraph-checkpoint = { path = "../checkpoint", editable = true }
 langgraph-checkpoint-sqlite = { path = "../checkpoint-sqlite", editable = true }
@@ -1320,6 +1320,98 @@ async def test_state_extraction_with_tool_call_with_context_async() -> None:
    assert "tool_call" not in state_seen[0]


+def _config_with_channel_read(
+    channel_values: dict[str, object],
+    store: BaseStore | None = None,
+) -> RunnableConfig:
+    """Build a config that mimics `CONFIG_KEY_READ` as Pregel installs it.
+
+    Pregel always installs a `functools.partial(local_read, scratchpad,
+    channels, managed, task)`, and `ToolNode` introspects that partial to
+    learn channel names. The stub matches the shape: partial whose second and
+    third positional args are `channels` and `managed` mappings.
+    """
+    import functools
+
+    channels_stub = {k: None for k in channel_values}
+    managed_stub: dict[str, object] = {}
+
+    # Shape matches pregel's real partial:
+    # functools.partial(local_read, scratchpad, channels, managed, task)
+    def _read(scratchpad, channels, managed, task, select, fresh):  # noqa: ARG001
+        if isinstance(select, str):
+            return channel_values[select]
+        return {k: channel_values[k] for k in select if k in channel_values}
+
+    read = functools.partial(_read, None, channels_stub, managed_stub, None)
+    cfg = _create_config_with_runtime(store)
+    cfg["configurable"]["__pregel_read"] = read
+    return cfg
+
+
+def test_list_form_send_hydrates_state_from_channel_read() -> None:
+    """Send('tools', [tool_call]) with no inlined state should hydrate
+    ToolRuntime.state from CONFIG_KEY_READ (full state read)."""
+    state_seen = []
+
+    def state_inspector_handler(
+        request: ToolCallRequest,
+        execute: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        state_seen.append(request.state)
+        return execute(request)
+
+    channel_values = {
+        "messages": [AIMessage("from channels")],
+        "files": {"/a.md": "body"},
+    }
+
+    tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
+
+    tool_call: ToolCall = {
+        "name": "add",
+        "args": {"a": 1, "b": 2},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+
+    tool_node.invoke([tool_call], config=_config_with_channel_read(channel_values))
+
+    assert len(state_seen) == 1
+    got = state_seen[0]
+    assert got == channel_values
+    assert "messages" in got and "files" in got
+
+
+async def test_list_form_send_hydrates_state_async() -> None:
+    state_seen = []
+
+    def state_inspector_handler(
+        request: ToolCallRequest,
+        execute: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        state_seen.append(request.state)
+        return execute(request)
+
+    channel_values = {"messages": [AIMessage("from channels")], "files": {}}
+
+    tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
+
+    tool_call: ToolCall = {
+        "name": "add",
+        "args": {"a": 1, "b": 2},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+
+    await tool_node.ainvoke(
+        [tool_call], config=_config_with_channel_read(channel_values)
+    )
+
+    assert len(state_seen) == 1
+    assert state_seen[0] == channel_values
+
+
 def test_tool_call_request_is_frozen() -> None:
    """Test that ToolCallRequest raises deprecation warnings on direct attribute reassignment."""
    tool_call: ToolCall = {"name": "add", "args": {"a": 1, "b": 2}, "id": "call_1"}
@@ -0,0 +1,307 @@
+"""Tests for ToolCallTransformer and the ToolCallStream projection."""
+
+from __future__ import annotations
+
+import time
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.stream._mux import StreamMux
+from langgraph.stream._types import ProtocolEvent
+from langgraph.stream.stream_channel import StreamChannel
+from langgraph.stream.transformers import (
+    MessagesTransformer,
+    ValuesTransformer,
+)
+from typing_extensions import TypedDict
+
+from langgraph.prebuilt import (
+    ToolCallTransformer,
+    ToolNode,
+    ToolRuntime,
+)
+from langgraph.prebuilt._tool_call_stream import ToolCallStream
+
+TS = int(time.time() * 1000)
+
+
+def _tool_event(
+    event: str,
+    tool_call_id: str,
+    *,
+    tool_name: str = "",
+    input: dict[str, Any] | None = None,
+    delta: Any = None,
+    output: Any = None,
+    message: str = "",
+    namespace: list[str] | None = None,
+) -> ProtocolEvent:
+    data: dict[str, Any] = {"event": event, "tool_call_id": tool_call_id}
+    if event == "tool-started":
+        data["tool_name"] = tool_name
+        if input is not None:
+            data["input"] = input
+    elif event == "tool-output-delta":
+        data["delta"] = delta
+    elif event == "tool-finished":
+        data["output"] = output
+    elif event == "tool-error":
+        data["message"] = message
+    return {
+        "type": "event",
+        "method": "tools",
+        "params": {
+            "namespace": namespace or [],
+            "timestamp": TS,
+            "data": data,
+        },
+    }
+
+
+def _subscribe(log: StreamChannel) -> None:
+    log._subscribed = True
+
+
+def _mux() -> tuple[StreamMux, ToolCallTransformer]:
+    transformer = ToolCallTransformer()
+    mux = StreamMux(
+        [
+            ValuesTransformer(),
+            MessagesTransformer(),
+            transformer,
+        ],
+        is_async=False,
+    )
+    _subscribe(transformer._log)
+    return mux, transformer
+
+
+class TestToolCallTransformerUnit:
+    def test_required_stream_modes_declares_tools(self) -> None:
+        assert ToolCallTransformer.required_stream_modes == ("tools",)
+
+    def test_tool_started_yields_handle(self) -> None:
+        mux, transformer = _mux()
+        mux.push(
+            _tool_event(
+                "tool-started",
+                "tc1",
+                tool_name="echo",
+                input={"text": "hi"},
+            )
+        )
+        handles = list(transformer._log._items)
+        assert len(handles) == 1
+        h = handles[0]
+        assert isinstance(h, ToolCallStream)
+        assert h.tool_call_id == "tc1"
+        assert h.tool_name == "echo"
+        assert h.input == {"text": "hi"}
+        assert h.completed is False
+
+    def test_delta_accumulates_on_active_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        _subscribe(transformer._active["tc1"]._output_deltas)
+        mux.push(_tool_event("tool-output-delta", "tc1", delta="a"))
+        mux.push(_tool_event("tool-output-delta", "tc1", delta="b"))
+        stream = transformer._active["tc1"]
+        assert list(stream._output_deltas._items) == ["a", "b"]
+
+    def test_finish_closes_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        stream = transformer._active["tc1"]
+        mux.push(_tool_event("tool-finished", "tc1", output="done"))
+        assert stream.completed is True
+        assert stream.output == "done"
+        assert stream.error is None
+        assert "tc1" not in transformer._active
+
+    def test_error_closes_stream(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "tc1", tool_name="boom"))
+        stream = transformer._active["tc1"]
+        mux.push(_tool_event("tool-error", "tc1", message="nope"))
+        assert stream.completed is True
+        assert stream.output is None
+        assert stream.error == "nope"
+        assert "tc1" not in transformer._active
+
+    def test_concurrent_tool_calls_do_not_bleed(self) -> None:
+        mux, transformer = _mux()
+        mux.push(_tool_event("tool-started", "a", tool_name="t"))
+        mux.push(_tool_event("tool-started", "b", tool_name="t"))
+        for tc in ("a", "b"):
+            _subscribe(transformer._active[tc]._output_deltas)
+        mux.push(_tool_event("tool-output-delta", "a", delta="A1"))
+        mux.push(_tool_event("tool-output-delta", "b", delta="B1"))
+        mux.push(_tool_event("tool-output-delta", "a", delta="A2"))
+        assert list(transformer._active["a"]._output_deltas._items) == ["A1", "A2"]
+        assert list(transformer._active["b"]._output_deltas._items) == ["B1"]
+
+    def test_tools_event_passes_through_main_log(self) -> None:
+        mux, transformer = _mux()
+        _subscribe(mux._events)
+        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
+        kept = [e for e in mux._events._items if e["method"] == "tools"]
+        assert len(kept) == 1
+
+
+# ---------------------------------------------------------------------------
+# End-to-end tests with a real graph
+# ---------------------------------------------------------------------------
+
+
+class _State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def _build_graph(caller, tools):
+    sg = StateGraph(_State)
+    sg.add_node("caller", caller)
+    sg.add_node("tools", ToolNode(tools))
+    sg.add_edge(START, "caller")
+    sg.add_edge("caller", "tools")
+    sg.add_edge("tools", END)
+    return sg.compile()
+
+
+class TestToolCallTransformerEndToEnd:
+    def test_sync_streaming_tool_populates_tool_calls(self) -> None:
+        @tool
+        def streamer(text: str, runtime: ToolRuntime) -> str:
+            """streams chunks."""
+            for chunk in ("one", "two"):
+                runtime.emit_output_delta(chunk)
+            return text
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "streamer", "args": {"text": "x"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [streamer])
+        run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
+
+        tool_calls: list[ToolCallStream] = []
+        for tc in run.tool_calls:
+            tool_calls.append(tc)
+            deltas = list(tc.output_deltas)
+            assert deltas == ["one", "two"]
+        assert len(tool_calls) == 1
+        tc = tool_calls[0]
+        assert tc.tool_call_id == "tc1"
+        assert tc.tool_name == "streamer"
+        assert tc.completed is True
+        assert tc.error is None
+
+    def test_stream_modes_union_includes_tools(self) -> None:
+        @tool
+        def echo(text: str) -> str:
+            """echo."""
+            return text
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "echo", "args": {"text": "x"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [echo])
+        # Without ToolCallTransformer, no tool_calls projection is
+        # exposed and no `tools` events flow through (required_stream_modes
+        # omits it).
+        run_no_tc = graph.stream_v2({"messages": []})
+        assert "tool_calls" not in run_no_tc._mux.extensions  # type: ignore[attr-defined]
+
+        # With ToolCallTransformer, the projection is present.
+        run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
+        assert "tool_calls" in run._mux.extensions  # type: ignore[attr-defined]
+        # Drain so the run closes cleanly.
+        list(run.tool_calls)
+
+    @pytest.mark.anyio
+    async def test_async_streaming_tool_populates_tool_calls(self) -> None:
+        @tool
+        async def astreamer(text: str, runtime: ToolRuntime) -> str:
+            """async streams."""
+            runtime.emit_output_delta(text)
+            runtime.emit_output_delta(text + "!")
+            return text
+
+        async def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[
+                            {"name": "astreamer", "args": {"text": "hi"}, "id": "tc1"}
+                        ],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [astreamer])
+        run = await graph.astream_v2(
+            {"messages": []}, transformers=[ToolCallTransformer]
+        )
+
+        collected: list[ToolCallStream] = []
+        async for tc in run.tool_calls:
+            collected.append(tc)
+            deltas = [d async for d in tc.output_deltas]
+            assert deltas == ["hi", "hi!"]
+        assert len(collected) == 1
+        assert collected[0].completed is True
+        assert collected[0].error is None
+
+    def test_tool_error_populates_error_field(self) -> None:
+        @tool
+        def boom() -> str:
+            """raises."""
+            raise ValueError("nope")
+
+        def caller(state: _State) -> dict:
+            return {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        tool_calls=[{"name": "boom", "args": {}, "id": "tc1"}],
+                    )
+                ]
+            }
+
+        graph = _build_graph(caller, [boom])
+        run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
+
+        collected: list[ToolCallStream] = []
+        with pytest.raises(ValueError, match="nope"):
+            for tc in run.tool_calls:
+                collected.append(tc)
+                # Drain deltas so the error field is populated before we
+                # inspect it below.
+                list(tc.output_deltas)
+
+        assert len(collected) == 1
+        assert collected[0].error == "nope"
+        assert collected[0].output is None
+        assert collected[0].completed is True
@@ -2223,3 +2223,195 @@ def test_tool_node_injected_state_overwrites_llm_value() -> None:
    )
    tool_message = result["messages"][-1]
    assert tool_message.content == "PUBLIC_DATA"
+
+
+class _ReturningTool(BaseTool):
+    """A tool that returns a configured value verbatim."""
+
+    name: str = "list_tool"
+    description: str = "Returns a configured value"
+    return_value: Any = None
+
+    def _run(self, **kwargs: Any) -> Any:
+        return self.return_value
+
+    async def _arun(self, **kwargs: Any) -> Any:
+        return self.return_value
+
+
+def _list_tool_call(outer_id: str = "call-1") -> dict[str, Any]:
+    return {"name": "list_tool", "args": {}, "id": outer_id, "type": "tool_call"}
+
+
+def _invoke_returning(
+    return_value: Any,
+    *,
+    outer_id: str = "call-1",
+    handle_tool_errors: bool = True,
+) -> Any:
+    node = ToolNode(
+        [_ReturningTool(return_value=return_value)],
+        handle_tool_errors=handle_tool_errors,
+    )
+    return node.invoke(
+        {"messages": [AIMessage("", tool_calls=[_list_tool_call(outer_id)])]},
+        config=_create_config_with_runtime(),
+    )
+
+
+def test_tool_node_list_return_command_and_tool_message() -> None:
+    """Valid: tool returns [Command(update={...}), ToolMessage(...)]."""
+    outer_id = "call-1"
+    result = _invoke_returning(
+        [
+            Command(update={"foo": "bar"}),
+            ToolMessage(content="done", tool_call_id=outer_id),
+        ]
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 1
+    assert commands[0].update == {"foo": "bar"}
+    non_commands = [r for r in result if not isinstance(r, Command)]
+    assert len(non_commands) == 1
+    assert isinstance(non_commands[0], dict)
+    msgs = non_commands[0]["messages"]
+    assert len(msgs) == 1
+    assert isinstance(msgs[0], ToolMessage)
+    assert msgs[0].content == "done"
+    assert msgs[0].tool_call_id == outer_id
+
+
+def test_tool_node_list_return_nested_terminator() -> None:
+    """Valid: terminator nested inside Command.update['messages']."""
+    outer_id = "call-1"
+    result = _invoke_returning(
+        [
+            Command(update={"foo": "bar"}),
+            Command(
+                update={
+                    "messages": [ToolMessage(content="done", tool_call_id=outer_id)]
+                }
+            ),
+        ]
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 2
+    updates = [c.update for c in commands]
+    assert {"foo": "bar"} in updates
+    msgs_update = next(u for u in updates if "messages" in (u or {}))
+    assert any(
+        isinstance(m, ToolMessage) and m.tool_call_id == outer_id
+        for m in msgs_update["messages"]
+    )
+
+
+def test_tool_node_list_return_parent_goto_with_terminator() -> None:
+    """Valid: [Command(graph=PARENT, goto=[Send(...)]), ToolMessage(...)]."""
+    outer_id = "call-1"
+    result = _invoke_returning(
+        [
+            Command(graph=Command.PARENT, goto=[Send("child", {})]),
+            ToolMessage(content="ok", tool_call_id=outer_id),
+        ]
+    )
+    assert isinstance(result, list)
+    parent_cmds = [
+        r for r in result if isinstance(r, Command) and r.graph is Command.PARENT
+    ]
+    assert len(parent_cmds) == 1
+    assert isinstance(parent_cmds[0].goto, list)
+    assert any(isinstance(s, Send) for s in parent_cmds[0].goto)
+    non_commands = [r for r in result if not isinstance(r, Command)]
+    assert len(non_commands) == 1
+
+
+def test_tool_node_list_return_no_terminator_raises() -> None:
+    """Invalid: list with no terminating ToolMessage."""
+    with pytest.raises(ValueError, match="0 messages bound to tool_call_id"):
+        _invoke_returning([Command(update={"foo": "bar"})], handle_tool_errors=False)
+
+
+def test_tool_node_list_return_multiple_terminators_raises() -> None:
+    """Invalid: list with two terminating ToolMessages."""
+    outer_id = "call-1"
+    with pytest.raises(ValueError, match="2 messages bound to tool_call_id"):
+        _invoke_returning(
+            [
+                ToolMessage(content="a", tool_call_id=outer_id),
+                ToolMessage(content="b", tool_call_id=outer_id),
+            ],
+            handle_tool_errors=False,
+        )
+
+
+def test_tool_node_list_return_validation_error_handled() -> None:
+    """handle_tool_errors=True converts validation errors to an error ToolMessage."""
+    result = _invoke_returning([Command(update={"foo": "bar"})])
+    assert isinstance(result, dict)
+    msg = result["messages"][0]
+    assert isinstance(msg, ToolMessage)
+    assert msg.status == "error"
+    assert "0 messages bound to tool_call_id" in msg.content
+
+
+async def test_tool_node_list_return_async_smoke() -> None:
+    """Async path parallels sync for the happy case."""
+    outer_id = "call-1"
+    node = ToolNode(
+        [
+            _ReturningTool(
+                return_value=[
+                    Command(update={"foo": "bar"}),
+                    ToolMessage(content="done", tool_call_id=outer_id),
+                ]
+            )
+        ]
+    )
+    result = await node.ainvoke(
+        {"messages": [AIMessage("", tool_calls=[_list_tool_call(outer_id)])]},
+        config=_create_config_with_runtime(),
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 1 and commands[0].update == {"foo": "bar"}
+
+
+def test_tool_node_list_return_mixed_with_regular_tool() -> None:
+    """List-returning tool and a regular tool dispatched from the same AIMessage."""
+    list_tool_id = "call-list"
+    regular_tool_id = "call-regular"
+    list_tool = _ReturningTool(
+        return_value=[
+            Command(update={"foo": "bar"}),
+            ToolMessage(content="list done", tool_call_id=list_tool_id),
+        ]
+    )
+
+    def regular_tool(x: int) -> str:
+        """A normal tool."""
+        return f"regular: {x}"
+
+    tool_calls = [
+        {"name": "list_tool", "args": {}, "id": list_tool_id, "type": "tool_call"},
+        {
+            "name": "regular_tool",
+            "args": {"x": 7},
+            "id": regular_tool_id,
+            "type": "tool_call",
+        },
+    ]
+    node = ToolNode([list_tool, regular_tool])
+    result = node.invoke(
+        {"messages": [AIMessage("", tool_calls=tool_calls)]},
+        config=_create_config_with_runtime(),
+    )
+    assert isinstance(result, list)
+    commands = [r for r in result if isinstance(r, Command)]
+    assert len(commands) == 1
+    assert commands[0].update == {"foo": "bar"}
+    all_msgs = [m for r in result if isinstance(r, dict) for m in r["messages"]]
+    tool_call_ids = {m.tool_call_id for m in all_msgs}
+    assert list_tool_id in tool_call_ids
+    assert regular_tool_id in tool_call_ids
@@ -249,10 +249,11 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.0"
-source = { registry = "https://pypi.org/simple" }
+version = "1.3.2"
+source = { git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates#aee50839376e379891c99fcbe6d5264f66dedc68" }
 dependencies = [
    { name = "jsonpatch" },
+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -261,14 +262,22 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
+
+[[package]]
+name = "langchain-protocol"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/bf/efb5e2ed832e4d6d45590e25a9e5191986b291b543bc6a807b48bee070b0/langchain_protocol-0.0.14.tar.gz", hash = "sha256:bc1e8553122e6ede310280462d5813023a172ff2785ccbbdec54d43f3a15e5f2", size = 5862, upload-time = "2026-04-29T16:40:18.657Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/e9/06c47ecb2aff08f83dfa30058da3bf86be64862c19569043ed5331bbeecd/langchain_protocol-0.0.14-py3-none-any.whl", hash = "sha256:ffc35089779bd8ca217015180cef5e660fc3b074efdaa0f2e95df73583f1a047", size = 6984, upload-time = "2026-04-29T16:40:17.841Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.9"
+version = "1.1.10"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -281,7 +290,7 @@ dependencies = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=1.3.0,<2" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
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    { name = "jupyter" },
-    { name = "langchain-core", specifier = ">=1.0.0" },
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    { name = "langgraph-checkpoint-sqlite", editable = "../checkpoint-sqlite" },
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-    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
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    { name = "langgraph-checkpoint-sqlite", editable = "../checkpoint-sqlite" },
@@ -352,7 +361,7 @@ test = [

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+version = "4.0.3"
 source = { editable = "../checkpoint" }
 dependencies = [
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@@ -361,7 +370,7 @@ dependencies = [

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+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
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@@ -490,7 +499,7 @@ test = [

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@@ -535,14 +544,14 @@ test = [

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+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
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 dev = [
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-    { name = "langchain-core" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
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    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-checkpoint-postgres", editable = "../checkpoint-postgres" },
@@ -562,7 +571,7 @@ lint = [
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 ]
 test = [
-    { name = "langchain-core" },
+    { name = "langchain-core", git = "https://github.com/langchain-ai/langchain?subdirectory=libs%2Fcore&branch=cb%2Fchat-model-updates" },
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    { name = "langgraph-checkpoint-postgres", editable = "../checkpoint-postgres" },
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    { name = "pytest-watch" },
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-    { name = "ruff", specifier = "==0.15.6" },
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@@ -30,9 +30,9 @@ test = [
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+    "ruff==0.15.12",
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@@ -1,6 +1,10 @@
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 revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]

 [[package]]
 name = "annotated-types"
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+    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
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 [[package]]
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-version = "1.1.9"
+version = "1.1.10"
 source = { editable = "../langgraph" }
 dependencies = [
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@@ -294,7 +311,7 @@ dependencies = [

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 requires-dist = [
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+    { name = "langchain-core", specifier = ">=1.3.2,<2" },
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@@ -365,7 +382,7 @@ test = [

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+version = "4.0.3"
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@@ -413,7 +430,7 @@ test = [

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+    { name = "langchain-core", specifier = ">=1.3.1" },
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 [[package]]
@@ -1,133 +0,0 @@
-# feat(channels): DeltaChannel — O(N) incremental checkpoint storage
-
-## The problem
-
-LangGraph checkpoints store the **full accumulated value** of every channel on every step. For a `messages` channel backed by `add_messages`, that means each checkpoint blob contains the entire conversation history up to that point.
-
-Storage cost grows **O(N²)** in the number of turns:
-
-| Step | Checkpoint blob |
-|------|----------------|
-| 1    | [msg_1] |
-| 2    | [msg_1, msg_2] |
-| N    | [msg_1, ..., msg_N] |
-
-At 100K tokens of conversation data, a single thread accumulates ~250 MB; with large messages or file attachments costs scale even faster.
-
-## The fix: `DeltaChannel`
-
-`DeltaChannel` is an opt-in wrapper around any binary reducer that stores only a **sentinel marker** in `checkpoint_blobs` rather than the full accumulated value. The actual per-step writes stay in `checkpoint_writes` (which every checkpointer already writes unconditionally). At read time the saver walks the ancestor chain, collects all writes for the channel, and replays them through the reducer.
-
-Storage scales **O(N)** — the sentinel blob is effectively zero bytes, and the writes table already exists.
-
-```python
-from langgraph.channels.delta import DeltaChannel
-from langgraph.graph.message import add_messages
-
-class State(TypedDict):
-    # Before: O(N²) storage
-    messages: Annotated[list[AnyMessage], add_messages]
-
-    # After: O(N) storage
-    messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
-```
-
-## Benchmarks
-
-Simulated with realistic paragraph-length messages (~100 tokens each, ~400 chars). Each turn = one human + one AI message (~200 tokens total).
-
-### Storage (InMemorySaver)
-
-| turns | ctx | add_msgs | delta | savings |
-|------:|----:|---------:|------:|--------:|
-| 10 | ~2K tok | 108.6 KB | 4.0 KB | 27x |
-| 25 | ~5K tok | 649.0 KB | 10.1 KB | 64x |
-| 50 | ~10K tok | 2.6 MB | 20.2 KB | 126x |
-| 100 | ~20K tok | 10.2 MB | 40.5 KB | 251x |
-| 500 | ~100K tok | 252.6 MB | 202.8 KB | 1245x |
-
-Savings grow with N because `add_messages` is O(N²) while `DeltaChannel` is O(N). The sentinel blob itself is essentially zero bytes.
-
-### Read latency (avg of 5 `get_state` calls = cost per `invoke`)
-
-| turns | ctx | add_msgs | delta |
-|------:|----:|---------:|------:|
-| 10 | ~2K tok | 0.1ms | 0.2ms |
-| 25 | ~5K tok | 0.2ms | 0.5ms |
-| 50 | ~10K tok | 0.4ms | 1.5ms |
-| 100 | ~20K tok | 0.7ms | 4.8ms |
-| 500 | ~100K tok | 5.8ms | 114.9ms |
-
-**This cost is paid once per `invoke`/`stream` call, not per node.** Within a single invocation, all channels are loaded into memory once at the start and shared across every node — there is no per-node reconstruction. The 114.9ms at 500 turns is what you pay each time a user sends a new message, not on each step of the graph.
-
-## How it works
-
-**Write:** `DeltaChannel.checkpoint()` always emits `DeltaChannelSentinel()` — a tiny marker (zero payload bytes) stored in `checkpoint_blobs`. Per-step writes flow into `checkpoint_writes` as they normally do for every channel.
-
-**Read:** The saver detects `DeltaChannelSentinel` values in `channel_values` and replaces them by calling `get_channel_writes` / `aget_channel_writes`, which walks the ancestor checkpoint chain and collects all writes for that channel (oldest→newest). `DeltaChannel.from_checkpoint()` replays those writes through the operator to reconstruct the full value.
-
-**Saver implementations:**
- `InMemorySaver` — direct dict traversal of `self.storage` and `self.writes`, no I/O
- `PostgresSaver` (sync + async) — two queries: one cheap ID walk across the thread, one `ANY()` fetch of writes; no recursive CTE
- All other savers — `BaseCheckpointSaver.get_channel_writes` fallback via `list()`, with a re-entrancy guard to prevent infinite recursion
-
-## Changes
-
-**`libs/checkpoint`**
- `base/__init__.py` — add `DeltaChannelSentinel` marker dataclass; add `get_channel_writes` / `aget_channel_writes` to `BaseCheckpointSaver` with a `list()`-based fallback and re-entrancy guard
-
-**`libs/checkpoint/memory`**
- `memory/__init__.py` — `get_channel_writes` via direct dict traversal; `_resolve_delta_channels` helper called in `get_tuple` / `aget_tuple` to replace sentinels with reconstructed write lists
-
-**`libs/langgraph`**
- `channels/delta.py` — `DeltaChannel` implementation: `checkpoint()` always emits sentinel, `from_checkpoint()` replays writes list
- `channels/__init__.py` — export `DeltaChannel`
- `graph/state.py` — recognize `DeltaChannel` as a valid channel annotation
- `pregel/_checkpoint.py` / `pregel/_loop.py` — wire `after_checkpoint` hook; call it after each checkpointing step so `DeltaChannel` can advance internal state
-
-**`libs/checkpoint-postgres`**
- `postgres/base.py` — `_get_channel_writes_cur` two-query ancestor walk (sync); `_resolve_delta_channels` called after `_load_blobs`
- `postgres/aio.py` — `_aget_channel_writes_cur` (async counterpart)
-
-## Open questions
-
-**Should we add a compile-time capability check?**
-
-Currently misconfiguring `DeltaChannel` with an unsupported saver only errors at runtime on first reload. A protocol-based check at `compile()` time would give an early warning without requiring a manual boolean flag.
-
-**`snapshot_every` for bounded reconstruction cost?**
-
-Both per-invoke read latency and total write wall time grow O(N) per invoke / O(N²) total as the conversation lengthens. A `snapshot_every` parameter — periodically store a full snapshot in `checkpoint_blobs` to cap chain depth — would bound reconstruction cost and is a natural follow-up once the core design is stable.
-
-## Backwards compatibility
-
-| Scenario | Behaviour |
-|----------|-----------|
-| Existing graph using `add_messages` | Unaffected — no code or schema changes |
-| `DeltaChannel` loading an old full-list checkpoint blob | Handled via backwards-compat path in `from_checkpoint` |
-| `DeltaChannel` with `InMemorySaver` or `PostgresSaver` | Fully supported |
-| Time-travel to a past checkpoint | Ancestor walk uses the version at that checkpoint — correct by construction |
-| `Overwrite` value | Resets the effective chain; reconstruction starts from that step |
-
-## Test plan
-
- [x] `DeltaChannel` unit tests: `update` → `checkpoint` lifecycle, `from_checkpoint` chain replay, backwards-compat with plain list, `Overwrite` resets chain
- [x] `InMemorySaver` `get_channel_writes`: assembles write list from dict storage
- [x] Serde round-trip for `DeltaChannelSentinel`
- [x] End-to-end graph tests: multi-turn conversations accumulate correctly, time-travel reconstructs correct partial history
- [x] `PostgresSaver` two-query chain reconstruction (sync + async)
- [x] `BaseCheckpointSaver` fallback path via `list()` with re-entrancy guard
- [x] Storage benchmark: `DeltaChannel` uses strictly less storage than `add_messages` at all measured turn counts
-
---
-
-## Changes from previous base branch
-
-The previous version stored `DeltaValue` objects (containing the per-step writes) directly in `checkpoint_blobs` and used a `DeltaChainValue` to represent the assembled chain. Reconstruction required a dedicated `get_delta_chain` / `aget_delta_chain` protocol and a recursive CTE in Postgres.
-
-This version pivots to a simpler design:
- **Sentinel in blobs, writes in `checkpoint_writes`** — `checkpoint_blobs` stores only a zero-byte `DeltaChannelSentinel` marker. The actual per-step data already lives in `checkpoint_writes` (written unconditionally by every checkpointer), so blob storage is essentially free. This is why storage savings jump to 1245x at 500 turns.
- **No custom serde type for the delta payload** — `DeltaValue` / `DeltaChainValue` and the `"delta"` serde type tag are gone. Writes are deserialized with the same serde path they were originally written with.
- **Postgres: two queries instead of a recursive CTE** — fetch all `(checkpoint_id, parent_checkpoint_id)` pairs for the thread, walk the ancestor chain in Python, then fetch writes with a plain `ANY()` filter.
- **Universal fallback on `BaseCheckpointSaver`** — the base class now provides `get_channel_writes` via `list()`, so any third-party saver works without modification.
- **`snapshot_every` removed** — deferred as a follow-up; the simpler design is easier to reason about and delivers larger storage savings.