refactor(add_messages): clearer step-by-step structure

Restructure add_messages so fast vs. slow path, REMOVE_ALL handling, and format application each live in a single numbered section with a short lead-in comment. No behavior changes; the previous commits' optimizations are preserved. - fold the two path branches into one `if pure_append else slow_path` so format handling happens at a single exit instead of being duplicated between fast and slow paths - drop the now-redundant `left_seq` local; index `left` directly after the coerce step (with a single `cast(list, left)` for the type checker) - use `set.isdisjoint` for the overlap check Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
test(add_messages): cover fast-path guards and format handling
2026-09-01 04:39:01 +02:00 · 2026-04-24 07:31:03 -04:00 · 2026-04-24 07:31:03 -04:00 · 2026-04-24 07:31:03 -04:00 · 2026-04-24 07:31:03 -04:00 · 2026-04-24 07:29:52 -04:00
70 changed files with 6055 additions and 6607 deletions
@@ -100,3 +100,4 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
+.worktrees/
@@ -306,7 +306,7 @@ test = [

 [[package]]
 name = "langsmith"
-version = "0.7.3"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "httpx" },
@@ -319,9 +319,9 @@ dependencies = [
    { name = "xxhash" },
    { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/8d/bc/8172fefad4f2da888a6d564a27d1fb7d4dbf3c640899c2b40c46235cbe98/langsmith-0.7.3.tar.gz", hash = "sha256:0223b97021af62d2cf53c8a378a27bd22e90a7327e45b353e0069ae60d5d6f9e", size = 988575, upload-time = "2026-02-13T23:25:32.916Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/9d/5a68b6b5e313ffabbb9725d18a71edb48177fd6d3ad329c07801d2a8e862/langsmith-0.7.3-py3-none-any.whl", hash = "sha256:03659bf9274e6efcead361c9c31a7849ea565ae0d6c0d73e1d8b239029eff3be", size = 325718, upload-time = "2026-02-13T23:25:31.52Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]

 [[package]]
@@ -8,11 +8,13 @@ from typing import Any

 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
    WRITES_IDX_MAP,
    ChannelVersions,
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
+    _ChannelWritesHistory,
    get_checkpoint_id,
    get_serializable_checkpoint_metadata,
 )
@@ -23,7 +25,12 @@ from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool

 from langgraph.checkpoint.postgres import _internal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_BLOBS_SQL,
+    SELECT_DELTA_PARENTS_SQL,
+    SELECT_DELTA_WRITES_SQL,
+    BasePostgresSaver,
+)
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver

 Conn = _internal.Conn  # For backward compatibility
@@ -430,6 +437,42 @@ class PostgresSaver(BasePostgresSaver):
                with conn.cursor(binary=True, row_factory=dict_row) as cur:
                    yield cur

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

 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
    WRITES_IDX_MAP,
    ChannelVersions,
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
+    _ChannelWritesHistory,
    get_checkpoint_id,
    get_serializable_checkpoint_metadata,
 )
@@ -23,7 +25,12 @@ from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool

 from langgraph.checkpoint.postgres import _ainternal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_BLOBS_SQL,
+    SELECT_DELTA_PARENTS_SQL,
+    SELECT_DELTA_WRITES_SQL,
+    BasePostgresSaver,
+)
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver

 Conn = _ainternal.Conn  # For backward compatibility
@@ -391,6 +398,45 @@ class AsyncPostgresSaver(BasePostgresSaver):
                async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                    yield cur

+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._aget_channel_writes_history`.
+
+        Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
+        `checkpoint_blobs`); rows assembled by the shared pure helper on
+        `BasePostgresSaver`. Rationale + benchmark in
+        `notes/delta_channel_query_bench.md`.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = await self.aget_tuple(config)
+            if target is None:
+                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+        async with self._cursor() as cur:
+            await cur.execute(
+                SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns)
+            )
+            parents_rows = await cur.fetchall()
+            await cur.execute(
+                SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel)
+            )
+            writes_rows = await cur.fetchall()
+            await cur.execute(
+                SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel)
+            )
+            blobs_rows = await cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            target_id=checkpoint_id,
+            parents_rows=parents_rows,
+            writes_rows=writes_rows,
+            blobs_rows=blobs_rows,
+        )
+
    async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.
@@ -403,11 +449,18 @@ class AsyncPostgresSaver(BasePostgresSaver):
            including its configuration, metadata, parent checkpoint (if any),
            and pending writes.
        """
+        thread_id = value["thread_id"]
+        checkpoint_ns = value["checkpoint_ns"]
+        blob_values = value["channel_values"]
+        channel_values: dict[str, Any] = {}
+        if blob_values:
+            channel_values = self._load_blobs(blob_values)
+
        return CheckpointTuple(
            {
                "configurable": {
-                    "thread_id": value["thread_id"],
-                    "checkpoint_ns": value["checkpoint_ns"],
+                    "thread_id": thread_id,
+                    "checkpoint_ns": checkpoint_ns,
                    "checkpoint_id": value["checkpoint_id"],
                }
            },
@@ -415,15 +468,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
                **value["checkpoint"],
                "channel_values": {
                    **(value["checkpoint"].get("channel_values") or {}),
-                    **self._load_blobs(value["channel_values"]),
+                    **channel_values,
                },
            },
            value["metadata"],
            (
                {
                    "configurable": {
-                        "thread_id": value["thread_id"],
-                        "checkpoint_ns": value["checkpoint_ns"],
+                        "thread_id": thread_id,
+                        "checkpoint_ns": checkpoint_ns,
                        "checkpoint_id": value["parent_checkpoint_id"],
                    }
                }
@@ -8,9 +8,12 @@ from typing import Any, cast

 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
    ChannelVersions,
+    PendingWrite,
+    _ChannelWritesHistory,
    get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
@@ -152,6 +155,30 @@ INSERT_CHECKPOINT_WRITES_SQL = """
    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
 """

+# DeltaChannel reconstruction: three plain indexed SELECTs per channel.
+# Bench (notes/delta_channel_query_bench.md) showed the prior recursive CTE
+# carried a hidden O(ancestors x blobs_in_thread) join; plain SELECTs are
+# 3x-100x faster in the realistic depth range and the Python walk is O(n).
+SELECT_DELTA_PARENTS_SQL = """
+    SELECT checkpoint_id,
+           parent_checkpoint_id,
+           checkpoint -> 'channel_versions' ->> %s AS ver
+    FROM checkpoints
+    WHERE thread_id = %s AND checkpoint_ns = %s
+"""
+
+SELECT_DELTA_WRITES_SQL = """
+    SELECT checkpoint_id, type, blob, task_id, idx
+    FROM checkpoint_writes
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+"""
+
+SELECT_DELTA_BLOBS_SQL = """
+    SELECT version, type, blob
+    FROM checkpoint_blobs
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+"""
+

 class BasePostgresSaver(BaseCheckpointSaver[str]):
    SELECT_SQL = SELECT_SQL
@@ -185,16 +212,91 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
        )

    def _load_blobs(
-        self, blob_values: list[tuple[bytes, bytes, bytes]]
+        self,
+        blob_values: Any,
    ) -> dict[str, Any]:
        if not blob_values:
            return {}
-        return {
-            k.decode(): self.serde.loads_typed((t.decode(), v))
-            for k, t, v in blob_values
-            if t.decode() != "empty"
+        result: dict[str, Any] = {}
+        for k, t, v in blob_values:
+            type_tag = t.decode()
+            if type_tag != "empty":
+                result[k.decode()] = self.serde.loads_typed((type_tag, v))
+        return result
+
+    def _build_delta_channel_writes_history(
+        self,
+        *,
+        channel: str,
+        target_id: str,
+        parents_rows: Sequence[Any],
+        writes_rows: Sequence[Any],
+        blobs_rows: Sequence[Any],
+    ) -> _ChannelWritesHistory:
+        """Reconstruct one delta channel's history from rows of the three SELECTs.
+
+        Pure data transform shared by sync (`PostgresSaver`) and async
+        (`AsyncPostgresSaver`); both paths run the queries themselves and
+        feed the rows here.
+
+        Walk is newest → oldest from the target's parent. A non-sentinel
+        blob in `checkpoint_blobs` (a pre-delta snapshot) terminates the
+        walk and is returned as the seed so replay starts from it.
+
+        Writes stored at `target_id` itself are pending writes for the next
+        step and are excluded — the walk begins at the target's parent.
+        """
+        parent_of: dict[str, str | None] = {}
+        ver_of: dict[str, str | None] = {}
+        for r in parents_rows:
+            cid = r["checkpoint_id"]
+            parent_of[cid] = r["parent_checkpoint_id"]
+            ver_of[cid] = r["ver"]
+
+        ancestors: list[str] = []
+        cid = parent_of.get(target_id)
+        while cid is not None:
+            ancestors.append(cid)
+            cid = parent_of.get(cid)
+        if not ancestors:
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+        ancestor_set = set(ancestors)
+
+        # Group writes by ancestor cid; sort within (task_id DESC, idx DESC)
+        # to match the prior CTE ordering — newest write first per ancestor.
+        writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
+        for r in writes_rows:
+            cid = r["checkpoint_id"]
+            if cid not in ancestor_set:
+                continue
+            writes_by_cid.setdefault(cid, []).append(
+                (r["type"], r["blob"], r["task_id"], r["idx"])
+            )
+        for ws in writes_by_cid.values():
+            ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
+
+        blob_by_ver: dict[str, tuple[str, bytes]] = {
+            r["version"]: (r["type"], r["blob"]) for r in blobs_rows
        }

+        collected: list[PendingWrite] = []  # newest first; reversed at the end
+        for cid in ancestors:
+            # Pre-delta blob terminator: subsumes any writes at this ancestor.
+            ver = ver_of.get(cid)
+            if ver is not None:
+                seed_blob = blob_by_ver.get(ver)
+                if seed_blob is not None and seed_blob[0] != "empty":
+                    blob_value = self.serde.loads_typed(seed_blob)
+                    if blob_value is not DELTA_SENTINEL:
+                        collected.reverse()
+                        return _ChannelWritesHistory(seed=blob_value, writes=collected)
+            for type_tag, write_blob, task_id, _idx in writes_by_cid.get(cid, []):
+                val = self.serde.loads_typed((type_tag, write_blob))
+                collected.append((task_id, channel, val))
+
+        collected.reverse()  # oldest → newest
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
    def _dump_blobs(
        self,
        thread_id: str,
@@ -361,9 +361,9 @@ async def test_get_checkpoint_no_channel_values(

        load_checkpoint_tuple = saver._load_checkpoint_tuple

-        def patched_load_checkpoint_tuple(value):
+        async def patched_load_checkpoint_tuple(value):
            value["checkpoint"].pop("channel_values", None)
-            return load_checkpoint_tuple(value)
+            return await load_checkpoint_tuple(value)

        monkeypatch.setattr(
            saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
@@ -371,3 +371,47 @@ async def test_get_checkpoint_no_channel_values(

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

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -382,7 +382,7 @@ test = [

 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "httpx" },
@@ -392,11 +392,12 @@ dependencies = [
    { name = "requests" },
    { name = "requests-toolbelt" },
    { name = "uuid-utils" },
+    { name = "xxhash" },
    { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]

 [[package]]
@@ -1284,6 +1285,124 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" },
 ]

+[[package]]
+name = "xxhash"
+version = "3.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/02/84/30869e01909fb37a6cc7e18688ee8bf1e42d57e7e0777636bd47524c43c7/xxhash-3.6.0.tar.gz", hash = "sha256:f0162a78b13a0d7617b2845b90c763339d1f1d82bb04a4b07f4ab535cc5e05d6", size = 85160, upload-time = "2025-10-02T14:37:08.097Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/34/ee/f9f1d656ad168681bb0f6b092372c1e533c4416b8069b1896a175c46e484/xxhash-3.6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:87ff03d7e35c61435976554477a7f4cd1704c3596a89a8300d5ce7fc83874a71", size = 32845, upload-time = "2025-10-02T14:33:51.573Z" },
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+
 [[package]]
 name = "zstandard"
 version = "0.25.0"
@@ -1,9 +1,10 @@
 from __future__ import annotations

+import contextvars
 import copy
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
-from typing import (  # noqa: UP035
+from typing import (
    Any,
    Generic,
    Literal,
@@ -18,6 +19,9 @@ from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.types import (
+    DELTA_SENTINEL as DELTA_SENTINEL,
+)
 from langgraph.checkpoint.serde.types import (
    ERROR,
    INTERRUPT,
@@ -28,6 +32,16 @@ from langgraph.checkpoint.serde.types import (

 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
+
+# Task-local guard: ContextVar is copied per asyncio Task, so concurrent
+# requests on the same event-loop thread do not share this flag. A plain
+# `threading.local()` would leak across tasks and let one in-flight
+# reconstruction silently short-circuit another.
+_DELTA_RECONSTRUCTION: contextvars.ContextVar[bool] = contextvars.ContextVar(
+    "_DELTA_RECONSTRUCTION", default=False
+)
+
+
 logger = logging.getLogger(__name__)


@@ -119,6 +133,30 @@ class CheckpointTuple(NamedTuple):
    pending_writes: list[PendingWrite] | None = None


+class _ChannelWritesHistory(NamedTuple):
+    """Result of `BaseCheckpointSaver._get_channel_writes_history`.
+
+    Storage-level view of what one channel wrote across the ancestor chain
+    of a target checkpoint:
+
+      * `seed` — the nearest ancestor's stored blob value for this channel,
+        or `DELTA_SENTINEL` if the walk reached the root without finding a
+        stored value. A non-sentinel seed typically indicates a pre-delta
+        snapshot preserved across a channel-type migration (e.g.
+        `BinaryOperatorAggregate` storage extended under `DeltaChannel`).
+      * `writes` — on-path deltas oldest→newest, one `PendingWrite` per
+        step that wrote to this channel. Writes stored at the target
+        checkpoint itself are pending for the next super-step and are
+        excluded.
+
+    Experimental: method surface may change; the NamedTuple shape is the
+    contract.
+    """
+
+    seed: Any
+    writes: list[PendingWrite]
+
+
 class BaseCheckpointSaver(Generic[V]):
    """Base class for creating a graph checkpointer.

@@ -457,6 +495,104 @@ class BaseCheckpointSaver(Generic[V]):
        """
        raise NotImplementedError

+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """**Experimental.** Query one channel's writes along the parent chain.
+
+        Storage-level query, not channel semantics: returns `(seed, writes)`
+        reflecting what storage knows about a single channel across the
+        ancestor chain of the target checkpoint identified by `config`.
+
+        * `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
+          Writes stored at the target `checkpoint_id` itself are pending
+          for the next super-step and are excluded.
+        * `seed` — the nearest ancestor's stored blob value for this
+          channel; `DELTA_SENTINEL` if the walk reached the root without
+          finding a stored value. A non-sentinel seed typically indicates
+          a pre-delta snapshot preserved across a channel-type migration.
+
+        Walks the **parent chain** (not `list(before=...)`): for forked
+        threads, only on-path ancestors contribute.
+
+        Reference implementation walks `get_tuple` + `parent_config`,
+        inspecting each ancestor's `channel_values[channel]` for the seed
+        terminator. Savers with direct storage access (`InMemorySaver`,
+        `PostgresSaver`) override for performance; the return contract is
+        fixed here.
+
+        Underscore-prefixed because the method surface is experimental.
+        """
+        # Guard against re-entrant calls: when get_tuple() triggers
+        # reconstruction which calls get_tuple() again, the inner call
+        # short-circuits here.
+        if _DELTA_RECONSTRUCTION.get():
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        token = _DELTA_RECONSTRUCTION.set(True)
+        try:
+            collected: list[PendingWrite] = []  # newest first; reversed at the end
+            target_tuple = self.get_tuple(config)
+            cursor_config: RunnableConfig | None = (
+                target_tuple.parent_config if target_tuple else None
+            )
+            while cursor_config is not None:
+                tup = self.get_tuple(cursor_config)
+                if tup is None:
+                    break
+                # Pre-delta seed terminator: if the ancestor has a stored
+                # (non-sentinel) value for this channel, that snapshot
+                # subsumes any earlier writes on the chain. Stop here.
+                ancestor_value = tup.checkpoint["channel_values"].get(channel)
+                if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                    collected.reverse()
+                    return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+                if tup.pending_writes:
+                    # Within a superstep, pending_writes are oldest→newest;
+                    # reverse to scan newest-first.
+                    for write in reversed(tup.pending_writes):
+                        if write[1] != channel:
+                            continue
+                        collected.append(write)
+                cursor_config = tup.parent_config
+            collected.reverse()
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+        finally:
+            _DELTA_RECONSTRUCTION.reset(token)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Async version of `_get_channel_writes_history`. See docstring there."""
+        if _DELTA_RECONSTRUCTION.get():
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        token = _DELTA_RECONSTRUCTION.set(True)
+        try:
+            collected: list[PendingWrite] = []
+            target_tuple = await self.aget_tuple(config)
+            cursor_config: RunnableConfig | None = (
+                target_tuple.parent_config if target_tuple else None
+            )
+            while cursor_config is not None:
+                tup = await self.aget_tuple(cursor_config)
+                if tup is None:
+                    break
+                ancestor_value = tup.checkpoint["channel_values"].get(channel)
+                if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                    collected.reverse()
+                    return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+                if tup.pending_writes:
+                    for write in reversed(tup.pending_writes):
+                        if write[1] != channel:
+                            continue
+                        collected.append(write)
+                cursor_config = tup.parent_config
+            collected.reverse()
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+        finally:
+            _DELTA_RECONSTRUCTION.reset(token)
+
    def get_next_version(self, current: V | None, channel: None) -> V:
        """Generate the next version ID for a channel.

@@ -9,18 +9,21 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any
+from typing import Any, cast

 from langchain_core.runnables import RunnableConfig

 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
    ChannelVersions,
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
+    PendingWrite,
    SerializerProtocol,
+    _ChannelWritesHistory,
    get_checkpoint_id,
    get_checkpoint_metadata,
 )
@@ -121,16 +124,91 @@ class InMemorySaver(
        return self.stack.__exit__(__exc_type, __exc_value, __traceback)

    def _load_blobs(
-        self, thread_id: str, checkpoint_ns: str, versions: ChannelVersions
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        versions: ChannelVersions,
    ) -> dict[str, Any]:
-        channel_values: dict[str, Any] = {}
-        for k, v in versions.items():
-            kk = (thread_id, checkpoint_ns, k, v)
-            if kk in self.blobs:
-                vv = self.blobs[kk]
-                if vv[0] != "empty":
-                    channel_values[k] = self.serde.loads_typed(vv)
-        return channel_values
+        result: dict[str, Any] = {}
+        for k, ver in versions.items():
+            kk = (thread_id, checkpoint_ns, k, ver)
+            if kk not in self.blobs:
+                continue
+            vv = self.blobs[kk]
+            if vv[0] == "empty":
+                continue
+            result[k] = self.serde.loads_typed(vv)
+        return result
+
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = config["configurable"].get("checkpoint_id", "")
+        ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
+        # Walk the parent chain newest→oldest. Skip the target itself —
+        # writes stored AT `checkpoint_id` are pending for the next step
+        # (pregel applies them via `apply_writes`; they aren't part of the
+        # snapshot value AT `checkpoint_id`).
+        chain: list[str] = []
+        target_entry = ns_storage.get(checkpoint_id)
+        current: str | None = target_entry[2] if target_entry is not None else None
+        while current is not None:
+            entry = ns_storage.get(current)
+            if entry is None:
+                break
+            chain.append(current)
+            _, _, parent = entry
+            current = parent
+        # Scan newest→oldest. A pre-delta blob on an ancestor terminates the
+        # walk and is bound as `seed`; without this, a thread migrated from
+        # pre-delta storage would replay ancestor writes all the way to the
+        # root AND miss any value that lived only in the old blob (e.g. from
+        # `update_state`).
+        #
+        # At each ancestor, check the blob BEFORE processing its pending
+        # writes: a pre-delta blob represents the state AT that ancestor,
+        # which already subsumes any writes stored under it. Processing
+        # those writes first would fold them into the reconstructed value
+        # twice (once via the blob, once via replay).
+        collected: list[PendingWrite] = []  # newest first
+        for cp_id in chain:  # newest → oldest
+            entry = ns_storage.get(cp_id)
+            if entry is not None:
+                ckpt = self.serde.loads_typed(entry[0])
+                ver = ckpt.get("channel_versions", {}).get(channel)
+                if ver is not None:
+                    blob_entry = self.blobs.get(
+                        (thread_id, checkpoint_ns, channel, ver)
+                    )
+                    if blob_entry is not None and blob_entry[0] != "empty":
+                        blob_value = self.serde.loads_typed(blob_entry)
+                        if blob_value is not DELTA_SENTINEL:
+                            # Pre-delta snapshot terminator. Skip this
+                            # ancestor's writes — the blob subsumes them.
+                            collected.reverse()
+                            return _ChannelWritesHistory(
+                                seed=blob_value, writes=collected
+                            )
+
+            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
+            # Within a superstep, sorted by (task_id, idx) = oldest → newest;
+            # reverse for newest-first scan.
+            for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
+                step_writes.items(), reverse=True
+            ):
+                if ch != channel:
+                    continue
+                val = self.serde.loads_typed(serialized)
+                collected.append((tid, ch, val))
+        collected.reverse()
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        return self._get_channel_writes_history(config, channel)

    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
        """Get a checkpoint tuple from the in-memory storage.
@@ -153,13 +231,16 @@ class InMemorySaver(
                checkpoint, metadata, parent_checkpoint_id = saved
                writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
                return CheckpointTuple(
                    config=config,
                    checkpoint={
                        **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                    },
                    metadata=self.serde.loads_typed(metadata),
                    pending_writes=[
@@ -183,19 +264,26 @@ class InMemorySaver(
                checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
                writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                checkpoint_ = self.serde.loads_typed(checkpoint)
-                return CheckpointTuple(
-                    config={
+                resolved_config = cast(
+                    RunnableConfig,
+                    {
                        "configurable": {
                            "thread_id": thread_id,
                            "checkpoint_ns": checkpoint_ns,
                            "checkpoint_id": checkpoint_id,
                        }
                    },
+                )
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
+                return CheckpointTuple(
+                    config=resolved_config,
                    checkpoint={
                        **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                    },
                    metadata=self.serde.loads_typed(metadata),
                    pending_writes=[
@@ -290,21 +378,27 @@ class InMemorySaver(

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

-                    yield CheckpointTuple(
-                        config={
+                    list_config = cast(
+                        RunnableConfig,
+                        {
                            "configurable": {
                                "thread_id": thread_id,
                                "checkpoint_ns": checkpoint_ns,
                                "checkpoint_id": checkpoint_id,
                            }
                        },
+                    )
+                    channel_values = self._load_blobs(
+                        thread_id,
+                        checkpoint_ns,
+                        checkpoint_["channel_versions"],
+                    )
+
+                    yield CheckpointTuple(
+                        config=list_config,
                        checkpoint={
                            **checkpoint_,
-                            "channel_values": self._load_blobs(
-                                thread_id,
-                                checkpoint_ns,
-                                checkpoint_["channel_versions"],
-                            ),
+                            "channel_values": channel_values,
                        },
                        metadata=metadata,
                        parent_config=(
@@ -33,19 +33,35 @@ from langchain_core.load.load import Reviver
 from langgraph.checkpoint.serde import _msgpack as _lg_msgpack
 from langgraph.checkpoint.serde.base import SerializerProtocol
 from langgraph.checkpoint.serde.event_hooks import emit_serde_event
-from langgraph.checkpoint.serde.types import SendProtocol
+from langgraph.checkpoint.serde.types import DELTA_SENTINEL, SendProtocol
 from langgraph.store.base import Item

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

 LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
 logger = logging.getLogger(__name__)

+# Dedup log warnings across process lifetime; cap bounds state if types are
+# dynamically generated (also acts as a circuit breaker on warning volume).
+# Dedup is best-effort: racing threads may each emit once for the same key,
+# and warnings are silently dropped once _MAX_WARNED_TYPES is reached.
+_MAX_WARNED_TYPES = 1000
+_warned_unregistered_types: set[tuple[str, str]] = set()
+_warned_blocked_types: set[tuple[str, str]] = set()
+
+
+def _warn_once(
+    seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
+) -> None:
+    if key in seen or len(seen) >= _MAX_WARNED_TYPES:
+        return
+    seen.add(key)
+    logger.warning(msg, *args)
+

 class JsonPlusSerializer(SerializerProtocol):
    """Serializer that uses ormsgpack, with optional fallbacks.
@@ -235,6 +251,8 @@ class JsonPlusSerializer(SerializerProtocol):
    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
        if obj is None:
            return "null", EMPTY_BYTES
+        elif obj is DELTA_SENTINEL:
+            return "delta", EMPTY_BYTES
        elif isinstance(obj, bytes):
            return "bytes", obj
        elif isinstance(obj, bytearray):
@@ -261,6 +279,8 @@ class JsonPlusSerializer(SerializerProtocol):
            return ormsgpack.unpackb(
                data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
            )
+        elif type_ == "delta":
+            return DELTA_SENTINEL
        elif self.pickle_fallback and type_ == "pickle":
            return pickle.loads(data_)
        else:
@@ -534,7 +554,9 @@ def _create_msgpack_ext_hook(
                    "name": name,
                }
            )
-            logger.warning(
+            _warn_once(
+                _warned_unregistered_types,
+                key,
                "Deserializing unregistered type %s.%s from checkpoint. "
                "This will be blocked in a future version. "
                "Set LANGGRAPH_STRICT_MSGPACK=true to block now, or add "
@@ -556,7 +578,9 @@ def _create_msgpack_ext_hook(
                "name": name,
            }
        )
-        logger.warning(
+        _warn_once(
+            _warned_blocked_types,
+            key,
            "Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
            "Add to allowed_msgpack_modules to allow: [(%r, %r)]",
            module,
@@ -14,6 +14,25 @@ INTERRUPT = "__interrupt__"
 RESUME = "__resume__"
 TASKS = "__pregel_tasks"

+
+class _DeltaSentinel:
+    """Singleton marker stored (as zero bytes) in checkpoint_blobs for a
+    DeltaChannel field. The actual per-step writes live in checkpoint_writes
+    and are replayed through the reducer at load time.
+
+    Compare with `is DELTA_SENTINEL` — `loads_typed` always returns the same
+    module-level instance.
+    """
+
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "DELTA_SENTINEL"
+
+
+DELTA_SENTINEL = _DeltaSentinel()
+
+
 Value = TypeVar("Value", covariant=True)
 Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"
@@ -29,6 +29,8 @@ from langgraph.checkpoint.serde.jsonplus import (
    EXT_METHOD_SINGLE_ARG,
    JsonPlusSerializer,
    _msgpack_enc,
+    _warned_blocked_types,
+    _warned_unregistered_types,
 )


@@ -102,6 +104,13 @@ def test_msgpack_method_pathlib_blocked_encrypted_strict(
 class TestEncryptedSerializerMsgpackAllowlist:
    """Test msgpack allowlist behavior through EncryptedSerializer."""

+    @pytest.fixture(autouse=True)
+    def _reset_warned_types(self) -> None:
+        # Warning dedup state is process-global; reset per-test so each case
+        # sees a fresh slate and assertions about warning emission are stable.
+        _warned_unregistered_types.clear()
+        _warned_blocked_types.clear()
+
    def test_safe_types_no_warning(self, caplog: pytest.LogCaptureFixture) -> None:
        """Test safe types deserialize without warnings through encryption."""
        serde = _make_encrypted_serde()
@@ -35,6 +35,8 @@ from langgraph.checkpoint.serde.jsonplus import (
    JsonPlusSerializer,
    _msgpack_enc,
    _msgpack_ext_hook_to_json,
+    _warned_blocked_types,
+    _warned_unregistered_types,
 )
 from langgraph.store.base import Item

@@ -580,6 +582,14 @@ def test_msgpack_safe_types_no_warning(caplog: pytest.LogCaptureFixture) -> None
        assert result is not None


+@pytest.fixture(autouse=True)
+def _reset_warned_types() -> None:
+    # Warning dedup state is process-global; reset per-test so each case sees
+    # a fresh slate and assertions about warning emission are stable.
+    _warned_unregistered_types.clear()
+    _warned_blocked_types.clear()
+
+
 def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) -> None:
    """Pydantic models not in allowlist should log warning but still deserialize."""
    current = _lg_msgpack.STRICT_MSGPACK_ENABLED
@@ -595,6 +605,12 @@ def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) ->
    assert "unregistered type" in caplog.text.lower()
    assert "allowed_msgpack_modules" in caplog.text
    assert result == obj
+
+    # Second deserialization of the same type should NOT produce another warning
+    caplog.clear()
+    result2 = serde.loads_typed(dumped)
+    assert "unregistered type" not in caplog.text.lower()
+    assert result2 == obj
    _lg_msgpack.STRICT_MSGPACK_ENABLED = current


@@ -639,7 +655,6 @@ def test_msgpack_allowlist_silences_warning(caplog: pytest.LogCaptureFixture) ->

 def test_msgpack_none_blocks_unregistered(caplog: pytest.LogCaptureFixture) -> None:
    """allowed_msgpack_modules=None should block unregistered types."""
-
    serde = JsonPlusSerializer(allowed_msgpack_modules=None)

    obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
@@ -657,7 +672,6 @@ def test_msgpack_allowlist_blocks_non_listed(
    caplog: pytest.LogCaptureFixture,
 ) -> None:
    """Allowlists should block unregistered types even if msgpack is enabled."""
-
    serde = JsonPlusSerializer(
        allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
    )
@@ -983,3 +997,16 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
    # No blocking should occur - inner is serialized as dict, not ext
    assert "blocked" not in caplog.text.lower()
    assert result == obj
+
+
+def test_delta_sentinel_serde_round_trip() -> None:
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+    serde = JsonPlusSerializer()
+    type_tag, blob = serde.dumps_typed(DELTA_SENTINEL)
+    # Zero-byte "delta" tag — no allowlist change needed.
+    assert type_tag == "delta"
+    assert blob == b""
+    loaded = serde.loads_typed((type_tag, blob))
+    assert loaded is DELTA_SENTINEL
@@ -6,19 +6,32 @@ from langchain_core.runnables import RunnableConfig
 from pydantic import BaseModel

 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
    Checkpoint,
    CheckpointMetadata,
    create_checkpoint,
    empty_checkpoint,
 )
 from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.jsonplus import (
+    JsonPlusSerializer,
+    _warned_blocked_types,
+    _warned_unregistered_types,
+)


 class MemoryPydantic(BaseModel):
    foo: str


+@pytest.fixture(autouse=True)
+def _reset_warned_types() -> None:
+    # Warning dedup state is process-global; reset per-test so each case sees
+    # a fresh slate and assertions about warning emission are stable.
+    _warned_unregistered_types.clear()
+    _warned_blocked_types.clear()
+
+
 class TestMemorySaver:
    @pytest.fixture(autouse=True)
    def setup(self) -> None:
@@ -196,8 +209,6 @@ class TestMemorySaver:


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

@@ -308,3 +319,347 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
    assert direct is not None
    expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
    assert direct.checkpoint["channel_values"]["foo"] == expected
+
+
+class TestInMemorySaverDeltaChannel:
+    def test_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
+        """_load_blobs returns DELTA_SENTINEL for delta channels (reconstruction deferred)."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+        v1 = "00000000000000000000000000000001.0000000000000000"
+
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(DELTA_SENTINEL)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+
+        result = saver._load_blobs(thread_id, ns, {channel: v1})
+        assert channel in result
+        assert result[channel] is DELTA_SENTINEL
+
+    def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
+        """_get_channel_writes_history collects ancestor writes oldest→newest,
+        and excludes writes stored at the target checkpoint itself (those are
+        pending writes for the next step, applied separately by pregel)."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+        }
+        # Writes stored at cp1 produced the cp1 snapshot; part of history.
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "hi"}),
+            "",
+        )
+        # Writes stored at cp2 are pending — they will produce cp3 when the
+        # step that loaded cp2 completes. They MUST NOT appear in the
+        # reconstructed snapshot value at cp2.
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp2",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "hi"}]
+
+    def test_get_channel_writes_at_root_returns_empty(self) -> None:
+        """Reconstructing the root checkpoint's state: no ancestors → []."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp1",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        assert result.writes == []
+
+
+class TestBaseFallbackGetChannelWrites:
+    """Exercises the `BaseCheckpointSaver._get_channel_writes_history` default
+    implementation — the path third-party savers inherit when they don't
+    override `_get_channel_writes_history` themselves.
+
+    Regression guard for a bug where the fallback passed the caller's config
+    (with `checkpoint_id`) straight to `self.list()`, which most savers
+    collapse to a single row — causing the fallback to return `[]`.
+    """
+
+    def _build_saver_with_chain(self) -> tuple[InMemorySaver, str, str]:
+        """Build an InMemorySaver with a 3-checkpoint chain and per-step writes
+        for a `messages` channel.
+
+        Returns `(saver, thread_id, namespace)`. The saver subclass deletes the
+        InMemorySaver override so the base class fallback is exercised.
+        """
+
+        class _ThirdPartyStyleSaver(InMemorySaver):
+            _get_channel_writes_history = (
+                InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+            )
+            _aget_channel_writes_history = (
+                InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+            )
+
+        saver = _ThirdPartyStyleSaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp0 = empty_checkpoint()
+        cp0["id"] = "00000000000000000000000000000001.0000000000000000"
+        cp1 = empty_checkpoint()
+        cp1["id"] = "00000000000000000000000000000002.0000000000000000"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "00000000000000000000000000000003.0000000000000000"
+        saver.storage[thread_id][ns] = {
+            cp0["id"]: (serde.dumps_typed(cp0), serde.dumps_typed({}), None),
+            cp1["id"]: (serde.dumps_typed(cp1), serde.dumps_typed({}), cp0["id"]),
+            cp2["id"]: (serde.dumps_typed(cp2), serde.dumps_typed({}), cp1["id"]),
+        }
+        # Writes under cp0 produced cp1's state; writes under cp1 produced cp2's.
+        saver.writes[(thread_id, ns, cp0["id"])][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "first"}),
+            "",
+        )
+        saver.writes[(thread_id, ns, cp1["id"])][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "second"}),
+            "",
+        )
+        return saver, thread_id, ns
+
+    def test_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = await saver._aget_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_concurrent_tasks_do_not_interfere(self) -> None:
+        """Regression: the re-entrancy guard must be task-local, not thread-local.
+
+        Two concurrent `_aget_channel_writes_history` calls on the same
+        event-loop thread must each see their full reconstructed writes. A
+        `threading.local()` guard would let whichever task set it first
+        short-circuit the other to `writes=[]`.
+        """
+        import asyncio
+
+        saver, thread_id, ns = self._build_saver_with_chain()
+
+        # Force the two tasks to interleave across the `set(True)` boundary:
+        # each `aget_tuple` yields control, so if the guard were thread-local
+        # the second task would observe `active=True` set by the first.
+        orig_aget_tuple = saver.aget_tuple
+
+        async def slow_aget_tuple(config: RunnableConfig) -> Any:
+            await asyncio.sleep(0)
+            return await orig_aget_tuple(config)
+
+        saver.aget_tuple = slow_aget_tuple  # type: ignore[method-assign]
+
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        results = await asyncio.gather(
+            saver._aget_channel_writes_history(config, "messages"),
+            saver._aget_channel_writes_history(config, "messages"),
+        )
+
+        expected_values = [{"content": "first"}, {"content": "second"}]
+        for result in results:
+            assert result.seed is DELTA_SENTINEL
+            values = [v for _, _, v in result.writes]
+            assert values == expected_values
+
+
+class TestPreDeltaBlobTerminator:
+    """Verify the pre-delta blob terminator: when the ancestor walk hits a
+    checkpoint whose blob for the channel is a real value (not
+    DELTA_SENTINEL), reconstruction seeds from it and stops. This guards
+
+      * back-compat: a thread written by pre-delta code, then extended under
+        delta — reconstruction must return the correct value without walking
+        past the last pre-delta ancestor;
+      * perf: without the terminator, every reconstruct-after-migration would
+        walk all the way to the thread root.
+    """
+
+    def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
+        """Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
+        write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
+        seed=[A] + writes=[B, C].
+
+        Returns `(saver, thread_id, ns, channel, cp3_id)`.
+        """
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        v1 = "00000000000000000000000000000001.0"
+        v2 = "00000000000000000000000000000002.0"
+        v3 = "00000000000000000000000000000003.0"
+
+        # Pre-delta: cp1 stored a real blob for the channel.
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
+        # Delta-era: cp2 and cp3 store sentinels; real writes in checkpoint_writes.
+        saver.blobs[(thread_id, ns, channel, v2)] = serde.dumps_typed(DELTA_SENTINEL)
+        saver.blobs[(thread_id, ns, channel, v3)] = serde.dumps_typed(DELTA_SENTINEL)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        cp2["channel_versions"][channel] = v2
+        cp3 = empty_checkpoint()
+        cp3["id"] = "cp3"
+        cp3["channel_versions"][channel] = v3
+
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+            "cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
+        }
+        # Write under cp1 would be from the pre-delta era and MUST be ignored
+        # (the blob already captures it). We add one and assert it is not
+        # folded into the reconstructed result.
+        saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
+            "task0",
+            channel,
+            serde.dumps_typed("PRE-DELTA-WRITE"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed("B"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
+            "task3",
+            channel,
+            serde.dumps_typed("PENDING-AT-TARGET"),
+            "",
+        )
+        return saver, thread_id, ns, channel, "cp3"
+
+    def test_seed_from_pre_delta_ancestor_blob(self) -> None:
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        # Seed came from the pre-delta blob at cp1.
+        assert result.seed == ["A"]
+        # Delta-era writes from cp2 replay through the reducer on top of seed.
+        # cp3 is the target — its own write is pending for the NEXT step and
+        # must be excluded.
+        values = [v for _, _, v in result.writes]
+        assert values == ["B"]
+
+    def test_pre_delta_blob_terminates_walk_before_older_writes(self) -> None:
+        """Writes stored at the pre-delta ancestor itself must not be replayed
+        (the blob subsumes them)."""
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        values = [v for _, _, v in result.writes]
+        # The pre-delta write under cp1 must not appear (the blob subsumes it).
+        assert "PRE-DELTA-WRITE" not in values
+        # And the pending write at the target is never folded in.
+        assert "PENDING-AT-TARGET" not in values
@@ -286,7 +286,7 @@ wheels = [

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 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "." }
 dependencies = [
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 source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/7b/d9/8d95e906764a386a3d3b596f3c68bb63687dfca806373509f51ce8eea81f/xxhash-3.6.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:15e0dac10eb9309508bfc41f7f9deaa7755c69e35af835db9cb10751adebc35d", size = 31565, upload-time = "2025-10-02T14:37:06.966Z" },
+]
+
 [[package]]
 name = "zstandard"
 version = "0.25.0"
@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==1.0.1"
+  "langchain-openai==1.1.14"
 ]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==1.0.0a2",
+  "langchain-openai==1.1.14",
  "langchain-anthropic==1.0.0a5",
  "langgraph==1.1.5"
 ]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==1.0.0a2",
+  "langchain-openai==1.1.14",
  "langgraph==1.1.2",
  "langchain_community>=0.3.0",
 ]
@@ -3676,9 +3676,9 @@ keyv@^4.5.4:
    json-buffer "3.0.1"

 "langsmith@>=0.5.0 <1.0.0":
-  version "0.5.18"
-  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
-  integrity sha512-3zuZUWffTHQ+73EAwnodADtf534VNEZUpXr9jC12qyG8/IQuJET7PRsCpTb9wX2lmBspakwLUpqpj3tNm/0bVA==
+  version "0.5.20"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
+  integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
  dependencies:
    p-queue "6.6.2"
    uuid "10.0.0"
@@ -1328,9 +1328,9 @@ keyv@^4.5.4:
    json-buffer "3.0.1"

 "langsmith@>=0.5.0 <1.0.0":
-  version "0.5.18"
-  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
-  integrity sha512-3zuZUWffTHQ+73EAwnodADtf534VNEZUpXr9jC12qyG8/IQuJET7PRsCpTb9wX2lmBspakwLUpqpj3tNm/0bVA==
+  version "0.5.20"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
+  integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
  dependencies:
    p-queue "6.6.2"
    uuid "10.0.0"
@@ -1 +1 @@
-__version__ = "0.4.21"
+__version__ = "0.4.23"
@@ -26,8 +26,15 @@ class LogData(TypedDict):
    params: dict[str, Any]


-def get_anonymized_params(kwargs: dict[str, Any]) -> dict[str, bool]:
-    params = {}
+def get_anonymized_params(
+    kwargs: dict[str, Any], *, cli_command: str
+) -> dict[str, bool | str]:
+    params: dict[str, bool | str] = {}
+
+    if cli_command == "deploy" and (
+        analytics_source := os.getenv("LANGGRAPH_CLI_ANALYTICS_SOURCE")
+    ):
+        params["source"] = analytics_source

    # anonymize params with values
    if config := kwargs.get("config"):
@@ -88,7 +95,7 @@ def log_command(func):
            "python_version": platform.python_version(),
            "cli_version": __version__,
            "cli_command": func.__name__,
-            "params": get_anonymized_params(kwargs),
+            "params": get_anonymized_params(kwargs, cli_command=func.__name__),
        }

        background_thread = threading.Thread(target=log_data, args=(data,))
@@ -23,7 +23,7 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.5.35,<0.9.0 ; python_version >= '3.11'",
    "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
 ]

@@ -290,7 +290,7 @@ wheels = [

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

 [[package]]
@@ -266,7 +266,7 @@ wheels = [

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

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

 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")
@@ -110,7 +107,6 @@ RESERVED = {
    CONFIG_KEY_CHECKPOINT_ID,
    CONFIG_KEY_CHECKPOINT_NS,
    CONFIG_KEY_RESUME_MAP,
-    CONFIG_KEY_STREAM_MESSAGES_V2,
    # other constants
    PUSH,
    PULL,
@@ -245,15 +245,6 @@ class _GraphCallbackManager(BaseCallbackManager):
            run_id=run_id,
        )

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

-    def add_handler(
-        self,
-        handler: BaseCallbackHandler,
-        inherit: bool = True,  # noqa: FBT001,FBT002
-    ) -> None:
-        if not isinstance(handler, GraphCallbackHandler):
-            raise TypeError("handlers must inherit GraphCallbackHandler")
-        super().add_handler(handler, inherit=inherit)
-
    def copy(
        self,
        *,
@@ -0,0 +1,177 @@
+from __future__ import annotations
+
+import copy as _copy
+from collections.abc import Callable, Sequence
+from typing import Any, Generic
+
+from langgraph.checkpoint.base import DELTA_SENTINEL, PendingWrite
+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",)
+
+
+def _empty(typ: Any) -> Any:
+    try:
+        return typ()
+    except Exception:
+        return []
+
+
+class DeltaChannel(Generic[Value], BaseChannel[Any, Any, Any]):
+    """Experimental — private API, subject to change or removal without notice.
+
+    Imported from the underscored module `langgraph.channels._delta` on purpose;
+    not re-exported from `langgraph.channels`. Intended for internal use only
+    while we validate the design on real workloads.
+
+    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.
+
+    Reconstruction replays every ancestor write through the operator, so
+    per-get cost scales with thread depth. Compaction for deep threads is
+    a follow-up — today, use this on threads of a few hundred turns.
+
+    Usage::
+
+        from langgraph.channels._delta import DeltaChannel
+
+        class State(TypedDict):
+            messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
+    """
+
+    __slots__ = (
+        "value",
+        "operator",
+    )
+
+    def __init__(
+        self,
+        operator: Callable[[Any, Any], Any],
+    ) -> None:
+        super().__init__(list)
+        self.operator = operator
+        self.value: Any = []
+
+    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 self.typ
+
+    @property
+    def UpdateType(self) -> Any:
+        return self.typ
+
+    def copy(self) -> Self:
+        new: DeltaChannel[Value] = DeltaChannel(self.operator)
+        new.typ = self.typ
+        new.key = self.key
+        new.value = self.value if self.value is MISSING else _copy.copy(self.value)
+        return new
+
+    def _apply_write(self, value: Any, write: Any) -> Any:
+        """Apply one write to `value` and return the new value.
+
+        An `Overwrite` replaces the value; any other write is folded through
+        the operator. Centralizes the Overwrite/reducer branching used by both
+        `update` (live super-step) and `from_checkpoint` (ancestor replay).
+        """
+        is_overwrite, overwrite_value = _get_overwrite(write)
+        if is_overwrite:
+            return (
+                _copy.copy(overwrite_value)
+                if overwrite_value is not None
+                else _empty(self.typ)
+            )
+        base = _empty(self.typ) if value is MISSING else value
+        return self.operator(base, write)
+
+    def from_checkpoint(self, checkpoint: Any) -> Self:
+        """Initialize from a seed value.
+
+        Pregel's hydration path calls this with the `seed` returned by
+        `saver.get_channel_history`:
+
+          * `MISSING` / `DELTA_SENTINEL` → channel starts empty. The walk
+            either reached the root (fresh delta thread) or found nothing
+            to seed from.
+          * any other value → use as the base value. Typically a pre-delta
+            blob preserved across a channel-type migration; `replay_writes`
+            folds subsequent deltas on top.
+        """
+        new: DeltaChannel[Value] = DeltaChannel(self.operator)
+        new.typ = self.typ
+        new.key = self.key
+        if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
+            new.value = _empty(new.typ)
+        else:
+            new.value = checkpoint
+        return new
+
+    def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
+        """Fold a sequence of `PendingWrite` tuples into the current value.
+
+        Called after `from_checkpoint` during pregel hydration to replay
+        per-step deltas from on-path ancestors through the reducer. Writes
+        are oldest→newest. `Overwrite` values inside the stream reset the
+        reducer state at that point, same as during a live super-step.
+        The `task_id` and `channel` fields of each `PendingWrite` are
+        ignored — `_get_channel_writes_history` has already filtered to
+        this channel.
+        """
+        for _, _, value in writes:
+            self.value = self._apply_write(self.value, value)
+
+    def update(self, values: Sequence[Any]) -> bool:
+        if not values:
+            return False
+        seen_overwrite = False
+        for value in values:
+            is_overwrite, _ = _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)
+                seen_overwrite = True
+            elif seen_overwrite:
+                # Post-Overwrite writes within the same super-step are dropped.
+                continue
+            self.value = self._apply_write(self.value, value)
+        return True
+
+    def get(self) -> Any:
+        if self.value is MISSING:
+            raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING
+
+    def checkpoint(self) -> Any:
+        return DELTA_SENTINEL
@@ -184,63 +184,97 @@ def add_messages(
        ```

    """
-    remove_all_idx = None
-    # coerce to list
+    # 1. Coerce scalars to lists.
    if not isinstance(left, list):
        left = [left]  # type: ignore[assignment]
    if not isinstance(right, list):
        right = [right]  # type: ignore[assignment]
-    # coerce to message
-    left = [
-        message_chunk_to_message(cast(BaseMessageChunk, m))
-        for m in convert_to_messages(left)
-    ]
-    right = [
+    left = cast(list, left)
+
+    # 2. Normalize `left`. After the first call, `left` is the previous return
+    #    value of `add_messages` — a list of fully-resolved BaseMessages with
+    #    IDs — and needs no work. Fresh user input (dicts, tuples, message
+    #    chunks, BaseMessages without IDs) falls through to full conversion.
+    left_msgs: list[BaseMessage]
+    if (
+        left
+        and isinstance(left[0], BaseMessage)
+        and not isinstance(left[0], BaseMessageChunk)
+        and left[0].id is not None
+    ):
+        left_msgs = left
+    else:
+        left_msgs = [
+            message_chunk_to_message(cast(BaseMessageChunk, m))
+            for m in convert_to_messages(left)
+        ]
+        for m in left_msgs:
+            if m.id is None:
+                m.id = str(uuid.uuid4())
+
+    # 3. Normalize `right` — always fresh input. Assign missing IDs and detect
+    #    any RemoveMessage sentinels in a single pass.
+    right_msgs: list[BaseMessage] = [
        message_chunk_to_message(cast(BaseMessageChunk, m))
        for m in convert_to_messages(right)
    ]
-    # assign missing ids
-    for m in left:
+    remove_all_idx: int | None = None
+    has_remove = False
+    for idx, m in enumerate(right_msgs):
        if m.id is None:
            m.id = str(uuid.uuid4())
-    for idx, m in enumerate(right):
-        if m.id is None:
-            m.id = str(uuid.uuid4())
-        if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
-            remove_all_idx = idx
+        if isinstance(m, RemoveMessage):
+            has_remove = True
+            if m.id == REMOVE_ALL_MESSAGES:
+                remove_all_idx = idx

+    # 4. REMOVE_ALL_MESSAGES: discard everything up to and including the sentinel.
    if remove_all_idx is not None:
-        return right[remove_all_idx + 1 :]
+        return right_msgs[remove_all_idx + 1 :]

-    # merge
-    merged = left.copy()
-    merged_by_id = {m.id: i for i, m in enumerate(merged)}
-    ids_to_remove = set()
-    for m in right:
-        if (existing_idx := merged_by_id.get(m.id)) is not None:
-            if isinstance(m, RemoveMessage):
-                ids_to_remove.add(m.id)
+    # 5. Decide fast vs. slow path. The fast path (pure append) is only valid
+    #    when `right` has no removals, no intra-right duplicate IDs, and no IDs
+    #    that overlap with `left` — any of those would force the indexed merge
+    #    below to update or dedup.
+    pure_append = False
+    if not has_remove:
+        left_ids = {m.id for m in left_msgs}
+        right_ids = {m.id for m in right_msgs}
+        pure_append = len(right_ids) == len(right_msgs) and right_ids.isdisjoint(
+            left_ids
+        )
+
+    if pure_append:
+        merged = left_msgs + right_msgs
+    else:
+        # 6. Slow path: build id→index map over `left`, then replay `right`.
+        #    In-place replacement for matching IDs, append for new IDs, and a
+        #    deferred removal pass so RemoveMessages can target either side.
+        merged = left_msgs.copy()
+        merged_by_id = {m.id: i for i, m in enumerate(merged)}
+        ids_to_remove = set()
+        for m in right_msgs:
+            if (existing_idx := merged_by_id.get(m.id)) is not None:
+                if isinstance(m, RemoveMessage):
+                    ids_to_remove.add(m.id)
+                else:
+                    ids_to_remove.discard(m.id)
+                    merged[existing_idx] = m
            else:
-                ids_to_remove.discard(m.id)
-                merged[existing_idx] = m
-        else:
-            if isinstance(m, RemoveMessage):
-                raise ValueError(
-                    f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
-                )
-
-            merged_by_id[m.id] = len(merged)
-            merged.append(m)
-    merged = [m for m in merged if m.id not in ids_to_remove]
+                if isinstance(m, RemoveMessage):
+                    raise ValueError(
+                        f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
+                    )
+                merged_by_id[m.id] = len(merged)
+                merged.append(m)
+        merged = [m for m in merged if m.id not in ids_to_remove]

+    # 7. Apply optional output format.
    if format == "langchain-openai":
-        merged = _format_messages(merged)
-    elif format:
+        return _format_messages(merged)
+    if format:
        msg = f"Unrecognized {format=}. Expected one of 'langchain-openai', None."
        raise ValueError(msg)
-    else:
-        pass
-
    return merged


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

+import collections.abc
 import inspect
 import logging
 import typing
@@ -47,7 +48,8 @@ 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
+from langgraph.channels.binop import BinaryOperatorAggregate, _strip_extras
+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 (
@@ -1045,7 +1047,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        interrupt_after: All | list[str] | None = None,
        debug: bool = False,
        name: str | None = None,
-        transformers: Sequence[Callable[[], Any]] | None = None,
    ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
        """Compiles the `StateGraph` into a `CompiledStateGraph` object.

@@ -1078,16 +1079,12 @@ 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 zero-arg factories returning
-                `StreamTransformer` instances. Registered on the compiled
-                graph and instantiated per-run whenever `stream_v2` /
-                `astream_v2` is called. Appended after the built-in
-                `ValuesTransformer` and `MessagesTransformer`.

        Returns:
            CompiledStateGraph: The compiled `StateGraph`.
        """
        checkpointer = ensure_valid_checkpointer(checkpointer)
+
        serde_allowlist: set[tuple[str, ...]] | None = None
        if _serde.STRICT_MSGPACK_ENABLED:
            schema_types: list[type[Any]] = [
@@ -1165,7 +1162,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            store=store,
            cache=cache,
            name=name or "LangGraph",
-            stream_transformers=transformers,
        )
        compiled._serde_allowlist = serde_allowlist

@@ -1674,6 +1670,36 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
        # Search through all annotated medata to find channel annotations
        for item in meta:
            if isinstance(item, BaseChannel):
+                if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
+                    origin = typ.__origin__
+                    # Unwrap parameterized Required[X]/NotRequired[X] to X
+                    # (e.g. Annotated[NotRequired[dict[...]], ...]).
+                    if hasattr(origin, "__origin__") and origin.__origin__ in (
+                        Required,
+                        NotRequired,
+                    ):
+                        origin = origin.__args__[0]
+                    outer = _strip_extras(origin)
+                    if outer in (
+                        collections.abc.Sequence,
+                        collections.abc.MutableSequence,
+                    ):
+                        outer = list
+                    elif outer in (
+                        collections.abc.Mapping,
+                        collections.abc.MutableMapping,
+                    ):
+                        outer = dict
+                    elif outer in (
+                        collections.abc.Set,
+                        collections.abc.MutableSet,
+                    ):
+                        outer = set
+                    item.typ = outer
+                    try:
+                        item.value = outer()
+                    except Exception:
+                        item.value = []
                return item
            elif isclass(item) and issubclass(item, BaseChannel):
                # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]
@@ -3,11 +3,13 @@ from __future__ import annotations
 from collections.abc import Mapping
 from datetime import datetime, timezone

-from langgraph.checkpoint.base import Checkpoint
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import DELTA_SENTINEL, BaseCheckpointSaver, Checkpoint
 from langgraph.checkpoint.base.id import uuid6

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

 LATEST_VERSION = 4
@@ -58,8 +60,22 @@ def create_checkpoint(
 def channels_from_checkpoint(
    specs: Mapping[str, BaseChannel | ManagedValueSpec],
    checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
 ) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Get channels from a checkpoint."""
+    """Hydrate channels from a checkpoint.
+
+    For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
+    is sufficient — the stored value IS the reconstructed state.
+
+    `DeltaChannel` is the exception: its stored value is a sentinel; the
+    full state is spread across `checkpoint_writes` along the ancestor
+    chain. When `saver` and `config` are provided, this function fetches
+    that history via `saver._get_channel_writes_history` and folds it
+    through the channel's reducer. Without them (static contexts — graph
+    drawing, unit tests), delta channels fall back to empty.
+    """
    channel_specs: dict[str, BaseChannel] = {}
    managed_specs: dict[str, ManagedValueSpec] = {}
    for k, v in specs.items():
@@ -67,13 +83,67 @@ def channels_from_checkpoint(
            channel_specs[k] = v
        else:
            managed_specs[k] = v
-    return (
-        {
-            k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
-            for k, v in channel_specs.items()
-        },
-        managed_specs,
-    )
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if (
+            isinstance(spec, DeltaChannel)
+            and saver is not None
+            and config is not None
+            and (stored is MISSING or stored is DELTA_SENTINEL)
+        ):
+            # Target's own blob is empty/sentinel — walk ancestors for
+            # seed + writes. Skipping this when `stored` is a real value
+            # preserves state written via `update_state` or sitting at the
+            # tip of a pre-migration thread: the saver's ancestor walk
+            # intentionally excludes the target's own blob, so without
+            # this short-circuit we'd lose it.
+            history = saver._get_channel_writes_history(config, k)
+            delta_ch = spec.from_checkpoint(history.seed)
+            delta_ch.replay_writes(history.writes)
+            ch = delta_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs
+
+
+async def achannels_from_checkpoint(
+    specs: Mapping[str, BaseChannel | ManagedValueSpec],
+    checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
+) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
+    """Async version of `channels_from_checkpoint`. See docstring there."""
+    channel_specs: dict[str, BaseChannel] = {}
+    managed_specs: dict[str, ManagedValueSpec] = {}
+    for k, v in specs.items():
+        if isinstance(v, BaseChannel):
+            channel_specs[k] = v
+        else:
+            managed_specs[k] = v
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if (
+            isinstance(spec, DeltaChannel)
+            and saver is not None
+            and config is not None
+            and (stored is MISSING or stored is DELTA_SENTINEL)
+        ):
+            history = await saver._aget_channel_writes_history(config, k)
+            delta_ch = spec.from_checkpoint(history.seed)
+            delta_ch.replay_writes(history.writes)
+            ch = delta_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs


 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -92,6 +92,7 @@ from langgraph.pregel._algo import (
    task_path_str,
 )
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
    channels_from_checkpoint,
    copy_checkpoint,
    create_checkpoint,
@@ -692,7 +693,7 @@ class PregelLoop:
        # writes so that interrupt() calls re-fire instead of returning
        # stale values. But if we're actively resuming, keep them —
        # multi-interrupt scenarios need previously resolved values preserved.
-        if self.is_replaying and (
+        is_time_traveling = self.is_replaying and (
            # Time-travel to a subgraph checkpoint: the parent sets
            # RESUMING=True (it can't distinguish time-travel from resume),
            # so we check if this subgraph's own ns is in checkpoint_map.
@@ -710,7 +711,8 @@ class PregelLoop:
                # (subgraph input is a Send arg, not a Command)
                or configurable.get(CONFIG_KEY_RESUMING, False)
            )
-        ):
+        )
+        if is_time_traveling:
            self.checkpoint_pending_writes = [
                w for w in self.checkpoint_pending_writes if w[1] != RESUME
            ]
@@ -765,6 +767,26 @@ class PregelLoop:
                if k in self.checkpoint["channel_versions"]:
                    version = self.checkpoint["channel_versions"][k]
                    self.checkpoint["versions_seen"][INTERRUPT][k] = version
+            # When time-traveling (replaying from a specific checkpoint),
+            # save a fork checkpoint so the replayed execution creates a
+            # new branch. Without this, if the execution hits an interrupt
+            # before after_tick() runs, no new checkpoint is created —
+            # the parent's latest checkpoint remains the old one and
+            # subsequent resumes load the wrong state.
+            # Skip for update_state forks (source=update/fork) since they
+            # already have their own fork checkpoint.
+            if is_time_traveling and self.checkpoint_metadata.get("source") not in (
+                "update",
+                "fork",
+            ):
+                # Clear old INTERRUPT writes from the loaded checkpoint.
+                # The fork will have a new checkpoint_id which changes
+                # task IDs — stale interrupt writes would accumulate and
+                # confuse the multiple-interrupt check in future resumes.
+                self.checkpoint_pending_writes = [
+                    w for w in self.checkpoint_pending_writes if w[1] != INTERRUPT
+                ]
+                self._put_checkpoint({"source": "fork"})
            # produce values output
            self._emit(
                "values", map_output_values, self.output_keys, True, self.channels
@@ -807,14 +829,28 @@ class PregelLoop:
        if not self.is_nested:
            # Pass the resolved before-bound checkpoint ID so subgraphs can
            # find their corresponding checkpoint without re-fetching the
-            # parent. For forks (source=update), use the fork's parent
+            # parent. For forks (source=update/fork), use the fork's parent
            # checkpoint ID since the fork was created after the subgraph's
            # checkpoints from the original execution.
+            #
+            # Only gate on is_time_traveling (not is_replaying). When the
+            # client resumes with an explicit checkpoint_id that happens to
+            # point at the current head (e.g. LangGraph Studio sending
+            # `checkpoint: {checkpoint_id}` alongside Command(resume=...)),
+            # is_replaying is True but is_time_traveling is False. In that
+            # case subgraphs should load their latest checkpoint normally,
+            # not go through ReplayState's before-bound lookup which would
+            # miss subgraph checkpoints created during processing of the
+            # current parent step.
            replay_state: ReplayState | None = None
-            if self.is_replaying:
+            if is_time_traveling:
                replay_checkpoint_id = self.checkpoint["id"]
                if (
-                    self.checkpoint_metadata.get("source") == "update"
+                    self.checkpoint_metadata.get("source")
+                    in (
+                        "update",
+                        "fork",
+                    )
                    and self.prev_checkpoint_config
                ):
                    replay_checkpoint_id = self.prev_checkpoint_config[CONF].get(
@@ -1238,7 +1274,10 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
        )
        self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
        self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
        )
        self.stack.push(self._suppress_interrupt)
        self.status = "input"
@@ -1441,8 +1480,11 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
        self.submit = await self.stack.enter_async_context(
            AsyncBackgroundExecutor(self.config)
        )
-        self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+        self.channels, self.managed = await achannels_from_checkpoint(
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
        )
        self.stack.push(self._suppress_interrupt)
        self.status = "input"
@@ -14,7 +14,7 @@ from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
 from pydantic import BaseModel

-from langgraph._internal._constants import NS_END, NS_SEP
+from langgraph._internal._constants import NS_SEP
 from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
 from langgraph.pregel.protocol import StreamChunk
 from langgraph.types import Command
@@ -24,11 +24,6 @@ try:
 except ImportError:
    _StreamingCallbackHandler = object  # type: ignore

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

@@ -137,23 +132,15 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
        **kwargs: Any,
    ) -> Any:
        if metadata and (not tags or (TAG_NOSTREAM not in tags)):
-            task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
-            checkpoint_ns = (
-                f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
-                if NS_END in task_checkpoint_ns
-                else task_checkpoint_ns
-            )
-            ns = tuple(task_checkpoint_ns.split(NS_SEP))[:-1]
+            ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
+                :-1
+            ]
            if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
                return
-            stream_metadata = dict(metadata)
-            stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
-            # Preserve backwards-compatible streamed checkpoint metadata shape.
-            stream_metadata["checkpoint_ns"] = checkpoint_ns
            if tags:
                if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
-                    stream_metadata["tags"] = filtered_tags
-            self.metadata[run_id] = (ns, stream_metadata)
+                    metadata["tags"] = filtered_tags
+            self.metadata[run_id] = (ns, metadata)

    def on_llm_new_token(
        self,
@@ -261,78 +248,3 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
        **kwargs: Any,
    ) -> Any:
        self.metadata.pop(run_id, None)
-
-
-class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler):
-    """v2 variant of `StreamMessagesHandler`.
-
-    Declaring `_V2StreamingCallbackHandler` as a base flips
-    `BaseChatModel.invoke` to route through `_stream_chat_model_events`
-    (firing `on_stream_event`) instead of `_stream` (firing
-    `on_llm_new_token`). Inherits `on_stream_event` from the parent,
-    which forwards protocol events onto the messages stream channel.
-
-    Pregel attaches this class instead of the v1 handler only when
-    `StreamingHandler` opts in via the internal
-    `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
-    `graph.stream(stream_mode="messages")` callers keep the v1
-    AIMessageChunk shape.
-    """
-
-    def on_llm_new_token(
-        self,
-        token: str,
-        *,
-        chunk: ChatGenerationChunk | None = None,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Intentional no-op — v1 chunks are not used on v2-flagged runs.
-
-        The v2 marker already steers `invoke` to the event generator, so
-        `on_llm_new_token` should not fire under normal routing. This
-        override stays a pass-through (no call to `super()`) to make
-        the intent explicit and to guard against any caller (e.g. a
-        node that calls `model.stream()` directly, which still fires
-        the v1 callback) leaking AIMessageChunks onto a v2-flagged
-        messages stream.
-        """
-        # Intentionally empty: v2 handler does not forward v1 chunks.
-
-    def 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":
-                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)))
@@ -73,7 +73,6 @@ 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,
@@ -123,6 +122,7 @@ from langgraph.pregel._algo import (
 )
 from langgraph.pregel._call import identifier
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
    channels_from_checkpoint,
    copy_checkpoint,
    create_checkpoint,
@@ -134,10 +134,7 @@ from langgraph.pregel._loop import (
    AsyncPregelLoop,
    SyncPregelLoop,
 )
-from langgraph.pregel._messages import (
-    StreamMessagesHandler,
-    StreamMessagesHandlerV2,
-)
+from langgraph.pregel._messages import StreamMessagesHandler
 from langgraph.pregel._read import DEFAULT_BOUND, PregelNode
 from langgraph.pregel._retry import RetryPolicy
 from langgraph.pregel._runner import PregelRunner
@@ -344,69 +341,6 @@ class NodeBuilder:
        )


-_STREAM_V2_MODES: list[StreamMode] = [
-    "values",
-    "updates",
-    "messages",
-    "custom",
-    "checkpoints",
-    "tasks",
-    "debug",
-]
-
-
-def _build_stream_factories(
-    compile_time: Sequence[Callable[..., Any]],
-    call_site: Sequence[Any] | None,
-) -> list[Callable[..., Any]]:
-    """Assemble the factory list handed to `StreamMux(factories=...)`.
-
-    Prepends the auto-registered built-ins — `ValuesTransformer`
-    (state snapshots backing `run.output` / `run.interrupted`),
-    `MessagesTransformer` (LLM token streaming), and
-    `SubgraphTransformer` (in-process subgraph handle discovery) —
-    then appends the graph's compile-time `stream_transformers`
-    followed by any call-site additions. Factories flow down into
-    subgraph mini-muxes, so per-scope instances propagate
-    automatically.
-
-    `LifecycleTransformer` is opt-in: add it via compile-time
-    `stream_transformers=[...]` or the per-call `transformers=[...]`
-    kwarg on `stream_v2()` / `astream_v2()`. Without it, no
-    `lifecycle` wire events are emitted and `run.lifecycle` is
-    absent.
-    """
-    from langgraph.stream.transformers import (
-        MessagesTransformer,
-        SubgraphTransformer,
-        ValuesTransformer,
-    )
-
-    builtins: list[Callable[..., Any]] = [
-        ValuesTransformer,
-        MessagesTransformer,
-        SubgraphTransformer,
-    ]
-    return [*builtins, *compile_time, *(call_site or ())]
-
-
-def _merge_v2_messages_flag(
-    config: RunnableConfig | None,
-) -> RunnableConfig:
-    """Return a config with the v2 messages flag set in `configurable`.
-
-    Signals to pregel that `stream_mode="messages"` should attach
-    `StreamMessagesHandlerV2` for this call so invoke-time model runs
-    route through the v2 event generator and their protocol events
-    reach the messages channel.
-    """
-    merged: RunnableConfig = dict(config or {})  # type: ignore[assignment]
-    configurable = dict(merged.get(CONF) or {})
-    configurable[CONFIG_KEY_STREAM_MESSAGES_V2] = True
-    merged[CONF] = configurable
-    return merged
-
-
 class Pregel(
    PregelProtocol[StateT, ContextT, InputT, OutputT],
    Generic[StateT, ContextT, InputT, OutputT],
@@ -738,7 +672,6 @@ class Pregel(
        config: RunnableConfig | None = None,
        trigger_to_nodes: Mapping[str, Sequence[str]] | None = None,
        name: str = "LangGraph",
-        stream_transformers: Sequence[Callable[..., Any]] | None = None,
        **deprecated_kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
        if (
@@ -785,9 +718,6 @@ class Pregel(
        self.config = config
        self.trigger_to_nodes = trigger_to_nodes or {}
        self.name = name
-        self._stream_transformers: tuple[Callable[..., Any], ...] = tuple(
-            stream_transformers or ()
-        )
        self._serde_allowlist: set[tuple[str, ...]] | None = None
        if auto_validate:
            self.validate()
@@ -1123,6 +1053,10 @@ class Pregel(
        channels, managed = channels_from_checkpoint(
            self.channels,
            saved.checkpoint,
+            saver=self.checkpointer
+            if isinstance(self.checkpointer, BaseCheckpointSaver)
+            else None,
+            config=saved.config,
        )
        # tasks for this checkpoint
        next_tasks = prepare_next_tasks(
@@ -1239,9 +1173,13 @@ class Pregel(

        step = saved.metadata.get("step", -1) + 1
        stop = step + 2
-        channels, managed = channels_from_checkpoint(
+        channels, managed = await achannels_from_checkpoint(
            self.channels,
            saved.checkpoint,
+            saver=self.checkpointer
+            if isinstance(self.checkpointer, BaseCheckpointSaver)
+            else None,
+            config=saved.config,
        )
        # tasks for this checkpoint
        next_tasks = prepare_next_tasks(
@@ -1612,6 +1550,11 @@ class Pregel(
            channels, managed = channels_from_checkpoint(
                self.channels,
                checkpoint,
+                saver=self.checkpointer
+                if saved is not None
+                and isinstance(self.checkpointer, BaseCheckpointSaver)
+                else None,
+                config=saved.config if saved is not None else None,
            )
            values, as_node = updates[0][:2]

@@ -2055,9 +1998,14 @@ class Pregel(
            )
            if saved:
                checkpoint_config = patch_configurable(config, saved.config[CONF])
-            channels, managed = channels_from_checkpoint(
+            channels, managed = await achannels_from_checkpoint(
                self.channels,
                checkpoint,
+                saver=self.checkpointer
+                if saved is not None
+                and isinstance(self.checkpointer, BaseCheckpointSaver)
+                else None,
+                config=saved.config if saved is not None else None,
            )
            values, as_node = updates[0][:2]
            # no values, just clear all tasks
@@ -2697,13 +2645,8 @@ class Pregel(
            # set up messages stream mode
            if "messages" in stream_modes:
                ns_ = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
-                messages_handler_cls = (
-                    StreamMessagesHandlerV2
-                    if config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
-                    else StreamMessagesHandler
-                )
                run_manager.inheritable_handlers.append(
-                    messages_handler_cls(
+                    StreamMessagesHandler(
                        stream.put,
                        subgraphs,
                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
@@ -3079,13 +3022,8 @@ 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))
-                messages_handler_cls = (
-                    StreamMessagesHandlerV2
-                    if config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
-                    else StreamMessagesHandler
-                )
                run_manager.inheritable_handlers.append(
-                    messages_handler_cls(
+                    StreamMessagesHandler(
                        stream_put,
                        subgraphs,
                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
@@ -3318,98 +3256,6 @@ class Pregel(
            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[Any] | None = None,
-    ) -> Any:
-        """Start a sync v2 streaming run driven by transformer projections.
-
-        Builds a `StreamMux` from the auto-registered built-ins
-        (`ValuesTransformer`, `MessagesTransformer`,
-        `SubgraphTransformer`), 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.
-
-        `LifecycleTransformer` (emits `lifecycle` wire events,
-        exposes `run.lifecycle`) is opt-in — add it via
-        `stream_transformers` at compile time or via the
-        `transformers=` kwarg here.
-
-        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 instances appended after
-                compile-time `stream_transformers`.
-
-        Returns:
-            A `GraphRunStream` the caller iterates to drive the run.
-        """
-        from langgraph.stream._mux import StreamMux
-        from langgraph.stream.run_stream import GraphRunStream
-
-        factories = _build_stream_factories(self._stream_transformers, transformers)
-        mux = StreamMux(factories=factories, is_async=False)
-        graph_iter = iter(
-            self.stream(
-                input,
-                _merge_v2_messages_flag(config),
-                stream_mode=_STREAM_V2_MODES,
-                subgraphs=True,
-                version="v2",
-                interrupt_before=interrupt_before,
-                interrupt_after=interrupt_after,
-            )
-        )
-        return GraphRunStream(graph_iter, mux)
-
-    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[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.
-
-        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 instances appended after
-                compile-time `stream_transformers`.
-        """
-        from langgraph.stream._mux import StreamMux
-        from langgraph.stream.run_stream import AsyncGraphRunStream
-
-        factories = _build_stream_factories(self._stream_transformers, transformers)
-        mux = StreamMux(factories=factories, is_async=True)
-        graph_aiter = self.astream(
-            input,
-            _merge_v2_messages_flag(config),
-            stream_mode=_STREAM_V2_MODES,
-            subgraphs=True,
-            version="v2",
-            interrupt_before=interrupt_before,
-            interrupt_after=interrupt_after,
-        ).__aiter__()
-        return AsyncGraphRunStream(graph_aiter, mux)
-
    @overload
    def invoke(
        self,
@@ -653,11 +653,10 @@ class RemoteGraph(PregelProtocol):
        # coerce to list, or add default stream mode
        if stream_mode:
            if isinstance(stream_mode, str):
-                updated_stream_modes.append(cast(StreamModeSDK, stream_mode))
+                updated_stream_modes.append(stream_mode)
            else:
                req_single = False
-                for m in stream_mode:
-                    updated_stream_modes.append(cast(StreamModeSDK, m))
+                updated_stream_modes.extend(stream_mode)
        else:
            updated_stream_modes.append(default)
        requested_stream_modes = updated_stream_modes.copy()
@@ -666,8 +665,7 @@ class RemoteGraph(PregelProtocol):
            (config or {}).get(CONF, {}).get(CONFIG_KEY_STREAM)
        )
        if stream:
-            for m in stream.modes:
-                updated_stream_modes.append(cast(StreamModeSDK, m))
+            updated_stream_modes.extend(stream.modes)
        # map "messages" to "messages-tuple"
        if "messages" in updated_stream_modes:
            updated_stream_modes.remove("messages")
@@ -1,20 +0,0 @@
-"""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._event_log import EventLog
-from langgraph.stream._types import ProtocolEvent, StreamTransformer
-from langgraph.stream.run_stream import AsyncGraphRunStream, GraphRunStream
-from langgraph.stream.stream_channel import StreamChannel
-
-__all__ = [
-    "AsyncGraphRunStream",
-    "EventLog",
-    "GraphRunStream",
-    "ProtocolEvent",
-    "StreamChannel",
-    "StreamTransformer",
-]
@@ -1,32 +0,0 @@
-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,
-    }
@@ -1,318 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-from collections import deque
-from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
-from typing import Generic, TypeVar
-
-T = TypeVar("T")
-
-
-class EventLog(Generic[T]):
-    """Single-consumer drainable queue for streaming events.
-
-    Items are popped off the front as the consumer advances — there is
-    no retention beyond what's currently queued. A log accepts exactly
-    one subscriber; a second `__iter__` / `__aiter__` call raises. Use
-    `tee(n)` / `atee(n)` for fan-out.
-
-    Starts unbound — neither `__iter__` nor `__aiter__` is available
-    until the StreamMux calls `_bind(is_async)`. After binding, only
-    the matching iteration protocol works; the other raises `TypeError`.
-
-    Pump wiring (set by the run stream, not by `_bind`):
-        - `_request_more`: sync pump callable, returns True if a new
-          event was produced.
-        - `_arequest_more`: async pump coroutine factory, same contract.
-
-    Memory is bounded by caller pace: both sync and async use caller-
-    driven pumps, so each cursor advance produces at most one event.
-    The only shape where a log can accumulate meaningfully is
-    concurrent async consumers at unequal rates — a slow consumer's
-    log grows while fast consumers drive the shared pump. That's the
-    documented tradeoff for concurrent consumption; consume at similar
-    rates or use a single consumer if memory matters.
-
-    Lazy-subscribe: `push` is a no-op when no subscriber has registered.
-    Transformers still execute `process()` (so scalar state like
-    `ValuesTransformer._latest` stays current); only the log append is
-    skipped.
-    """
-
-    def __init__(self, maxlen: int | None = None, *, retain: bool = False) -> None:
-        """Initialize an empty, unbound log.
-
-        Args:
-            maxlen: Accepted for forward compatibility; currently unused.
-                The caller-driven pump bounds memory naturally for
-                single-consumer use.
-            retain: If True, `push()` retains items regardless of whether
-                a consumer has subscribed yet. Used for projections
-                whose consumer only becomes visible after events have
-                already flowed (e.g. mini-mux logs inside dynamically
-                discovered subgraph handles, or the `lifecycle` channel
-                iterated after draining `values`). Subscription
-                exclusivity on `__iter__` is unchanged.
-
-        Raises:
-            ValueError: If `maxlen` is not a positive integer or `None`.
-        """
-        if maxlen is not None and maxlen <= 0:
-            raise ValueError("EventLog maxlen must be a positive int or None")
-        self._items: deque[T] = deque()
-        self._maxlen: int | None = maxlen
-        self._closed = False
-        self._error: BaseException | None = None
-
-        # Binding state — None means unbound.
-        self._is_async: bool | None = None
-
-        # Flipped on first __iter__ / __aiter__. Pre-subscription
-        # pushes are silent no-ops unless `_retain` is True.
-        self._subscribed = False
-        self._retain = retain
-
-        # Pump wiring set by the run stream after bind.
-        self._request_more: Callable[[], bool] | None = None
-        self._arequest_more: Callable[[], Awaitable[bool]] | None = None
-
-    # ------------------------------------------------------------------
-    # Binding
-    # ------------------------------------------------------------------
-
-    def _bind(self, *, is_async: bool) -> None:
-        """Bind this log to sync or async mode.
-
-        Called by the StreamMux after transformer registration. Must be
-        called exactly once before any iteration.
-
-        Args:
-            is_async: True to enable async iteration, False for sync.
-
-        Raises:
-            RuntimeError: If the log has already been bound.
-        """
-        if self._is_async is not None:
-            raise RuntimeError("EventLog is already bound")
-        self._is_async = is_async
-
-    # ------------------------------------------------------------------
-    # Producer API
-    # ------------------------------------------------------------------
-
-    def push(self, item: T) -> None:
-        """Append an item. No-op when no subscriber is registered.
-
-        Non-blocking in both sync and async — matches v1's
-        `put_nowait` producer shape. Memory is bounded by caller pace
-        via the caller-driven pump.
-
-        When `retain=True` was set at construction, items are appended
-        regardless of subscription — for projections whose consumer
-        only reaches them after events have already flowed.
-
-        Raises:
-            RuntimeError: If the log is closed (and subscribed).
-        """
-        if not self._subscribed and not self._retain:
-            return
-        if self._closed:
-            raise RuntimeError("Cannot push to a closed EventLog")
-        self._items.append(item)
-
-    def close(self) -> None:
-        """Mark the log as complete."""
-        self._closed = True
-
-    def fail(self, err: BaseException) -> None:
-        """Mark the log as errored.
-
-        Args:
-            err: The exception to surface to the subscriber.
-        """
-        self._error = err
-        self._closed = True
-
-    # ------------------------------------------------------------------
-    # Sync iteration (caller-driven pump)
-    # ------------------------------------------------------------------
-
-    def __iter__(self) -> Iterator[T]:
-        """Subscribe and return a sync cursor. Can be called only once.
-
-        Raises:
-            TypeError: If the log is unbound or bound to async mode.
-            RuntimeError: If the log already has a subscriber.
-        """
-        if self._is_async is None:
-            raise TypeError(
-                "EventLog has not been bound yet. "
-                "Register the transformer with a StreamMux first."
-            )
-        if self._is_async:
-            raise TypeError(
-                "This EventLog is bound to async mode — use 'async for' instead."
-            )
-        if self._subscribed:
-            raise RuntimeError(
-                "EventLog already has a subscriber; use .tee(n) for fan-out."
-            )
-        self._subscribed = True
-        return self._sync_cursor()
-
-    def _sync_cursor(self) -> Iterator[T]:
-        while True:
-            if self._items:
-                yield self._items.popleft()
-            elif self._closed:
-                if self._error is not None:
-                    raise self._error
-                return
-            elif self._request_more is not None:
-                if not self._request_more():
-                    if not self._items and not self._closed:
-                        return
-            else:
-                return
-
-    # ------------------------------------------------------------------
-    # Async iteration (caller-driven pump)
-    # ------------------------------------------------------------------
-
-    def __aiter__(self) -> AsyncIterator[T]:
-        """Subscribe and return an async cursor. Can be called only once.
-
-        Raises:
-            TypeError: If the log is unbound or bound to sync mode.
-            RuntimeError: If the log already has a subscriber.
-        """
-        if self._is_async is None:
-            raise TypeError(
-                "EventLog has not been bound yet. "
-                "Register the transformer with a StreamMux first."
-            )
-        if not self._is_async:
-            raise TypeError("This EventLog is bound to sync mode — use 'for' instead.")
-        if self._subscribed:
-            raise RuntimeError(
-                "EventLog already has a subscriber; use .atee(n) for fan-out."
-            )
-        self._subscribed = True
-        return self._async_cursor()
-
-    async def _async_cursor(self) -> AsyncIterator[T]:
-        while True:
-            if self._items:
-                yield self._items.popleft()
-            elif self._closed:
-                if self._error is not None:
-                    raise self._error
-                return
-            elif self._arequest_more is not None:
-                if not await self._arequest_more():
-                    if not self._items and not self._closed:
-                        return
-            else:
-                return
-
-    # ------------------------------------------------------------------
-    # Fan-out via tee
-    # ------------------------------------------------------------------
-
-    def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
-        """Subscribe and return `n` independent sync iterators.
-
-        Each branch has its own buffer; items pulled from the
-        underlying cursor are copied into every branch. Branches are
-        naturally bounded by caller pace since the sync pump is
-        caller-driven.
-
-        Args:
-            n: Number of branches to create. Must be >= 1.
-
-        Returns:
-            A tuple of `n` iterators over the same underlying stream.
-
-        Raises:
-            TypeError: If the log is unbound or bound to async mode.
-            RuntimeError: If the log already has a subscriber.
-            ValueError: If `n` < 1.
-        """
-        if n < 1:
-            raise ValueError("tee() requires n >= 1")
-        source = self.__iter__()
-        buffers: list[deque[T]] = [deque() for _ in range(n)]
-        exhausted = [False]
-
-        def branch(i: int) -> Iterator[T]:
-            buf = buffers[i]
-            while True:
-                if buf:
-                    yield buf.popleft()
-                elif exhausted[0]:
-                    return
-                else:
-                    try:
-                        item = next(source)
-                    except StopIteration:
-                        exhausted[0] = True
-                        return
-                    for b in buffers:
-                        b.append(item)
-
-        return tuple(branch(i) for i in range(n))
-
-    def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
-        """Subscribe and return `n` independent async iterators.
-
-        Caller-driven fan-out: each branch's `__anext__` either pops
-        from its own buffer or, under a shared `asyncio.Lock`, pulls
-        one item from the underlying cursor and distributes it to
-        every branch's buffer.
-
-        Args:
-            n: Number of branches to create. Must be >= 1.
-
-        Returns:
-            A tuple of `n` async iterators over the same underlying
-            stream.
-
-        Raises:
-            TypeError: If the log is unbound or bound to sync mode.
-            RuntimeError: If the log already has a subscriber.
-            ValueError: If `n` < 1.
-        """
-        if n < 1:
-            raise ValueError("atee() requires n >= 1")
-        source = self.__aiter__()
-        buffers: list[deque[T]] = [deque() for _ in range(n)]
-        exhausted = [False]
-        error: list[BaseException | None] = [None]
-        lock = asyncio.Lock()
-
-        async def branch(i: int) -> AsyncIterator[T]:
-            buf = buffers[i]
-            while True:
-                if buf:
-                    yield buf.popleft()
-                    continue
-                if exhausted[0]:
-                    if error[0] is not None:
-                        raise error[0]
-                    return
-                async with lock:
-                    if buf or exhausted[0]:
-                        continue
-                    try:
-                        item = await source.__anext__()
-                    except StopAsyncIteration:
-                        exhausted[0] = True
-                        continue
-                    except Exception as e:
-                        error[0] = e
-                        exhausted[0] = True
-                        continue
-                    for b in buffers:
-                        b.append(item)
-
-        return tuple(branch(i) for i in range(n))
@@ -1,514 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import time
-from collections.abc import Awaitable, Callable
-from typing import Any
-
-from langgraph.stream._event_log import EventLog
-from langgraph.stream._types import (
-    ProtocolEvent,
-    StreamTransformer,
-    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` (root or mini-mux) with the mux's scope
-— typically a subgraph's namespace or `()` for the root. Standard
-transformer classes (`ValuesTransformer`, `MessagesTransformer`,
-`SubgraphTransformer`) accept a single positional scope argument, so
-the class itself is a valid factory. User transformers can close over
-their config: `lambda scope: MyTransformer(scope, foo=...)`.
-"""
-
-
-class StreamMux:
-    """Central event dispatcher for the streaming infrastructure.
-
-    Owns the main event log and routes events through a transformer
-    pipeline. StreamChannels discovered in transformer projections are
-    auto-wired so that every `push()` also injects a `ProtocolEvent`
-    into the main log.
-
-    Pass `is_async=True` when the mux will be consumed via async
-    iteration (`handler.astream()`). All EventLog and 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, ...] = (),
-    ) -> None:
-        """Initialize the mux and register transformers in order.
-
-        Callers pass either `transformers` (pre-built instances) or
-        `factories` (callables producing fresh instances per mux). A
-        factory list is preferred — mini-muxes built by `make_child()`
-        inherit the factory list, so transformers propagate naturally
-        into every subgraph's scope. `transformers` is kept for
-        back-compat tests that exercise the mux directly.
-
-        Each transformer's `init()` is called once during registration,
-        projections are merged into `extensions`, `_native` keys are
-        recorded in `native_keys`, and any EventLog / StreamChannel
-        instances are bound and wired.
-
-        Args:
-            transformers: Already-built transformer instances. Mutually
-                exclusive with `factories`.
-            is_async: True for async dispatch (`apush` / `aclose` /
-                `afail`), False for the sync path.
-            factories: Zero-or-one-argument callables producing
-                transformers. Called with this mux's `scope`.
-            scope: The namespace the mux operates within. The root mux
-                is `()`; mini-muxes for subgraphs use the subgraph's
-                namespace tuple.
-
-        Raises:
-            RuntimeError: If any transformer requires an async run but
-                the mux is in sync mode.
-            TypeError: If a transformer's `init()` doesn't return a dict.
-            ValueError: If transformers' projection keys collide, or if
-                both `transformers` and `factories` are supplied.
-        """
-        if transformers is not None and factories is not None:
-            raise ValueError("Pass either `transformers` or `factories`, not both.")
-
-        self._is_async = is_async
-        self._factories: list[TransformerFactory] = list(factories or ())
-        self.scope: tuple[str, ...] = scope
-        self._pump_fn: Callable[[], bool] | None = None
-        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
-
-        self._events: EventLog[ProtocolEvent] = EventLog()
-        self._events._bind(is_async=is_async)
-        self._transformers: list[StreamTransformer] = []
-        self._channels: list[StreamChannel[Any]] = []
-        self._logs: list[EventLog[Any]] = []
-        self._seq = 0
-
-        self.extensions: dict[str, Any] = {}
-        self.native_keys: set[str] = set()
-        self._projection_owners: dict[str, str] = {}
-        self._transformer_by_key: dict[str, StreamTransformer] = {}
-
-        if factories is not None:
-            for factory in factories:
-                self._register(factory(scope))
-        else:
-            for transformer in transformers or ():
-                self._register(transformer)
-
-    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 binding (so cursors on its projection
-        logs drive the root pump) and carries the same factory list
-        forward to any grandchild subgraphs.
-
-        Raises:
-            RuntimeError: If the mux was not built from a factory list
-                (i.e., constructed with `transformers=`). Mini-muxes
-                require factories so each scope gets its own fresh
-                transformer instances.
-        """
-        if not self._factories:
-            raise RuntimeError(
-                "StreamMux.make_child requires the mux to be constructed "
-                "with factories; pre-built transformers can't be cloned "
-                "to a new scope."
-            )
-        child = StreamMux(
-            factories=self._factories,
-            is_async=self._is_async,
-            scope=scope,
-        )
-        # Mini-muxes are created during the pump, after the first
-        # event at the child's scope has already been dispatched.
-        # Consumers reach child projections via the parent's
-        # `subgraphs` handle — necessarily after that first event.
-        # Flip retain on every log and channel so pushes are buffered
-        # until the consumer subscribes.
-        child._events._retain = True
-        for value in child.extensions.values():
-            if isinstance(value, EventLog):
-                value._retain = True
-            elif isinstance(value, StreamChannel):
-                value._log._retain = True
-        if self._pump_fn is not None:
-            child.bind_pump(self._pump_fn)
-        if self._apump_fn is not None:
-            child.bind_apump(self._apump_fn)
-        return child
-
-    def bind_pump(self, fn: Callable[[], bool]) -> None:
-        """Wire the sync pull callback onto every EventLog in the mux.
-
-        Also propagates to transformers that expose `_bind_pump` so
-        nested handles (e.g., `ChatModelStream` instances produced by
-        `MessagesTransformer`) can drive the graph pump from their
-        projection cursors.
-        """
-        self._pump_fn = fn
-        self._events._request_more = fn
-        for value in self.extensions.values():
-            if isinstance(value, EventLog):
-                value._request_more = fn
-            elif isinstance(value, StreamChannel):
-                value._log._request_more = fn
-        for transformer in self._transformers:
-            bind = getattr(transformer, "_bind_pump", None)
-            if bind is not None:
-                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 value in self.extensions.values():
-            if isinstance(value, EventLog):
-                value._arequest_more = fn
-            elif isinstance(value, StreamChannel):
-                value._log._arequest_more = fn
-        for transformer in self._transformers:
-            abind = getattr(transformer, "_bind_apump", None)
-            if abind is not None:
-                abind(fn)
-
-    def _register(self, transformer: StreamTransformer) -> None:
-        """Register a single transformer.
-
-        Calls `transformer.init()`, stores the transformer for event
-        processing, binds any EventLog or StreamChannel instances in
-        the projection, and merges the projection into `extensions`.
-        """
-        if transformer_requires_async(transformer) and not self._is_async:
-            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}"
-            )
-        self._transformers.append(transformer)
-        is_native = bool(getattr(transformer, "_native", False))
-        self._bind_and_wire(projection, is_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())
-        on_register = getattr(transformer, "_on_register", None)
-        if on_register is not None:
-            on_register(self)
-
-    def transformer_by_key(self, key: str) -> StreamTransformer | None:
-        """Return the transformer that owns the projection at `key`, if any."""
-        return self._transformer_by_key.get(key)
-
-    def push(self, event: ProtocolEvent) -> None:
-        """Route an event through all transformers, then append to the main log.
-
-        Each transformer's `process()` is called in registration order
-        — except when the transformer has `scope_exact = True` (the
-        default) and the event's namespace differs from the mux's
-        `scope`, in which case the transformer is skipped. Transformers
-        that need to see cross-scope events opt out by setting
-        `scope_exact = False` (e.g. `SubgraphTransformer`).
-
-        If any transformer returns False, the event is suppressed from
-        the main log, but transformers that already saw it keep their
-        side effects.
-
-        Seq is assigned right before an event enters the main log, not
-        before the transformer pipeline runs. This ensures that events
-        auto-forwarded from StreamChannels during `process()` get
-        earlier seq numbers than the original event, preserving
-        monotonic ordering in the log.
-
-        Args:
-            event: The protocol event to dispatch.
-        """
-        ns = tuple(event["params"]["namespace"])
-        in_scope = ns == self.scope
-        keep = True
-        for transformer in self._transformers:
-            if transformer.scope_exact and not in_scope:
-                continue
-            if not transformer.process(event):
-                keep = False
-        if keep:
-            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.
-
-        EventLogs and StreamChannels discovered in transformer
-        projections are auto-closed after `finalize()` runs —
-        transformers don't need to close them manually. If any
-        transformer's `finalize()` raises, the remaining transformers,
-        projections, and the main log are still closed; the first error
-        is re-raised after cleanup completes.
-
-        Raises:
-            BaseException: The first error raised by a transformer's
-                `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 log in self._logs:
-            if not log._closed:
-                log.close()
-        for ch in self._channels:
-            if not ch._log._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.
-
-        EventLogs and StreamChannels discovered in transformer
-        projections are auto-failed — transformers don't need to fail
-        them manually. If any transformer's `fail()` raises, the
-        remaining transformers, projections, and the main log are still
-        failed.
-
-        Args:
-            err: The exception that ended the run.
-        """
-        for transformer in self._transformers:
-            try:
-                transformer.fail(err)
-            except BaseException:
-                pass
-        for log in self._logs:
-            if not log._closed:
-                log.fail(err)
-        for ch in self._channels:
-            if not ch._log._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 — except when the transformer
-        has `scope_exact = True` and the event's namespace differs from
-        `self.scope`, in which case it is skipped. A slow `aprocess`
-        serializes the pipeline by design — that's the guarantee that
-        lets a later transformer (or a synchronous consumer) see the
-        result of the async work. For decoupled work, use `schedule()`
-        from inside `process` / `aprocess` instead.
-
-        The main log append is a non-blocking `push` — matching v1's
-        `put_nowait` shape. Memory is bounded by caller pace via the
-        caller-driven pump; see `EventLog` for the full tradeoff story.
-
-        Args:
-            event: The protocol event to dispatch.
-        """
-        ns = tuple(event["params"]["namespace"])
-        in_scope = ns == self.scope
-        keep = True
-        for transformer in self._transformers:
-            if transformer.scope_exact and not in_scope:
-                continue
-            if not await transformer.aprocess(event):
-                keep = False
-        if keep:
-            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 logs, channels, and the main event log.
-
-        If any scheduled task raised under `on_error="raise"`, or any
-        transformer's `afinalize` raises, the exception propagates.
-        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 log in self._logs:
-            if not log._closed:
-                log.close()
-        for ch in self._channels:
-            if not ch._log._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 logs, 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 log in self._logs:
-            if not log._closed:
-                log.fail(err)
-        for ch in self._channels:
-            if not ch._log._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], *, is_native: bool = False
-    ) -> None:
-        """Bind and wire EventLog / StreamChannel instances in a projection.
-
-        `is_native` controls wire naming: native transformer channels
-        emit events with `method` equal to the channel name, while
-        non-native channels get a `custom:` prefix to keep user-defined
-        projections from colliding with built-in method names.
-        """
-        for value in projection.values():
-            if isinstance(value, StreamChannel):
-                value._bind(is_async=self._is_async)
-                self._channels.append(value)
-                channel_name = value.name
-
-                def _make_forward(name: str, native: bool) -> Callable[[Any], None]:
-                    def _forward(item: Any) -> None:
-                        self._forward(name, item, native=native)
-
-                    return _forward
-
-                value._wire(_make_forward(channel_name, is_native))
-            elif isinstance(value, EventLog):
-                value._bind(is_async=self._is_async)
-                self._logs.append(value)
-
-    def _forward(self, channel_name: str, item: Any, *, native: bool) -> 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 the main event log but are not passed through
-        transformers' `process()` methods.
-
-        Native transformers emit with `method` equal to the channel
-        name (e.g. `"lifecycle"`); non-native transformers get a
-        `custom:` prefix so user-defined projections can't collide
-        with built-in method names.
-        """
-        self._seq += 1
-        method = channel_name if native else f"custom:{channel_name}"
-        event: ProtocolEvent = {
-            "type": "event",
-            "seq": self._seq,
-            "method": method,
-            "params": {
-                "namespace": [],
-                "timestamp": int(time.time() * 1000),
-                "data": item,
-            },
-        }
-        self._events.push(event)
@@ -1,303 +0,0 @@
-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 StreamMux.
-    Consumers that need a total order across 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 (EventLogs, StreamChannels, promises,
-    etc.).
-
-    Set `_native = True` on a transformer to have its projection keys
-    exposed as direct attributes on the run stream (in addition to
-    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.
-    EventLog and StreamChannel instances in the projection dict are
-    auto-closed / auto-failed by the mux, so most transformers don't
-    need `finalize` or `fail` at all.
-
-    Transformers that need async work pick the async lane by:
-
-    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, a subgraph's namespace tuple inside a mini-mux.
-            Set at construction from the mux's scope (each factory is
-            called as `factory(scope)`). Transformers that only care
-            about events at their own namespace compare against
-            `self.scope`; subgraph-aware transformers can treat it as
-            a parent path.
-        scope_exact: If True (the default), the mux only calls
-            `process` / `aprocess` for events whose namespace equals
-            `self.scope` — user transformers get scope-scoped events
-            for free with no boilerplate. Set False for transformers
-            that need to see events across scopes (e.g.
-            `SubgraphTransformer` forwards deeper events into child
-            mini-muxes).
-        requires_async: Explicit opt-in for transformers that need a
-            running event loop but don't override any async method (for
-            example, transformers that call `schedule()` from a sync
-            `process`). The mux also auto-detects the async lane when
-            `aprocess`, `afinalize`, or `afail` is overridden.
-    """
-
-    requires_async: ClassVar[bool] = False
-    scope_exact: ClassVar[bool] = True
-
-    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, the subgraph's namespace inside a
-                mini-mux. 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 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 EventLogs, resolve promises, or perform other
-        teardown. StreamChannel instances are auto-closed by the mux.
-        """
-
-    async def afinalize(self) -> None:
-        """Called when the run ends normally (async lane).
-
-        By the time this runs, the mux has already awaited every task
-        started via `schedule()`, so EventLogs 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 EventLogs, reject promises, or perform other
-        teardown. StreamChannel instances 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`).
-
-    Args:
-        transformer: The transformer to inspect.
-
-    Returns:
-        True if the transformer cannot run under sync `stream()`.
-    """
-    if transformer.requires_async:
-        return True
-    cls = type(transformer)
-    for name in ("aprocess", "afinalize", "afail"):
-        if getattr(cls, name) is not getattr(StreamTransformer, name):
-            return True
-    return False
@@ -1,408 +0,0 @@
-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 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 BaseRunStream:
-    """Shared shape for any object that wraps a `StreamMux`.
-
-    Root (`GraphRunStream` / `AsyncGraphRunStream`) and scoped
-    (`SubgraphRunStream`) streams both compose a `StreamMux`. The mux
-    owns the projections — `values`, `messages`, `subgraphs`, and any
-    user-registered keys — all exposed via `extensions`. Native
-    projections (`_native = True`) are also bound as direct attributes
-    (`run.values`, `run.messages`, …) for ergonomics.
-
-    Raw iteration (`for event in run` / `async for event in run`) and
-    the `interleave(...)` helper both live here so every subclass
-    behaves consistently. Subclasses only add pump ownership, scope
-    metadata, or sync/async flavor.
-    """
-
-    def __init__(self, mux: StreamMux) -> None:
-        self._mux = mux
-        self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
-        for key in mux.native_keys:
-            setattr(self, key, mux.extensions[key])
-
-    @property
-    def _values_transformer(self) -> ValuesTransformer:
-        """Look up the `ValuesTransformer` registered on this mux.
-
-        `output` / `interrupted` / `interrupts` need scalar state from
-        the `ValuesTransformer` without threading it through the
-        constructor. Raises if none is registered — `stream_v2` /
-        `astream_v2` always register one, so hitting this path means
-        the caller assembled the mux themselves and forgot.
-        """
-        from langgraph.stream.transformers import ValuesTransformer
-
-        for t in self._mux._transformers:
-            if isinstance(t, ValuesTransformer):
-                return t
-        raise RuntimeError(
-            "No ValuesTransformer is registered on this mux — "
-            "`output` / `interrupted` / `interrupts` are unavailable."
-        )
-
-    def __iter__(self) -> Iterator[ProtocolEvent]:
-        """Sync iteration of protocol events on this mux's main log.
-
-        Raises at the EventLog level if the mux is async-bound.
-        """
-        return iter(self._mux._events)
-
-    def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
-        """Async iteration of protocol events on this mux's main log.
-
-        Raises at the EventLog level if the mux is sync-bound.
-        """
-        return self._mux._events.__aiter__()
-
-    def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
-        """Iterate multiple projections round-robin, yielding ``(name, item)``.
-
-        Each turn advances one projection's cursor; when a cursor's
-        buffer is empty, pulling from it drives the pump once, which
-        fans out to every subscribed projection log. Projections whose
-        items aren't consumed on this turn sit in their own buffers
-        only until the next turn reaches them, bounding memory by the
-        skew between projection rates rather than letting any single
-        log grow to the full run length.
-
-        Projections are exhausted independently; a projection that
-        finishes early drops out of the rotation while others
-        continue. The overall iterator ends once all named projections
-        are done.
-
-        Args:
-            *names: Projection keys to interleave. Must match keys in
-                `extensions`.
-
-        Yields:
-            `(name, item)` tuples in round-robin order across the named
-            projections.
-
-        Raises:
-            KeyError: If a name doesn't match a registered projection.
-
-        Example:
-            ```python
-            for name, item in run.interleave("messages", "values"):
-                if name == "messages":
-                    print("msg:", item)
-                else:
-                    print("val:", item)
-            ```
-        """
-        cursors: dict[str, Iterator[Any]] = {
-            name: iter(self.extensions[name]) for name in names
-        }
-        done: set[str] = set()
-        while len(done) < len(cursors):
-            for name, cursor in cursors.items():
-                if name in done:
-                    continue
-                try:
-                    item = next(cursor)
-                except StopIteration:
-                    done.add(name)
-                    continue
-                yield (name, item)
-
-
-class GraphRunStream(BaseRunStream):
-    """Sync run stream with caller-driven pumping.
-
-    The caller's iteration on any projection (`values`, `messages`,
-    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.
-    """
-
-    def __init__(
-        self,
-        graph_iter: Iterator[Any],
-        mux: StreamMux,
-    ) -> None:
-        """Initialize the run stream.
-
-        Args:
-            graph_iter: Pull-based iterator over the graph's stream.
-            mux: The StreamMux owning projections and the main log.
-                Must have a `ValuesTransformer` registered for
-                `output` / `interrupted` / `interrupts` to work.
-        """
-        super().__init__(mux)
-        self._graph_iter = graph_iter
-        self._exhausted = False
-        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.
-        """
-        if self._exhausted:
-            return False
-        try:
-            part = next(self._graph_iter)
-        except StopIteration:
-            self._mux.close()
-            self._exhausted = True
-            return False
-        except Exception as e:
-            self._mux.fail(e)
-            self._exhausted = True
-            return False
-        self._mux.push(convert_to_protocol_event(part))
-        return True
-
-    def abort(self) -> None:
-        """Stop the run early.
-
-        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)
-        err = self._values_transformer.error
-        if err 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)
-        err = self._values_transformer.error
-        if err 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)
-        err = self._values_transformer.error
-        if err is not None:
-            raise err
-        return self._values_transformer._interrupts
-
-
-class AsyncGraphRunStream(BaseRunStream):
-    """Async run stream with caller-driven pumping.
-
-    Async iteration on any projection drives the graph forward — there
-    is no background task. Concurrent consumers share a single-flight
-    pump via an `asyncio.Lock`, so each awaiting cursor contributes
-    one event per acquisition. Backpressure comes from the logs: when
-    a subscribed log's buffer reaches `maxlen`, `apush` awaits the
-    subscriber to drain, which holds back the pump and paces the
-    graph.
-
-    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],
-        mux: StreamMux,
-    ) -> None:
-        """Initialize the async run stream.
-
-        Args:
-            graph_aiter: Async iterator over the graph's stream.
-            mux: The StreamMux owning projections and the main log.
-                Must have a `ValuesTransformer` registered for
-                `output` / `interrupted` / `interrupts` to work.
-        """
-        super().__init__(mux)
-        self._graph_aiter = graph_aiter
-        self._exhausted = False
-        self._pump_cond = asyncio.Condition()
-        self._pumping = False
-        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:
-                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__()
-            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
-            await self._mux.apush(convert_to_protocol_event(part))
-            return True
-        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
@@ -1,115 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import AsyncIterator, Callable, Iterator
-from typing import Generic, TypeVar
-
-from langgraph.stream._event_log import EventLog
-
-T = TypeVar("T")
-
-
-class StreamChannel(Generic[T]):
-    """A named projection channel with optional protocol auto-forwarding.
-
-    Wraps an event log and declares a protocol channel name. When the
-    StreamMux detects a StreamChannel in a transformer's `init()`
-    return value, it automatically wires every `push()` to inject a
-    `ProtocolEvent` into the main event stream using the channel's
-    name as the method.
-
-    Auto-forwarded events bypass the transformer pipeline — other
-    transformers' `process()` / `aprocess()` methods do not see
-    `custom:<name>` events produced by a channel push. This prevents a
-    transformer that pushes to its own channel during `process()` from
-    re-triggering itself, but it also means filter- or tap-style
-    transformers cannot observe channel output from peer transformers.
-    Consumers that need that should iterate the main event stream.
-
-    In-process consumers iterate the channel directly (`for item in ch`
-    or `async for item in ch`). Remote SDK clients subscribe via
-    `session.subscribe("custom:<channelName>")`.
-
-    Like EventLog, a StreamChannel starts unbound. The mux calls
-    `_bind(is_async)` during registration so the correct iteration
-    protocol is available by the time user code sees it.
-
-    Lifecycle (`_close` / `_fail`) is managed by the mux — transformers
-    using only StreamChannels don't need `finalize` or `fail` hooks.
-    """
-
-    def __init__(
-        self, name: str, *, maxlen: int | None = None, retain: bool = False
-    ) -> None:
-        """Initialize the channel with an empty inner log.
-
-        Args:
-            name: The protocol channel name used for auto-forwarded
-                events (`custom:<name>` on the wire).
-            maxlen: Optional retention cap on the inner EventLog. See
-                `EventLog.__init__` for semantics.
-            retain: If True, the inner log retains pushes before any
-                consumer subscribes — needed for channels whose
-                consumer iterates after events have already flowed
-                (e.g. `lifecycle` inspected after draining `values`).
-        """
-        self.name = name
-        self._log: EventLog[T] = EventLog(maxlen=maxlen, retain=retain)
-        self._wire_fn: Callable[[T], None] | None = None
-
-    def _bind(self, *, is_async: bool) -> None:
-        """Bind the underlying event log to sync or async mode.
-
-        Args:
-            is_async: True for async iteration, False for sync.
-        """
-        self._log._bind(is_async=is_async)
-
-    def push(self, item: T) -> None:
-        """Append an item to the log and auto-forward if wired.
-
-        Args:
-            item: The item to push.
-        """
-        self._log.push(item)
-        if self._wire_fn is not None:
-            self._wire_fn(item)
-
-    # ------------------------------------------------------------------
-    # Mux lifecycle hooks (not called by transformers directly)
-    # ------------------------------------------------------------------
-
-    def _wire(self, fn: Callable[[T], None]) -> None:
-        """Install the auto-forward callback (called by StreamMux)."""
-        self._wire_fn = fn
-
-    def _close(self) -> None:
-        """Close the underlying log (called by StreamMux on run end)."""
-        self._log.close()
-
-    def _fail(self, err: BaseException) -> None:
-        """Fail the underlying log (called by StreamMux on run error)."""
-        self._log.fail(err)
-
-    # ------------------------------------------------------------------
-    # Iteration — delegates to the inner event log (multi-cursor)
-    # ------------------------------------------------------------------
-
-    def __iter__(self) -> Iterator[T]:
-        return iter(self._log)
-
-    def __aiter__(self) -> AsyncIterator[T]:
-        return self._log.__aiter__()
-
-    def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
-        """Fan out the channel into `n` independent sync iterators.
-
-        Delegates to the underlying EventLog's `tee()`.
-        """
-        return self._log.tee(n)
-
-    def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
-        """Fan out the channel into `n` independent async iterators.
-
-        Delegates to the underlying EventLog's `atee()`.
-        """
-        return self._log.atee(n)
@@ -1,585 +0,0 @@
-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 CheckpointRef, MessagesData
-from typing_extensions import TypedDict
-
-from langgraph.errors import GraphInterrupt
-from langgraph.stream._event_log import EventLog
-from langgraph.stream._types import ProtocolEvent, StreamTransformer
-from langgraph.stream.run_stream import BaseRunStream
-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__)
-
-
-SubgraphStatus = Literal["started", "completed", "failed", "interrupted"]
-_TERMINAL_STATUSES: frozenset[SubgraphStatus] = frozenset(
-    {"completed", "failed", "interrupted"}
-)
-
-
-def _is_new_direct_child(
-    ns: tuple[str, ...],
-    scope: tuple[str, ...],
-    seen: set[tuple[str, ...]] | dict[tuple[str, ...], Any],
-) -> bool:
-    """Return True iff `ns` is a direct child of `scope` not yet seen.
-
-    Shared by `SubgraphTransformer` (in-process handle discovery) and
-    `LifecycleTransformer` (wire event emission) so the two can't
-    disagree on what counts as a new subgraph.
-    """
-    return len(ns) == len(scope) + 1 and ns[:-1] == scope and ns not in seen
-
-
-def _parse_ns_segment(segment: str) -> tuple[str, str | None]:
-    """Split `node_name:task_id` into (node_name, task_id).
-
-    Task ids are present when Pregel spawned the subgraph as a task;
-    absent on synthesized namespaces (tests, hand-crafted events).
-    """
-    node_name, sep, task_id = segment.partition(":")
-    if not sep:
-        return segment, None
-    return node_name, task_id or None
-
-
-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`).
-
-    `scope` (inherited from `StreamTransformer`) is the namespace the
-    transformer captures values for. `()` matches the root graph;
-    subgraph mini-muxes pass their subgraph's namespace, so each
-    instance sees only its own level.
-    """
-
-    _native = True
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: EventLog[dict[str, Any]] = EventLog()
-        self._latest: dict[str, Any] | None = None
-        self._interrupted = False
-        self._interrupts: list[Any] = []
-
-    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:
-        # Namespace filtering is handled by the mux via `scope_exact`.
-        if event["method"] != "values":
-            return True
-        params = event["params"]
-        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.
-
-    `scope` (inherited from `StreamTransformer`) is the namespace the
-    transformer captures messages for. `()` matches the root graph;
-    subgraph mini-muxes pass their subgraph's namespace, so each
-    instance sees only its own level.
-
-    Native transformer — the `messages` projection is exposed as a
-    direct attribute on the run stream.
-    """
-
-    _native = True
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: EventLog[ChatModelStream] = EventLog()
-        # Correlate protocol events back to a ChatModelStream by run_id
-        # (attached to the event's metadata by StreamMessagesHandler).
-        self._by_run: dict[str, ChatModelStream] = {}
-        self._pump_fn: Callable[[], bool] | None = None
-        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
-
-    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:
-        # Namespace filtering is handled by the mux via `scope_exact`.
-        if event["method"] != "messages":
-            return True
-        params = event["params"]
-
-        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=list(self.scope),
-                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=list(self.scope),
-            node=node,
-            message_id=message.id,
-        )
-        for evt in message_to_events(message, message_id=message.id):
-            stream.dispatch(evt)
-        self._log.push(stream)
-
-    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()
-
-
-class SubgraphRunStream(BaseRunStream):
-    """Scoped view of a single nested subgraph execution.
-
-    Yielded on `run.subgraphs` (or `parent.subgraphs` for grandchildren)
-    when a nested `Pregel` spawns. Wraps a mini-`StreamMux` built with
-    the same transformer factories as the root mux, so `.values`,
-    `.messages`, `.subgraphs` are populated by the standard
-    transformers scoped to this handle's namespace — no duplicated
-    routing logic. The mini-mux borrows the root's pump via
-    `make_child`'s pump inheritance, so any cursor on a subagent
-    projection drives the whole run forward.
-
-    Handle fields:
-
-    - `path`: the namespace tuple — stable for the life of the handle.
-    - `graph_name` / `trigger_call_id`: parsed from the namespace
-      segment at discovery (`node_name:task_id`).
-    - `status`: `started` on discovery; advances to `completed` when
-      the parent mux closes, or `failed` / `interrupted` when it
-      errors.
-    - `error`: set on terminal error.
-    - `checkpoint`: unused by the current discovery path — kept for
-      compatibility with consumers that inspect it.
-
-    `.output` is a snapshot of the latest values seen at this
-    namespace — it doesn't drive the pump (unlike root's
-    `GraphRunStream.output`), because advancing a subgraph to
-    completion is only meaningful as part of advancing the whole run.
-    """
-
-    def __init__(
-        self,
-        path: tuple[str, ...],
-        mux: StreamMux,
-        *,
-        graph_name: str | None = None,
-        trigger_call_id: str | None = None,
-    ) -> None:
-        super().__init__(mux)
-        self.path: tuple[str, ...] = path
-        self.graph_name: str | None = graph_name
-        self.trigger_call_id: str | None = trigger_call_id
-        self.status: SubgraphStatus = "started"
-        self.error: str | None = None
-        self.checkpoint: CheckpointRef | None = None
-
-    @property
-    def output(self) -> dict[str, Any] | None:
-        """Latest values snapshot at this namespace, or `None`.
-
-        Snapshot-only — iterating other projections or the root's
-        `.output` is what drives the pump.
-        """
-        values_t = self._mux.transformer_by_key("values")
-        if isinstance(values_t, ValuesTransformer):
-            return values_t._latest
-        return None
-
-
-class SubgraphTransformer(StreamTransformer):
-    """Discover subgraphs and route events into per-subgraph mini-muxes.
-
-    Thin dispatcher. At its own `scope` (inherited from
-    `StreamTransformer`, determined by the enclosing mux), it watches
-    for the first event at exactly one namespace level deeper to
-    discover a direct child. Each discovered child gets its own
-    `SubgraphRunStream` backed by a mini-`StreamMux` — built via
-    `parent_mux.make_child(path)`, so the same factory list produces
-    fresh transformer instances at the child's scope.
-
-    Every incoming event that falls under one of the direct children
-    (ns starts with a child's `path`) is forwarded into that child's
-    mini-mux via `push`. The standard transformers in that mini-mux
-    (`ValuesTransformer`, `MessagesTransformer`, and another
-    `SubgraphTransformer` for grandchildren) handle the rest. No
-    duplicated routing or assembly logic.
-
-    Discovery is method-agnostic: the first event of any mode whose
-    namespace places it directly below `scope` spawns the handle.
-    `graph_name` and `trigger_call_id` are parsed from the namespace
-    segment, which encodes `node_name:task_id`.
-
-    Terminal status for each handle is set by the parent mux's
-    `close` / `fail` path. `finalize` transitions still-open handles
-    to `completed`; `fail` transitions them to `failed` or
-    `interrupted` depending on the error.
-
-    Native transformer — `subgraphs` exposes the direct-children log.
-
-    `scope_exact = False`: this transformer sees events at any
-    namespace, because it forwards out-of-scope events to the matching
-    direct-child mini-mux.
-    """
-
-    _native = True
-    scope_exact = False
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._root_log: EventLog[SubgraphRunStream] = EventLog()
-        # Direct children only (namespace = scope + one segment).
-        self._by_ns: dict[tuple[str, ...], SubgraphRunStream] = {}
-        self._mux: StreamMux | None = None
-
-    def init(self) -> dict[str, Any]:
-        return {"subgraphs": self._root_log}
-
-    def _on_register(self, mux: StreamMux) -> None:
-        """Capture the enclosing mux so we can build child mini-muxes."""
-        self._mux = mux
-
-    def process(self, event: ProtocolEvent) -> bool:
-        ns = tuple(event["params"]["namespace"])
-        depth = len(self.scope)
-
-        # 1. Discover: first-seen direct-child namespace registers a
-        #    handle. Any event method triggers discovery — no dedicated
-        #    channel.
-        if _is_new_direct_child(ns, self.scope, self._by_ns):
-            self._on_started(ns)
-
-        # 2. Forward the event to the matching direct-child mini-mux.
-        #    Prefix-match: ns must start with some child's path.
-        direct_child_ns = ns[: depth + 1] if len(ns) > depth else None
-        if direct_child_ns is not None and direct_child_ns in self._by_ns:
-            self._by_ns[direct_child_ns]._mux.push(event)
-
-        return True
-
-    def _on_started(self, ns: tuple[str, ...]) -> None:
-        # `_on_register` is called by the mux during registration, which
-        # happens before any event can be dispatched — so this should
-        # always be set by the time we process an event.
-        assert self._mux is not None, (
-            "SubgraphTransformer processed an event before _on_register; "
-            "transformer registration ordering is broken."
-        )
-        graph_name, trigger_call_id = _parse_ns_segment(ns[-1])
-        child_mux = self._mux.make_child(ns)
-        handle = SubgraphRunStream(
-            path=ns,
-            mux=child_mux,
-            graph_name=graph_name,
-            trigger_call_id=trigger_call_id,
-        )
-        self._by_ns[ns] = handle
-        self._root_log.push(handle)
-
-    @staticmethod
-    def _close_handle_mux(handle: SubgraphRunStream) -> None:
-        # Idempotent close — mux.close() runs finalize on its transformers
-        # (which cascades through grandchildren) and closes projection logs.
-        if not handle._mux._events._closed:
-            try:
-                handle._mux.close()
-            except Exception:
-                logger.warning(
-                    "Error closing subgraph mini-mux at %s; subscribers "
-                    "may not see a clean close.",
-                    handle.path,
-                    exc_info=True,
-                )
-
-    def finalize(self) -> None:
-        """Transition any still-open direct children to `completed`.
-
-        Subgraph interrupts surface as a values event with a populated
-        `interrupts` field rather than as an exception at the graph
-        boundary — the parent pump exhausts normally and `finalize`
-        runs the close path. Inspect each child's `ValuesTransformer`
-        to distinguish "completed cleanly" from "interrupted".
-        """
-        for handle in self._by_ns.values():
-            if handle.status not in _TERMINAL_STATUSES:
-                values_t = handle._mux.transformer_by_key("values")
-                if isinstance(values_t, ValuesTransformer) and values_t._interrupted:
-                    handle.status = "interrupted"
-                else:
-                    handle.status = "completed"
-            self._close_handle_mux(handle)
-
-    def fail(self, err: BaseException) -> None:
-        """Transition any still-open direct children to `failed` / `interrupted`."""
-        is_interrupt = isinstance(err, GraphInterrupt)
-        terminal: SubgraphStatus = "interrupted" if is_interrupt else "failed"
-        error_str = None if is_interrupt else str(err)
-        for handle in self._by_ns.values():
-            if handle.status not in _TERMINAL_STATUSES:
-                handle.status = terminal
-                if error_str is not None and handle.error is None:
-                    handle.error = error_str
-            if not handle._mux._events._closed:
-                try:
-                    handle._mux.fail(err)
-                except Exception:
-                    logger.warning(
-                        "Error failing subgraph mini-mux at %s; subscribers "
-                        "may not see the terminal error.",
-                        handle.path,
-                        exc_info=True,
-                    )
-
-
-class LifecyclePayload(TypedDict, total=False):
-    """Payload of a lifecycle event emitted by `LifecycleTransformer`."""
-
-    event: SubgraphStatus
-    namespace: list[str]
-    graph_name: str | None
-    trigger_call_id: str | None
-    error: str | None
-
-
-class LifecycleTransformer(StreamTransformer):
-    """Synthesize subgraph lifecycle events from observed namespaces.
-
-    Observes the same namespace signal `SubgraphTransformer` uses for
-    in-process discovery and emits `started` / `completed` / `failed`
-    / `interrupted` payloads onto its `lifecycle` channel. Consumers
-    subscribed to that channel see the events in-process; wire
-    consumers receive them as protocol events with `method:
-    "lifecycle"` (unprefixed because this transformer is `_native`).
-
-    No `running` event: the ns-discovery signal only fires once a
-    subgraph has emitted output, so `started` already implies
-    execution. Consumers needing finer-grained task-start visibility
-    should read the `tasks` stream mode alongside.
-
-    No root `started`: the run object itself signals run start.
-
-    Terminal events are synthesized — `finalize` emits `completed` for
-    still-open handles; `fail` emits `failed` or `interrupted`
-    depending on whether the error is a `GraphInterrupt`.
-
-    `scope_exact = False` so the transformer sees events at any
-    namespace (needed for discovery of direct children).
-    """
-
-    _native = True
-    scope_exact = False
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        # retain=True: lifecycle events are low-volume and consumers
-        # commonly inspect them after draining `values`; without
-        # retention those pushes would be dropped.
-        self._channel: StreamChannel[LifecyclePayload] = StreamChannel(
-            "lifecycle", retain=True
-        )
-        self._seen: set[tuple[str, ...]] = set()
-        self._open: set[tuple[str, ...]] = set()
-
-    def init(self) -> dict[str, Any]:
-        return {"lifecycle": self._channel}
-
-    def process(self, event: ProtocolEvent) -> bool:
-        ns = tuple(event["params"]["namespace"])
-        if _is_new_direct_child(ns, self.scope, self._seen):
-            self._emit_started(ns)
-        return True
-
-    def _emit_started(self, ns: tuple[str, ...]) -> None:
-        graph_name, trigger_call_id = _parse_ns_segment(ns[-1])
-        self._seen.add(ns)
-        self._open.add(ns)
-        payload: LifecyclePayload = {
-            "event": "started",
-            "namespace": list(ns),
-        }
-        if graph_name:
-            payload["graph_name"] = graph_name
-        if trigger_call_id is not None:
-            payload["trigger_call_id"] = trigger_call_id
-        self._channel.push(payload)
-
-    def finalize(self) -> None:
-        """Emit `completed` for every still-open direct child."""
-        for ns in list(self._open):
-            self._channel.push({"event": "completed", "namespace": list(ns)})
-        self._open.clear()
-
-    def fail(self, err: BaseException) -> None:
-        """Emit `failed` / `interrupted` for every still-open direct child.
-
-        Closes the channel after emitting rather than letting the mux
-        auto-fail it — the "failed" payload is the signal to
-        consumers, so they should be able to iterate it. A failed
-        channel would raise on iteration and hide the events that just
-        got pushed.
-        """
-        is_interrupt = isinstance(err, GraphInterrupt)
-        event_type: SubgraphStatus = "interrupted" if is_interrupt else "failed"
-        error_str = None if is_interrupt else str(err)
-        for ns in list(self._open):
-            payload: LifecyclePayload = {
-                "event": event_type,
-                "namespace": list(ns),
-            }
-            if error_str is not None:
-                payload["error"] = error_str
-            self._channel.push(payload)
-        self._open.clear()
-        self._channel._close()
@@ -116,13 +116,7 @@ def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:


 StreamMode = Literal[
-    "values",
-    "updates",
-    "checkpoints",
-    "tasks",
-    "debug",
-    "messages",
-    "custom",
+    "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
 ]
 """How the stream method should emit outputs.

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

 [project]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -24,7 +24,7 @@ classifiers = [
    'Programming Language :: Python :: 3.13',
 ]
 dependencies = [
-    "langchain-core==1.3.2",
+    "langchain-core>=1.3.0,<2",
    "langgraph-checkpoint>=2.1.0,<5.0.0",
    "langgraph-sdk>=0.3.0,<0.4.0",
    "langgraph-prebuilt>=1.0.9,<1.1.0",
@@ -0,0 +1,290 @@
+"""Benchmark: add_messages fast-path optimizations.
+
+Both implementations are inlined so the benchmark is self-contained and
+immune to import-cache or installed-vs-local confusion.
+
+Run directly:
+    python tests/test_add_messages_benchmark.py
+
+Or via pytest (correctness only, numbers printed to stdout):
+    pytest tests/test_add_messages_benchmark.py -s -v
+"""
+
+import statistics
+import time
+import tracemalloc
+import uuid
+from typing import cast
+
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    BaseMessageChunk,
+    HumanMessage,
+    RemoveMessage,
+    convert_to_messages,
+    message_chunk_to_message,
+)
+
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
+
+# ── original implementation (pre-optimisation) ────────────────────────────────
+
+
+def _add_messages_original(left, right):
+    remove_all_idx = None
+    if not isinstance(left, list):
+        left = [left]
+    if not isinstance(right, list):
+        right = [right]
+    left = [
+        message_chunk_to_message(cast(BaseMessageChunk, m))
+        for m in convert_to_messages(left)
+    ]
+    right = [
+        message_chunk_to_message(cast(BaseMessageChunk, m))
+        for m in convert_to_messages(right)
+    ]
+    for m in left:
+        if m.id is None:
+            m.id = str(uuid.uuid4())
+    for idx, m in enumerate(right):
+        if m.id is None:
+            m.id = str(uuid.uuid4())
+        if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
+            remove_all_idx = idx
+    if remove_all_idx is not None:
+        return right[remove_all_idx + 1 :]
+    merged = left.copy()
+    merged_by_id = {m.id: i for i, m in enumerate(merged)}
+    ids_to_remove = set()
+    for m in right:
+        if (existing_idx := merged_by_id.get(m.id)) is not None:
+            if isinstance(m, RemoveMessage):
+                ids_to_remove.add(m.id)
+            else:
+                ids_to_remove.discard(m.id)
+                merged[existing_idx] = m
+        else:
+            if isinstance(m, RemoveMessage):
+                raise ValueError(
+                    f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
+                )
+            merged_by_id[m.id] = len(merged)
+            merged.append(m)
+    return [m for m in merged if m.id not in ids_to_remove]
+
+
+# ── optimised implementation ──────────────────────────────────────────────────
+
+
+def _add_messages_optimized(left, right):
+    if not isinstance(left, list):
+        left = [left]
+    if not isinstance(right, list):
+        right = [right]
+
+    # Optimisation 1: skip conversion + ID assignment on left when it already
+    # contains fully-resolved BaseMessage objects (the common case after the
+    # first call, since add_messages always returns list[BaseMessage] with IDs).
+    if (
+        left
+        and isinstance(left[0], BaseMessage)
+        and not isinstance(left[0], BaseMessageChunk)
+    ):
+        left = cast(list[BaseMessage], left)
+    else:
+        left = [
+            message_chunk_to_message(cast(BaseMessageChunk, m))
+            for m in convert_to_messages(left)
+        ]
+        for m in left:
+            if m.id is None:
+                m.id = str(uuid.uuid4())
+
+    # always normalise right — it's fresh external input
+    right = [
+        message_chunk_to_message(cast(BaseMessageChunk, m))
+        for m in convert_to_messages(right)
+    ]
+    remove_all_idx = None
+    has_remove = False
+    for idx, m in enumerate(right):
+        if m.id is None:
+            m.id = str(uuid.uuid4())
+        if isinstance(m, RemoveMessage):
+            has_remove = True
+            if m.id == REMOVE_ALL_MESSAGES:
+                remove_all_idx = idx
+
+    if remove_all_idx is not None:
+        return right[remove_all_idx + 1 :]
+
+    # Optimisation 2: pure-append fast path — no removals and no ID overlaps.
+    # Builds one set over left instead of copying left + building a full dict.
+    if not has_remove:
+        left_ids = {m.id for m in left}
+        if not any(m.id in left_ids for m in right):
+            return left + right
+
+    # slow path: updates or removals present — full indexed merge
+    merged = left.copy()
+    merged_by_id = {m.id: i for i, m in enumerate(merged)}
+    ids_to_remove = set()
+    for m in right:
+        if (existing_idx := merged_by_id.get(m.id)) is not None:
+            if isinstance(m, RemoveMessage):
+                ids_to_remove.add(m.id)
+            else:
+                ids_to_remove.discard(m.id)
+                merged[existing_idx] = m
+        else:
+            if isinstance(m, RemoveMessage):
+                raise ValueError(
+                    f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
+                )
+            merged_by_id[m.id] = len(merged)
+            merged.append(m)
+    return [m for m in merged if m.id not in ids_to_remove]
+
+
+# ── helpers ───────────────────────────────────────────────────────────────────
+
+
+def _make_messages(n: int) -> list[BaseMessage]:
+    return [
+        (HumanMessage if i % 2 == 0 else AIMessage)(
+            content=f"message {i}", id=str(uuid.uuid4())
+        )
+        for i in range(n)
+    ]
+
+
+def _bench_time(fn, left, right, *, iters: int = 2_000) -> float:
+    """Return median latency in microseconds."""
+    for _ in range(100):
+        fn(list(left), list(right))
+    times = []
+    for _ in range(iters):
+        left_copy, right_copy = list(left), list(right)
+        t0 = time.perf_counter()
+        fn(left_copy, right_copy)
+        times.append(time.perf_counter() - t0)
+    return statistics.median(times) * 1e6
+
+
+def _bench_memory(fn, left, right) -> int:
+    """Return peak memory allocated during a single call (bytes)."""
+    # one warm-up so any lazy init is excluded
+    fn(list(left), list(right))
+    left_copy, right_copy = list(left), list(right)
+    tracemalloc.start()
+    tracemalloc.clear_traces()
+    fn(left_copy, right_copy)
+    _, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    return peak
+
+
+# ── scenarios ─────────────────────────────────────────────────────────────────
+
+SCENARIOS = [
+    ("pure append  1 → 1  msg", 1, 1, "append"),
+    ("pure append  10 → 1  msg", 10, 1, "append"),
+    ("pure append  100 → 1  msg", 100, 1, "append"),
+    ("pure append  1000 → 1  msg", 1000, 1, "append"),
+    ("pure append  1000 → 5  msgs", 1000, 5, "append"),
+    ("update existing  100 → 1  msg", 100, 1, "update"),
+    ("remove message  100 → 1  msg", 100, 1, "remove"),
+]
+
+
+def _make_inputs(n_left, n_right, mode):
+    left = _make_messages(n_left)
+    right = _make_messages(n_right)
+    if mode == "update":
+        right[0] = AIMessage(content="updated", id=left[0].id)
+    elif mode == "remove":
+        right = [RemoveMessage(id=left[0].id)]
+    return left, right
+
+
+# ── main output ───────────────────────────────────────────────────────────────
+
+COL = 36
+
+
+def run_benchmarks() -> None:
+    print()
+    print("=" * 88)
+    print("add_messages benchmark — time (µs, median of 2 000 iterations)")
+    print("=" * 88)
+    print(f"{'Scenario':<{COL}} {'Original':>10} {'Optimized':>11} {'Speedup':>8}")
+    print("-" * 88)
+
+    for label, n_left, n_right, mode in SCENARIOS:
+        left, right = _make_inputs(n_left, n_right, mode)
+        t_orig = _bench_time(_add_messages_original, left, right)
+        t_opt = _bench_time(_add_messages_optimized, left, right)
+        print(f"{label:<{COL}} {t_orig:>10.2f} {t_opt:>11.2f} {t_orig / t_opt:>7.2f}x")
+
+    print()
+    print("=" * 88)
+    print("add_messages benchmark — peak memory allocated per call (bytes)")
+    print("=" * 88)
+    print(f"{'Scenario':<{COL}} {'Original':>10} {'Optimized':>11} {'Reduction':>10}")
+    print("-" * 88)
+
+    for label, n_left, n_right, mode in SCENARIOS:
+        left, right = _make_inputs(n_left, n_right, mode)
+        m_orig = _bench_memory(_add_messages_original, left, right)
+        m_opt = _bench_memory(_add_messages_optimized, left, right)
+        reduction = (1 - m_opt / m_orig) * 100 if m_orig else 0.0
+        print(f"{label:<{COL}} {m_orig:>10,} {m_opt:>11,} {reduction:>9.1f}%")
+
+    print()
+    print("=" * 88)
+    print("Simulated long thread — 200 steps × 2 msgs appended per step")
+    print("=" * 88)
+    for name, fn in [
+        ("original", _add_messages_original),
+        ("optimized", _add_messages_optimized),
+    ]:
+        state: list = []
+        t0 = time.perf_counter()
+        for step in range(200):
+            new_msgs = [
+                HumanMessage(content=f"step {step} human", id=str(uuid.uuid4())),
+                AIMessage(content=f"step {step} ai", id=str(uuid.uuid4())),
+            ]
+            state = fn(state, new_msgs)
+        elapsed = (time.perf_counter() - t0) * 1_000
+        print(f"  {name:<12}  {elapsed:.2f} ms  ({len(state)} messages)")
+    print()
+
+
+# ── pytest entry-points ───────────────────────────────────────────────────────
+
+
+def test_add_messages_correctness():
+    """Optimised implementation must match original output for every scenario."""
+    for label, n_left, n_right, mode in SCENARIOS:
+        left, right = _make_inputs(n_left, n_right, mode)
+        expected = _add_messages_original(list(left), list(right))
+        actual = _add_messages_optimized(list(left), list(right))
+        assert len(actual) == len(expected), f"[{label}] length mismatch"
+        for a, b in zip(actual, expected):
+            assert type(a) is type(b), f"[{label}] type mismatch"
+            assert a.id == b.id, f"[{label}] id mismatch"
+            assert a.content == b.content, f"[{label}] content mismatch"
+
+
+def test_add_messages_benchmark(capsys):
+    run_benchmarks()
+    out = capsys.readouterr().out
+    assert "Speedup" in out
+    assert "Optimized" in out
+
+
+if __name__ == "__main__":
+    run_benchmarks()
@@ -2,13 +2,17 @@ import operator
 from collections.abc import Sequence

 import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.base import DELTA_SENTINEL

 from langgraph._internal._typing import MISSING
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels._delta import DeltaChannel
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
+from langgraph.graph.message import add_messages

 pytestmark = pytest.mark.anyio

@@ -117,3 +121,503 @@ def test_untracked_value() -> None:
    new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
    with pytest.raises(EmptyChannelError):
        new_channel.get()
+
+
+def test_delta_channel_basic_two_steps() -> None:
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.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 d1 is DELTA_SENTINEL
+
+    # Step 2: another message
+    ch.update([AIMessage(content="hello", id="a1")])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    # Full accumulated value is preserved in memory
+    assert len(ch.get()) == 2
+    assert ch.get()[0].content == "hi"
+    assert ch.get()[1].content == "hello"
+
+
+def test_delta_channel_from_checkpoint_writes_list() -> None:
+    """replay_writes on a fresh channel replays through the operator."""
+    from langchain_core.messages import AIMessage, HumanMessage
+
+    from langgraph.channels._delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", HumanMessage(content="bye", id="h2")),
+        ]
+    )
+    msgs = ch.get()
+    assert len(msgs) == 3
+    assert msgs[0].content == "hi"
+    assert msgs[1].content == "hello"
+    assert msgs[2].content == "bye"
+
+
+def test_delta_channel_from_checkpoint_backwards_compat() -> None:
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.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 DELTA_SENTINEL
+
+    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 d is DELTA_SENTINEL
+    # After overwrite, value is reset to only the new message
+    assert len(ch.get()) == 1
+    assert ch.get()[0].content == "new"
+
+
+def test_delta_channel_remove_message_and_replay() -> None:
+    """RemoveMessage must round-trip correctly when writes are replayed."""
+    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+
+    from langgraph.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
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", RemoveMessage(id="a1")),
+        ]
+    )
+    assert ch2.get() == [HumanMessage(content="hi", id="h1")]
+
+
+def test_delta_channel_update_by_id_and_replay() -> None:
+    """Updating a message by ID must round-trip correctly through writes replay."""
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.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
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="original", id="h1")),
+            ("t1", "messages", HumanMessage(content="updated", id="h1")),
+        ]
+    )
+    assert len(ch2.get()) == 1
+    assert ch2.get()[0].content == "updated"
+
+
+def test_delta_channel_checkpoint_returns_sentinel() -> None:
+    """checkpoint() always returns DELTA_SENTINEL regardless of state."""
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.channels._delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    assert ch.checkpoint() is DELTA_SENTINEL
+
+    from langchain_core.messages import HumanMessage
+
+    ch.update([HumanMessage(content="hi", id="h1")])
+    assert ch.checkpoint() is DELTA_SENTINEL
+
+
+def test_delta_channel_inmemory_saver_assembles_writes() -> None:
+    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.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 returns raw storage shape — channel_values stores DELTA_SENTINEL
+    # for delta channels; the reconstructed writes flow separately via
+    # saver._get_channel_writes_history.
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "messages" in saved.checkpoint["channel_values"]
+    assert saved.checkpoint["channel_values"]["messages"] is DELTA_SENTINEL
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
+
+
+def _delta_channel_with_type(operator, typ):
+    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
+    from typing import Annotated
+
+    from langgraph.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 DELTA_SENTINEL
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+
+    ch.update([{"a": 1}])
+    d1 = ch.checkpoint()
+    assert d1 is DELTA_SENTINEL
+
+    ch.update([{"b": 2}])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
+    """replay_writes on a fresh channel replays through a dict merge reducer."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", {"b": 2}),
+            ("t2", "files", {"c": 3}),
+        ]
+    )
+    assert ch.get() == {"a": 1, "b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_with_deletions() -> None:
+    """Dict reducer that treats None values as deletions works end-to-end (deepagents pattern)."""
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
+
+    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
+
+    # Delete file1, add file3
+    ch.update([{"file1.py": None, "file3.py": "content3"}])
+
+    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+    # Confirm writes reconstruction produces the same result
+    spec = _delta_channel_with_type(merge_files, dict)
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
+            ("t1", "files", {"file1.py": None, "file3.py": "content3"}),
+        ]
+    )
+    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+
+def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
+    """Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
+    from langgraph.types import Overwrite
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    ch.update([{"a": 1}])
+    ch.update([Overwrite({"b": 2, "c": 3})])
+
+    assert ch.get() == {"b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
+    """Overwrite(dict) embedded in replayed writes must reconstruct as dict."""
+    from langgraph.types import Overwrite
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", Overwrite({"x": 10, "y": 20})),
+            ("t2", "files", {"z": 30}),
+        ]
+    )
+    assert ch.get() == {"x": 10, "y": 20, "z": 30}
+
+
+def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
+    """DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`.
+
+    This is the shape the deepagents filesystem middleware uses for its
+    `files` field; without unwrapping NotRequired we'd fall through to `list`
+    and blow up on the first dict operator call.
+    """
+    from typing import Annotated
+
+    from typing_extensions import NotRequired
+
+    from langgraph.channels._delta import DeltaChannel
+    from langgraph.graph.state import _get_channel
+
+    def merge_dicts(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return dict(right)
+        return {**left, **right}
+
+    annotation = Annotated[
+        NotRequired[dict[str, int]],
+        DeltaChannel(merge_dicts),
+    ]
+    ch = _get_channel("files", annotation).from_checkpoint(MISSING)
+    assert ch.get() == {}
+    ch.update([{"a": 1}])
+    ch.update([{"b": 2}])
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
+    """End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel.
+
+    Mirrors the deepagents filesystem pattern: `files: Annotated[dict, reducer]`
+    where the reducer merges dicts and treats None values as deletions.
+    """
+    from typing import Annotated
+
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.channels._delta import DeltaChannel
+    from langgraph.graph import START, StateGraph
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    class State(TypedDict):
+        files: Annotated[dict[str, str], DeltaChannel(merge_files)]
+
+    turn = {"v": 0}
+
+    def write_file(state: State) -> dict:
+        turn["v"] += 1
+        n = turn["v"]
+        return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
+
+    builder = StateGraph(State)
+    builder.add_node("write_file", write_file)
+    builder.add_edge(START, "write_file")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "fs"}}
+
+    for _ in range(3):
+        graph.invoke({"files": {}}, config)
+
+    # Checkpoint stores only the sentinel — per-step writes live in checkpoint_writes.
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    cv = saved.checkpoint["channel_values"]["files"]
+    assert cv is DELTA_SENTINEL
+
+    state = graph.get_state(config)
+    assert state.values["files"] == {
+        "/doc_1.txt": "content for turn 1",
+        "/doc_2.txt": "content for turn 2",
+        "/doc_3.txt": "content for turn 3",
+    }
+
+    # Deletion path must round-trip through writes replay.
+    def delete_file(state: State) -> dict:
+        return {"files": {"/doc_1.txt": None}}
+
+    builder2 = StateGraph(State)
+    builder2.add_node("write_file", write_file)
+    builder2.add_node("delete_file", delete_file)
+    builder2.add_edge(START, "write_file")
+    builder2.add_edge("write_file", "delete_file")
+    turn["v"] = 0
+    saver2 = InMemorySaver()
+    graph2 = builder2.compile(checkpointer=saver2)
+    config2 = {"configurable": {"thread_id": "fs2"}}
+    graph2.invoke({"files": {}}, config2)
+    state2 = graph2.get_state(config2)
+    assert state2.values["files"] == {}
+
+
+def test_delta_channel_dict_reducer_backwards_compat() -> None:
+    """A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    old_value = {"a": 1, "b": 2}
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+# ---------------------------------------------------------------------------
+# seed / pre-delta migration
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_from_checkpoint_honors_seed() -> None:
+    """A non-sentinel value to from_checkpoint is used as the pre-delta seed.
+
+    Guards the pre-delta migration path: when the saver's ancestor walk hits
+    a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
+    the post-migration state correctly rather than replaying from empty.
+    """
+    spec = DeltaChannel(add_messages)
+    seed = [HumanMessage(content="pre-delta", id="p1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes(
+        [
+            ("t0", "messages", AIMessage(content="delta-1", id="d1")),
+            ("t1", "messages", HumanMessage(content="delta-2", id="d2")),
+        ]
+    )
+    msgs = ch.get()
+    assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
+
+
+def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
+    """Reconstruction at a pre-delta ancestor with no newer deltas returns
+    just the seed — the saver's terminator fired immediately."""
+    spec = DeltaChannel(add_messages)
+    seed = [HumanMessage(content="only-snap", id="s1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes([])
+    assert ch.get() == seed
+
+
+def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
+    """`seed=None` must start replay from None, not from an empty channel.
+
+    The DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
+    explicitly should feed None to the reducer as the left operand.
+    """
+
+    def replace(left, right):
+        return right
+
+    spec = DeltaChannel(replace)
+    ch = spec.from_checkpoint(None)
+    ch.replay_writes([("t0", "x", "after")])
+    # Reducer replaces; seed=None → first write produces "after".
+    assert ch.get() == "after"
@@ -0,0 +1,398 @@
+"""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. "
+    "We've had prior incidents in this area and want to be deliberate. "
+    "What should we prioritize first, and are there known failure modes we should design around from the start?"
+)
+
+_AI_TEMPLATE = (
+    "Great question about {topic}. The key insight here is that {concern} introduces "
+    "a subtle ordering dependency that most teams overlook until they hit it in production. "
+    "For your {component} layer specifically, I'd recommend starting with a careful audit "
+    "of the interface boundaries before making any structural changes. This will give you "
+    "a clear picture of the blast radius and let you sequence the migration safely."
+)
+
+_TOPICS = [
+    "distributed tracing",
+    "eventual consistency",
+    "schema migration",
+    "backpressure handling",
+    "idempotency guarantees",
+    "cache invalidation",
+    "connection pooling",
+    "rate limiting",
+    "circuit breaking",
+    "observability pipelines",
+]
+
+_CONCERNS = [
+    "concurrency model",
+    "retry semantics",
+    "state management",
+    "error propagation",
+    "latency budget",
+]
+
+_COMPONENTS = [
+    "persistence",
+    "routing",
+    "ingestion",
+    "aggregation",
+    "serialization",
+]
+
+
+def _human_content(i: int) -> str:
+    return _HUMAN_TEMPLATE.format(
+        topic=_TOPICS[i % len(_TOPICS)],
+        concern=_CONCERNS[i % len(_CONCERNS)],
+        component=_COMPONENTS[i % len(_COMPONENTS)],
+    )
+
+
+def _ai_content(i: int) -> str:
+    return _AI_TEMPLATE.format(
+        topic=_TOPICS[i % len(_TOPICS)],
+        concern=_CONCERNS[i % len(_CONCERNS)],
+        component=_COMPONENTS[i % len(_COMPONENTS)],
+    )
+
+
+# ---------------------------------------------------------------------------
+# State definitions
+# ---------------------------------------------------------------------------
+
+
+class BinaryState(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+class DeltaState(TypedDict):
+    messages: Annotated[list, DeltaChannel(add_messages)]
+
+
+# ---------------------------------------------------------------------------
+# Graph factory
+# ---------------------------------------------------------------------------
+
+
+def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
+    def human_node(state: Any) -> dict:
+        return {}
+
+    def ai_node(state: Any) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
+
+    g = StateGraph(state_cls)
+    g.add_node("human", human_node)
+    g.add_node("ai", ai_node)
+    g.add_edge("human", "ai")
+    g.add_edge("ai", END)
+    g.set_entry_point("human")
+    return g.compile(checkpointer=checkpointer or MemorySaver())
+
+
+# ---------------------------------------------------------------------------
+# Measurement helpers
+# ---------------------------------------------------------------------------
+
+
+def _total_blob_bytes(saver: MemorySaver) -> int:
+    total = 0
+    for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
+        if blob is not None:
+            total += len(blob)
+    return total
+
+
+def _run_turns(
+    n_turns: int,
+    state_cls: type,
+    checkpointer: Any = None,
+) -> tuple[float, float, int]:
+    """Run n_turns conversation turns.
+
+    Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
+    blob_bytes is -1 for savers without in-memory blob stores (e.g. SQLite).
+    Read latency is measured as the time to invoke the graph with no new
+    messages after the full history is built — this forces state rehydration.
+    """
+    graph = _make_graph(state_cls, checkpointer)
+    config = {"configurable": {"thread_id": "bench"}}
+
+    t0 = time.perf_counter()
+    for i in range(n_turns):
+        graph.invoke(
+            {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
+            config,
+        )
+    write_elapsed = time.perf_counter() - t0
+
+    # Measure read/rehydration: get_state forces the channel to rebuild
+    t1 = time.perf_counter()
+    for _ in range(5):
+        graph.get_state(config)
+    read_elapsed = (time.perf_counter() - t1) / 5
+
+    if isinstance(graph.checkpointer, MemorySaver):
+        blob_bytes = _total_blob_bytes(graph.checkpointer)
+    else:
+        blob_bytes = -1
+    return write_elapsed, read_elapsed, blob_bytes
+
+
+def _fmt_bytes(n: int) -> str:
+    if n >= 1_000_000:
+        return f"{n / 1_000_000:.1f} MB"
+    if n >= 1_000:
+        return f"{n / 1_000:.1f} KB"
+    return f"{n} B"
+
+
+def _approx_tokens(n_turns: int) -> str:
+    # ~100 tokens human + ~100 tokens AI per turn
+    tokens = n_turns * 200
+    if tokens >= 1_000_000:
+        return f"~{tokens / 1_000_000:.1f}M tok"
+    if tokens >= 1_000:
+        return f"~{tokens / 1_000:.0f}K tok"
+    return f"~{tokens} tok"
+
+
+# ---------------------------------------------------------------------------
+# Benchmark matrix
+# ---------------------------------------------------------------------------
+
+# Turn counts chosen to demonstrate O(N²) vs O(N) storage growth without running too long.
+# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
+TURN_COUNTS = [10, 25, 50, 100, 500]
+
+# Deep-thread counts where add_messages blob storage would exceed 1 GB;
+# only DeltaChannel runs here.
+DELTA_ONLY_TURN_COUNTS = [1000]
+
+
+def _checkpointer_factories() -> list[tuple[str, Any]]:
+    """Return (label, context_manager_or_none) pairs for available checkpointers."""
+    return [("InMemory", None)]
+
+
+def run_benchmark() -> None:
+    print()
+    print(
+        "DeltaChannel vs add_messages (BinaryOperatorAggregate) — checkpoint storage & latency"
+    )
+    print("Simulating realistic multi-turn conversations up to ~1M-token histories")
+    print("(5,000 turns × ~200 tokens/turn ≈ 1M tokens — Claude's full context window)")
+    print()
+
+    checkpointers: list[tuple[str, Any]] = [("InMemory", None)]
+    if _POSTGRES_AVAILABLE:
+        try:
+            import psycopg
+
+            psycopg.connect(_POSTGRES_URI).close()
+            checkpointers.append(("Postgres (plain SELECT)", "postgres"))
+        except Exception:
+            pass
+
+    for cp_label, cp_hint in checkpointers:
+        print(f"--- Checkpointer: {cp_label} ---")
+        _run_benchmark_for_checkpointer(cp_hint)
+
+
+def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
+    import contextlib
+    import tempfile
+
+    @contextlib.contextmanager
+    def _make_saver():
+        if cp_hint is None:
+            yield None
+        elif cp_hint == "postgres":
+            with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
+                saver.setup()
+                with saver._cursor() as cur:
+                    cur.execute("DELETE FROM checkpoints WHERE thread_id = 'bench'")
+                    cur.execute(
+                        "DELETE FROM checkpoint_blobs WHERE thread_id = 'bench'"
+                    )
+                    cur.execute(
+                        "DELETE FROM checkpoint_writes WHERE thread_id = 'bench'"
+                    )
+                yield saver
+        else:
+            with tempfile.NamedTemporaryFile(suffix=".db") as f:
+                with SqliteSaver.from_conn_string(f.name) as saver:
+                    yield saver
+
+    rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
+    for turns in TURN_COUNTS:
+        with _make_saver() as saver:
+            b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
+        with _make_saver() as saver:
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt))
+    for turns in DELTA_ONLY_TURN_COUNTS:
+        with _make_saver() as saver:
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, None, d_bytes, None, d_rt, None, d_wt))
+
+    # ── Table 1: Storage ─────────────────────────────────────────────────────
+    W = 64
+    print("Storage (checkpoint blob bytes)")
+    print("=" * W)
+    print(
+        f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}  "
+        f"{'savings':>8}"
+    )
+    print("-" * W)
+
+    def _bytes_or_na(v: Any) -> str:
+        if v is None:
+            return "n/a"
+        if v < 0:
+            return "n/a"
+        return _fmt_bytes(v)
+
+    def _ms_or_na(v: Any) -> str:
+        return "n/a" if v is None else f"{v * 1000:.1f}ms"
+
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        if b_bytes is None or b_bytes < 0 or d_bytes is None or d_bytes < 0:
+            ratio_str = "n/a"
+        else:
+            ratio = b_bytes / d_bytes if d_bytes else float("inf")
+            ratio_str = f"{ratio:.0f}x"
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_bytes_or_na(b_bytes):>12}  {_bytes_or_na(d_bytes):>12}  "
+            f"{ratio_str:>8}"
+        )
+    print("=" * W)
+    print()
+
+    # ── Table 2: Read latency ─────────────────────────────────────────────────
+    print("Read latency (avg of 5 get_state calls)")
+    print("=" * W)
+    print(f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}")
+    print("-" * W)
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_ms_or_na(b_rt):>12}  {_ms_or_na(d_rt):>12}"
+        )
+    print("=" * W)
+    print()
+
+    # ── Table 3: Per-invoke latency (total write_elapsed / turns) ─────────────
+    print("Per-invoke latency (total graph.invoke time / turns)")
+    print("=" * W)
+    print(f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}")
+    print("-" * W)
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+
+        def _per(wt: Any) -> str:
+            if wt is None:
+                return "n/a"
+            return f"{(wt / turns) * 1000:.1f}ms"
+
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_per(b_wt):>12}  {_per(d_wt):>12}"
+        )
+    print("=" * W)
+    print()
+
+    print("Legend:")
+    print("  add_msgs  = Annotated[list, add_messages]  — O(N²) storage")
+    print("  delta     = Annotated[list, DeltaChannel(add_messages)]  — O(N) storage")
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Pytest entry point
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+def test_delta_channel_benchmark(capsys: Any) -> None:
+    """Storage grows O(N²) for add_messages, O(N) for DeltaChannel."""
+    with capsys.disabled():
+        run_benchmark()
+
+    # Correctness assertion: DeltaChannel must use less storage at scale.
+    for turns in [25, 50]:
+        _, _, b_bytes = _run_turns(turns, BinaryState)
+        _, _, d_bytes = _run_turns(turns, DeltaState)
+        assert d_bytes < b_bytes, (
+            f"DeltaChannel should use less storage at {turns} turns, "
+            f"got delta={d_bytes} binary={b_bytes}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Script entry point
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    run_benchmark()
+    sys.exit(0)
@@ -0,0 +1,504 @@
+"""Tests for the BinaryOperatorAggregate -> DeltaChannel migration path.
+
+A thread written under `BinaryOperatorAggregate(...)` must keep working
+after its annotation is swapped to `DeltaChannel(...)` on the same
+checkpointer — pre-migration state visible at each *settled* ancestor
+checkpoint is preserved, and post-migration writes fold on top through
+the reducer.
+
+Mechanism under test: the saver's `_get_channel_writes_history(config,
+channel)` walks the parent chain; when it encounters an ancestor whose
+`channel_values[channel]` is a real value (not `DELTA_SENTINEL`), it
+returns that as the `seed`. `DeltaChannel.from_checkpoint(seed)` uses
+it as the base value, and `replay_writes(writes)` folds on-path deltas.
+
+Scenarios covered:
+
+1. **Basic migration (sync + async)**: build pre-migration state with
+   `BinaryOperatorAggregate`, swap the annotation to `DeltaChannel` on
+   the same checkpointer, and verify that every settled pre-migration
+   super-step boundary (`next=('__start__',)`) round-trips exactly
+   under the delta-channel view.
+2. **Time travel into a pre-migration checkpoint** after migration —
+   `graph.get_state(pre_migration_config)` at a settled ancestor
+   returns the same state as under the binop channel.
+3. **Continuing a migrated thread**: driving one more super-step after
+   migration produces a state that includes the pre-migration settled
+   prefix plus the new delta write — proving `from_checkpoint(seed)` +
+   `replay_writes` correctly fold post-migration deltas onto the
+   pre-migration seed.
+4. **Base-saver fallback path**: a third-party-style subclass that
+   removes the optimized `InMemorySaver` override and falls back to
+   `BaseCheckpointSaver._get_channel_writes_history` must produce the
+   same result as the optimized path.
+5. **Channel-type isolation across threads**: two threads on the same
+   checkpointer under the delta-channel graph — one freshly-started,
+   one migrated from pre-migration state — don't cross-contaminate.
+   The parent-chain walk is scoped to the thread.
+
+TODO: add postgres variants in the existing `libs/checkpoint-postgres`
+test files (different fixture setup; not this file).
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels._delta import DeltaChannel
+from langgraph.graph import END, START, StateGraph
+
+pytestmark = pytest.mark.anyio
+
+
+# ---------------------------------------------------------------------------
+# Graph factories
+#
+# A minimal reducer (`operator.add` on lists of str) with a noop node keeps
+# state change localized to the HumanMessage-like payload passed through
+# `invoke`. That isolates the pre/post-migration parity assertions to
+# channel-hydration semantics.
+# ---------------------------------------------------------------------------
+
+
+def _noop(_state: Any) -> dict:
+    return {}
+
+
+def _binop_graph(checkpointer: Any) -> Any:
+    class BinopState(TypedDict):
+        items: Annotated[list, BinaryOperatorAggregate(list, operator.add)]
+
+    return (
+        StateGraph(BinopState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_graph(checkpointer: Any) -> Any:
+    class DeltaState(TypedDict):
+        items: Annotated[list, DeltaChannel(operator.add)]
+
+    return (
+        StateGraph(DeltaState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _drive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        graph.invoke({"items": [f"{tag}{i}"]}, config)
+
+
+async def _adrive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        await graph.ainvoke({"items": [f"{tag}{i}"]}, config)
+
+
+def _settled_boundaries(history: list) -> list[tuple[dict, list]]:
+    """Return `[(config, items), ...]` for every checkpoint in `history`
+    whose `next == ('__start__',)` — the stable boundaries between invokes.
+    """
+    return [
+        (s.config, list(s.values.get("items", [])))
+        for s in history
+        if s.next == ("__start__",)
+    ]
+
+
+# ---------------------------------------------------------------------------
+# 1. Basic migration (sync + async)
+# ---------------------------------------------------------------------------
+
+
+def test_basic_migration_preserves_pre_migration_state() -> None:
+    """Build state under `BinaryOperatorAggregate`, migrate to
+    `DeltaChannel` on the same checkpointer, and verify that every
+    settled pre-migration super-step boundary round-trips exactly.
+
+    Settled boundaries (`next=('__start__',)`) are the stable hydration
+    targets for the migration path: writes that produced the NEXT
+    super-step are kept as `pending_writes` on the ancestor, so walking
+    from a descendant finds the ancestor's blob as the seed and
+    reconstructs the correct state.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-sync"}}
+
+    # Pre-migration: accumulate items across 3 invokes.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert len(pre_boundaries) >= 2, "expected multiple settled boundaries"
+
+    # Migrate: swap the annotation on the same checkpointer.
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = delta.get_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"snapshot mismatch at {cfg['configurable']['checkpoint_id']}: "
+            f"expected {items}, got {snap.values.get('items', [])}"
+        )
+
+
+async def test_basic_migration_preserves_pre_migration_state_async() -> None:
+    """Async variant of the basic migration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    pre_history = [s async for s in binop.aget_state_history(config)]
+    pre_boundaries = _settled_boundaries(pre_history)
+    assert len(pre_boundaries) >= 2
+
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = await delta.aget_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"async snapshot mismatch at {cfg['configurable']['checkpoint_id']}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 2. Time travel into a pre-migration checkpoint after migration
+# ---------------------------------------------------------------------------
+
+
+def test_time_travel_into_pre_migration_checkpoint() -> None:
+    """After migration, `graph.get_state(pre_migration_config)` at a
+    settled ancestor returns the state as stored at that point."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "time-travel"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert pre_boundaries, "no settled ancestors to time-travel to"
+
+    delta = _delta_graph(checkpointer)
+
+    # Pick the oldest non-empty boundary — a long distance to walk back.
+    non_empty = [(cfg, items) for cfg, items in pre_boundaries if items]
+    assert non_empty, "expected at least one non-empty boundary"
+    target_cfg, expected_items = non_empty[-1]
+
+    snap = delta.get_state(target_cfg)
+    assert list(snap.values.get("items", [])) == expected_items
+
+
+# ---------------------------------------------------------------------------
+# 3. Continuing a migrated thread: deltas fold onto pre-migration seed
+# ---------------------------------------------------------------------------
+
+
+def test_continuing_migrated_thread_folds_deltas_on_seed() -> None:
+    """Resume a pre-migration settled ancestor via `invoke(None, cfg)`
+    under the delta-channel graph. Since the pre-migration checkpoint
+    has an existing `pending_writes` entry (the input for the NEXT
+    super-step), re-running from that ancestor reproduces the same
+    post-ancestor state as the original binop run.
+
+    This proves the seed-terminator + write-replay pipeline works
+    end-to-end across the migration boundary.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "continue"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Pick the oldest settled boundary with non-empty state.
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    target_cfg, seed_items = next(
+        (cfg, items) for cfg, items in reversed(pre_boundaries) if items
+    )
+    assert seed_items, "need a non-empty seed boundary"
+
+    # Migrate and resume from the pre-migration ancestor. `invoke(None,
+    # cfg)` replays the pending writes staged at `cfg` under the new
+    # channel; the reducer folds those deltas onto the seed.
+    delta = _delta_graph(checkpointer)
+    result = delta.invoke(None, target_cfg)
+
+    # The resumed state must include the pre-migration seed items in order.
+    result_items = list(result.get("items", []))
+    for idx, prefix_item in enumerate(seed_items):
+        assert result_items[idx] == prefix_item, (
+            f"pre-migration seed item at {idx} not preserved: "
+            f"got {result_items[: idx + 1]}, expected {seed_items}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 4. Base-saver fallback path
+# ---------------------------------------------------------------------------
+
+
+class _ThirdPartyStyleSaver(InMemorySaver):
+    """Simulates a third-party saver that inherits the reference
+    `_get_channel_writes_history` implementation from
+    `BaseCheckpointSaver` rather than overriding it.
+
+    We rebind the two methods to the base-class versions (via MRO) so
+    the fallback path is exercised even though the storage layer is
+    still the in-memory one.
+    """
+
+    # MRO: [_ThirdPartyStyleSaver, InMemorySaver, BaseCheckpointSaver, ...]
+    _get_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+    )
+    _aget_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+    )
+
+
+def test_base_saver_fallback_matches_optimized_override() -> None:
+    """The reference `BaseCheckpointSaver` implementation must produce
+    the same migration behavior as the optimized `InMemorySaver`
+    override. We drive the same migration scenario through both savers
+    and assert per-snapshot parity in the delta-channel view."""
+
+    # Fast path: optimized InMemorySaver override.
+    fast_saver = InMemorySaver()
+    fast_config = {"configurable": {"thread_id": "fast"}}
+    fast_binop = _binop_graph(fast_saver)
+    _drive(fast_binop, fast_config, "u", 3)
+    fast_delta = _delta_graph(fast_saver)
+    fast_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in fast_delta.get_state_history(fast_config)
+    ]
+
+    # Slow path: base-class fallback.
+    slow_saver = _ThirdPartyStyleSaver()
+    slow_config = {"configurable": {"thread_id": "slow"}}
+    slow_binop = _binop_graph(slow_saver)
+    _drive(slow_binop, slow_config, "u", 3)
+    slow_delta = _delta_graph(slow_saver)
+    slow_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in slow_delta.get_state_history(slow_config)
+    ]
+
+    assert slow_history == fast_history, (
+        "base-saver fallback should match optimized-override behavior; "
+        f"fast={fast_history}, slow={slow_history}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 5. Thread isolation under mixed-generation storage
+# ---------------------------------------------------------------------------
+
+
+def test_delta_and_migrated_threads_do_not_cross_contaminate() -> None:
+    """Two threads sharing a checkpointer — one migrated from
+    pre-migration state, one freshly-started under DeltaChannel — must
+    maintain independent state. The parent-chain walk in
+    `_get_channel_writes_history` must be scoped to the target thread.
+    """
+
+    checkpointer = InMemorySaver()
+    migrated_cfg = {"configurable": {"thread_id": "migrated"}}
+    fresh_cfg = {"configurable": {"thread_id": "fresh"}}
+
+    # Thread A: pre-migration build-up.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, migrated_cfg, "m", 2)
+
+    # Thread B: fresh delta-channel run.
+    delta = _delta_graph(checkpointer)
+    _drive(delta, fresh_cfg, "f", 2)
+
+    # Thread A: migrate and confirm its state is anchored in its own
+    # thread's pre-migration history (tag 'm'), never mixing in tag 'f'.
+    migrated_boundaries = _settled_boundaries(
+        list(delta.get_state_history(migrated_cfg))
+    )
+    assert migrated_boundaries, "migrated thread has no settled boundaries"
+    for _, items in migrated_boundaries:
+        for it in items:
+            assert it.startswith("m"), (
+                f"migrated thread leaked item from other thread: {it}"
+            )
+
+    # Thread B: settled boundaries must only contain 'f' tags.
+    fresh_boundaries = _settled_boundaries(list(delta.get_state_history(fresh_cfg)))
+    assert fresh_boundaries, "fresh thread has no settled boundaries"
+    for _, items in fresh_boundaries:
+        for it in items:
+            assert it.startswith("f"), (
+                f"fresh thread leaked item from migrated thread: {it}"
+            )
+
+
+# ---------------------------------------------------------------------------
+# 6. Tip-of-pre-migration hydration: the latest checkpoint from a binop-run
+# thread has a real accumulated value in its own `channel_values["items"]`.
+# When hydrated under the delta-channel graph via `get_state(config)` with no
+# `checkpoint_id`, the short-circuit must use that value directly instead of
+# walking ancestors (which would skip the tip's own blob).
+# ---------------------------------------------------------------------------
+
+
+def test_tip_of_pre_migration_hydrates_directly() -> None:
+    """`graph.get_state(config)` at the latest (pre-migration) checkpoint
+    returns the full accumulated list stored in that checkpoint's own
+    `channel_values`. The hydration must not walk ancestors past it."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-sync"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    binop_tip = binop.get_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"], (
+        f"sanity: pre-migration tip should accumulate all 3 items, got {expected_items}"
+    )
+
+    delta = _delta_graph(checkpointer)
+
+    snap = delta.get_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+async def test_tip_of_pre_migration_hydrates_directly_async() -> None:
+    """Async variant of the tip-of-pre-migration hydration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    binop_tip = await binop.aget_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"]
+
+    delta = _delta_graph(checkpointer)
+
+    snap = await delta.aget_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"async tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 7. `update_state` after migration writes a real value to the new
+# checkpoint's `channel_values` (not a sentinel). Hydration must use it
+# directly — the ancestor walk would skip this blob and return stale state.
+# ---------------------------------------------------------------------------
+
+
+def test_update_state_after_migration_uses_written_value() -> None:
+    """After migrating and running at least one post-migration super-step
+    (so the thread's tip has a `DELTA_SENTINEL`), `update_state` writes a
+    concrete value to a new checkpoint's `channel_values`. `get_state`
+    must reflect that concrete value."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "update-state"}}
+
+    # Pre-migration: accumulate a little state.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Migrate and run one more super-step so the tip is a post-migration
+    # checkpoint with `DELTA_SENTINEL` in its own `channel_values`.
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # `update_state` writes a concrete value into a new checkpoint's blob
+    # via the reducer against the hydrated prior state.
+    delta.update_state(config, {"items": ["x", "y"]})
+
+    snap = delta.get_state(config)
+    updated_items = list(snap.values.get("items", []))
+    # Must include the "x","y" update; without the hydration fix, the
+    # update_state-written blob would be skipped in favor of an ancestor
+    # walk, and the update values would disappear.
+    assert "x" in updated_items and "y" in updated_items, (
+        f"update_state values missing from snapshot: {updated_items}"
+    )
+    # The "x","y" items should be folded onto the prior accumulated state,
+    # not stand alone. This verifies the update-written blob is used
+    # directly by `get_state` (no ancestor walk past it).
+    assert len(updated_items) >= 4, (
+        f"update_state snapshot should preserve pre-update state, got {updated_items}"
+    )
+    assert updated_items[-2:] == ["x", "y"], (
+        f"update_state deltas should be at the tail, got {updated_items}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 8. Fork from an `update_state` checkpoint: a new run branched off the
+# update_state-produced checkpoint must see that checkpoint's concrete
+# `channel_values` as its base, with new deltas folded on top.
+# ---------------------------------------------------------------------------
+
+
+def test_fork_from_update_state_checkpoint() -> None:
+    """Branching a new run from the checkpoint produced by `update_state`
+    must use that checkpoint's concrete blob as the base. Additional
+    deltas from the forked run fold onto it through the reducer."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "fork"}}
+
+    # Pre-migration build-up, then migrate and add one post-migration step.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # Apply `update_state` and capture the returned config (references
+    # the new checkpoint produced by the update).
+    update_cfg = delta.update_state(config, {"items": ["x", "y"]})
+
+    update_snap = delta.get_state(update_cfg)
+    base_items = list(update_snap.values.get("items", []))
+    assert "x" in base_items and "y" in base_items, (
+        f"update_state values missing from snapshot: {base_items}"
+    )
+    assert base_items[-2:] == ["x", "y"], (
+        f"sanity: update_state deltas should be at the tail, got {base_items}"
+    )
+
+    # Fork: invoke from the update_state checkpoint with a new delta.
+    forked = delta.invoke({"items": ["fork0"]}, update_cfg)
+    forked_items = list(forked.get("items", []))
+    # The fork must see the update_state-written blob as its base (not
+    # walk past it), and the new delta must fold on top of it.
+    assert forked_items[: len(base_items)] == base_items, (
+        f"fork lost update_state base: base={base_items}, forked={forked_items}"
+    )
+    assert forked_items[-1] == "fork0", f"fork delta not appended: {forked_items}"
@@ -275,3 +275,70 @@ def test_graph_callbacks_accept_base_callback_manager() -> None:

    assert "__interrupt__" in first
    assert len(graph_handler.interrupt_events) == 1
+
+
+def test_non_graph_handler_via_add_handler_does_not_crash() -> None:
+    """Non-GraphCallbackHandler added via add_handler should not raise.
+
+    Libraries like opentelemetry-instrumentation-langchain monkey-patch
+    BaseCallbackManager.__init__ and inject handlers via add_handler().
+    These handlers inherit from BaseCallbackHandler, not
+    GraphCallbackHandler. They must be silently accepted — graph lifecycle
+    events will simply not be dispatched to them.
+    """
+    from langgraph.callbacks import _GraphCallbackManager
+
+    manager = _GraphCallbackManager()
+    plain_handler = _LangChainCustomEventHandler()
+
+    manager.add_handler(plain_handler, inherit=True)
+    assert plain_handler in manager.handlers
+
+
+def test_non_graph_handler_does_not_receive_lifecycle_events() -> None:
+    """Non-GraphCallbackHandler added alongside a GraphCallbackHandler
+    should not interfere with lifecycle event dispatch."""
+    graph = _build_interrupt_graph()
+    graph_handler = _GraphEventHandler()
+    plain_handler = _LangChainCustomEventHandler()
+
+    config = {
+        "configurable": {"thread_id": "graph-callback-mixed-handlers"},
+        "callbacks": [plain_handler, graph_handler],
+    }
+
+    first = graph.invoke({"answer": None}, config)
+    assert "__interrupt__" in first
+
+    assert len(graph_handler.interrupt_events) == 1
+    assert plain_handler.events == []
+
+    resumed = graph.invoke(Command(resume="done"), config)
+    assert resumed == {"answer": "done"}
+    assert len(graph_handler.resume_events) == 1
+    assert plain_handler.events == []
+
+
+@pytest.mark.anyio
+@NEEDS_CONTEXTVARS
+async def test_non_graph_handler_does_not_receive_lifecycle_events_async() -> None:
+    """Async variant: non-GraphCallbackHandler should not interfere."""
+    graph = _build_interrupt_graph()
+    graph_handler = _GraphEventHandler()
+    plain_handler = _LangChainCustomEventHandler()
+
+    config = {
+        "configurable": {"thread_id": "graph-callback-mixed-handlers-async"},
+        "callbacks": [plain_handler, graph_handler],
+    }
+
+    first = await graph.ainvoke({"answer": None}, config)
+    assert "__interrupt__" in first
+
+    assert len(graph_handler.interrupt_events) == 1
+    assert plain_handler.events == []
+
+    resumed = await graph.ainvoke(Command(resume="done"), config)
+    assert resumed == {"answer": "done"}
+    assert len(graph_handler.resume_events) == 1
+    assert plain_handler.events == []
@@ -5,6 +5,7 @@ import langchain_core
 import pytest
 from langchain_core.messages import (
    AIMessage,
+    AIMessageChunk,
    AnyMessage,
    HumanMessage,
    RemoveMessage,
@@ -338,6 +339,123 @@ def test_remove_all_messages():
    ]


+def test_fast_path_preserves_format_openai():
+    """Pure-append fast path must still apply the `langchain-openai` formatter."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [
+        AIMessage(
+            content=[
+                {
+                    "type": "tool_use",
+                    "name": "foo",
+                    "input": {"bar": "baz"},
+                    "id": "t1",
+                }
+            ],
+            id="2",
+        )
+    ]
+    result = add_messages(left, right, format="langchain-openai")
+    assert isinstance(result[0], HumanMessage)
+    assert result[0].content == "prior"
+    assert isinstance(result[1], AIMessage)
+    # formatter collapses the tool_use content block into `tool_calls`
+    assert result[1].content == ""
+    assert len(result[1].tool_calls) == 1
+    assert result[1].tool_calls[0]["name"] == "foo"
+    assert result[1].tool_calls[0]["args"] == {"bar": "baz"}
+    assert result[1].tool_calls[0]["id"] == "t1"
+
+
+def test_fast_path_rejects_invalid_format():
+    """Pure-append fast path must validate the `format` arg like the slow path."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [AIMessage(content="new", id="2")]
+    with pytest.raises(ValueError, match="Unrecognized format="):
+        add_messages(left, right, format="bogus")  # type: ignore[arg-type]
+
+
+def test_left_starting_with_chunk_is_normalized():
+    """Opt-1 guard: a `BaseMessageChunk` at left[0] must trigger full conversion."""
+    chunk = AIMessageChunk(content="chunk", id="c1")
+    result = add_messages([chunk], [HumanMessage(content="h", id="h1")])
+    assert len(result) == 2
+    # chunk must be converted to a non-chunk message
+    assert type(result[0]).__name__ == "AIMessage"
+    assert result[0].id == "c1"
+    assert result[1].id == "h1"
+
+
+def test_left_as_dicts_is_normalized():
+    """Opt-1 guard: dicts at left[0] must trigger full conversion."""
+    left = [{"role": "user", "content": "hi", "id": "d1"}]
+    right = [AIMessage(content="reply", id="a1")]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage)
+    assert result[0].id == "d1"
+    assert result[0].content == "hi"
+
+
+def test_left_as_tuples_is_normalized():
+    """Opt-1 guard: tuple-form messages must trigger full conversion."""
+    left = [("user", "hi")]
+    right = [AIMessage(content="reply", id="a1")]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    assert isinstance(result[0], HumanMessage)
+    # id is auto-assigned
+    assert isinstance(result[0].id, str) and UUID(result[0].id, version=4)
+
+
+def test_left_first_msg_missing_id_is_normalized():
+    """Opt-1 guard: a BaseMessage without an id at left[0] falls to the else branch."""
+    left = [HumanMessage(content="hi")]  # no id
+    right = [AIMessage(content="reply", id="a1")]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    # left's id must have been auto-assigned
+    assert isinstance(result[0].id, str) and UUID(result[0].id, version=4)
+
+
+def test_duplicate_ids_in_right_with_nonempty_left():
+    """Opt-2 guard: intra-right duplicate ids must take slow path (dedup kept)."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [
+        AIMessage(content="first", id="2"),
+        AIMessage(content="second", id="2"),
+    ]
+    result = add_messages(left, right)
+    assert len(result) == 2
+    assert result[0].id == "1"
+    assert result[1].id == "2"
+    assert result[1].content == "second"
+
+
+def test_right_with_none_ids_pure_append():
+    """Fast path still correct when right entries start with id=None (fresh uuids assigned)."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [AIMessage(content="a"), AIMessage(content="b")]
+    result = add_messages(left, right)
+    assert len(result) == 3
+    assert result[0].id == "1"
+    for m in result[1:]:
+        assert isinstance(m.id, str) and UUID(m.id, version=4)
+    # fresh uuids must be distinct
+    assert result[1].id != result[2].id
+
+
+def test_fast_path_returns_fresh_list():
+    """Fast path must return a new list object (not mutate or alias left)."""
+    left = [HumanMessage(content="prior", id="1")]
+    right = [AIMessage(content="new", id="2")]
+    result = add_messages(left, right)
+    assert result is not left
+    # left must be untouched
+    assert len(left) == 1
+    assert left[0].id == "1"
+
+
 def test_push_messages_in_graph():
    class MessagesState(TypedDict):
        messages: Annotated[list[AnyMessage], add_messages]
@@ -615,8 +615,11 @@ def test_run_from_checkpoint_id_retains_previous_writes(
        )
    ]

-    assert len(new_history) == len(history) + 1
-    for original, new in zip(history, new_history[1:]):
+    # +2: one fork checkpoint from time travel, one from the new execution
+    assert len(new_history) == len(history) + 2
+    # new_history[0] is the new execution result, new_history[1] is the fork
+    assert new_history[1].metadata["source"] == "fork"
+    for original, new in zip(history, new_history[2:]):
        assert original.values == new.values
        assert original.next == new.next
        assert original.metadata["step"] == new.metadata["step"]
@@ -624,7 +627,7 @@ def test_run_from_checkpoint_id_retains_previous_writes(
    def _get_tasks(hist: list, start: int):
        return [h.tasks for h in hist[start:]]

-    assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
+    assert _get_tasks(new_history, 2) == _get_tasks(history, 0)


 def test_batch_two_processes_in_out() -> None:
@@ -9397,3 +9400,190 @@ 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"
@@ -2086,8 +2086,11 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
        )
    ]

-    assert len(new_history) == len(history) + 1
-    for original, new in zip(history, new_history[1:]):
+    # +2: one fork checkpoint from time travel, one from the new execution
+    assert len(new_history) == len(history) + 2
+    # new_history[0] is the new execution result, new_history[1] is the fork
+    assert new_history[1].metadata["source"] == "fork"
+    for original, new in zip(history, new_history[2:]):
        assert original.values == new.values
        assert original.next == new.next
        assert original.metadata["step"] == new.metadata["step"]
@@ -2095,7 +2098,7 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
    def _get_tasks(hist: list, start: int):
        return [h.tasks for h in hist[start:]]

-    assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
+    assert _get_tasks(new_history, 2) == _get_tasks(history, 0)


 async def test_cond_edge_after_send() -> None:
@@ -1,258 +0,0 @@
-"""Tests for LifecycleTransformer — derives subgraph lifecycle from ns discovery."""
-
-from __future__ import annotations
-
-import operator
-import time
-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.stream._mux import StreamMux
-from langgraph.stream._types import ProtocolEvent
-from langgraph.stream.transformers import (
-    LifecycleTransformer,
-    ValuesTransformer,
-)
-
-TS = int(time.time() * 1000)
-
-
-def _event(method: str, data: Any, *, namespace: list[str]) -> ProtocolEvent:
-    return {
-        "type": "event",
-        "method": method,
-        "params": {
-            "namespace": namespace,
-            "timestamp": TS,
-            "data": data,
-        },
-    }
-
-
-def _drain_channel(mux: StreamMux) -> list[dict[str, Any]]:
-    ch = mux.extensions["lifecycle"]
-    return list(ch._log._items)  # type: ignore[attr-defined]
-
-
-def _drain_main_events(mux: StreamMux) -> list[ProtocolEvent]:
-    return list(mux._events._items)  # type: ignore[attr-defined]
-
-
-def _subscribe(mux: StreamMux) -> None:
-    ch = mux.extensions["lifecycle"]
-    ch._log._subscribed = True  # type: ignore[attr-defined]
-    mux._events._subscribed = True  # type: ignore[attr-defined]
-
-
-class TestLifecycleTransformerUnit:
-    def _mux(self) -> StreamMux:
-        mux = StreamMux(
-            factories=[ValuesTransformer, LifecycleTransformer], is_async=False
-        )
-        _subscribe(mux)
-        return mux
-
-    def test_first_event_at_child_ns_emits_started(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=["child:task_a"]))
-
-        events = _drain_channel(mux)
-        assert events == [
-            {
-                "event": "started",
-                "namespace": ["child:task_a"],
-                "graph_name": "child",
-                "trigger_call_id": "task_a",
-            }
-        ]
-
-    def test_no_started_at_root_ns(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=[]))
-        assert _drain_channel(mux) == []
-
-    def test_method_agnostic_discovery(self) -> None:
-        mux = self._mux()
-        mux.push(_event("messages", "x", namespace=["c:t"]))
-
-        (started,) = _drain_channel(mux)
-        assert started["event"] == "started"
-        assert started["namespace"] == ["c:t"]
-
-    def test_repeated_events_single_started(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=["c:t"]))
-        mux.push(_event("values", {"v": 2}, namespace=["c:t"]))
-        mux.push(_event("updates", {"n": "x"}, namespace=["c:t"]))
-
-        events = _drain_channel(mux)
-        assert len(events) == 1
-        assert events[0]["event"] == "started"
-
-    def test_ns_without_task_id(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=["child"]))
-
-        (started,) = _drain_channel(mux)
-        assert started["graph_name"] == "child"
-        assert "trigger_call_id" not in started
-
-    def test_finalize_emits_completed(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=["c:t"]))
-        mux.close()
-
-        events = _drain_channel(mux)
-        assert events == [
-            {
-                "event": "started",
-                "namespace": ["c:t"],
-                "graph_name": "c",
-                "trigger_call_id": "t",
-            },
-            {"event": "completed", "namespace": ["c:t"]},
-        ]
-
-    def test_fail_with_graph_interrupt(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=["c:t"]))
-        mux.fail(GraphInterrupt())
-
-        events = _drain_channel(mux)
-        assert events[-1] == {"event": "interrupted", "namespace": ["c:t"]}
-
-    def test_fail_with_generic_error(self) -> None:
-        mux = self._mux()
-        mux.push(_event("values", {"v": 1}, namespace=["c:t"]))
-        mux.fail(RuntimeError("boom"))
-
-        events = _drain_channel(mux)
-        assert events[-1] == {
-            "event": "failed",
-            "namespace": ["c:t"],
-            "error": "boom",
-        }
-
-
-class TestLifecycleWireFormat:
-    """Native transformer: method on the wire is `"lifecycle"`, no `custom:` prefix."""
-
-    def test_wire_method_is_lifecycle_unprefixed(self) -> None:
-        mux = StreamMux(factories=[LifecycleTransformer], is_async=False)
-        _subscribe(mux)
-        mux.push(_event("values", {"v": 1}, namespace=["c:t"]))
-
-        # Find the lifecycle event in the main log.
-        lifecycle_events = [
-            ev for ev in _drain_main_events(mux) if ev["method"] == "lifecycle"
-        ]
-        assert len(lifecycle_events) == 1
-        assert lifecycle_events[0]["params"]["data"]["event"] == "started"
-        # Also verify no `custom:lifecycle` leaks through.
-        assert not any(
-            ev["method"].startswith("custom:") for ev in _drain_main_events(mux)
-        )
-
-    def test_started_precedes_originating_event_on_wire(self) -> None:
-        """Seq ordering: synthesized lifecycle event lands before the event that triggered it."""
-        mux = StreamMux(
-            factories=[ValuesTransformer, LifecycleTransformer], is_async=False
-        )
-        _subscribe(mux)
-        mux.push(_event("values", {"v": 1}, namespace=["c:t"]))
-
-        wire = _drain_main_events(mux)
-        methods = [ev["method"] for ev in wire]
-        # Lifecycle's synthetic event is forwarded during process() and
-        # gets an earlier seq than the originating values event.
-        assert methods.index("lifecycle") < methods.index("values")
-
-
-# ---------------------------------------------------------------------------
-# End-to-end via stream_v2
-# ---------------------------------------------------------------------------
-
-
-class SimpleState(TypedDict):
-    value: str
-    items: Annotated[list[str], operator.add]
-
-
-def _build_nested_graph():
-    def inner_node(state: SimpleState) -> dict:
-        return {"value": state["value"] + "X", "items": ["x"]}
-
-    inner_builder = StateGraph(SimpleState)
-    inner_builder.add_node("inner_node", inner_node)
-    inner_builder.add_edge(START, "inner_node")
-    inner_builder.add_edge("inner_node", END)
-    inner = inner_builder.compile()
-
-    outer_builder = StateGraph(SimpleState)
-    outer_builder.add_node("sub", inner)
-    outer_builder.add_edge(START, "sub")
-    outer_builder.add_edge("sub", END)
-    return outer_builder.compile()
-
-
-class TestLifecycleEndToEnd:
-    def test_real_run_emits_started_and_completed(self) -> None:
-        graph = _build_nested_graph()
-        run = graph.stream_v2(
-            {"value": "", "items": []}, transformers=[LifecycleTransformer]
-        )
-
-        # Drain the run so finalize fires.
-        list(run.values)
-
-        lifecycle = list(run.lifecycle)  # type: ignore[attr-defined]
-        events = [e["event"] for e in lifecycle]
-        assert "started" in events
-        assert "completed" in events
-
-    def test_real_run_error_emits_failed(self) -> None:
-        def boom(state: SimpleState) -> dict:
-            raise RuntimeError("kaboom")
-
-        inner_builder = StateGraph(SimpleState)
-        inner_builder.add_node("inner", boom)
-        inner_builder.add_edge(START, "inner")
-        inner_builder.add_edge("inner", END)
-        inner = inner_builder.compile()
-
-        outer_builder = StateGraph(SimpleState)
-        outer_builder.add_node("sub", inner)
-        outer_builder.add_edge(START, "sub")
-        outer_builder.add_edge("sub", END)
-        graph = outer_builder.compile()
-
-        run = graph.stream_v2(
-            {"value": "", "items": []}, transformers=[LifecycleTransformer]
-        )
-        with pytest.raises(RuntimeError):
-            list(run.values)
-
-        lifecycle = list(run.lifecycle)  # type: ignore[attr-defined]
-        events = [e["event"] for e in lifecycle]
-        assert "failed" in events
-
-    def test_lifecycle_and_subgraphs_agree(self) -> None:
-        """SubgraphTransformer and LifecycleTransformer share the discovery predicate."""
-        graph = _build_nested_graph()
-        run = graph.stream_v2(
-            {"value": "", "items": []}, transformers=[LifecycleTransformer]
-        )
-
-        subs = list(run.subgraphs)
-        lifecycle = list(run.lifecycle)  # type: ignore[attr-defined]
-
-        sub_paths = {tuple(s.path) for s in subs}
-        started_paths = {
-            tuple(e["namespace"]) for e in lifecycle if e["event"] == "started"
-        }
-        assert sub_paths == started_paths
@@ -1,907 +0,0 @@
-"""Tests for the MessagesTransformer content-block upgrade (B2).
-
-Verifies that `MessagesTransformer` routes protocol events (emitted by
-`stream_v2` via `on_stream_event`) to `ChatModelStream` objects keyed by
-run_id, and replays whole `AIMessage` payloads via `message_to_events`.
-Legacy v1 `AIMessageChunk` tuples (from `on_llm_new_token`) are ignored.
-"""
-
-from __future__ import annotations
-
-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 langgraph.constants import END, START
-from langgraph.graph import MessagesState, StateGraph
-from langgraph.stream._event_log import EventLog
-from langgraph.stream._mux import StreamMux
-from langgraph.stream.run_stream import GraphRunStream
-from langgraph.stream.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)."""
-    metadata: dict[str, Any] = {"langgraph_node": node, "run_id": run_id}
-    return {
-        "type": "event",
-        "method": "messages",
-        "params": {
-            "namespace": [],
-            "timestamp": TS,
-            "data": (event, metadata),
-        },
-    }
-
-
-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] = {}
-    if finish:
-        rm["finish_reason"] = "stop"
-    message = AIMessageChunk(content=text, id=msg_id, response_metadata=rm)
-    metadata: dict[str, Any] = {"langgraph_node": node}
-    return {
-        "type": "event",
-        "method": "messages",
-        "params": {
-            "namespace": [],
-            "timestamp": TS,
-            "data": (message, metadata),
-        },
-    }
-
-
-def _whole_msg(
-    text: str,
-    msg_id: str = "msg-10",
-    *,
-    node: str = "node",
-) -> dict[str, Any]:
-    """Build a messages ProtocolEvent carrying a completed AIMessage."""
-    message = AIMessage(content=text, id=msg_id)
-    metadata: dict[str, Any] = {"langgraph_node": node}
-    return {
-        "type": "event",
-        "method": "messages",
-        "params": {
-            "namespace": [],
-            "timestamp": TS,
-            "data": (message, metadata),
-        },
-    }
-
-
-def _make_sync_transformer() -> tuple[MessagesTransformer, EventLog[ChatModelStream]]:
-    t = MessagesTransformer()
-    proj = t.init()
-    log: EventLog[ChatModelStream] = proj["messages"]
-    log._bind(is_async=False)
-    # Production subscribes via `iter(log)` from the graph consumer — do that
-    # up front so `push` during `process` isn't a no-op. Tests read buffered
-    # items via `log._items` directly rather than re-iterating.
-    log._subscribed = True
-    t._bind_pump(lambda: False)
-    return t, log
-
-
-def _make_async_transformer() -> tuple[MessagesTransformer, EventLog[ChatModelStream]]:
-    t = MessagesTransformer()
-    proj = t.init()
-    log: EventLog[ChatModelStream] = proj["messages"]
-    log._bind(is_async=True)
-    log._subscribed = True
-    return t, log
-
-
-# Standard lifecycle events for one streaming LLM call.
-def _lifecycle(
-    *,
-    text: str = "hello world",
-    message_id: str = "run-1",
-) -> list[dict[str, Any]]:
-    """Produce a valid protocol event lifecycle: start, delta, finish, end."""
-    # Split text into two deltas to exercise delta accumulation.
-    half = len(text) // 2
-    first, second = text[:half], text[half:]
-    return [
-        {"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"},
-    ]
-
-
-# ---------------------------------------------------------------------------
-# Primary path: 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",
-            )
-        )
-        # Stream is in the log immediately.
-        log.close()
-        streams = list(log._items)
-        assert len(streams) == 1
-        assert isinstance(streams[0], ChatModelStream)
-        assert streams[0].message_id == "run-1"
-
-    def test_full_lifecycle_yields_done_stream(self) -> None:
-        t, log = _make_sync_transformer()
-        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:
-        """Orphan delta events (no preceding message-start) are dropped silently."""
-        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:
-        """Two interleaved LLM calls each produce their own stream."""
-        t, log = _make_sync_transformer()
-        # Interleave events from two different run_ids.
-        life_a = _lifecycle(text="aaaa", message_id="run-a")
-        life_b = _lifecycle(text="bbbb", message_id="run-b")
-        for a, b in zip(life_a, life_b):
-            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)
-        deltas = list(stream._text_proj._deltas)
-        assert "".join(deltas) == "abcdef"
-
-    def test_stream_pushed_on_message_start_not_finish(self) -> None:
-        """Consumer can see the stream before it finishes."""
-        t, log = _make_sync_transformer()
-        t.process(
-            _proto_event(
-                {"event": "message-start", "role": "ai", "message_id": "run-1"},
-                run_id="run-1",
-            )
-        )
-        # The log has the stream immediately — even though message-finish
-        # hasn't arrived yet.
-        assert len(log._items) == 1
-
-    def test_node_metadata_set_on_stream(self) -> None:
-        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,) = [*log._items]
-        assert stream.node == "my_llm"
-
-
-# ---------------------------------------------------------------------------
-# Non-streaming (whole AIMessage) 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)
-        event_types = [e["event"] for e in stream._events]
-        assert event_types == [
-            "message-start",
-            "content-block-start",
-            "content-block-delta",
-            "content-block-finish",
-            "message-finish",
-        ]
-
-
-# ---------------------------------------------------------------------------
-# Legacy v1 chunks are ignored (users must migrate to stream_v2)
-# ---------------------------------------------------------------------------
-
-
-class TestLegacyChunksIgnored:
-    def test_aimessage_chunk_tuple_is_dropped(self) -> None:
-        t, log = _make_sync_transformer()
-        t.process(_v1_chunk("hello"))
-        t.process(_v1_chunk(" world", finish=True))
-        log.close()
-        assert list(log._items) == []
-
-
-# ---------------------------------------------------------------------------
-# Filtering behaviors
-# ---------------------------------------------------------------------------
-
-
-class TestFiltering:
-    def test_non_messages_events_pass_through(self) -> None:
-        t, _ = _make_sync_transformer()
-        values_event = {
-            "type": "event",
-            "method": "values",
-            "params": {"namespace": [], "timestamp": TS, "data": {"x": 1}},
-        }
-        assert t.process(values_event) is True
-
-    def test_subgraph_namespace_dropped(self) -> None:
-        """Root MessagesTransformer (via the mux) ignores non-root events."""
-        from langgraph.stream._mux import StreamMux
-
-        mux = StreamMux([MessagesTransformer()], is_async=False)
-        t = mux.transformer_by_key("messages")
-        assert isinstance(t, MessagesTransformer)
-        t._log._subscribed = True
-        t._bind_pump(lambda: False)
-
-        mux.push(
-            {
-                "type": "event",
-                "method": "messages",
-                "params": {
-                    "namespace": ["subgraph"],
-                    "timestamp": TS,
-                    "data": (
-                        {"event": "message-start", "message_id": "run-x"},
-                        {"run_id": "run-x"},
-                    ),
-                },
-            }
-        )
-        t._log.close()
-        assert list(t._log._items) == []
-
-
-# ---------------------------------------------------------------------------
-# Lifecycle: finalize / fail
-# ---------------------------------------------------------------------------
-
-
-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 (AsyncChatModelStream)
-# ---------------------------------------------------------------------------
-
-
-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))
-        streams = list(log._items)
-        assert len(streams) == 1
-        assert isinstance(streams[0], AsyncChatModelStream)
-
-    @pytest.mark.anyio
-    async def test_async_text_projection_yields_deltas(self) -> None:
-        t, log = _make_async_transformer()
-        for evt in _lifecycle(text="hello world"):
-            t.process(_proto_event(evt))
-        (stream,) = list(log._items)
-        assert isinstance(stream, AsyncChatModelStream)
-        collected = []
-        async for delta in stream.text:
-            collected.append(delta)
-        assert "".join(collected) == "hello world"
-
-    @pytest.mark.anyio
-    async def test_async_output_awaitable(self) -> None:
-        t, log = _make_async_transformer()
-        for evt in _lifecycle(text="async"):
-            t.process(_proto_event(evt))
-        (stream,) = list(log._items)
-        msg = await stream.output
-        assert msg.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)
-        # After wire, the transformer's pump callback is set.
-        assert messages_t._pump_fn is not None
-        # And calling it invokes GraphRunStream._pump_next (drains an empty
-        # graph_iter, returns False).
-        assert messages_t._pump_fn() is False
-        assert run._exhausted
-
-    def test_created_streams_have_request_more(self) -> None:
-        values_t = ValuesTransformer()
-        messages_t = MessagesTransformer()
-        mux = StreamMux([values_t, messages_t], is_async=False)
-
-        GraphRunStream(iter([]), mux)
-        log: EventLog[ChatModelStream] = mux.extensions["messages"]
-        log._subscribed = True
-
-        for evt in _lifecycle():
-            messages_t.process(_proto_event(evt))
-
-        (stream,) = list(log._items)
-        # Pump was threaded through: the stream's _request_more points at
-        # the same callable the transformer was bound with.
-        assert stream._request_more is messages_t._pump_fn
-
-
-# ---------------------------------------------------------------------------
-# End-to-end via StreamMux
-# ---------------------------------------------------------------------------
-
-
-class TestViaMux:
-    def test_streaming_via_mux(self) -> None:
-        t = MessagesTransformer()
-        v = ValuesTransformer()
-        mux = StreamMux([v, t], is_async=False)
-        t._bind_pump(lambda: False)
-        log: EventLog[ChatModelStream] = mux.extensions["messages"]
-        # Simulate a consumer subscribing (as `run.messages` iteration would).
-        log._subscribed = True
-
-        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 = MessagesTransformer()
-        v = ValuesTransformer()
-        mux = StreamMux([v, t], is_async=False)
-        t._bind_pump(lambda: False)
-        log: EventLog[ChatModelStream] = mux.extensions["messages"]
-        log._subscribed = True
-
-        mux.push(_whole_msg("result"))
-        mux.close()
-
-        (stream,) = list(log._items)
-        assert stream.output.text == "result"
-
-    @pytest.mark.anyio
-    async def test_async_streaming_via_mux(self) -> None:
-        t = MessagesTransformer()
-        v = ValuesTransformer()
-        mux = StreamMux([v, t], is_async=True)
-        log: EventLog[ChatModelStream] = mux.extensions["messages"]
-        log._subscribed = True
-
-        for evt in _lifecycle(text="async mux"):
-            await mux.apush(_proto_event(evt))
-
-        streams = list(log._items)
-        assert len(streams) == 1
-        msg = await streams[0].output
-        assert msg.text == "async mux"
-        await mux.aclose()
-
-
-# ---------------------------------------------------------------------------
-# End-to-end: full graph → stream_v2 → run.messages
-# ---------------------------------------------------------------------------
-
-
-class TestEndToEnd:
-    """Prove the full pipeline works when a node calls `model.stream_v2()`.
-
-    These tests exercise the path that the new messages projection is
-    designed for: a user node invokes `stream_v2` on a chat model,
-    `on_stream_event` fires on `StreamMessagesHandler`, the handler
-    forwards to the mux, and the transformer routes events into a
-    `ChatModelStream` exposed on `run.messages`.
-
-    Nothing in Pregel calls `stream_v2` automatically yet; the planned
-    `graph.stream_v2()` API (B4) and the `create_react_agent`
-    integration (C2) will wire that up. Until then, populating the
-    messages projection is opt-in at the node level.
-    """
-
-    def test_node_calling_stream_v2_populates_messages(self) -> None:
-        model = GenericFakeChatModel(messages=iter(["hello world"]))
-
-        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"})
-        streams = list(run.messages)
-
-        assert len(streams) == 1
-        assert isinstance(streams[0], ChatModelStream)
-        assert streams[0].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"})
-
-        # Pull the stream handle out, then iterate its text deltas.
-        (stream,) = list(run.messages)
-        text = "".join(stream.text)
-        assert text == "streamed answer"
-
-    def test_non_llm_message_returned_from_node(self) -> None:
-        """Node returns a finalized AIMessage directly — whole-message fallback."""
-
-        def return_message(state: MessagesState) -> dict[str, Any]:
-            return {"messages": AIMessage(content="hardcoded", id="msg-abc")}
-
-        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"})
-        streams = list(run.messages)
-
-        assert len(streams) == 1
-        assert streams[0].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"])
-            msg = await stream
-            return {"messages": msg}
-
-        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 = []
-        async for stream in run.messages:
-            streams.append(stream)
-
-        assert len(streams) == 1
-        assert isinstance(streams[0], AsyncChatModelStream)
-        msg = await streams[0].output
-        assert msg.text == "async answer"
-
-    @pytest.mark.anyio
-    async def test_nested_async_iteration_yields_text_deltas(self) -> None:
-        """Iterate `stream.text` inside `async for stream in run.messages`.
-
-        The inner `stream.text` cursor drives the shared graph pump via
-        `AsyncProjection._arequest_more`, wired by
-        `MessagesTransformer._bind_apump` and
-        `AsyncGraphRunStream._wire_arequest_more`.
-        """
-        import asyncio
-
-        model = GenericFakeChatModel(messages=iter(["hello world"]))
-
-        async def call_model(state: MessagesState) -> dict[str, Any]:
-            stream = await model.astream_v2(state["messages"])
-            msg = await stream
-            return {"messages": msg}
-
-        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_nested() -> list[str]:
-            collected: list[str] = []
-            async for stream in run.messages:
-                async for delta in stream.text:
-                    collected.append(delta)
-            return collected
-
-        deltas = await asyncio.wait_for(consume_nested(), timeout=2.0)
-        assert "".join(deltas) == "hello world"
-
-
-class TestEndToEndV2Invoke:
-    """Nodes call `model.invoke()`; `stream_v2` routes through v2.
-
-    Exercises the auto-routing path added in
-    `feat(core): route invoke through v2 event path for
-    _V2StreamingCallbackHandler`: `stream_v2` injects
-    `CONFIG_KEY_STREAM_MESSAGES_V2` into the config, pregel attaches
-    `StreamMessagesHandlerV2`, `BaseChatModel._should_stream_v2` sees the
-    v2 marker and drives the protocol event generator, and
-    `on_stream_event` forwards each event onto the messages channel.
-    """
-
-    def test_invoke_with_v2_marker_populates_messages(self) -> None:
-        """Node calling `model.invoke()` produces one ChatModelStream with v2 events."""
-        model = GenericFakeChatModel(messages=iter(["hello world"]))
-
-        def 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"})
-        streams = list(run.messages)
-
-        assert len(streams) == 1, (
-            "Expected exactly one ChatModelStream — the streamed invoke and "
-            "the node's return of the same AIMessage must dedupe."
-        )
-        stream = streams[0]
-        assert isinstance(stream, ChatModelStream)
-        assert stream.output.text == "hello world"
-
-    def test_invoke_v2_emits_protocol_events(self) -> None:
-        """Iterating the stream yields the full v2 lifecycle (not v1 chunks)."""
-        model = GenericFakeChatModel(messages=iter(["streamed answer"]))
-
-        def call_model(state: MessagesState) -> dict[str, Any]:
-            return {"messages": model.invoke(state["messages"])}
-
-        graph = (
-            StateGraph(MessagesState)
-            .add_node("call_model", call_model)
-            .add_edge(START, "call_model")
-            .add_edge("call_model", END)
-            .compile()
-        )
-
-        run = graph.stream_v2({"messages": "go"})
-        (stream,) = list(run.messages)
-
-        events = list(stream)
-        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_live(self) -> None:
-        """`.text` projection yields deltas in order."""
-        model = GenericFakeChatModel(messages=iter(["delta streaming works"]))
-
-        def call_model(state: MessagesState) -> dict[str, Any]:
-            return {"messages": model.invoke(state["messages"])}
-
-        graph = (
-            StateGraph(MessagesState)
-            .add_node("call_model", call_model)
-            .add_edge(START, "call_model")
-            .add_edge("call_model", END)
-            .compile()
-        )
-
-        run = graph.stream_v2({"messages": "hi"})
-        (stream,) = list(run.messages)
-
-        assembled = "".join(stream.text)
-        assert assembled == "delta streaming works"
-
-    def test_invoke_dedupe_survives_multi_node_graph(self) -> None:
-        """Two model-invoking nodes produce exactly two streams, each once."""
-        model_a = GenericFakeChatModel(messages=iter(["alpha"]))
-        model_b = GenericFakeChatModel(messages=iter(["beta"]))
-
-        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()
-        )
-
-        run = graph.stream_v2({"messages": "hi"})
-        streams = list(run.messages)
-
-        assert len(streams) == 2
-        contents = {s.output.text for s in streams}
-        assert contents == {"alpha", "beta"}
-
-    def test_invoke_plus_constructed_message_two_streams(self) -> None:
-        """A v2-streamed node + a node that returns a constructed AIMessage
-        produces two ChatModelStreams — one from the live event lifecycle,
-        one synthesized from the constructed message via `message_to_events`.
-        """
-        model = GenericFakeChatModel(messages=iter(["live stream"]))
-
-        def streaming_node(state: MessagesState) -> dict[str, Any]:
-            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_with_v2_marker_populates_messages(self) -> None:
-        """Async mirror: `model.ainvoke()` + `astream_v2`."""
-        model = GenericFakeChatModel(messages=iter(["async invoke"]))
-
-        async def call_model(state: MessagesState) -> dict[str, Any]:
-            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 = []
-        async for stream in run.messages:
-            streams.append(stream)
-
-        assert len(streams) == 1
-        assert isinstance(streams[0], AsyncChatModelStream)
-        msg = await streams[0].output
-        assert msg.text == "async invoke"
-
-
-class TestDirectMessagesModeStaysV1:
-    """Regression guard: direct `graph.stream(stream_mode="messages")`
-    (no `stream_v2`) must keep the v1 `(AIMessageChunk, metadata)`
-    tuple shape. The v2 flag is only injected by `stream_v2` / `astream_v2`.
-    """
-
-    def test_direct_graph_stream_messages_yields_ai_message_chunks(self) -> None:
-        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"))
-        # Should have at least one streamed chunk; each part is
-        # (AIMessageChunk, metadata) — not a v2 event dict.
-        assert parts, "expected stream_mode='messages' to emit tuples"
-        for part in parts:
-            payload, _metadata = part
-            assert isinstance(payload, AIMessageChunk), (
-                "direct graph.stream(stream_mode='messages') leaked v2 "
-                "event dicts — stream_v2 flag bled through."
-            )
-        assembled = "".join(
-            p[0].content for p in parts if isinstance(p[0].content, str)
-        )
-        assert assembled == "legacy path"
-
-
-class TestStreamMessagesHandlerV2Unit:
-    """Unit tests on the handler class itself."""
-
-    def test_on_llm_new_token_is_noop(self) -> None:
-        """v2 handler must not emit v1 chunks even if `on_llm_new_token` fires
-        (e.g. from a node calling `model.stream()` directly on a v2-flagged run).
-        """
-        from uuid import uuid4
-
-        from langchain_core.outputs import ChatGenerationChunk
-
-        from langgraph.pregel._messages import StreamMessagesHandlerV2
-
-        emitted: list[Any] = []
-        handler = StreamMessagesHandlerV2(emitted.append, subgraphs=False)
-        run_id = uuid4()
-        # Register a fake run so `self.metadata.get(run_id)` would succeed for
-        # other callbacks — this makes sure the no-op is unconditional, not a
-        # side effect of missing metadata.
-        handler.metadata[run_id] = ((), {"langgraph_node": "x"})
-
-        handler.on_llm_new_token(
-            "hello",
-            chunk=ChatGenerationChunk(message=AIMessageChunk(content="hello")),
-            run_id=run_id,
-        )
-
-        assert emitted == [], (
-            "StreamMessagesHandlerV2.on_llm_new_token must not push to the "
-            "messages stream — it's the v2 marker's guarantee."
-        )
@@ -1,411 +0,0 @@
-"""Tests for SubgraphTransformer namespace-based discovery."""
-
-from __future__ import annotations
-
-import operator
-import time
-from typing import Annotated, Any
-
-import pytest
-from langgraph.checkpoint.memory import InMemorySaver
-from typing_extensions import TypedDict
-
-from langgraph.constants import END, START
-from langgraph.errors import GraphInterrupt
-from langgraph.graph import StateGraph
-from langgraph.stream._event_log import EventLog
-from langgraph.stream._mux import StreamMux
-from langgraph.stream._types import ProtocolEvent
-from langgraph.stream.transformers import (
-    MessagesTransformer,
-    SubgraphRunStream,
-    SubgraphTransformer,
-    ValuesTransformer,
-)
-from langgraph.types import interrupt
-
-TS = int(time.time() * 1000)
-
-
-def _values(payload: dict[str, Any], *, namespace: list[str]) -> ProtocolEvent:
-    return {
-        "type": "event",
-        "method": "values",
-        "params": {
-            "namespace": namespace,
-            "timestamp": TS,
-            "data": payload,
-        },
-    }
-
-
-def _subscribe(log: EventLog) -> None:
-    """Flip `_subscribed = True` so pushes retain items for test inspection."""
-    log._subscribed = True
-
-
-# ---------------------------------------------------------------------------
-# Unit tests: feed events directly into the transformer
-# ---------------------------------------------------------------------------
-
-
-_FACTORIES = [ValuesTransformer, MessagesTransformer, SubgraphTransformer]
-
-
-def _handle_values_items(handle: SubgraphRunStream) -> list:
-    return list(handle._mux.extensions["values"]._items)  # type: ignore[attr-defined]
-
-
-def _handle_subgraphs_items(handle: SubgraphRunStream) -> list:
-    return list(handle._mux.extensions["subgraphs"]._items)  # type: ignore[attr-defined]
-
-
-def _pre_subscribe_handle(handle: SubgraphRunStream) -> None:
-    """Flip `_subscribed` on every EventLog inside the handle's mini-mux.
-
-    The mini-mux is built via `make_child` with the full factory list,
-    so values / messages / subgraphs logs all exist as projections.
-    Tests that feed events directly need them subscribed so pushes
-    retain items in the deque for `_items` inspection.
-    """
-    for value in handle._mux.extensions.values():
-        if isinstance(value, EventLog):
-            _subscribe(value)
-
-
-class TestSubgraphTransformerUnit:
-    def _mux(self) -> tuple[StreamMux, SubgraphTransformer]:
-        mux = StreamMux(factories=_FACTORIES, is_async=False)
-        transformer = mux.transformer_by_key("subgraphs")
-        assert isinstance(transformer, SubgraphTransformer)
-        _subscribe(transformer._root_log)
-        return mux, transformer
-
-    def _handle(self, transformer: SubgraphTransformer) -> SubgraphRunStream:
-        (handle,) = list(transformer._root_log._items)
-        return handle
-
-    def test_root_event_does_not_create_handle(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=[]))
-        assert list(transformer._root_log._items) == []
-        assert transformer._by_ns == {}
-
-    def test_first_event_at_child_depth_yields_handle(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["child:task_a"]))
-
-        handle = self._handle(transformer)
-        assert handle.path == ("child:task_a",)
-        assert handle.graph_name == "child"
-        assert handle.trigger_call_id == "task_a"
-        assert handle.status == "started"
-
-    def test_handle_without_task_id_suffix(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["child"]))
-
-        handle = self._handle(transformer)
-        assert handle.path == ("child",)
-        assert handle.graph_name == "child"
-        assert handle.trigger_call_id is None
-
-    def test_discovery_is_method_agnostic(self) -> None:
-        mux, transformer = self._mux()
-        # Method-agnostic means "any event method triggers discovery".
-        # Use `updates` — neither ValuesTransformer nor
-        # MessagesTransformer care about it, so the test only exercises
-        # SubgraphTransformer's discovery path.
-        mux.push(
-            {
-                "type": "event",
-                "method": "updates",
-                "params": {"namespace": ["c:t"], "timestamp": TS, "data": "x"},
-            }
-        )
-        handle = self._handle(transformer)
-        assert handle.path == ("c:t",)
-
-    def test_grandchild_surfaces_under_child(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["child:t"]))
-
-        child = self._handle(transformer)
-        _pre_subscribe_handle(child)
-
-        mux.push(_values({"value": 2}, namespace=["child:t", "grand:u"]))
-
-        (grand,) = _handle_subgraphs_items(child)
-        assert grand.path == ("child:t", "grand:u")
-        assert grand.graph_name == "grand"
-        assert grand.trigger_call_id == "u"
-
-    def test_values_routed_into_handle(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["c:t"]))
-
-        handle = self._handle(transformer)
-        _pre_subscribe_handle(handle)
-
-        mux.push(_values({"value": 2}, namespace=["c:t"]))
-
-        # Both the discovery event and subsequent values land in the child.
-        assert _handle_values_items(handle) == [{"value": 1}, {"value": 2}]
-        assert handle.output == {"value": 2}
-
-    def test_root_values_not_routed(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["c:t"]))
-        handle = self._handle(transformer)
-        _pre_subscribe_handle(handle)
-
-        # Values event at root namespace — must not leak into child handle.
-        mux.push(_values({"value": "root"}, namespace=[]))
-        assert _handle_values_items(handle) == [{"value": 1}]
-
-    def test_finalize_closes_dangling(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["c:t"]))
-        handle = self._handle(transformer)
-
-        mux.close()
-        assert handle.status == "completed"
-        assert handle._mux.extensions["values"]._closed
-        assert handle._mux.extensions["subgraphs"]._closed
-
-    def test_fail_with_graph_interrupt_marks_interrupted(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["c:t"]))
-        handle = self._handle(transformer)
-
-        mux.fail(GraphInterrupt())
-        assert handle.status == "interrupted"
-
-    def test_fail_with_generic_error_marks_failed(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["c:t"]))
-        handle = self._handle(transformer)
-
-        mux.fail(RuntimeError("explode"))
-        assert handle.status == "failed"
-        assert handle.error == "explode"
-
-    def test_repeated_events_same_ns_single_handle(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_values({"value": 1}, namespace=["c:t"]))
-        mux.push(_values({"value": 2}, namespace=["c:t"]))
-        mux.push(_values({"value": 3}, namespace=["c:t"]))
-
-        handles = list(transformer._root_log._items)
-        assert len(handles) == 1
-        assert handles[0].path == ("c:t",)
-
-
-# ---------------------------------------------------------------------------
-# End-to-end tests via stream_v2 on real graphs
-# ---------------------------------------------------------------------------
-
-
-class SimpleState(TypedDict):
-    value: str
-    items: Annotated[list[str], operator.add]
-
-
-def _build_nested_graph():
-    """Parent graph with a compiled subgraph node."""
-
-    def inner_node(state: SimpleState) -> dict:
-        return {"value": state["value"] + "X", "items": ["x"]}
-
-    inner_builder = StateGraph(SimpleState)
-    inner_builder.add_node("inner_node", inner_node)
-    inner_builder.add_edge(START, "inner_node")
-    inner_builder.add_edge("inner_node", END)
-    inner = inner_builder.compile()
-
-    def outer_node(state: SimpleState) -> dict:
-        return {"value": state["value"] + "Y", "items": ["y"]}
-
-    outer_builder = StateGraph(SimpleState)
-    outer_builder.add_node("outer_node", outer_node)
-    outer_builder.add_node("sub", inner)
-    outer_builder.add_edge(START, "outer_node")
-    outer_builder.add_edge("outer_node", "sub")
-    outer_builder.add_edge("sub", END)
-    return outer_builder.compile()
-
-
-class TestSubgraphTransformerEndToEnd:
-    def test_flat_graph_yields_no_subgraphs(self) -> None:
-        builder = StateGraph(SimpleState)
-        builder.add_node("n", lambda s: {"value": s["value"] + "!", "items": ["!"]})
-        builder.add_edge(START, "n")
-        builder.add_edge("n", END)
-        graph = builder.compile()
-
-        run = graph.stream_v2({"value": "", "items": []})
-
-        collected: list[SubgraphRunStream] = []
-        for sub in run.subgraphs:
-            collected.append(sub)
-        assert collected == []
-        # Output still resolves.
-        assert run.output is not None
-
-    def test_nested_graph_yields_one_child(self) -> None:
-        graph = _build_nested_graph()
-        run = graph.stream_v2({"value": "", "items": []})
-
-        collected: list[SubgraphRunStream] = []
-        for sub in run.subgraphs:
-            collected.append(sub)
-
-        assert len(collected) == 1
-        child = collected[0]
-        assert len(child.path) == 1
-        assert child.path[0].startswith("sub:")
-        assert child.status == "completed"
-
-    def test_error_in_subgraph_fails_child(self) -> None:
-        def boom(state: SimpleState) -> dict:
-            raise RuntimeError("subgraph_failed")
-
-        inner_builder = StateGraph(SimpleState)
-        inner_builder.add_node("inner", boom)
-        inner_builder.add_edge(START, "inner")
-        inner_builder.add_edge("inner", END)
-        inner = inner_builder.compile()
-
-        outer_builder = StateGraph(SimpleState)
-        outer_builder.add_node("sub", inner)
-        outer_builder.add_edge(START, "sub")
-        outer_builder.add_edge("sub", END)
-        graph = outer_builder.compile()
-
-        run = graph.stream_v2({"value": "", "items": []})
-
-        collected: list[SubgraphRunStream] = []
-        with pytest.raises(RuntimeError):
-            for sub in run.subgraphs:
-                collected.append(sub)
-
-        assert len(collected) == 1
-        assert collected[0].status == "failed"
-
-
-class TestSubgraphTransformerAsyncEndToEnd:
-    @pytest.mark.anyio
-    async def test_nested_graph_yields_one_child(self) -> None:
-        async def inner(state: SimpleState) -> dict:
-            return {"value": state["value"] + "X", "items": ["x"]}
-
-        inner_builder = StateGraph(SimpleState)
-        inner_builder.add_node("inner", inner)
-        inner_builder.add_edge(START, "inner")
-        inner_builder.add_edge("inner", END)
-        inner_graph = inner_builder.compile()
-
-        outer_builder = StateGraph(SimpleState)
-        outer_builder.add_node("sub", inner_graph)
-        outer_builder.add_edge(START, "sub")
-        outer_builder.add_edge("sub", END)
-        graph = outer_builder.compile()
-
-        run = await graph.astream_v2({"value": "", "items": []})
-
-        collected: list[SubgraphRunStream] = []
-        async for sub in run.subgraphs:
-            collected.append(sub)
-
-        assert len(collected) == 1
-        child = collected[0]
-        assert child.status == "completed"
-
-
-class TestSubgraphTriggerCallId:
-    """Confirm `trigger_call_id` flows from real pregel metadata."""
-
-    def test_trigger_call_id_populated_end_to_end(self) -> None:
-        graph = _build_nested_graph()
-        run = graph.stream_v2({"value": "", "items": []})
-
-        collected: list[SubgraphRunStream] = list(run.subgraphs)
-        assert len(collected) == 1
-        child = collected[0]
-
-        # The child's single-segment path encodes `node_name:task_id`.
-        # Both the parsed task_id (`trigger_call_id`) and the segment
-        # should match the same task_id suffix.
-        assert ":" in child.path[0]
-        node_name, _, task_id = child.path[0].partition(":")
-        assert node_name == "sub"
-        assert task_id  # non-empty
-        assert child.trigger_call_id == task_id
-
-
-class TestSubgraphInterrupt:
-    """Interrupts raised inside a subgraph surface as status=interrupted."""
-
-    def _build_interrupt_subgraph(self):
-        def inner_node(state: SimpleState) -> dict:
-            interrupt("need approval")
-            return {"value": state["value"] + "X", "items": ["x"]}
-
-        inner_builder = StateGraph(SimpleState)
-        inner_builder.add_node("inner_node", inner_node)
-        inner_builder.add_edge(START, "inner_node")
-        inner_builder.add_edge("inner_node", END)
-        inner = inner_builder.compile()
-
-        outer_builder = StateGraph(SimpleState)
-        outer_builder.add_node("sub", inner)
-        outer_builder.add_edge(START, "sub")
-        outer_builder.add_edge("sub", END)
-        return outer_builder.compile(checkpointer=InMemorySaver())
-
-    def test_interrupt_in_subgraph_marks_handle_interrupted(self) -> None:
-        graph = self._build_interrupt_subgraph()
-        run = graph.stream_v2(
-            {"value": "", "items": []},
-            config={"configurable": {"thread_id": "t1"}},
-        )
-
-        collected: list[SubgraphRunStream] = list(run.subgraphs)
-
-        assert run.interrupted is True
-        assert len(collected) == 1
-        assert collected[0].status == "interrupted"
-
-
-class TestSubgraphNameCollision:
-    """The subgraph's compiled `name` equaling its node name is detected.
-
-    Primary detector `name != langgraph_node` fails here; the
-    parent_run_id fallback in `_is_nested_pregel_start` is what keeps
-    the subgraph visible.
-    """
-
-    def test_name_equals_node_name_still_detected(self) -> None:
-        def inner_node(state: SimpleState) -> dict:
-            return {"value": state["value"] + "X", "items": ["x"]}
-
-        inner_builder = StateGraph(SimpleState)
-        inner_builder.add_node("inner_node", inner_node)
-        inner_builder.add_edge(START, "inner_node")
-        inner_builder.add_edge("inner_node", END)
-        # Compile with the same name as the node it will be registered as.
-        inner = inner_builder.compile(name="sub")
-
-        outer_builder = StateGraph(SimpleState)
-        outer_builder.add_node("sub", inner)
-        outer_builder.add_edge(START, "sub")
-        outer_builder.add_edge("sub", END)
-        graph = outer_builder.compile()
-
-        run = graph.stream_v2({"value": "", "items": []})
-
-        collected: list[SubgraphRunStream] = list(run.subgraphs)
-        assert len(collected) == 1
-        child = collected[0]
-        assert child.graph_name == "sub"
-        assert child.status == "completed"
@@ -37,6 +37,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
    Returns a list of dicts (newest-first, matching get_state_history order) with:
      - id: short checkpoint id suffix (last 6 chars)
      - parent_id: short parent checkpoint id suffix or None
+      - source: checkpoint metadata source (input, loop, fork, update)
      - next: tuple of next node names
      - values: channel values snapshot
    """
@@ -52,6 +53,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
            {
                "id": cid[-6:],
                "parent_id": pid[-6:] if pid else None,
+                "source": s.metadata.get("source"),
                "next": s.next,
                "values": s.values,
            }
@@ -280,6 +282,116 @@ def test_replay_from_before_interrupt_refires(
    assert call_count["node_b"] == 1  # NOT re-executed (after interrupt)


+def test_replay_from_before_interrupt_then_resume(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Replay from checkpoint before interrupt node, then resume with a new
+    answer and verify the graph completes with the new value.
+
+    Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
+
+    Original run:
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(node_a,)     values=[]
+      source=loop   next=(ask_human,)  values=[a]            <-- replay from here
+      source=loop   next=(node_b,)     values=[a, human:old_answer]
+      source=loop   next=()            values=[a, human:old_answer, b]
+
+    After replay (fork created) + resume with "new_answer":
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(node_a,)     values=[]
+      source=loop   next=(ask_human,)  values=[a]            <-- branch point
+      source=loop   next=(node_b,)     values=[a, human:old_answer]
+      source=loop   next=()            values=[a, human:old_answer, b]  (old branch)
+      source=fork   next=(ask_human,)  values=[a]            <-- fork from branch point
+      source=loop   next=(node_b,)     values=[a, human:new_answer]
+      source=loop   next=()            values=[a, human:new_answer, b]  (new branch)
+    """
+
+    called: list[str] = []
+
+    def node_a(state: State) -> State:
+        called.append("node_a")
+        return {"value": ["a"]}
+
+    def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("What is your input?")
+        return {"value": [f"human:{answer}"]}
+
+    def node_b(state: State) -> State:
+        called.append("node_b")
+        return {"value": ["b"]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("ask_human", ask_human)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "ask_human")
+        .add_edge("ask_human", "node_b")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: invoke until interrupt, then resume to complete ---
+    graph.invoke({"value": []}, config)
+    graph.invoke(Command(resume="old_answer"), config)
+
+    original_history = list(graph.get_state_history(config))
+    original = _checkpoint_summary(original_history)
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Replay from checkpoint before ask_human ---
+    before_ask = next(s for s in original_history if s.next == ("ask_human",))
+
+    called.clear()
+    replay_result = graph.invoke(None, before_ask.config)
+    assert replay_result["__interrupt__"][0].value == "What is your input?"
+    assert "ask_human" in called
+    assert "node_a" not in called  # before the replay point, not re-executed
+
+    # A fork checkpoint is now the latest — it branches from the replay point
+    post_replay = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"]) for s in post_replay] == [
+        ("fork", ("ask_human",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("node_b",)),
+        ("loop", ("ask_human",)),  # branch point
+        ("loop", ("node_a",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer ---
+    called.clear()
+    final_result = graph.invoke(Command(resume="new_answer"), config)
+    assert final_result["value"] == ["a", "human:new_answer", "b"]
+    assert "ask_human" in called
+    assert "node_b" in called
+
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from fork)
+        ("loop", (), {"value": ["a", "human:new_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
+        ("fork", ("ask_human",), {"value": ["a"]}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
 def test_replay_interrupt_stable_across_replays(
    sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -320,8 +432,14 @@ def test_replay_interrupt_stable_across_replays(
        r = graph.invoke(None, before_ask.config)
        results.append(r)

-    assert all(r == results[0] for r in results)
-    assert "__interrupt__" in results[0]
+    # Each replay creates a fork with a unique interrupt ID, so we compare
+    # interrupt values and state values rather than full equality.
+    assert all("__interrupt__" in r for r in results)
+    assert all(
+        r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
+        for r in results
+    )
+    assert all(r["value"] == results[0]["value"] for r in results)


 def test_fork_from_before_interrupt_refires(
@@ -854,6 +972,354 @@ def test_subgraph_interrupt_replay_from_interrupt_checkpoint(
    assert "step_b" not in called


+def test_subgraph_interrupt_replay_from_parent_then_resume(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Replay from the parent checkpoint where a subgraph interrupt fired,
+    then resume with a new answer. Verifies that a fork is created and the
+    full graph completes. Checks full checkpoint history at each stage."""
+
+    called: list[str] = []
+
+    def router(state: State) -> State:
+        called.append("router")
+        return {"value": ["routed"]}
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["sub_a"]}
+
+    def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("Provide input:")
+        return {"value": [f"human:{answer}"]}
+
+    def step_b(state: State) -> State:
+        called.append("step_b")
+        return {"value": ["sub_b"]}
+
+    subgraph = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_human", ask_human)
+        .add_node("step_b", step_b)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_human")
+        .add_edge("ask_human", "step_b")
+        .compile(checkpointer=True)
+    )
+
+    def post_process(state: State) -> State:
+        called.append("post_process")
+        return {"value": ["post"]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("router", router)
+        .add_node("subgraph_node", subgraph)
+        .add_node("post_process", post_process)
+        .add_edge(START, "router")
+        .add_edge("router", "subgraph_node")
+        .add_edge("subgraph_node", "post_process")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt, then resume to complete
+    graph.invoke({"value": []}, config)
+    graph.invoke(Command(resume="old_answer"), config)
+
+    # Original parent history (newest first)
+    original_history = list(graph.get_state_history(config))
+    assert [s.next for s in original_history] == [
+        (),  # done
+        ("post_process",),
+        ("subgraph_node",),  # subgraph ran, interrupt fired here
+        ("router",),
+        ("__start__",),
+    ]
+
+    # Find the parent checkpoint where the interrupt fired
+    interrupt_checkpoint = next(
+        s for s in original_history if s.next == ("subgraph_node",)
+    )
+
+    # Replay from parent checkpoint — subgraph re-executes, interrupt re-fires
+    called.clear()
+    replay_result = graph.invoke(None, interrupt_checkpoint.config)
+    assert "__interrupt__" in replay_result
+    assert replay_result["__interrupt__"][0].value == "Provide input:"
+    assert "step_a" in called
+    assert "ask_human" in called
+    assert "step_b" not in called
+
+    # Verify fork checkpoint was created
+    post_replay_history = list(graph.get_state_history(config))
+    assert [s.next for s in post_replay_history] == [
+        ("subgraph_node",),  # fork (interrupt pending)
+        (),  # original done
+        ("post_process",),
+        ("subgraph_node",),
+        ("router",),
+        ("__start__",),
+    ]
+    assert [s.metadata["source"] for s in post_replay_history] == [
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+    fork = post_replay_history[0]
+    assert (
+        fork.parent_config["configurable"]["checkpoint_id"]
+        == interrupt_checkpoint.config["configurable"]["checkpoint_id"]
+    )
+
+    # Resume with a new answer — full graph should complete
+    called.clear()
+    final_result = graph.invoke(Command(resume="new_answer"), config)
+    assert "__interrupt__" not in final_result
+    assert "human:new_answer" in final_result["value"]
+    assert "sub_b" in final_result["value"]
+    assert "post" in final_result["value"]
+    assert "ask_human" in called
+    assert "step_b" in called
+    assert "post_process" in called
+
+    # Final checkpoint history
+    final_history = list(graph.get_state_history(config))
+    assert [s.next for s in final_history] == [
+        (),  # new branch done
+        ("post_process",),  # new branch post_process
+        ("subgraph_node",),  # fork
+        (),  # original done
+        ("post_process",),
+        ("subgraph_node",),
+        ("router",),
+        ("__start__",),
+    ]
+    assert [s.metadata["source"] for s in final_history] == [
+        "loop",
+        "loop",
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+
+
+def test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Resume with Command(resume=...) plus the current head checkpoint_id
+    in config. The subgraph must continue from the interrupted node, not
+    restart from scratch. Explicit checkpoint_id triggers is_replaying but
+    this is a resume, not a time-travel, so ReplayState should not apply."""
+
+    called: list[str] = []
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["sub_a"]}
+
+    def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("Provide input:")
+        return {"value": [f"human:{answer}"]}
+
+    def step_b(state: State) -> State:
+        called.append("step_b")
+        return {"value": ["sub_b"]}
+
+    subgraph = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_human", ask_human)
+        .add_node("step_b", step_b)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_human")
+        .add_edge("ask_human", "step_b")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("subgraph_node", subgraph)
+        .add_edge(START, "subgraph_node")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt fires in subgraph
+    graph.invoke({"value": []}, config)
+    assert called == ["step_a", "ask_human"]
+
+    # Resume with explicit head checkpoint_id in config
+    head_checkpoint_id = graph.get_state(config).config["configurable"]["checkpoint_id"]
+    called.clear()
+    resume_config = {
+        "configurable": {
+            "thread_id": "1",
+            "checkpoint_id": head_checkpoint_id,
+            "checkpoint_ns": "",
+        }
+    }
+    result = graph.invoke(Command(resume="answer"), resume_config)
+
+    assert called == ["ask_human", "step_b"]
+    assert "__interrupt__" not in result
+    assert result["value"] == ["sub_a", "human:answer", "sub_b"]
+
+
+def test_subgraph_replay_loads_accumulated_state_then_resume(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Two parent invocations, then replay from before the subgraph in the
+    2nd invocation. The subgraph (checkpointer=True) should load its
+    accumulated state from the 1st invocation via ReplayState, re-fire
+    the interrupt, and then resume + complete.
+
+    This tests the ReplayState path: the parent is replaying and the
+    subgraph uses list(before=parent_checkpoint_id) to find its
+    corresponding checkpoint from the original execution.
+    """
+
+    class SubState(TypedDict):
+        value: Annotated[list[str], operator.add]
+
+    class ParentState(TypedDict):
+        results: Annotated[list[str], operator.add]
+
+    started_state: list[dict] = []
+
+    def step_a(state: SubState) -> SubState:
+        started_state.append(dict(state))
+        answer = interrupt("question_a")
+        return {"value": [f"a:{answer}"]}
+
+    subgraph = (
+        StateGraph(SubState)
+        .add_node("step_a", step_a)
+        .add_edge(START, "step_a")
+        .compile(checkpointer=True)
+    )
+
+    def parent_node(state: ParentState) -> ParentState:
+        return {"results": ["p"]}
+
+    graph = (
+        StateGraph(ParentState)
+        .add_node("parent_node", parent_node)
+        .add_node("sub_node", subgraph)
+        .add_edge(START, "parent_node")
+        .add_edge("parent_node", "sub_node")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # === 1st invocation: complete with answer "a1" ===
+    graph.invoke({"results": []}, config)
+    graph.invoke(Command(resume="a1"), config)
+
+    # step_a saw empty state (fresh subgraph)
+    assert started_state[0] == {"value": []}
+
+    # === 2nd invocation: complete with answer "a2" ===
+    started_state.clear()
+    graph.invoke({"results": []}, config)
+    graph.invoke(Command(resume="a2"), config)
+
+    # Stateful subgraph retained state from 1st invocation
+    assert started_state[0] == {"value": ["a:a1"]}
+
+    # Original history (newest first)
+    original_history = list(graph.get_state_history(config))
+    assert [s.next for s in original_history] == [
+        (),  # 2nd done
+        ("sub_node",),  # 2nd sub_node
+        ("parent_node",),  # 2nd parent_node
+        ("__start__",),  # 2nd input
+        (),  # 1st done
+        ("sub_node",),  # 1st sub_node
+        ("parent_node",),  # 1st parent_node
+        ("__start__",),  # 1st input
+    ]
+
+    # Replay from before sub_node in 2nd invocation (newest match)
+    before_sub_2nd = [s for s in original_history if s.next == ("sub_node",)][0]
+    started_state.clear()
+    replay = graph.invoke(None, before_sub_2nd.config)
+    assert "__interrupt__" in replay
+
+    # Subgraph should see accumulated state from END of 1st invocation
+    assert started_state[0] == {"value": ["a:a1"]}
+
+    # Verify fork was created
+    post_replay_history = list(graph.get_state_history(config))
+    assert [s.next for s in post_replay_history] == [
+        ("sub_node",),  # fork (interrupt pending)
+        (),  # 2nd done
+        ("sub_node",),  # 2nd sub_node
+        ("parent_node",),  # 2nd parent_node
+        ("__start__",),  # 2nd input
+        (),  # 1st done
+        ("sub_node",),  # 1st sub_node
+        ("parent_node",),  # 1st parent_node
+        ("__start__",),  # 1st input
+    ]
+    assert [s.metadata["source"] for s in post_replay_history] == [
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+
+    # Resume with a new answer
+    started_state.clear()
+    final = graph.invoke(Command(resume="a3"), config)
+    assert "__interrupt__" not in final
+    assert final["results"] == ["p", "p"]
+
+    # Final history
+    final_history = list(graph.get_state_history(config))
+    assert [s.next for s in final_history] == [
+        (),  # new branch done
+        ("sub_node",),  # fork
+        (),  # 2nd done
+        ("sub_node",),  # 2nd sub_node
+        ("parent_node",),  # 2nd parent_node
+        ("__start__",),  # 2nd input
+        (),  # 1st done
+        ("sub_node",),  # 1st sub_node
+        ("parent_node",),  # 1st parent_node
+        ("__start__",),  # 1st input
+    ]
+    assert [s.metadata["source"] for s in final_history] == [
+        "loop",
+        "fork",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+        "loop",
+        "loop",
+        "loop",
+        "input",
+    ]
+
+
 def test_subgraph_interrupt_full_flow(
    sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -1290,6 +1756,321 @@ def test_subgraph_time_travel_to_second_interrupt(
    assert "ask_1" not in called


+def test_subgraph_time_travel_resume_from_first_interrupt(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the first interrupt, then
+    resume through both interrupts with new answers.
+
+    This verifies the key bug fix: after time-traveling to a subgraph
+    checkpoint with an interrupt, a fork checkpoint is created so that
+    subsequent resumes find the correct state (not the old branch tip).
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+
+    Parent history after original run completes:
+      source=input  next=(__start__,)   values=[]
+      source=loop   next=(executor,)    values=[]
+      source=loop   next=()             values=[step_a_done, ask_1:answer_1, ask_2:answer_2]
+
+    After time-traveling to 1st interrupt + resuming with new answers:
+      source=input  next=(__start__,)   values=[]
+      source=loop   next=(executor,)    values=[]          <-- branch point
+      source=loop   next=()             values=[..., ask_2:answer_2]  (old branch)
+      source=fork   next=(executor,)    values=[]          <-- fork from time travel
+      source=loop   next=()             values=[..., ask_2:new_answer_2]  (new branch)
+    """
+
+    called: list[str] = []
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    graph.invoke({"value": []}, config)
+    sub_config_at_first = graph.get_state(config, subgraphs=True).tasks[0].state.config
+    graph.invoke(Command(resume="answer_1"), config)
+    graph.invoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to first interrupt's subgraph checkpoint ---
+    called.clear()
+    replay_result = graph.invoke(None, sub_config_at_first)
+    assert replay_result["__interrupt__"][0].value == "Question 1?"
+    assert "step_a" not in called  # before interrupt, not re-executed
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume both interrupts with new answers ---
+    called.clear()
+    resume_1 = graph.invoke(Command(resume="new_answer_1"), config)
+    assert resume_1["__interrupt__"][0].value == "Question 2?"
+    assert "ask_1" in called
+
+    called.clear()
+    resume_2 = graph.invoke(Command(resume="new_answer_2"), config)
+    assert resume_2["value"] == [
+        "step_a_done",
+        "ask_1:new_answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history: original branch preserved, new branch appended
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+def test_subgraph_time_travel_resume_from_second_interrupt(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the second interrupt, then
+    resume with a new answer. The first interrupt's answer should be preserved.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+
+    Key assertion: after resuming from a time-travel to the 2nd interrupt,
+    the final state keeps ask_1's original answer but uses the new ask_2 answer.
+    """
+
+    called: list[str] = []
+
+    def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    graph.invoke({"value": []}, config)
+    graph.invoke(Command(resume="answer_1"), config)
+    sub_config_at_second = graph.get_state(config, subgraphs=True).tasks[0].state.config
+    graph.invoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to second interrupt ---
+    called.clear()
+    replay_result = graph.invoke(None, sub_config_at_second)
+    assert replay_result["__interrupt__"][0].value == "Question 2?"
+    assert "step_a" not in called
+    assert "ask_1" not in called  # already resolved, not re-executed
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer for ask_2 only ---
+    called.clear()
+    resume_result = graph.invoke(Command(resume="new_answer_2"), config)
+    # ask_1's original answer preserved, ask_2 uses the new answer
+    assert resume_result["value"] == [
+        "step_a_done",
+        "ask_1:answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history: original branch preserved, new branch appended
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+def test_subgraph_time_travel_checkpoint_pattern(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Verify the checkpoint pattern created by time travel to a subgraph
+    interrupt. A fork checkpoint should branch from the replay point and
+    become the latest parent checkpoint.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> ask (interrupt) --> END
+
+    Original run (after completing):
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(executor,)   values=[]          <-- replay point
+      source=loop   next=()            values=[a:first]
+
+    After time travel to interrupt + resume with "second":
+      source=input  next=(__start__,)  values=[]
+      source=loop   next=(executor,)   values=[]          <-- branch point
+      source=loop   next=()            values=[a:first]   (old branch)
+      source=fork   next=(executor,)   values=[]          <-- fork
+      source=loop   next=()            values=[a:second]  (new branch)
+    """
+
+    def ask(state: State) -> State:
+        answer = interrupt("Q?")
+        return {"value": [f"a:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("ask", ask)
+        .add_edge(START, "ask")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt, then complete
+    graph.invoke({"value": []}, config)
+    sub_config = graph.get_state(config, subgraphs=True).tasks[0].state.config
+    graph.invoke(Command(resume="first"), config)
+
+    original = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # Time travel to the interrupt
+    graph.invoke(None, sub_config)
+
+    # Fork is now the latest, branching from the original replay point
+    post_tt = list(graph.get_state_history(config))
+    post_tt_summary = _checkpoint_summary(post_tt)
+    assert [(s["source"], s["next"]) for s in post_tt_summary] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),
+        ("loop", ("executor",)),  # <-- replay point / fork parent
+        ("input", ("__start__",)),
+    ]
+    # Verify the fork's parent is the original replay point
+    replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
+    assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
+
+    # Resume from the fork — graph completes with new answer
+    result = graph.invoke(Command(resume="second"), config)
+    assert result["value"] == ["a:second"]
+
+    final = _checkpoint_summary(list(graph.get_state_history(config)))
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch
+        ("loop", (), {"value": ["a:second"]}),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
 def test_subgraph_time_travel_after_completion(
    sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -2283,14 +3064,16 @@ def test_replay_creates_branch_preserving_old_checkpoints(
    # -- Post-replay checkpoint history (newest first) --
    post_replay_history = list(graph.get_state_history(config))
    post_summary = _checkpoint_summary(post_replay_history)
-    assert len(post_summary) == 7  # 5 original + 2 new branch checkpoints
+    # 5 original + 1 fork + 2 new branch checkpoints = 8
+    assert len(post_summary) == 8

    # Verify the full shape after replay
    assert [s["next"] for s in post_summary] == [
-        (),  # new branch tip (C6)
-        ("node_c",),  # new branch (C5)
-        (),  # old branch tip (C4)
-        ("node_c",),  # old (C3)
+        (),  # new branch tip
+        ("node_c",),  # new branch
+        ("node_b",),  # fork from replay point
+        (),  # old branch tip
+        ("node_c",),  # old
        ("node_b",),  # branch point (C2)
        ("node_a",),  # old (C1)
        ("__start__",),  # old (C0)
@@ -2298,6 +3081,7 @@ def test_replay_creates_branch_preserving_old_checkpoints(
    assert [s["values"] for s in post_summary] == [
        {"value": ["a", "b2", "c"]},  # new branch tip
        {"value": ["a", "b2"]},  # new: node_b re-ran with call_count=2
+        {"value": ["a"]},  # fork from replay point
        {"value": ["a", "b1", "c"]},  # old branch tip preserved
        {"value": ["a", "b1"]},  # old
        {"value": ["a"]},  # branch point
@@ -46,6 +46,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
    Returns a list of dicts (newest-first, matching get_state_history order) with:
      - id: short checkpoint id suffix (last 6 chars)
      - parent_id: short parent checkpoint id suffix or None
+      - source: checkpoint metadata source (input, loop, fork, update)
      - next: tuple of next node names
      - values: channel values snapshot
    """
@@ -61,6 +62,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
            {
                "id": cid[-6:],
                "parent_id": pid[-6:] if pid else None,
+                "source": s.metadata.get("source"),
                "next": s.next,
                "values": s.values,
            }
@@ -335,8 +337,14 @@ async def test_replay_interrupt_stable_across_replays(
        r = await graph.ainvoke(None, before_ask.config)
        results.append(r)

-    assert all(r == results[0] for r in results)
-    assert "__interrupt__" in results[0]
+    # Each replay creates a fork with a unique interrupt ID, so we compare
+    # interrupt values and state values rather than full equality.
+    assert all("__interrupt__" in r for r in results)
+    assert all(
+        r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
+        for r in results
+    )
+    assert all(r["value"] == results[0]["value"] for r in results)


@NEEDS_CONTEXTVARS
@@ -1261,6 +1269,391 @@ async def test_subgraph_time_travel_after_completion_async(
    assert "ask_2:answer_2" in replay_result["value"]


+@NEEDS_CONTEXTVARS
+async def test_replay_from_before_interrupt_then_resume_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Replay from checkpoint before interrupt node, then resume with a new
+    answer and verify the graph completes with the new value.
+
+    Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
+    """
+
+    called: list[str] = []
+
+    async def node_a(state: State) -> State:
+        called.append("node_a")
+        return {"value": ["a"]}
+
+    async def ask_human(state: State) -> State:
+        called.append("ask_human")
+        answer = interrupt("What is your input?")
+        return {"value": [f"human:{answer}"]}
+
+    async def node_b(state: State) -> State:
+        called.append("node_b")
+        return {"value": ["b"]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("ask_human", ask_human)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "ask_human")
+        .add_edge("ask_human", "node_b")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: invoke until interrupt, then resume to complete ---
+    await graph.ainvoke({"value": []}, config)
+    await graph.ainvoke(Command(resume="old_answer"), config)
+
+    original_history = [s async for s in graph.aget_state_history(config)]
+    original = _checkpoint_summary(original_history)
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Replay from checkpoint before ask_human ---
+    before_ask = next(s for s in original_history if s.next == ("ask_human",))
+
+    called.clear()
+    replay_result = await graph.ainvoke(None, before_ask.config)
+    assert replay_result["__interrupt__"][0].value == "What is your input?"
+    assert "ask_human" in called
+    assert "node_a" not in called
+
+    # A fork checkpoint is now the latest
+    post_replay = _checkpoint_summary(
+        [s async for s in graph.aget_state_history(config)]
+    )
+    assert [(s["source"], s["next"]) for s in post_replay] == [
+        ("fork", ("ask_human",)),
+        ("loop", ()),
+        ("loop", ("node_b",)),
+        ("loop", ("ask_human",)),
+        ("loop", ("node_a",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer ---
+    called.clear()
+    final_result = await graph.ainvoke(Command(resume="new_answer"), config)
+    assert final_result["value"] == ["a", "human:new_answer", "b"]
+    assert "ask_human" in called
+    assert "node_b" in called
+
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from fork)
+        ("loop", (), {"value": ["a", "human:new_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
+        ("fork", ("ask_human",), {"value": ["a"]}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["a", "human:old_answer", "b"]}),
+        ("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
+        ("loop", ("ask_human",), {"value": ["a"]}),
+        ("loop", ("node_a",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_subgraph_time_travel_resume_from_first_interrupt_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the first interrupt, then
+    resume through both interrupts with new answers.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+    """
+
+    called: list[str] = []
+
+    async def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    async def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    async def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    await graph.ainvoke({"value": []}, config)
+    sub_config_at_first = (
+        (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
+    )
+    await graph.ainvoke(Command(resume="answer_1"), config)
+    await graph.ainvoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to first interrupt's subgraph checkpoint ---
+    called.clear()
+    replay_result = await graph.ainvoke(None, sub_config_at_first)
+    assert replay_result["__interrupt__"][0].value == "Question 1?"
+    assert "step_a" not in called
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume both interrupts with new answers ---
+    called.clear()
+    resume_1 = await graph.ainvoke(Command(resume="new_answer_1"), config)
+    assert resume_1["__interrupt__"][0].value == "Question 2?"
+    assert "ask_1" in called
+
+    called.clear()
+    resume_2 = await graph.ainvoke(Command(resume="new_answer_2"), config)
+    assert resume_2["value"] == [
+        "step_a_done",
+        "ask_1:new_answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history: original branch preserved, new branch appended
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_subgraph_time_travel_resume_from_second_interrupt_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Time travel to a subgraph checkpoint at the second interrupt, then
+    resume with a new answer. The first interrupt's answer should be preserved.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
+    """
+
+    called: list[str] = []
+
+    async def step_a(state: State) -> State:
+        called.append("step_a")
+        return {"value": ["step_a_done"]}
+
+    async def ask_1(state: State) -> State:
+        called.append("ask_1")
+        answer = interrupt("Question 1?")
+        return {"value": [f"ask_1:{answer}"]}
+
+    async def ask_2(state: State) -> State:
+        called.append("ask_2")
+        answer = interrupt("Question 2?")
+        return {"value": [f"ask_2:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("step_a", step_a)
+        .add_node("ask_1", ask_1)
+        .add_node("ask_2", ask_2)
+        .add_edge(START, "step_a")
+        .add_edge("step_a", "ask_1")
+        .add_edge("ask_1", "ask_2")
+        .add_edge("ask_2", "__end__")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # --- Original run: hit both interrupts and resume ---
+    await graph.ainvoke({"value": []}, config)
+    await graph.ainvoke(Command(resume="answer_1"), config)
+    sub_config_at_second = (
+        (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
+    )
+    await graph.ainvoke(Command(resume="answer_2"), config)
+
+    original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # --- Time travel to second interrupt ---
+    called.clear()
+    replay_result = await graph.ainvoke(None, sub_config_at_second)
+    assert replay_result["__interrupt__"][0].value == "Question 2?"
+    assert "step_a" not in called
+    assert "ask_1" not in called
+
+    # Fork is now the latest parent checkpoint
+    post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"]) for s in post_tt] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),  # original done
+        ("loop", ("executor",)),
+        ("input", ("__start__",)),
+    ]
+
+    # --- Resume with a new answer for ask_2 only ---
+    called.clear()
+    resume_result = await graph.ainvoke(Command(resume="new_answer_2"), config)
+    assert resume_result["value"] == [
+        "step_a_done",
+        "ask_1:answer_1",
+        "ask_2:new_answer_2",
+    ]
+
+    # Verify final history
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch (from time travel fork)
+        (
+            "loop",
+            (),
+            {"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
+        ),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch (preserved)
+        ("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_subgraph_time_travel_checkpoint_pattern_async(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Verify the checkpoint pattern created by time travel to a subgraph
+    interrupt. A fork checkpoint should branch from the replay point.
+
+    Parent:    START --> executor (subgraph, checkpointer=True) --> END
+    Executor:  START --> ask (interrupt) --> END
+    """
+
+    async def ask(state: State) -> State:
+        answer = interrupt("Q?")
+        return {"value": [f"a:{answer}"]}
+
+    executor = (
+        StateGraph(State)
+        .add_node("ask", ask)
+        .add_edge(START, "ask")
+        .compile(checkpointer=True)
+    )
+
+    graph = (
+        StateGraph(State)
+        .add_node("executor", executor)
+        .add_edge(START, "executor")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Run until interrupt, then complete
+    await graph.ainvoke({"value": []}, config)
+    sub_config = (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
+    await graph.ainvoke(Command(resume="first"), config)
+
+    original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in original] == [
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+    # Time travel to the interrupt
+    await graph.ainvoke(None, sub_config)
+
+    # Fork is now the latest, branching from the original replay point
+    post_tt = [s async for s in graph.aget_state_history(config)]
+    post_tt_summary = _checkpoint_summary(post_tt)
+    assert [(s["source"], s["next"]) for s in post_tt_summary] == [
+        ("fork", ("executor",)),  # <-- new fork (latest)
+        ("loop", ()),
+        ("loop", ("executor",)),  # <-- replay point / fork parent
+        ("input", ("__start__",)),
+    ]
+    # Verify the fork's parent is the original replay point
+    replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
+    assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
+
+    # Resume from the fork
+    result = await graph.ainvoke(Command(resume="second"), config)
+    assert result["value"] == ["a:second"]
+
+    final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
+    assert [(s["source"], s["next"], s["values"]) for s in final] == [
+        # New branch
+        ("loop", (), {"value": ["a:second"]}),
+        ("fork", ("executor",), {"value": []}),
+        # Original branch
+        ("loop", (), {"value": ["a:first"]}),
+        ("loop", ("executor",), {"value": []}),
+        ("input", ("__start__",), {"value": []}),
+    ]
+
+
@NEEDS_CONTEXTVARS
 async def test_3_levels_deep_time_travel_to_first_interrupt_async(
    async_checkpointer: BaseCheckpointSaver,
@@ -2088,13 +2481,15 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
    # -- Post-replay checkpoint history (newest first) --
    post_replay_history = [s async for s in graph.aget_state_history(config)]
    post_summary = _checkpoint_summary(post_replay_history)
-    assert len(post_summary) == 7  # 5 original + 2 new branch checkpoints
+    # 5 original + 1 fork + 2 new branch checkpoints = 8
+    assert len(post_summary) == 8

    assert [s["next"] for s in post_summary] == [
-        (),  # new branch tip (C6)
-        ("node_c",),  # new branch (C5)
-        (),  # old branch tip (C4)
-        ("node_c",),  # old (C3)
+        (),  # new branch tip
+        ("node_c",),  # new branch
+        ("node_b",),  # fork from replay point
+        (),  # old branch tip
+        ("node_c",),  # old
        ("node_b",),  # branch point (C2)
        ("node_a",),  # old (C1)
        ("__start__",),  # old (C0)
@@ -2102,6 +2497,7 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
    assert [s["values"] for s in post_summary] == [
        {"value": ["a", "b2", "c"]},  # new branch tip
        {"value": ["a", "b2"]},  # new: node_b re-ran with call_count=2
+        {"value": ["a"]},  # fork from replay point
        {"value": ["a", "b1", "c"]},  # old branch tip preserved
        {"value": ["a", "b1"]},  # old
        {"value": ["a"]},  # branch point
@@ -1348,11 +1348,10 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
-    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -1361,26 +1360,14 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a8/03/7219502e8ca728d65eb44d7a3eb60239230742a70dbfc9241b9bfd61c4ab/langchain_core-1.3.2.tar.gz", hash = "sha256:fd7a50b2f28ba561fd9d7f5d2760bc9e06cf00cdf820a3ccafe88a94ffa8d5b7", size = 911813, upload-time = "2026-04-24T15:49:23.699Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7d/d5/8fa4431007cbb7cfed7590f4d6a5dea3ad724f4174d248f6642ef5ce7d05/langchain_core-1.3.2-py3-none-any.whl", hash = "sha256:d44a66127f9f8db735bdfd0ab9661bccb47a97113cfd3f2d89c74864422b7274", size = 542390, upload-time = "2026-04-24T15:49:21.991Z" },
-]
-
-[[package]]
-name = "langchain-protocol"
-version = "0.0.11"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/d0/bb/38b5eaefa41c67735eedd9f9a2568b11c9eb376fa129a5edd7cc3dcde071/langchain_protocol-0.0.11.tar.gz", hash = "sha256:c276e2373b5ac691fc7ac9a72019d55182444ce8e89385c3f7e9f0185d0aace7", size = 6622, upload-time = "2026-04-23T22:13:16.771Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0b/fa/6a8ecad8472b182f2caf9d83fd89f40fc1590cb96546d90089b7869b7f5e/langchain_protocol-0.0.11-py3-none-any.whl", hash = "sha256:364da1faf6f5d3001413bede792c1a822c0f23ae55d1ce1266ca7d8e80e79011", size = 6778, upload-time = "2026-04-23T22:13:15.712Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -1452,7 +1439,7 @@ test = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-prebuilt", editable = "../prebuilt" },
    { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1561,7 +1548,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -1719,7 +1706,7 @@ inmem = [
 requires-dist = [
    { name = "click", specifier = ">=8.1.7" },
    { name = "httpx", specifier = ">=0.24.0" },
-    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
+    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.9.0" },
    { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
    { name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
    { name = "pathspec", specifier = ">=0.11.0" },
@@ -1755,7 +1742,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "../prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -1865,7 +1852,7 @@ test = [

 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "httpx" },
@@ -1875,11 +1862,12 @@ dependencies = [
    { name = "requests" },
    { name = "requests-toolbelt" },
    { name = "uuid-utils" },
+    { name = "xxhash" },
    { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
 ]

 [package.optional-dependencies]
@@ -614,6 +614,7 @@ class _InjectedArgs:
    store: str | None
    runtime: str | None
    all_injected_keys: set[str]
+    _optional_state_args: set[str]


 class ToolNode(RunnableCallable):
@@ -807,6 +808,7 @@ class ToolNode(RunnableCallable):
                context=runtime.context,
                store=runtime.store,
                stream_writer=runtime.stream_writer,
+                tools=list(self.tools_by_name.values()),
                execution_info=runtime.execution_info,
                server_info=runtime.server_info,
            )
@@ -841,6 +843,7 @@ class ToolNode(RunnableCallable):
                context=runtime.context,
                store=runtime.store,
                stream_writer=runtime.stream_writer,
+                tools=list(self.tools_by_name.values()),
                execution_info=runtime.execution_info,
                server_info=runtime.server_info,
            )
@@ -1333,7 +1336,7 @@ class ToolNode(RunnableCallable):
            return tool_call

        tool_call_copy: ToolCall = copy(tool_call)
-        injected_args = {}
+        injected_args: dict[str, Any] = {}

        # Inject state
        if injected.state:
@@ -1361,14 +1364,20 @@ class ToolNode(RunnableCallable):
            # Extract state values
            if isinstance(state, dict):
                for tool_arg, state_field in injected.state.items():
-                    injected_args[tool_arg] = (
-                        state[state_field] if state_field else state
-                    )
+                    if not state_field:
+                        injected_args[tool_arg] = state
+                    elif state_field in state:
+                        injected_args[tool_arg] = state[state_field]
+                    elif tool_arg not in injected._optional_state_args:
+                        raise KeyError(state_field)
            else:
                for tool_arg, state_field in injected.state.items():
-                    injected_args[tool_arg] = (
-                        getattr(state, state_field) if state_field else state
-                    )
+                    if not state_field:
+                        injected_args[tool_arg] = state
+                    elif hasattr(state, state_field):
+                        injected_args[tool_arg] = getattr(state, state_field)
+                    elif tool_arg not in injected._optional_state_args:
+                        raise AttributeError(state_field)

        # Inject store
        if injected.store:
@@ -1569,6 +1578,7 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
    - `context`: Runtime context (shared with `Runtime`)
    - `store`: `BaseStore` instance for persistent storage (shared with `Runtime`)
    - `stream_writer`: `StreamWriter` for streaming output (shared with `Runtime`)
+    - `tools`: List of all available `BaseTool` instances

    No `Annotated` wrapper is needed - just use `runtime: ToolRuntime`
    as a parameter.
@@ -1611,6 +1621,7 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
    context: ContextT
    config: RunnableConfig
    stream_writer: StreamWriter
+    tools: list[BaseTool]
    tool_call_id: str | None
    store: BaseStore | None
    execution_info: ExecutionInfo | None = None
@@ -1859,6 +1870,7 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
    store_arg: str | None = None
    runtime_arg: str | None = None
    all_injected_keys: set[str] = set()
+    _optional_state_args: set[str] = set()

    for name, type_ in all_annotations.items():
        # Track all InjectedToolArg-annotated params (including custom subclasses)
@@ -1873,6 +1885,9 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
        if state_inj := _get_injection_from_type(type_, InjectedState):
            if isinstance(state_inj, InjectedState) and state_inj.field:
                state_args[name] = state_inj.field
+                field_info = full_schema.model_fields.get(name)
+                if field_info and not field_info.is_required():
+                    _optional_state_args.add(name)
            else:
                state_args[name] = None

@@ -1889,4 +1904,5 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
        store=store_arg,
        runtime=runtime_arg,
        all_injected_keys=all_injected_keys,
+        _optional_state_args=_optional_state_args,
    )
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -0,0 +1,285 @@
+"""Test InjectedState with NotRequired state fields.
+
+This tests the fix for https://github.com/langchain-ai/langchain/issues/35585
+
+When using InjectedState(<field>) on a tool parameter, and the referenced field is
+declared as NotRequired in the custom state schema, the ToolNode should gracefully
+handle missing fields by injecting None instead of raising KeyError.
+"""
+
+import sys
+from typing import Annotated
+
+import pytest
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage
+from langchain_core.tools import tool
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from typing_extensions import NotRequired
+
+from langgraph.prebuilt import InjectedState, ToolNode, create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+
+from .model import FakeToolCallingModel
+
+
+class CustomAgentStateWithNotRequired(AgentState):
+    """Custom state with a NotRequired field (TypedDict style)."""
+
+    city: NotRequired[str]
+
+
+class CustomAgentStatePydanticWithDefault(BaseModel):
+    """Custom state with Optional field and default (Pydantic style)."""
+
+    messages: Annotated[list[AnyMessage], add_messages]
+    remaining_steps: int = Field(default=10)
+    city: str | None = Field(default=None)
+
+
+@tool
+def get_weather(city: Annotated[str | None, InjectedState("city")] = None) -> str:
+    """Get weather for a given city."""
+    if city is None:
+        return "No city provided"
+    return f"It's always sunny in {city}!"
+
+
+def _create_mock_runtime(
+    state: dict | None = None,
+    store=None,
+):
+    """Create a mock Runtime for testing ToolNode directly."""
+    from unittest.mock import Mock
+
+    from langgraph.runtime import Runtime
+
+    mock_runtime = Mock(spec=Runtime)
+    mock_runtime.context = {}
+    return mock_runtime
+
+
+def _create_config_with_runtime(store=None, state=None):
+    """Create a RunnableConfig with mocked runtime for direct ToolNode testing."""
+    from langgraph.prebuilt.tool_node import ToolRuntime
+
+    tool_runtime = ToolRuntime(
+        state=state or {},
+        config={},
+        context={},
+        store=store,
+        stream_writer=None,
+        tools=[],
+        tool_call_id="test_id",
+    )
+    return {
+        "configurable": {
+            "__pregel_runtime": _create_mock_runtime(),
+            "__tool_runtime__": tool_runtime,
+        }
+    }
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_injected_state_not_required_field_missing_injects_none():
+    """Test that InjectedState with NotRequired field injects None when field is missing.
+
+    This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
+    """
+    tool_node = ToolNode([get_weather])
+
+    tool_call = {
+        "name": "get_weather",
+        "args": {},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+    ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
+
+    # State WITHOUT the "city" field - should inject None instead of raising KeyError
+    state_without_city: CustomAgentStateWithNotRequired = {
+        "messages": [HumanMessage("What's the weather?"), ai_msg],
+    }
+
+    result = tool_node.invoke(
+        state_without_city,
+        config=_create_config_with_runtime(state=state_without_city),
+    )
+
+    assert len(result["messages"]) == 1
+    tool_msg = result["messages"][0]
+    assert isinstance(tool_msg, ToolMessage)
+    assert "No city provided" in tool_msg.content
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_injected_state_not_required_field_present_works():
+    """Test that InjectedState with NotRequired field works when field IS present."""
+    tool_node = ToolNode([get_weather])
+
+    tool_call = {
+        "name": "get_weather",
+        "args": {},
+        "id": "call_1",
+        "type": "tool_call",
+    }
+    ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
+
+    # State WITH the "city" field - this should work
+    state_with_city: CustomAgentStateWithNotRequired = {
+        "messages": [HumanMessage("What's the weather?"), ai_msg],
+        "city": "San Francisco",
+    }
+
+    result = tool_node.invoke(
+        state_with_city,
+        config=_create_config_with_runtime(state=state_with_city),
+    )
+
+    assert len(result["messages"]) == 1
+    tool_msg = result["messages"][0]
+    assert isinstance(tool_msg, ToolMessage)
+    assert "San Francisco" in tool_msg.content
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_create_react_agent_injected_state_not_required_field_missing():
+    """Test create_react_agent with InjectedState using NotRequired field that is missing.
+
+    This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
+    """
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather],
+        state_schema=CustomAgentStateWithNotRequired,
+    )
+
+    # Invoke WITHOUT the city field - should work, injecting None
+    result = agent.invoke(
+        {"messages": [HumanMessage("What's the weather?")]},
+    )
+
+    # Check that the tool was called successfully with None injected
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "No city provided" in tool_messages[0].content
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
+)
+def test_create_react_agent_injected_state_not_required_field_present():
+    """Test create_react_agent with InjectedState using NotRequired field that IS present."""
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather],
+        state_schema=CustomAgentStateWithNotRequired,
+    )
+
+    # Invoke WITH the city field
+    result = agent.invoke(
+        {
+            "messages": [HumanMessage("What's the weather?")],
+            "city": "San Francisco",
+        },
+    )
+
+    # Check that the tool was called successfully
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "San Francisco" in tool_messages[0].content
+
+
+@tool
+def get_weather_optional(city: Annotated[str | None, InjectedState("city")]) -> str:
+    """Get weather for a given city (accepts None)."""
+    if city is None:
+        return "Please provide a city!"
+    return f"It's always sunny in {city}!"
+
+
+def test_pydantic_state_with_default_field_missing_works():
+    """Test that Pydantic state with Optional field and default=None works when field is missing.
+
+    This is the workaround suggested in the issue comments - using Pydantic BaseModel
+    with `city: Optional[str] = Field(default=None)` instead of TypedDict with NotRequired.
+    """
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather_optional],
+        state_schema=CustomAgentStatePydanticWithDefault,
+    )
+
+    # Invoke WITHOUT the city field - should work because Pydantic provides default
+    result = agent.invoke(
+        {"messages": [HumanMessage("What's the weather?")]},
+    )
+
+    # Check that the tool was called successfully with None
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "Please provide a city!" in tool_messages[0].content
+
+
+def test_pydantic_state_with_default_field_present_works():
+    """Test that Pydantic state with Optional field works when field IS present."""
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
+            [],  # No more tool calls, agent should stop
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather_optional],
+        state_schema=CustomAgentStatePydanticWithDefault,
+    )
+
+    # Invoke WITH the city field
+    result = agent.invoke(
+        {
+            "messages": [HumanMessage("What's the weather?")],
+            "city": "San Francisco",
+        },
+    )
+
+    # Check that the tool was called successfully
+    messages = result["messages"]
+    tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
+    assert len(tool_messages) == 1
+    assert "San Francisco" in tool_messages[0].content
@@ -2016,8 +2016,8 @@ async def test_tool_node_inject_runtime_dynamic_tool_via_wrap_tool_call_async()
    assert tool_message.tool_call_id == "call_dynamic_2"


-def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
-    """Test that execution_info and server_info are forwarded from Runtime to ToolRuntime."""
+def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
+    """Test that execution_info, server_info, and tools are forwarded from Runtime to ToolRuntime."""
    from langgraph.runtime import ExecutionInfo, ServerInfo

    exec_info = ExecutionInfo(
@@ -2043,9 +2043,15 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
        """Tool that captures runtime info."""
        captured["execution_info"] = runtime.execution_info
        captured["server_info"] = runtime.server_info
+        captured["tools"] = runtime.tools
        return "ok"

-    node = ToolNode([info_tool])
+    @dec_tool
+    def other_tool(y: int) -> str:
+        """Another tool available to the runtime."""
+        return str(y)
+
+    node = ToolNode([info_tool, other_tool])
    tool_call = {
        "name": "info_tool",
        "args": {"x": 1},
@@ -2054,17 +2060,21 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
    }
    msg = AIMessage("", tool_calls=[tool_call])
    config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
-    node.invoke({"messages": [msg]}, config=config)
+    result = node.invoke({"messages": [msg]}, config=config)

+    assert result["messages"][-1].content == "ok"
    assert captured["execution_info"] is exec_info
    assert captured["execution_info"].thread_id == "t-1"
    assert captured["execution_info"].task_id == "tk-1"
    assert captured["server_info"] is server_info
    assert captured["server_info"].assistant_id == "asst-1"
+    assert [tool.name for tool in captured["tools"]] == ["info_tool", "other_tool"]


-async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> None:
-    """Test that execution_info and server_info are forwarded in async path."""
+async def test_tool_runtime_forwards_execution_info_server_info_and_tools_async() -> (
+    None
+):
+    """Test that execution_info, server_info, and tools are forwarded in async path."""
    from langgraph.runtime import ExecutionInfo, ServerInfo

    exec_info = ExecutionInfo(
@@ -2090,9 +2100,15 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
        """Async tool that captures runtime info."""
        captured["execution_info"] = runtime.execution_info
        captured["server_info"] = runtime.server_info
+        captured["tools"] = runtime.tools
        return "ok"

-    node = ToolNode([info_tool_async])
+    @dec_tool
+    async def other_tool_async(y: int) -> str:
+        """Another async tool available to the runtime."""
+        return str(y)
+
+    node = ToolNode([info_tool_async, other_tool_async])
    tool_call = {
        "name": "info_tool_async",
        "args": {"x": 1},
@@ -2101,12 +2117,17 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
    }
    msg = AIMessage("", tool_calls=[tool_call])
    config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
-    await node.ainvoke({"messages": [msg]}, config=config)
+    result = await node.ainvoke({"messages": [msg]}, config=config)

+    assert result["messages"][-1].content == "ok"
    assert captured["execution_info"] is exec_info
    assert captured["execution_info"].thread_id == "t-2"
    assert captured["server_info"] is server_info
    assert captured["server_info"].graph_id == "graph-2"
+    assert [tool.name for tool in captured["tools"]] == [
+        "info_tool_async",
+        "other_tool_async",
+    ]


 # --- InjectedToolArg security tests ---
@@ -249,11 +249,10 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.3.2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch" },
-    { name = "langchain-protocol" },
    { name = "langsmith" },
    { name = "packaging" },
    { name = "pydantic" },
@@ -262,26 +261,14 @@ dependencies = [
    { name = "typing-extensions" },
    { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a8/03/7219502e8ca728d65eb44d7a3eb60239230742a70dbfc9241b9bfd61c4ab/langchain_core-1.3.2.tar.gz", hash = "sha256:fd7a50b2f28ba561fd9d7f5d2760bc9e06cf00cdf820a3ccafe88a94ffa8d5b7", size = 911813, upload-time = "2026-04-24T15:49:23.699Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7d/d5/8fa4431007cbb7cfed7590f4d6a5dea3ad724f4174d248f6642ef5ce7d05/langchain_core-1.3.2-py3-none-any.whl", hash = "sha256:d44a66127f9f8db735bdfd0ab9661bccb47a97113cfd3f2d89c74864422b7274", size = 542390, upload-time = "2026-04-24T15:49:21.991Z" },
-]
-
-[[package]]
-name = "langchain-protocol"
-version = "0.0.11"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/d0/bb/38b5eaefa41c67735eedd9f9a2568b11c9eb376fa129a5edd7cc3dcde071/langchain_protocol-0.0.11.tar.gz", hash = "sha256:c276e2373b5ac691fc7ac9a72019d55182444ce8e89385c3f7e9f0185d0aace7", size = 6622, upload-time = "2026-04-23T22:13:16.771Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0b/fa/6a8ecad8472b182f2caf9d83fd89f40fc1590cb96546d90089b7869b7f5e/langchain_protocol-0.0.11-py3-none-any.whl", hash = "sha256:364da1faf6f5d3001413bede792c1a822c0f23ae55d1ce1266ca7d8e80e79011", size = 6778, upload-time = "2026-04-23T22:13:15.712Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.1.7a2"
+version = "1.1.9"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -294,7 +281,7 @@ dependencies = [

 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
    { name = "langgraph-checkpoint", editable = "../checkpoint" },
    { name = "langgraph-prebuilt", editable = "." },
    { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -365,7 +352,7 @@ test = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -503,7 +490,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -632,7 +619,7 @@ test = [

 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "httpx" },
@@ -642,11 +629,12 @@ dependencies = [
    { name = "requests" },
    { name = "requests-toolbelt" },
    { name = "uuid-utils" },
+    { name = "xxhash" },
    { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
 wheels = [
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@@ -307,7 +294,7 @@ dependencies = [

 [package.metadata]
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+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
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@@ -378,7 +365,7 @@ test = [

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