chore(prebuilt): fix ruff lint in tool_node.py

Remove unused `functools` import; drop redundant string annotation on
`_INJECTED_ARGS_CACHE` (already covered by `from __future__ import annotations`).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.gitignore

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

libs/checkpoint-conformance/uv.lock

@@ -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]]

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

@@ -4,7 +4,7 @@ import threading
 from collections import defaultdict
 from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
@@ -32,7 +32,7 @@ Conn = _internal.Conn  # For backward compatibility
 class PostgresSaver(BasePostgresSaver):
     """Checkpointer that stores checkpoints in a Postgres database."""
 
-    lock: threading.Lock
+    lock: threading.RLock
 
     def __init__(
         self,
@@ -48,7 +48,7 @@ class PostgresSaver(BasePostgresSaver):
 
         self.conn = conn
         self.pipe = pipe
-        self.lock = threading.Lock()
+        self.lock = threading.RLock()
         self.supports_pipeline = Capabilities().has_pipeline()
 
     @classmethod
@@ -442,6 +442,22 @@ class PostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
+        from langgraph.checkpoint.base import DeltaChannelSentinel
+
+        channel_values = self._load_blobs(value["channel_values"])
+        if any(isinstance(v, DeltaChannelSentinel) for v in channel_values.values()):
+            cp_config = cast(
+                RunnableConfig,
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["checkpoint_id"],
+                    }
+                },
+            )
+            with self._cursor() as cur:
+                self._resolve_delta_channels(cp_config, channel_values, cur)
         return CheckpointTuple(
             {
                 "configurable": {
@@ -454,7 +470,7 @@ class PostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **self._load_blobs(value["channel_values"]),
+                    **channel_values,
                 },
             },
             value["metadata"],

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

@@ -13,6 +13,7 @@ from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    DeltaChannelSentinel,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
@@ -391,6 +392,58 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    async def _aget_channel_writes_cur(
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        checkpoint_id: str,
+        channel: str,
+        cur: Any,
+    ) -> list[Any]:
+        """Async version of _get_channel_writes_cur — see sync version for rationale."""
+        await cur.execute(
+            "SELECT checkpoint_id, parent_checkpoint_id FROM checkpoints "
+            "WHERE thread_id = %s AND checkpoint_ns = %s",
+            (thread_id, checkpoint_ns),
+        )
+        parent_map: dict[str, str | None] = {
+            row["checkpoint_id"]: row["parent_checkpoint_id"]
+            for row in await cur.fetchall()
+        }
+        ancestor_ids: list[str] = []
+        cid: str | None = parent_map.get(checkpoint_id)
+        while cid is not None:
+            ancestor_ids.append(cid)
+            cid = parent_map.get(cid)
+        if not ancestor_ids:
+            return []
+        await cur.execute(
+            "SELECT checkpoint_id, type, blob FROM checkpoint_writes "
+            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
+            "  AND checkpoint_id = ANY(%s) "
+            "ORDER BY task_id, idx",
+            (thread_id, checkpoint_ns, channel, ancestor_ids),
+        )
+        writes_by_cp: dict[str, list[tuple[str, bytes]]] = defaultdict(list)
+        for row in await cur.fetchall():
+            writes_by_cp[row["checkpoint_id"]].append((row["type"], row["blob"]))
+        result = []
+        for cid in reversed(ancestor_ids):
+            for type_tag, blob in writes_by_cp.get(cid, []):
+                result.append(self.serde.loads_typed((type_tag, blob)))
+        return result
+
+    async def aget_channel_writes(
+        self, config: RunnableConfig, channel: str
+    ) -> list[Any]:
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = config["configurable"]["checkpoint_id"]
+        async with self._cursor() as cur:
+            return await self._aget_channel_writes_cur(
+                thread_id, checkpoint_ns, checkpoint_id, channel, cur
+            )
+
     async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.
@@ -403,11 +456,31 @@ class AsyncPostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
+        thread_id = value["thread_id"]
+        checkpoint_ns = value["checkpoint_ns"]
+        checkpoint_id = value["checkpoint_id"]
+        blob_values = value["channel_values"]
+
+        channel_values: dict[str, Any] = {}
+        if blob_values:
+            channel_values = self._load_blobs(blob_values)
+            delta_channels = [
+                ch
+                for ch, v in channel_values.items()
+                if isinstance(v, DeltaChannelSentinel)
+            ]
+            if delta_channels:
+                async with self._cursor() as cur:
+                    for channel in delta_channels:
+                        channel_values[channel] = await self._aget_channel_writes_cur(
+                            thread_id, checkpoint_ns, checkpoint_id, channel, cur
+                        )
+
         return CheckpointTuple(
             {
                 "configurable": {
-                    "thread_id": value["thread_id"],
-                    "checkpoint_ns": value["checkpoint_ns"],
+                    "thread_id": thread_id,
+                    "checkpoint_ns": checkpoint_ns,
                     "checkpoint_id": value["checkpoint_id"],
                 }
             },
@@ -415,15 +488,15 @@ 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"],
                     }
                 }

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

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import random
 import warnings
+from collections import defaultdict
 from collections.abc import Sequence
 from importlib.metadata import version as get_version
 from typing import Any, cast
@@ -11,6 +12,7 @@ from langgraph.checkpoint.base import (
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
+    DeltaChannelSentinel,
     get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
@@ -185,15 +187,78 @@ 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 _resolve_delta_channels(
+        self,
+        config: RunnableConfig,
+        channel_values: dict[str, Any],
+        cur: Any,
+    ) -> None:
+        for channel, value in list(channel_values.items()):
+            if isinstance(value, DeltaChannelSentinel):
+                channel_values[channel] = self._get_channel_writes_cur(
+                    config["configurable"]["thread_id"],
+                    config["configurable"].get("checkpoint_ns", ""),
+                    config["configurable"]["checkpoint_id"],
+                    channel,
+                    cur,
+                )
+
+    def _get_channel_writes_cur(
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        checkpoint_id: str,
+        channel: str,
+        cur: Any,
+    ) -> list[Any]:
+        """Fetch writes for `channel` across the checkpoint ancestor chain, oldest→newest.
+
+        Two queries:
+        1. Fetch all (checkpoint_id, parent_checkpoint_id) for the thread — cheap, IDs only.
+        2. Walk the ancestor chain in Python, then fetch writes with a plain ANY() filter.
+        """
+        cur.execute(
+            "SELECT checkpoint_id, parent_checkpoint_id FROM checkpoints "
+            "WHERE thread_id = %s AND checkpoint_ns = %s",
+            (thread_id, checkpoint_ns),
+        )
+        parent_map: dict[str, str | None] = {
+            row["checkpoint_id"]: row["parent_checkpoint_id"] for row in cur.fetchall()
         }
+        ancestor_ids: list[str] = []
+        cid: str | None = parent_map.get(checkpoint_id)
+        while cid is not None:
+            ancestor_ids.append(cid)
+            cid = parent_map.get(cid)
+        if not ancestor_ids:
+            return []
+        cur.execute(
+            "SELECT checkpoint_id, type, blob FROM checkpoint_writes "
+            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
+            "  AND checkpoint_id = ANY(%s) "
+            "ORDER BY task_id, idx",
+            (thread_id, checkpoint_ns, channel, ancestor_ids),
+        )
+        writes_by_cp: dict[str, list[tuple[str, bytes]]] = defaultdict(list)
+        for row in cur.fetchall():
+            writes_by_cp[row["checkpoint_id"]].append((row["type"], row["blob"]))
+        result = []
+        for cid in reversed(ancestor_ids):
+            for type_tag, blob in writes_by_cp.get(cid, []):
+                result.append(self.serde.loads_typed((type_tag, blob)))
+        return result
 
     def _dump_blobs(
         self,

libs/checkpoint-postgres/tests/test_async.py

@@ -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"

libs/checkpoint-postgres/uv.lock

@@ -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" },
+    { url = "https://files.pythonhosted.org/packages/a3/b1/93508d9460b292c74a09b83d16750c52a0ead89c51eea9951cb97a60d959/xxhash-3.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f572dfd3d0e2eb1a57511831cf6341242f5a9f8298a45862d085f5b93394a27d", size = 30807, upload-time = "2025-10-02T14:33:52.964Z" },
+    { url = "https://files.pythonhosted.org/packages/07/55/28c93a3662f2d200c70704efe74aab9640e824f8ce330d8d3943bf7c9b3c/xxhash-3.6.0-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:89952ea539566b9fed2bbd94e589672794b4286f342254fad28b149f9615fef8", size = 193786, upload-time = "2025-10-02T14:33:54.272Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/96/fec0be9bb4b8f5d9c57d76380a366f31a1781fb802f76fc7cda6c84893c7/xxhash-3.6.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:48e6f2ffb07a50b52465a1032c3cf1f4a5683f944acaca8a134a2f23674c2058", size = 212830, upload-time = "2025-10-02T14:33:55.706Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/a0/c706845ba77b9611f81fd2e93fad9859346b026e8445e76f8c6fd057cc6d/xxhash-3.6.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b5b848ad6c16d308c3ac7ad4ba6bede80ed5df2ba8ed382f8932df63158dd4b2", size = 211606, upload-time = "2025-10-02T14:33:57.133Z" },
+    { url = "https://files.pythonhosted.org/packages/67/1e/164126a2999e5045f04a69257eea946c0dc3e86541b400d4385d646b53d7/xxhash-3.6.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a034590a727b44dd8ac5914236a7b8504144447a9682586c3327e935f33ec8cc", size = 444872, upload-time = "2025-10-02T14:33:58.446Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/4b/55ab404c56cd70a2cf5ecfe484838865d0fea5627365c6c8ca156bd09c8f/xxhash-3.6.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8a8f1972e75ebdd161d7896743122834fe87378160c20e97f8b09166213bf8cc", size = 193217, upload-time = "2025-10-02T14:33:59.724Z" },
+    { url = "https://files.pythonhosted.org/packages/45/e6/52abf06bac316db33aa269091ae7311bd53cfc6f4b120ae77bac1b348091/xxhash-3.6.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ee34327b187f002a596d7b167ebc59a1b729e963ce645964bbc050d2f1b73d07", size = 210139, upload-time = "2025-10-02T14:34:02.041Z" },
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+
 [[package]]
 name = "zstandard"
 version = "0.25.0"

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

@@ -1,11 +1,14 @@
 from __future__ import annotations
 
 import copy
+import dataclasses
 import logging
+import threading
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
 from typing import (  # noqa: UP035
     Any,
     Generic,
+    List,
     Literal,
     NamedTuple,
     TypedDict,
@@ -28,6 +31,21 @@ from langgraph.checkpoint.serde.types import (
 
 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
+
+
+@dataclasses.dataclass
+class DeltaChannelSentinel:
+    """Marker stored in checkpoint_blobs for a DeltaChannel field.
+
+    No data is stored here — the actual per-step writes live in checkpoint_writes
+    and are replayed through the reducer at load time.
+    """
+
+    pass
+
+
+_DELTA_RECONSTRUCTION: threading.local = threading.local()
+
 logger = logging.getLogger(__name__)
 
 
@@ -457,6 +475,56 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
+    def get_channel_writes(self, config: RunnableConfig, channel: str) -> List[Any]:  # noqa: UP006
+        """Collect all writes for `channel` across this checkpoint's ancestry, oldest→newest.
+
+        Default implementation walks the full thread history via `list()`. Savers can
+        override with a more efficient query (InMemorySaver and PostgresSaver do this).
+        """
+        # Guard against re-entrant calls: when list() triggers reconstruction which
+        # calls list() again, the inner call returns tuples with DeltaChannelSentinel
+        # in channel_values (which get_channel_writes ignores — it only reads
+        # pending_writes). This breaks the recursion safely.
+        if getattr(_DELTA_RECONSTRUCTION, "active", False):
+            return []
+        _DELTA_RECONSTRUCTION.active = True
+        try:
+            result: list[Any] = []
+            target_id = config["configurable"].get("checkpoint_id")
+            for tup in self.list(config):
+                if tup.config["configurable"].get("checkpoint_id") == target_id:
+                    continue  # skip the checkpoint itself; we want its ancestors' writes
+                if tup.pending_writes:
+                    for _, ch, value in tup.pending_writes:
+                        if ch == channel:
+                            result.append(value)
+            result.reverse()  # list() yields newest→oldest; we want oldest→newest
+            return result
+        finally:
+            _DELTA_RECONSTRUCTION.active = False
+
+    async def aget_channel_writes(
+        self, config: RunnableConfig, channel: str
+    ) -> List[Any]:  # noqa: UP006
+        """Async version of get_channel_writes."""
+        if getattr(_DELTA_RECONSTRUCTION, "active", False):
+            return []
+        _DELTA_RECONSTRUCTION.active = True
+        try:
+            result: list[Any] = []
+            target_id = config["configurable"].get("checkpoint_id")
+            async for tup in self.alist(config):
+                if tup.config["configurable"].get("checkpoint_id") == target_id:
+                    continue
+                if tup.pending_writes:
+                    for _, ch, value in tup.pending_writes:
+                        if ch == channel:
+                            result.append(value)
+            result.reverse()
+            return result
+        finally:
+            _DELTA_RECONSTRUCTION.active = False
+
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.
 

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

@@ -9,7 +9,7 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 
@@ -20,6 +20,7 @@ from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    DeltaChannelSentinel,
     SerializerProtocol,
     get_checkpoint_id,
     get_checkpoint_metadata,
@@ -121,16 +122,60 @@ 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 _resolve_delta_channels(
+        self,
+        config: RunnableConfig,
+        channel_values: dict[str, Any],
+    ) -> None:
+        """Replace DeltaChannelSentinel entries with reconstructed write lists."""
+        for channel, value in list(channel_values.items()):
+            if isinstance(value, DeltaChannelSentinel):
+                channel_values[channel] = self.get_channel_writes(config, channel)
+
+    def get_channel_writes(self, config: RunnableConfig, channel: str) -> list[Any]:
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = config["configurable"].get("checkpoint_id", "")
+        ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
+        # Walk the parent chain newest→oldest collecting checkpoint IDs.
+        chain: list[str] = []
+        current: str | None = checkpoint_id
+        while current is not None:
+            entry = ns_storage.get(current)
+            if entry is None:
+                break
+            chain.append(current)
+            _, _, parent = entry
+            current = parent
+        # Collect writes oldest→newest.
+        result: list[Any] = []
+        for cp_id in reversed(chain):
+            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
+            for (_task_id, _idx), (_, ch, serialized, _) in sorted(step_writes.items()):
+                if ch == channel:
+                    result.append(self.serde.loads_typed(serialized))
+        return result
+
+    async def aget_channel_writes(
+        self, config: RunnableConfig, channel: str
+    ) -> list[Any]:
+        return self.get_channel_writes(config, channel)
 
     def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the in-memory storage.
@@ -153,13 +198,17 @@ class InMemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = saved
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
+                self._resolve_delta_channels(config, channel_values)
                 return CheckpointTuple(
                     config=config,
                     checkpoint={
                         **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
@@ -183,19 +232,27 @@ 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"],
+                )
+                self._resolve_delta_channels(resolved_config, channel_values)
+                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 +347,28 @@ 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"],
+                    )
+                    self._resolve_delta_channels(list_config, channel_values)
+
+                    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=(

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

@@ -46,6 +46,31 @@ 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)
+
+
+def _get_delta_sentinel_cls() -> type:
+    from langgraph.checkpoint.base import (
+        DeltaChannelSentinel,
+    )  # lazy import avoids circular dep
+
+    return DeltaChannelSentinel
+
 
 class JsonPlusSerializer(SerializerProtocol):
     """Serializer that uses ormsgpack, with optional fallbacks.
@@ -239,6 +264,8 @@ class JsonPlusSerializer(SerializerProtocol):
             return "bytes", obj
         elif isinstance(obj, bytearray):
             return "bytearray", obj
+        elif isinstance(obj, _get_delta_sentinel_cls()):
+            return "delta", b""
         else:
             try:
                 return "msgpack", _msgpack_enc(obj)
@@ -261,6 +288,10 @@ class JsonPlusSerializer(SerializerProtocol):
             return ormsgpack.unpackb(
                 data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
             )
+        elif type_ == "delta":
+            from langgraph.checkpoint.base import DeltaChannelSentinel
+
+            return DeltaChannelSentinel()
         elif self.pickle_fallback and type_ == "pickle":
             return pickle.loads(data_)
         else:
@@ -534,7 +565,9 @@ 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 +589,9 @@ 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,

libs/checkpoint/pyproject.toml

@@ -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"

libs/checkpoint/tests/test_encrypted.py

@@ -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()

libs/checkpoint/tests/test_jsonplus.py

@@ -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 +997,15 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
     # No blocking should occur - inner is serialized as dict, not ext
     assert "blocked" not in caplog.text.lower()
     assert result == obj
+
+
+def test_delta_channel_sentinel_serde_round_trip() -> None:
+    from langgraph.checkpoint.base import DeltaChannelSentinel
+    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+    serde = JsonPlusSerializer()
+    original = DeltaChannelSentinel()
+    type_tag, blob = serde.dumps_typed(original)
+    assert type_tag == "delta"
+    loaded = serde.loads_typed((type_tag, blob))
+    assert isinstance(loaded, DeltaChannelSentinel)

libs/checkpoint/tests/test_memory.py

@@ -12,13 +12,25 @@ from langgraph.checkpoint.base import (
     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:
@@ -308,3 +320,74 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
     assert direct is not None
     expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
     assert direct.checkpoint["channel_values"]["foo"] == expected
+
+
+class TestInMemorySaverDeltaChannel:
+    def test_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
+        """_load_blobs returns DeltaChannelSentinel for delta channels (reconstruction deferred)."""
+        from langgraph.checkpoint.base import (
+            DeltaChannelSentinel,
+            empty_checkpoint,
+        )
+
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+        v1 = "00000000000000000000000000000001.0000000000000000"
+
+        sentinel = DeltaChannelSentinel()
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(sentinel)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+
+        result = saver._load_blobs(thread_id, ns, {channel: v1})
+        assert channel in result
+        assert isinstance(result[channel], DeltaChannelSentinel)
+
+    def test_get_channel_writes_collects_writes(self) -> None:
+        """get_channel_writes collects per-step writes oldest→newest."""
+        from langgraph.checkpoint.base import empty_checkpoint
+
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+        }
+        # cp1 has a write for channel
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "hi"}),
+            "",
+        )
+        # cp2 has a write for channel
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "bye"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp2",
+            }
+        }
+        result = saver.get_channel_writes(config, channel)
+        assert result == [{"content": "hi"}, {"content": "bye"}]

libs/checkpoint/uv.lock

@@ -286,7 +286,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -369,7 +369,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -379,11 +379,12 @@ 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]]
@@ -1515,6 +1516,124 @@ 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" },
+    { url = "https://files.pythonhosted.org/packages/a3/b1/93508d9460b292c74a09b83d16750c52a0ead89c51eea9951cb97a60d959/xxhash-3.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f572dfd3d0e2eb1a57511831cf6341242f5a9f8298a45862d085f5b93394a27d", size = 30807, upload-time = "2025-10-02T14:33:52.964Z" },
+    { url = "https://files.pythonhosted.org/packages/07/55/28c93a3662f2d200c70704efe74aab9640e824f8ce330d8d3943bf7c9b3c/xxhash-3.6.0-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:89952ea539566b9fed2bbd94e589672794b4286f342254fad28b149f9615fef8", size = 193786, upload-time = "2025-10-02T14:33:54.272Z" },
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+    { url = "https://files.pythonhosted.org/packages/7b/d9/8d95e906764a386a3d3b596f3c68bb63687dfca806373509f51ce8eea81f/xxhash-3.6.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:15e0dac10eb9309508bfc41f7f9deaa7755c69e35af835db9cb10751adebc35d", size = 31565, upload-time = "2025-10-02T14:37:06.966Z" },
+]
+
 [[package]]
 name = "zstandard"
 version = "0.25.0"

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

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

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

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

libs/cli/examples/graph_prerelease_reqs_fail/pyproject.toml

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

libs/cli/js-examples/yarn.lock

@@ -3676,9 +3676,9 @@ keyv@^4.5.4:
     json-buffer "3.0.1"
 
 "langsmith@>=0.5.0 <1.0.0":
-  version "0.5.18"
-  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"

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

@@ -1328,9 +1328,9 @@ keyv@^4.5.4:
     json-buffer "3.0.1"
 
 "langsmith@>=0.5.0 <1.0.0":
-  version "0.5.18"
-  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"

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.21"
+__version__ = "0.4.23"

libs/cli/langgraph_cli/analytics.py

@@ -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,))

libs/cli/pyproject.toml

@@ -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'",
 ]
 

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

@@ -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]]

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

@@ -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]]

libs/langgraph/langgraph/_internal/_constants.py

@@ -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 GraphStreamer 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,

libs/langgraph/langgraph/callbacks.py

@@ -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,
         *,

libs/langgraph/langgraph/channels/__init__.py

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

libs/langgraph/langgraph/channels/base.py

@@ -119,3 +119,12 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
         Returns `True` if the channel was updated, `False` otherwise.
         """
         return False
+
+    def after_checkpoint(self, version: Any, checkpoint_id: str | None = None) -> None:
+        """Called after checkpoint() with the assigned version, and after
+        from_checkpoint() with the current channel version.
+
+        No-op by default. Override in channels that track their own version
+        for incremental checkpointing (e.g. DeltaChannel).
+        """
+        pass

libs/langgraph/langgraph/channels/delta.py

@@ -0,0 +1,137 @@
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+from typing import Any, Generic
+
+from langgraph.checkpoint.base import DeltaChannelSentinel
+from typing_extensions import Self
+
+from langgraph._internal._typing import MISSING
+from langgraph.channels.base import BaseChannel, Value
+from langgraph.channels.binop import _get_overwrite
+from langgraph.errors import EmptyChannelError
+
+__all__ = ("DeltaChannel",)
+
+
+class DeltaChannel(
+    Generic[Value], BaseChannel[list[Value], Value, DeltaChannelSentinel]
+):
+    """A channel that stores only a sentinel in checkpoints; per-step writes are
+    stored in checkpoint_writes and replayed through the operator at load time.
+
+    Use with append-style reducers (e.g. `add_messages`) on long-running threads
+    to eliminate O(N²) blob growth — storage is O(N) using the writes table that
+    every checkpointer already maintains.
+
+    Works with all checkpointers. Savers with dedicated implementations
+    (InMemorySaver, PostgresSaver) reconstruct in one pass; others fall back to
+    walking the checkpoint list.
+
+    Usage::
+
+        class State(TypedDict):
+            messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
+            # Dict-type reducer (type inferred from the Annotated outer type):
+            files: Annotated[dict, DeltaChannel(merge_files)]
+    """
+
+    __slots__ = ("value", "operator")
+
+    def __init__(
+        self,
+        operator: Callable[[list[Value], Any], list[Value]],
+    ) -> None:
+        super().__init__(list)
+        self.operator = operator
+        self.value: list[Value] = []
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, DeltaChannel):
+            return False
+        if (
+            self.operator.__name__ != "<lambda>"
+            and other.operator.__name__ != "<lambda>"
+        ):
+            return self.operator is other.operator
+        return True
+
+    @property
+    def ValueType(self) -> Any:
+        return list[self.typ]  # type: ignore[name-defined]
+
+    @property
+    def UpdateType(self) -> Any:
+        return self.typ | list[self.typ]  # type: ignore[name-defined]
+
+    def copy(self) -> Self:
+        new = DeltaChannel(self.operator)
+        new.typ = self.typ
+        new.key = self.key
+        new.value = self.value if self.value is MISSING else self.value.copy()
+        return new
+
+    def from_checkpoint(self, checkpoint: Any) -> Self:
+        new = DeltaChannel(self.operator)
+        new.typ = self.typ
+        new.key = self.key
+        if checkpoint is MISSING:
+            try:
+                new.value = new.typ()
+            except Exception:
+                new.value = []
+        elif isinstance(checkpoint, list):
+            # Flat list of write values (oldest→newest) from get_channel_writes.
+            try:
+                value: Any = new.typ()
+            except Exception:
+                value = []
+            for write in checkpoint:
+                value = new.operator(value, write)
+            new.value = value
+        else:
+            # Backward compat: plain accumulated value (e.g. from a migrated thread).
+            try:
+                new.value = list(checkpoint)
+            except Exception:
+                new.value = []
+        return new
+
+    def update(self, values: Sequence[Any]) -> bool:
+        if not values:
+            return False
+        seen_overwrite = False
+        for value in values:
+            is_overwrite, overwrite_value = _get_overwrite(value)
+            if is_overwrite:
+                if seen_overwrite:
+                    from langgraph.errors import (
+                        ErrorCode,
+                        InvalidUpdateError,
+                        create_error_message,
+                    )
+
+                    msg = create_error_message(
+                        message="Can receive only one Overwrite value per super-step.",
+                        error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
+                    )
+                    raise InvalidUpdateError(msg)
+                self.value = (
+                    list(overwrite_value) if overwrite_value is not None else self.typ()
+                )
+                seen_overwrite = True
+            elif not seen_overwrite:
+                base = self.typ() if self.value is MISSING else self.value
+                self.value = self.operator(base, value)
+        return True
+
+    def get(self) -> list[Value]:
+        if self.value is MISSING:
+            raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING
+
+    def checkpoint(self) -> DeltaChannelSentinel:
+        return DeltaChannelSentinel()

libs/langgraph/langgraph/config.py

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

libs/langgraph/langgraph/graph/state.py

@@ -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 +1670,18 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
         # Search through all annotated medata to find channel annotations
         for item in meta:
             if isinstance(item, BaseChannel):
+                if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
+                    outer = _strip_extras(typ.__origin__)
+                    if outer in (
+                        collections.abc.Sequence,
+                        collections.abc.MutableSequence,
+                    ):
+                        outer = list
+                    item.typ = outer
+                    try:
+                        item.value = outer()
+                    except Exception:
+                        item.value = []
                 return item
             elif isclass(item) and issubclass(item, BaseChannel):
                 # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]

libs/langgraph/langgraph/pregel/_checkpoint.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 from collections.abc import Mapping
 from datetime import datetime, timezone
 
@@ -12,6 +13,8 @@ from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec
 
 LATEST_VERSION = 4
 
+logger = logging.getLogger(__name__)
+
 
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
@@ -67,13 +70,12 @@ 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, v in channel_specs.items():
+        ch = v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
+        ch.after_checkpoint(checkpoint["channel_versions"].get(k), checkpoint.get("id"))
+        channels[k] = ch
+    return channels, managed_specs
 
 
 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:

libs/langgraph/langgraph/pregel/_lifecycle.py

@@ -1,274 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import AsyncIterator, Callable, Iterator
-from typing import Any, TypeVar, cast
-from uuid import UUID
-
-from langchain_core.callbacks import BaseCallbackHandler
-
-from langgraph._internal._constants import NS_SEP
-from langgraph.errors import GraphInterrupt
-from langgraph.pregel.protocol import StreamChunk
-
-try:
-    from langchain_core.tracers._streaming import _StreamingCallbackHandler
-except ImportError:
-    _StreamingCallbackHandler = object  # type: ignore[assignment,misc]
-
-
-T = TypeVar("T")
-
-
-_LANGGRAPH_SENTINEL_NODES = frozenset({"__start__", "__end__"})
-
-
-def _is_nested_pregel_start(
-    name: str | None,
-    metadata: dict[str, Any] | None,
-    parent_run_id: UUID | None,
-    task_run_ids: set[UUID],
-) -> bool:
-    """Recognize a nested `Pregel` invocation from its `on_chain_start` metadata.
-
-    When a compiled graph is added as a node, pregel fires two
-    `on_chain_start` callbacks at that task: first for the node chain
-    (whose `name` matches `metadata["langgraph_node"]`) and second for
-    the inner `Pregel` chain (whose `name` is the graph's `name`, not
-    the node name). Both share the same `langgraph_checkpoint_ns`.
-
-    Primary signal: a `langgraph_checkpoint_ns` is set AND `name`
-    differs from the owning task's `langgraph_node`. This covers the
-    common case where the compiled subgraph's name differs from the
-    node name it was registered under.
-
-    Fallback for name collisions (subgraph compiled with
-    `name == node_name`): the inner `Pregel` start's `parent_run_id`
-    is the run_id of the node chain's start event, which the handler
-    records in `task_run_ids` on the first start. Matching
-    `parent_run_id` to that set identifies the second start as the
-    nested `Pregel` even when names coincide.
-
-    Regular node chains are skipped; the root `Pregel` (which has no
-    `langgraph_node` metadata) isn't observed by this handler because
-    the root's start fires before the handler is attached.
-
-    Metadata-based detection is used because `on_chain_start`'s
-    `serialized` argument is `None` for compiled graphs in this
-    version of langchain-core, so class-based detection via
-    `serialized["id"]` isn't available.
-
-    Sentinel nodes (`__start__` / `__end__`) are excluded: conditional
-    edges from `START` fire an `on_chain_start` with `lg_node=__start__`
-    and the router function's name as `name`, which would otherwise
-    match the discriminator without representing an actual nested
-    `Pregel`.
-
-    Args:
-        name: The `name` kwarg from `on_chain_start`.
-        metadata: The `metadata` kwarg from `on_chain_start`.
-        parent_run_id: The `parent_run_id` kwarg from `on_chain_start`.
-        task_run_ids: The set of run_ids the handler has already seen
-            as node-chain starts (i.e. `name == langgraph_node`).
-    """
-    if not metadata:
-        return False
-    if not metadata.get("langgraph_checkpoint_ns"):
-        return False
-    lg_node = metadata.get("langgraph_node")
-    if lg_node is None or lg_node in _LANGGRAPH_SENTINEL_NODES:
-        return False
-    if name != lg_node:
-        return True
-    # Name collision fallback: the inner Pregel's parent_run_id is
-    # the node chain's run_id, which we recorded when that node
-    # chain's start fired.
-    return parent_run_id is not None and parent_run_id in task_run_ids
-
-
-class StreamLifecycleHandler(BaseCallbackHandler, _StreamingCallbackHandler):
-    """Callback handler that emits subgraph lifecycle events on the stream.
-
-    Pushes `LifecycleData`-shaped payloads onto the pregel stream under
-    the `"lifecycle"` mode, keyed by the subgraph's namespace tuple.
-    Drives the `started` → `running` → `completed` / `failed` /
-    `interrupted` state machine.
-
-    The handler is attached to `run_manager.inheritable_handlers` inside
-    a `Pregel.stream` / `astream` call, so it sees callbacks for every
-    descendant chain (nodes, nested `Pregel` subgraphs) but *not* for
-    the root `Pregel` whose start event has already fired. The root's
-    `started` event is emitted eagerly at construction; its terminal
-    state is emitted by `SubgraphTransformer.finalize` / `fail`.
-
-    `run_inline = True` keeps event ordering deterministic.
-    """
-
-    run_inline = True
-
-    def __init__(
-        self,
-        stream: Callable[[StreamChunk], None],
-        *,
-        root_graph_name: str | None = None,
-    ) -> None:
-        """Initialize the handler and emit the root graph's `started` event.
-
-        Args:
-            stream: Callable that accepts a `StreamChunk` tuple
-                `(namespace, mode, payload)` and enqueues it.
-            root_graph_name: The root `Pregel` instance's `name`, emitted
-                with the root's `started` lifecycle payload.
-        """
-        self.stream = stream
-        # Namespaces awaiting the started→running transition.
-        self._pending_running: set[tuple[str, ...]] = set()
-        # run_id → subgraph namespace; populated only for Pregel chains.
-        self._run_to_ns: dict[UUID, tuple[str, ...]] = {}
-        # run_ids of node-chain starts (name == langgraph_node); used
-        # as the parent_run_id fallback when a subgraph's name equals
-        # its node name. Cleared as each chain ends.
-        self._task_run_ids: set[UUID] = set()
-
-        root_payload: dict[str, Any] = {"event": "started"}
-        if root_graph_name is not None:
-            root_payload["graph_name"] = root_graph_name
-        self.stream(((), "lifecycle", root_payload))
-        self._pending_running.add(())
-
-    @staticmethod
-    def _subgraph_ns_from_metadata(metadata: dict[str, Any] | None) -> tuple[str, ...]:
-        """Return the running subgraph's own namespace from task metadata.
-
-        For a nested `Pregel` invoked as a node, `langgraph_checkpoint_ns`
-        ends at the node segment (no inner task appended yet), so
-        splitting on `NS_SEP` gives the subgraph's own namespace.
-        """
-        if not metadata:
-            return ()
-        nskey = metadata.get("langgraph_checkpoint_ns")
-        if not nskey:
-            return ()
-        return tuple(cast(str, nskey).split(NS_SEP))
-
-    @staticmethod
-    def _containing_ns_from_metadata(
-        metadata: dict[str, Any] | None,
-    ) -> tuple[str, ...]:
-        """Return the namespace of the subgraph that contains this task.
-
-        For an inner task with `langgraph_checkpoint_ns`
-        `"seg_a|seg_b"`, the containing subgraph is `("seg_a",)`.
-        """
-        if not metadata:
-            return ()
-        nskey = metadata.get("langgraph_checkpoint_ns")
-        if not nskey:
-            return ()
-        return tuple(cast(str, nskey).split(NS_SEP))[:-1]
-
-    def _emit(self, ns: tuple[str, ...], payload: dict[str, Any]) -> None:
-        self.stream((ns, "lifecycle", payload))
-
-    def tap_output_aiter(
-        self, run_id: UUID, output: AsyncIterator[T]
-    ) -> AsyncIterator[T]:
-        """Pass-through — required by the `_StreamingCallbackHandler` protocol.
-
-        Returns the iterator unchanged. A missing implementation lets
-        langchain's default `Protocol` body return `None`, which breaks
-        the `_consume_aiter` code path in `_runnable.py:900`.
-        """
-        return output
-
-    def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
-        """Pass-through — sync counterpart to `tap_output_aiter`."""
-        return output
-
-    def _fire_running_if_pending(self, ns: tuple[str, ...]) -> None:
-        if ns in self._pending_running:
-            self._pending_running.discard(ns)
-            self._emit(ns, {"event": "running"})
-
-    def on_chain_start(
-        self,
-        serialized: dict[str, Any],
-        inputs: dict[str, Any],
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        # Any descendant activity transitions the containing subgraph to running.
-        containing = self._containing_ns_from_metadata(metadata)
-        self._fire_running_if_pending(containing)
-
-        name = cast(str | None, kwargs.get("name"))
-        lg_node = (metadata or {}).get("langgraph_node")
-
-        # Record node-chain starts so the name-collision fallback in
-        # `_is_nested_pregel_start` can match the inner Pregel's
-        # parent_run_id to them.
-        if (
-            lg_node is not None
-            and lg_node not in _LANGGRAPH_SENTINEL_NODES
-            and name == lg_node
-        ):
-            self._task_run_ids.add(run_id)
-
-        if not _is_nested_pregel_start(
-            name, metadata, parent_run_id, self._task_run_ids
-        ):
-            return
-
-        ns = self._subgraph_ns_from_metadata(metadata)
-        if not ns:
-            return
-
-        self._run_to_ns[run_id] = ns
-        payload: dict[str, Any] = {"event": "started"}
-        if name:
-            payload["graph_name"] = name
-        # `cause` is intentionally not populated here: pregel does not know
-        # what on the parent namespace triggered this subgraph. Product-
-        # specific stream transformers populate `cause` before events
-        # reach the wire. See LifecycleCause in the protocol definition.
-        self._emit(ns, payload)
-        self._pending_running.add(ns)
-
-    def on_chain_end(
-        self,
-        response: Any,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        self._task_run_ids.discard(run_id)
-        ns = self._run_to_ns.pop(run_id, None)
-        if ns is None:
-            return
-        # Ensure started→running fired even for empty subgraphs.
-        if ns in self._pending_running:
-            self._pending_running.discard(ns)
-            self._emit(ns, {"event": "running"})
-        self._emit(ns, {"event": "completed"})
-
-    def on_chain_error(
-        self,
-        error: BaseException,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        self._task_run_ids.discard(run_id)
-        ns = self._run_to_ns.pop(run_id, None)
-        if ns is None:
-            return
-        self._pending_running.discard(ns)
-        if isinstance(error, GraphInterrupt):
-            self._emit(ns, {"event": "interrupted"})
-        else:
-            self._emit(ns, {"event": "failed", "error": str(error)})

libs/langgraph/langgraph/pregel/_loop.py

@@ -692,7 +692,7 @@ class PregelLoop:
         # writes so that interrupt() calls re-fire instead of returning
         # stale values. But if we're actively resuming, keep them —
         # multi-interrupt scenarios need previously resolved values preserved.
-        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 +710,8 @@ 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 +766,26 @@ 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 +828,28 @@ 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(
@@ -856,6 +891,12 @@ class PregelLoop:
             id=self.checkpoint["id"] if exiting else None,
             updated_channels=self.updated_channels,
         )
+        if do_checkpoint and self.channels:
+            for k, ch in self.channels.items():
+                ch.after_checkpoint(
+                    self.checkpoint["channel_versions"].get(k),
+                    self.checkpoint.get("id"),
+                )
         # sanitize TASK channel in the checkpoint before saving (durability=="exit")
         if TASKS in self.checkpoint["channel_values"] and any(
             isinstance(channel, UntrackedValue) for channel in self.channels.values()

libs/langgraph/langgraph/pregel/_messages.py

@@ -14,7 +14,7 @@ from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
 from pydantic import BaseModel
 
-from langgraph._internal._constants import NS_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,
@@ -261,185 +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
-    `GraphStreamer` opts in via the internal
-    `CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
-    `graph.stream(stream_mode="messages")` callers keep the v1
-    AIMessageChunk shape.
-    """
-
-    def on_chat_model_start(
-        self,
-        serialized: dict[str, Any],
-        messages: list[list[BaseMessage]],
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Record metadata with the FULL checkpoint namespace for v2.
-
-        v1's ``on_chat_model_start`` (inherited) slices the ns tuple
-        with ``[:-1]`` to re-position chat model tokens onto the
-        *containing pregel's* namespace — historically convenient for
-        consumers of ``stream_mode="messages"`` who want "where did
-        this node produce its output" rather than the chat-model's
-        own task ns.
-
-        For the protocol-v2 wire shape that is wrong: the client
-        subscribes the root feed at ``namespaces=[[]]`` with
-        ``depth=1``, and any message emitted at depth ``>=1`` that
-        still carries the containing node's ns must appear at the
-        *full* path from root so that depth filtering cleanly isolates
-        subgraph chatter from the root conversation. JS's
-        ``StreamMessagesHandlerV2`` already does
-        ``metadata.langgraph_checkpoint_ns.split("|")`` (no slice); this
-        override brings the Python v2 handler to the same shape.
-
-        Without this, a chat model invoked inside a nested subgraph
-        (e.g. ``research -> researcher``, ``research`` being a root
-        node that ``.ainvoke()``s a ``researcher`` subgraph) emits at
-        ``["research:<task>"]`` — a single level deep — which slips
-        through the root-feed depth-1 filter and pollutes the main
-        conversation with subgraph tokens. With this override we emit
-        at ``["research:<task>", "researcher:<task>"]`` so the client
-        routes those tokens to the subgraph card instead.
-        """
-        if metadata and (not tags or (TAG_NOSTREAM not in tags)):
-            task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
-            # Keep the trailing ``:<task_id>`` segment (unlike the v1
-            # handler which strips it via ``[:-1]``). The client's
-            # lifecycle events land on the same ns, so message deltas
-            # now correlate 1:1 with a ``lifecycle: started`` event —
-            # ``useMessages(stream, subgraph)`` picks them up without
-            # needing to collapse sibling namespaces.
-            ns = tuple(task_checkpoint_ns.split(NS_SEP))
-            if not self.subgraphs and len(ns) > 1 and ns != self.parent_ns:
-                return
-            stream_metadata = dict(metadata)
-            # Preserve the v1-shaped ``langgraph_checkpoint_ns`` (task
-            # id stripped, trailing ``NS_END`` retained) so downstream
-            # consumers reading checkpoint metadata off a streamed
-            # message see the same shape they did pre-v2. Only the ns
-            # tuple emitted on the wire changes.
-            checkpoint_ns = (
-                f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
-                if NS_END in task_checkpoint_ns
-                else task_checkpoint_ns
-            )
-            stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
-            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)
-
-    def on_chain_start(
-        self,
-        serialized: dict[str, Any],
-        inputs: dict[str, Any],
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Record chain (node) metadata with the FULL checkpoint ns.
-
-        Mirror of :meth:`on_chat_model_start` for the node-start path,
-        so messages returned by ``on_chain_end`` (``Command`` updates
-        and plain state dict outputs) land at the same full-path ns as
-        any chat-model deltas from within that node. See the
-        :meth:`on_chat_model_start` docstring for why the v1 ``[:-1]``
-        slice is dropped here.
-        """
-        if (
-            metadata
-            and kwargs.get("name") == metadata.get("langgraph_node")
-            and (not tags or TAG_HIDDEN not in tags)
-        ):
-            ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))
-            if not self.subgraphs and len(ns) > 1:
-                return
-            self.metadata[run_id] = (ns, metadata)
-            for value in _state_values(inputs):
-                if isinstance(value, BaseMessage):
-                    if value.id is not None:
-                        self.seen.add(value.id)
-                elif isinstance(value, Sequence) and not isinstance(value, str):
-                    for item in value:
-                        if isinstance(item, BaseMessage):
-                            if item.id is not None:
-                                self.seen.add(item.id)
-
-    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)))

libs/langgraph/langgraph/pregel/_tools.py

@@ -1,223 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import AsyncIterator, Callable, Iterator
-from contextvars import Token
-from typing import Any, TypeVar, cast
-from uuid import UUID
-
-from langchain_core.callbacks import BaseCallbackHandler
-
-from langgraph._internal._constants import NS_SEP
-from langgraph.config import _tool_call_writer
-from langgraph.pregel.protocol import StreamChunk
-
-try:
-    from langchain_core.tracers._streaming import _StreamingCallbackHandler
-except ImportError:
-    _StreamingCallbackHandler = object  # type: ignore[assignment,misc]
-
-
-T = TypeVar("T")
-
-ToolCallWriter = Callable[[Any], None]
-"""A closure bound to a single tool call that emits `tool-output-delta` events."""
-
-
-class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
-    """Callback handler that emits tool-call lifecycle events on the stream.
-
-    Fires on LangChain's `on_tool_*` callbacks and pushes to the `tools`
-    stream mode. Emits `tool-started` / `tool-output-delta` /
-    `tool-finished` / `tool-error` payloads keyed by `tool_call_id`.
-
-    While a tool is executing, this handler sets `_tool_call_writer` to a
-    closure bound to that call's namespace and `tool_call_id`. The
-    `emit_tool_output_delta` helper in `langgraph.config` reads that
-    ContextVar so tool bodies can stream partial output without threading
-    the writer through their own signature.
-
-    Attached by `Pregel.stream` / `astream` when `"tools"` is in
-    `stream_modes`. `run_inline = True` keeps event ordering
-    deterministic.
-    """
-
-    run_inline = True
-
-    def __init__(self, stream: Callable[[StreamChunk], None]) -> None:
-        """Initialize the handler.
-
-        Args:
-            stream: Callable that accepts a `StreamChunk` tuple
-                `(namespace, mode, payload)` and enqueues it.
-        """
-        self.stream = stream
-        # run_id → (namespace, tool_call_id, ContextVar token)
-        # `on_tool_end` does not receive `tool_call_id` in kwargs, so
-        # we correlate by `run_id` which is present on every callback.
-        self._run_to_call: dict[
-            UUID, tuple[tuple[str, ...], str, Token[ToolCallWriter | None]]
-        ] = {}
-
-    @staticmethod
-    def _containing_ns_from_metadata(
-        metadata: dict[str, Any] | None,
-    ) -> tuple[str, ...]:
-        """Return the namespace of the subgraph that contains this tool call.
-
-        `langgraph_checkpoint_ns` on a tool's callback metadata ends with
-        the `node_name:task_id` segment of the node that invoked the
-        tool. Dropping that segment gives the subgraph's own namespace,
-        which matches what other `tools` / `lifecycle` / `messages`
-        emitters use.
-        """
-        if not metadata:
-            return ()
-        nskey = metadata.get("langgraph_checkpoint_ns")
-        if not nskey:
-            return ()
-        return tuple(cast(str, nskey).split(NS_SEP))[:-1]
-
-    def _start(
-        self,
-        serialized: dict[str, Any] | None,
-        input_str: str,
-        *,
-        run_id: UUID,
-        metadata: dict[str, Any] | None,
-        inputs: dict[str, Any] | None,
-        kwargs: dict[str, Any],
-    ) -> None:
-        tool_call_id = cast("str | None", kwargs.get("tool_call_id")) or str(run_id)
-        tool_name = (
-            (serialized or {}).get("name")
-            or cast("str | None", kwargs.get("name"))
-            or ""
-        )
-        ns = self._containing_ns_from_metadata(metadata)
-
-        def writer(delta: Any) -> None:
-            self.stream(
-                (
-                    ns,
-                    "tools",
-                    {
-                        "event": "tool-output-delta",
-                        "tool_call_id": tool_call_id,
-                        "delta": delta,
-                    },
-                )
-            )
-
-        token = _tool_call_writer.set(writer)
-        self._run_to_call[run_id] = (ns, tool_call_id, token)
-
-        payload: dict[str, Any] = {
-            "event": "tool-started",
-            "tool_call_id": tool_call_id,
-            "tool_name": tool_name,
-        }
-        if inputs is not None:
-            payload["input"] = inputs
-        self.stream((ns, "tools", payload))
-
-    def _end(self, output: Any, *, run_id: UUID) -> None:
-        info = self._run_to_call.pop(run_id, None)
-        if info is None:
-            return
-        ns, tool_call_id, token = info
-        self._reset_writer(token)
-        self.stream(
-            (
-                ns,
-                "tools",
-                {
-                    "event": "tool-finished",
-                    "tool_call_id": tool_call_id,
-                    "output": output,
-                },
-            )
-        )
-
-    def _error(self, error: BaseException, *, run_id: UUID) -> None:
-        info = self._run_to_call.pop(run_id, None)
-        if info is None:
-            return
-        ns, tool_call_id, token = info
-        self._reset_writer(token)
-        self.stream(
-            (
-                ns,
-                "tools",
-                {
-                    "event": "tool-error",
-                    "tool_call_id": tool_call_id,
-                    "message": str(error),
-                },
-            )
-        )
-
-    def tap_output_aiter(
-        self, run_id: UUID, output: AsyncIterator[T]
-    ) -> AsyncIterator[T]:
-        """Pass-through — required by the `_StreamingCallbackHandler` protocol."""
-        return output
-
-    def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
-        """Pass-through — sync counterpart to `tap_output_aiter`."""
-        return output
-
-    @staticmethod
-    def _reset_writer(token: Token[ToolCallWriter | None]) -> None:
-        # Token is invalid if `on_tool_end` runs in a different context
-        # than `on_tool_start` (e.g. langchain may hand off to a thread
-        # worker without copying the context). Swallow that case; the
-        # ContextVar lifetime is bounded by the enclosing task anyway.
-        try:
-            _tool_call_writer.reset(token)
-        except ValueError:
-            pass
-
-    # ------------------------------------------------------------------
-    # Sync callbacks
-    # ------------------------------------------------------------------
-
-    def on_tool_start(
-        self,
-        serialized: dict[str, Any],
-        input_str: str,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        inputs: dict[str, Any] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        self._start(
-            serialized,
-            input_str,
-            run_id=run_id,
-            metadata=metadata,
-            inputs=inputs,
-            kwargs=kwargs,
-        )
-
-    def on_tool_end(
-        self,
-        output: Any,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        self._end(output, run_id=run_id)
-
-    def on_tool_error(
-        self,
-        error: BaseException,
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        self._error(error, run_id=run_id)

libs/langgraph/langgraph/pregel/_utils.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 import ast
+import functools
 import inspect
 import re
 import textwrap
+import types
 from collections.abc import Callable
 from typing import Any
 
@@ -64,46 +66,74 @@ def find_subgraph_pregel(candidate: Runnable) -> PregelProtocol | None:
     return None
 
 
+@functools.lru_cache(maxsize=256)
+def _get_nonlocal_names(code: types.CodeType) -> frozenset[str]:
+    """Return the set of nonlocal variable names referenced by a function.
+
+    Cached by code object so the expensive source fetch + AST parse only
+    happens once per unique function definition across repeated graph compiles.
+
+    Args:
+        code: The code object of the function to analyse.
+
+    Returns:
+        Frozenset of variable names that the function reads from its enclosing
+        scope (free variables and globals referenced in function bodies).
+    """
+    try:
+        source = inspect.getsource(code)
+        tree = ast.parse(textwrap.dedent(source))
+        visitor = FunctionNonLocals()
+        visitor.visit(tree)
+        return frozenset(visitor.nonlocals)
+    except (SyntaxError, TypeError, OSError, SystemError):
+        return frozenset()
+
+
 def get_function_nonlocals(func: Callable) -> list[Any]:
     """Get the nonlocal variables accessed by a function.
 
+    The expensive source-parsing step is cached by code object; only the
+    cheap closure-variable lookup runs on every call.
+
     Args:
         func: The function to check.
 
     Returns:
         List[Any]: The nonlocal variables accessed by the function.
     """
-    try:
-        code = inspect.getsource(func)
-        tree = ast.parse(textwrap.dedent(code))
-        visitor = FunctionNonLocals()
-        visitor.visit(tree)
-        values: list[Any] = []
-        closure = (
-            inspect.getclosurevars(func.__wrapped__)
-            if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
-            else inspect.getclosurevars(func)
-        )
-        candidates = {**closure.globals, **closure.nonlocals}
-        for k, v in candidates.items():
-            if k in visitor.nonlocals:
-                values.append(v)
-            for kk in visitor.nonlocals:
-                if "." in kk and kk.startswith(k):
-                    vv = v
-                    for part in kk.split(".")[1:]:
-                        if vv is None:
-                            break
-                        else:
-                            try:
-                                vv = getattr(vv, part)
-                            except AttributeError:
-                                break
-                    else:
-                        values.append(vv)
-    except (SyntaxError, TypeError, OSError, SystemError):
+    actual_func = (
+        func.__wrapped__
+        if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
+        else func
+    )
+    # Fast path: no free variables means nothing to scan.
+    if not actual_func.__code__.co_freevars:
         return []
 
+    nonlocal_names = _get_nonlocal_names(actual_func.__code__)
+    if not nonlocal_names:
+        return []
+
+    closure = inspect.getclosurevars(actual_func)
+    candidates = {**closure.globals, **closure.nonlocals}
+    values: list[Any] = []
+    for k, v in candidates.items():
+        if k in nonlocal_names:
+            values.append(v)
+        for kk in nonlocal_names:
+            if "." in kk and kk.startswith(k):
+                vv = v
+                for part in kk.split(".")[1:]:
+                    if vv is None:
+                        break
+                    else:
+                        try:
+                            vv = getattr(vv, part)
+                        except AttributeError:
+                            break
+                else:
+                    values.append(vv)
     return values
 
 

libs/langgraph/langgraph/pregel/main.py

@@ -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,
@@ -130,19 +129,14 @@ from langgraph.pregel._checkpoint import (
 )
 from langgraph.pregel._draw import draw_graph
 from langgraph.pregel._io import map_input, read_channels
-from langgraph.pregel._lifecycle import StreamLifecycleHandler
 from langgraph.pregel._loop import (
     AsyncPregelLoop,
     SyncPregelLoop,
 )
-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
-from langgraph.pregel._tools import StreamToolCallHandler
 from langgraph.pregel._utils import get_new_channel_versions
 from langgraph.pregel._validate import validate_graph, validate_keys
 from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
@@ -346,65 +340,6 @@ class NodeBuilder:
         )
 
 
-def _collect_stream_modes(mux: Any) -> list[StreamMode]:
-    """Return the union of `required_stream_modes` across registered transformers.
-
-    Transformers declare the stream modes they need to function, and
-    `stream_v2` asks the graph for exactly that union — no hardcoded
-    default set. If zero transformers are registered (or none declares
-    a given mode), the graph does not stream events for that mode.
-    """
-    modes: set[str] = set()
-    for transformer in mux._transformers:
-        modes.update(transformer.required_stream_modes)
-    return cast("list[StreamMode]", list(modes))
-
-
-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 built-in `ValuesTransformer`, `MessagesTransformer`,
-    and `SubgraphTransformer` factories, then appends the graph's
-    compile-time `stream_transformers` followed by any call-site
-    additions. Factories flow down into subgraph mini-muxes, so
-    per-scope instances propagate automatically.
-    """
-    from langgraph.stream.transformers import (
-        MessagesTransformer,
-        SubgraphTransformer,
-        ToolLifecycleTransformer,
-        ValuesTransformer,
-    )
-
-    builtins: list[Callable[..., Any]] = [
-        ValuesTransformer,
-        ToolLifecycleTransformer,
-        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],
@@ -736,7 +671,6 @@ 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 (
@@ -783,9 +717,6 @@ 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()
@@ -876,15 +807,6 @@ class Pregel(
 
     def copy(self, update: dict[str, Any] | None = None) -> Self:
         attrs = {k: v for k, v in self.__dict__.items() if k != "__orig_class__"}
-        # ``__init__`` accepts ``stream_transformers`` (public parameter) but
-        # the attribute is stored as ``_stream_transformers`` (private). Map
-        # the private key back onto the public kwarg so compile-time
-        # transformers survive ``copy()`` / ``with_config()``. Without this,
-        # ``_stream_transformers`` gets captured by ``**deprecated_kwargs``
-        # and the resulting instance silently has an empty transformer
-        # pipeline.
-        if "_stream_transformers" in attrs:
-            attrs["stream_transformers"] = attrs.pop("_stream_transformers")
         attrs.update(update or {})
         return self.__class__(**attrs)
 
@@ -1127,13 +1049,14 @@ class Pregel(
 
         step = saved.metadata.get("step", -1) + 1
         stop = step + 2
+        checkpoint = saved.checkpoint
         channels, managed = channels_from_checkpoint(
             self.channels,
-            saved.checkpoint,
+            checkpoint,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
-            saved.checkpoint,
+            checkpoint,
             saved.pending_writes or [],
             self.nodes,
             channels,
@@ -1246,13 +1169,14 @@ class Pregel(
 
         step = saved.metadata.get("step", -1) + 1
         stop = step + 2
+        checkpoint = saved.checkpoint
         channels, managed = channels_from_checkpoint(
             self.channels,
-            saved.checkpoint,
+            checkpoint,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
-            saved.checkpoint,
+            checkpoint,
             saved.pending_writes or [],
             self.nodes,
             channels,
@@ -1598,9 +1522,8 @@ class Pregel(
             saved = checkpointer.get_tuple(config)
             if saved is not None:
                 self._migrate_checkpoint(saved.checkpoint)
-            checkpoint = (
-                copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
-            )
+            base_checkpoint = saved.checkpoint if saved else empty_checkpoint()
+            checkpoint = copy_checkpoint(base_checkpoint) if saved else base_checkpoint
             checkpoint_previous_versions = (
                 saved.checkpoint["channel_versions"].copy() if saved else {}
             )
@@ -2044,9 +1967,8 @@ class Pregel(
             saved = await checkpointer.aget_tuple(config)
             if saved is not None:
                 self._migrate_checkpoint(saved.checkpoint)
-            checkpoint = (
-                copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
-            )
+            base_checkpoint = saved.checkpoint if saved else empty_checkpoint()
+            checkpoint = copy_checkpoint(base_checkpoint) if saved else base_checkpoint
             checkpoint_previous_versions = (
                 saved.checkpoint["channel_versions"].copy() if saved else {}
             )
@@ -2704,34 +2626,14 @@ 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,
                     )
                 )
 
-            # set up lifecycle stream mode
-            if "lifecycle" in stream_modes:
-                run_manager.inheritable_handlers.append(
-                    StreamLifecycleHandler(
-                        stream.put,
-                        root_graph_name=self.name,
-                    )
-                )
-
-            # set up tools stream mode
-            if "tools" in stream_modes:
-                run_manager.inheritable_handlers.append(
-                    StreamToolCallHandler(stream.put)
-                )
-
             # set up custom stream mode
             if "custom" in stream_modes:
 
@@ -3101,34 +3003,14 @@ 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,
                     )
                 )
 
-            # set up lifecycle stream mode
-            if "lifecycle" in stream_modes:
-                run_manager.inheritable_handlers.append(
-                    StreamLifecycleHandler(
-                        stream_put,
-                        root_graph_name=self.name,
-                    )
-                )
-
-            # set up tools stream mode
-            if "tools" in stream_modes:
-                run_manager.inheritable_handlers.append(
-                    StreamToolCallHandler(stream_put)
-                )
-
             # set up custom stream mode
             def stream_writer(c: Any) -> None:
                 aioloop.call_soon_threadsafe(
@@ -3355,106 +3237,6 @@ 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,
-        stream_modes: Sequence[StreamMode] | None = None,
-        output_keys: str | Sequence[str] | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Start a sync v2 streaming run driven by transformer projections.
-
-        Builds a `StreamMux` from the built-in `ValuesTransformer` /
-        `MessagesTransformer`, this graph's compile-time
-        `stream_transformers`, and any additional `transformers=`
-        supplied at the call site. Returns a `GraphRunStream` that the
-        caller drives by iterating any projection — no background
-        thread.
-
-        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)
-        requested_stream_modes = set(_collect_stream_modes(mux))
-        requested_stream_modes.update(stream_modes or ())
-        graph_iter = iter(
-            self.stream(
-                input,
-                _merge_v2_messages_flag(config),
-                stream_mode=list(requested_stream_modes),
-                subgraphs=True,
-                version="v2",
-                output_keys=output_keys,
-                interrupt_before=interrupt_before,
-                interrupt_after=interrupt_after,
-                **kwargs,
-            )
-        )
-        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,
-        stream_modes: Sequence[StreamMode] | None = None,
-        output_keys: str | Sequence[str] | None = None,
-        **kwargs: Any,
-    ) -> 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)
-        requested_stream_modes = set(_collect_stream_modes(mux))
-        requested_stream_modes.update(stream_modes or ())
-        graph_aiter = self.astream(
-            input,
-            _merge_v2_messages_flag(config),
-            stream_mode=list(requested_stream_modes),
-            subgraphs=True,
-            version="v2",
-            output_keys=output_keys,
-            interrupt_before=interrupt_before,
-            interrupt_after=interrupt_after,
-            **kwargs,
-        ).__aiter__()
-        return AsyncGraphRunStream(graph_aiter, mux)
-
     @overload
     def invoke(
         self,

libs/langgraph/langgraph/pregel/remote.py

@@ -650,46 +650,22 @@ class RemoteGraph(PregelProtocol):
         """
         updated_stream_modes: list[StreamModeSDK] = []
         req_single = True
-        # `"lifecycle"` is emitted locally by the `StreamLifecycleHandler`
-        # attached inside `Pregel.stream` / `astream`. The remote graph
-        # API has no corresponding mode, so requests for it against a
-        # `RemoteGraph` are silently stripped here and a warning is
-        # logged so the caller isn't left wondering why no lifecycle
-        # events arrive.
-        dropped_lifecycle = False
         # coerce to list, or add default stream mode
         if stream_mode:
             if isinstance(stream_mode, str):
-                if stream_mode != "lifecycle":
-                    updated_stream_modes.append(cast(StreamModeSDK, stream_mode))
-                else:
-                    dropped_lifecycle = True
+                updated_stream_modes.append(stream_mode)
             else:
                 req_single = False
-                for m in stream_mode:
-                    if m == "lifecycle":
-                        dropped_lifecycle = True
-                    else:
-                        updated_stream_modes.append(cast(StreamModeSDK, m))
+                updated_stream_modes.extend(stream_mode)
         else:
-            updated_stream_modes.append(default)  # type: ignore[arg-type]
+            updated_stream_modes.append(default)
         requested_stream_modes = updated_stream_modes.copy()
         # add any from parent graph
         stream: StreamProtocol | None = (
             (config or {}).get(CONF, {}).get(CONFIG_KEY_STREAM)
         )
         if stream:
-            for m in stream.modes:
-                if m == "lifecycle":
-                    dropped_lifecycle = True
-                else:
-                    updated_stream_modes.append(cast(StreamModeSDK, m))
-        if dropped_lifecycle:
-            logger.warning(
-                "Stream mode 'lifecycle' is not supported by RemoteGraph "
-                "and was stripped from the request; no lifecycle events "
-                "will be emitted for this remote run."
-            )
+            updated_stream_modes.extend(stream.modes)
         # map "messages" to "messages-tuple"
         if "messages" in updated_stream_modes:
             updated_stream_modes.remove("messages")
@@ -1020,99 +996,6 @@ class RemoteGraph(PregelProtocol):
             else:
                 yield chunk
 
-    def stream_v2(
-        self,
-        input: dict[str, Any] | Command | None,
-        config: RunnableConfig | None = None,
-        *,
-        context: Context | None = None,
-        interrupt_before: All | Sequence[str] | None = None,
-        interrupt_after: All | Sequence[str] | None = None,
-        transformers: Sequence[Any] | None = None,
-        stream_modes: Sequence[StreamMode] | None = None,
-        headers: dict[str, str] | None = None,
-        params: QueryParamTypes | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Start a sync v2 remote run driven by transformer projections."""
-        from langgraph.pregel.main import (
-            _build_stream_factories,
-            _collect_stream_modes,
-            _merge_v2_messages_flag,
-        )
-        from langgraph.stream._convert import convert_to_protocol_event
-        from langgraph.stream._mux import StreamMux
-        from langgraph.stream.run_stream import RemoteGraphRunStream
-
-        factories = _build_stream_factories((), transformers)
-        mux = StreamMux(factories=factories, is_async=False)
-        requested_stream_modes = set(_collect_stream_modes(mux))
-        requested_stream_modes.update(stream_modes or ())
-        remote_iter = (
-            convert_to_protocol_event(part)
-            for part in self.stream(
-                input,
-                _merge_v2_messages_flag(config),
-                context=context,
-                stream_mode=list(requested_stream_modes),
-                interrupt_before=interrupt_before,
-                interrupt_after=interrupt_after,
-                subgraphs=True,
-                headers=headers,
-                params=params,
-                version="v2",
-                **kwargs,
-            )
-        )
-        return RemoteGraphRunStream(iter(remote_iter), mux)
-
-    async def astream_v2(
-        self,
-        input: dict[str, Any] | Command | None,
-        config: RunnableConfig | None = None,
-        *,
-        context: Context | None = None,
-        interrupt_before: All | Sequence[str] | None = None,
-        interrupt_after: All | Sequence[str] | None = None,
-        transformers: Sequence[Any] | None = None,
-        stream_modes: Sequence[StreamMode] | None = None,
-        headers: dict[str, str] | None = None,
-        params: QueryParamTypes | None = None,
-        **kwargs: Any,
-    ) -> Any:
-        """Async counterpart to ``stream_v2`` for remote graphs."""
-        from langgraph.pregel.main import (
-            _build_stream_factories,
-            _collect_stream_modes,
-            _merge_v2_messages_flag,
-        )
-        from langgraph.stream._convert import convert_to_protocol_event
-        from langgraph.stream._mux import StreamMux
-        from langgraph.stream.run_stream import AsyncRemoteGraphRunStream
-
-        factories = _build_stream_factories((), transformers)
-        mux = StreamMux(factories=factories, is_async=True)
-        requested_stream_modes = set(_collect_stream_modes(mux))
-        requested_stream_modes.update(stream_modes or ())
-
-        async def remote_events() -> AsyncIterator[Any]:
-            async for part in self.astream(
-                input,
-                _merge_v2_messages_flag(config),
-                context=context,
-                stream_mode=list(requested_stream_modes),
-                interrupt_before=interrupt_before,
-                interrupt_after=interrupt_after,
-                subgraphs=True,
-                headers=headers,
-                params=params,
-                version="v2",
-                **kwargs,
-            ):
-                yield convert_to_protocol_event(part)
-
-        return AsyncRemoteGraphRunStream(remote_events().__aiter__(), mux)
-
     async def astream_events(
         self,
         input: Any,

libs/langgraph/langgraph/stream/__init__.py

@@ -1,27 +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,
-    AsyncRemoteGraphRunStream,
-    GraphRunStream,
-    RemoteGraphRunStream,
-)
-from langgraph.stream.stream_channel import StreamChannel
-
-__all__ = [
-    "AsyncGraphRunStream",
-    "AsyncRemoteGraphRunStream",
-    "EventLog",
-    "GraphRunStream",
-    "RemoteGraphRunStream",
-    "ProtocolEvent",
-    "StreamChannel",
-    "StreamTransformer",
-]

libs/langgraph/langgraph/stream/_convert.py

@@ -1,71 +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 _is_v2_messages_payload(data: Any) -> bool:
-    return isinstance(data, dict) and isinstance(data.get("event"), str)
-
-
-def _normalize_messages_data(data: dict[str, Any]) -> dict[str, Any]:
-    """Normalize Python Core message fields to the protocol wire shape."""
-    normalized = {**data}
-    if (
-        normalized["event"] == "message-start"
-        and "id" not in normalized
-        and isinstance(normalized.get("message_id"), str)
-    ):
-        normalized["id"] = normalized["message_id"]
-    if (
-        normalized["event"]
-        in ("content-block-start", "content-block-delta", "content-block-finish")
-        and "content" not in normalized
-        and isinstance(normalized.get("content_block"), dict)
-    ):
-        normalized["content"] = normalized["content_block"]
-    normalized.pop("message_id", None)
-    normalized.pop("content_block", None)
-    return normalized
-
-
-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)
-    data = part_dict["data"]
-    params: _ProtocolEventParams = {
-        "namespace": list(part_dict["ns"]),
-        "timestamp": int(time.time() * 1000),
-        "data": data,
-    }
-    if (
-        part_dict["type"] == "messages"
-        and isinstance(data, tuple)
-        and len(data) == 2
-        and _is_v2_messages_payload(data[0])
-        and isinstance(data[1], dict)
-    ):
-        payload, metadata = data
-        params["data"] = _normalize_messages_data(payload)
-        if isinstance(metadata.get("langgraph_node"), str):
-            params["node"] = metadata["langgraph_node"]
-        if isinstance(metadata.get("run_id"), str):
-            params["run_id"] = metadata["run_id"]
-    if "interrupts" in part_dict:
-        params["interrupts"] = part_dict["interrupts"]
-    return {
-        "type": "event",
-        "method": part_dict["type"],
-        "params": params,
-    }

libs/langgraph/langgraph/stream/_event_log.py

@@ -1,306 +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) -> None:
-        """Initialize an empty, unbound log.
-
-        Args:
-            maxlen: Accepted for forward compatibility; currently unused.
-                The caller-driven pump bounds memory naturally for
-                single-consumer use.
-
-        Raises:
-            ValueError: If `maxlen` is not a positive integer or `None`.
-        """
-        if maxlen is not None and maxlen <= 0:
-            raise ValueError("EventLog maxlen must be a positive int or None")
-        self._items: deque[T] = deque()
-        self._maxlen: int | None = maxlen
-        self._closed = False
-        self._error: BaseException | None = None
-
-        # Binding state — None means unbound.
-        self._is_async: bool | None = None
-
-        # Flipped on first __iter__ / __aiter__. Pre-subscription
-        # pushes are silent no-ops.
-        self._subscribed = False
-
-        # Pump wiring set by the run stream after bind.
-        self._request_more: Callable[[], bool] | None = None
-        self._arequest_more: Callable[[], Awaitable[bool]] | None = None
-
-    # ------------------------------------------------------------------
-    # Binding
-    # ------------------------------------------------------------------
-
-    def _bind(self, *, is_async: bool) -> None:
-        """Bind this log to sync or async mode.
-
-        Called by the StreamMux after transformer registration. Must be
-        called exactly once before any iteration.
-
-        Args:
-            is_async: True to enable async iteration, False for sync.
-
-        Raises:
-            RuntimeError: If the log has already been bound.
-        """
-        if self._is_async is not None:
-            raise RuntimeError("EventLog is already bound")
-        self._is_async = is_async
-
-    # ------------------------------------------------------------------
-    # Producer API
-    # ------------------------------------------------------------------
-
-    def push(self, item: T) -> None:
-        """Append an item. No-op when no subscriber is registered.
-
-        Non-blocking in both sync and async — matches v1's
-        `put_nowait` producer shape. Memory is bounded by caller pace
-        via the caller-driven pump.
-
-        Raises:
-            RuntimeError: If the log is closed (and subscribed).
-        """
-        if not self._subscribed:
-            return
-        if self._closed:
-            raise RuntimeError("Cannot push to a closed EventLog")
-        self._items.append(item)
-
-    def close(self) -> None:
-        """Mark the log as complete."""
-        self._closed = True
-
-    def fail(self, err: BaseException) -> None:
-        """Mark the log as errored.
-
-        Args:
-            err: The exception to surface to the subscriber.
-        """
-        self._error = err
-        self._closed = True
-
-    # ------------------------------------------------------------------
-    # Sync iteration (caller-driven pump)
-    # ------------------------------------------------------------------
-
-    def __iter__(self) -> Iterator[T]:
-        """Subscribe and return a sync cursor. Can be called only once.
-
-        Raises:
-            TypeError: If the log is unbound or bound to async mode.
-            RuntimeError: If the log already has a subscriber.
-        """
-        if self._is_async is None:
-            raise TypeError(
-                "EventLog has not been bound yet. "
-                "Register the transformer with a StreamMux first."
-            )
-        if self._is_async:
-            raise TypeError(
-                "This EventLog is bound to async mode — use 'async for' instead."
-            )
-        if self._subscribed:
-            raise RuntimeError(
-                "EventLog already has a subscriber; use .tee(n) for fan-out."
-            )
-        self._subscribed = True
-        return self._sync_cursor()
-
-    def _sync_cursor(self) -> Iterator[T]:
-        while True:
-            if self._items:
-                yield self._items.popleft()
-            elif self._closed:
-                if self._error is not None:
-                    raise self._error
-                return
-            elif self._request_more is not None:
-                if not self._request_more():
-                    if not self._items and not self._closed:
-                        return
-            else:
-                return
-
-    # ------------------------------------------------------------------
-    # Async iteration (caller-driven pump)
-    # ------------------------------------------------------------------
-
-    def __aiter__(self) -> AsyncIterator[T]:
-        """Subscribe and return an async cursor. Can be called only once.
-
-        Raises:
-            TypeError: If the log is unbound or bound to sync mode.
-            RuntimeError: If the log already has a subscriber.
-        """
-        if self._is_async is None:
-            raise TypeError(
-                "EventLog has not been bound yet. "
-                "Register the transformer with a StreamMux first."
-            )
-        if not self._is_async:
-            raise TypeError("This EventLog is bound to sync mode — use 'for' instead.")
-        if self._subscribed:
-            raise RuntimeError(
-                "EventLog already has a subscriber; use .atee(n) for fan-out."
-            )
-        self._subscribed = True
-        return self._async_cursor()
-
-    async def _async_cursor(self) -> AsyncIterator[T]:
-        while True:
-            if self._items:
-                yield self._items.popleft()
-            elif self._closed:
-                if self._error is not None:
-                    raise self._error
-                return
-            elif self._arequest_more is not None:
-                if not await self._arequest_more():
-                    if not self._items and not self._closed:
-                        return
-            else:
-                return
-
-    # ------------------------------------------------------------------
-    # Fan-out via tee
-    # ------------------------------------------------------------------
-
-    def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
-        """Subscribe and return `n` independent sync iterators.
-
-        Each branch has its own buffer; items pulled from the
-        underlying cursor are copied into every branch. Branches are
-        naturally bounded by caller pace since the sync pump is
-        caller-driven.
-
-        Args:
-            n: Number of branches to create. Must be >= 1.
-
-        Returns:
-            A tuple of `n` iterators over the same underlying stream.
-
-        Raises:
-            TypeError: If the log is unbound or bound to async mode.
-            RuntimeError: If the log already has a subscriber.
-            ValueError: If `n` < 1.
-        """
-        if n < 1:
-            raise ValueError("tee() requires n >= 1")
-        source = self.__iter__()
-        buffers: list[deque[T]] = [deque() for _ in range(n)]
-        exhausted = [False]
-
-        def branch(i: int) -> Iterator[T]:
-            buf = buffers[i]
-            while True:
-                if buf:
-                    yield buf.popleft()
-                elif exhausted[0]:
-                    return
-                else:
-                    try:
-                        item = next(source)
-                    except StopIteration:
-                        exhausted[0] = True
-                        return
-                    for b in buffers:
-                        b.append(item)
-
-        return tuple(branch(i) for i in range(n))
-
-    def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
-        """Subscribe and return `n` independent async iterators.
-
-        Caller-driven fan-out: each branch's `__anext__` either pops
-        from its own buffer or, under a shared `asyncio.Lock`, pulls
-        one item from the underlying cursor and distributes it to
-        every branch's buffer.
-
-        Args:
-            n: Number of branches to create. Must be >= 1.
-
-        Returns:
-            A tuple of `n` async iterators over the same underlying
-            stream.
-
-        Raises:
-            TypeError: If the log is unbound or bound to sync mode.
-            RuntimeError: If the log already has a subscriber.
-            ValueError: If `n` < 1.
-        """
-        if n < 1:
-            raise ValueError("atee() requires n >= 1")
-        source = self.__aiter__()
-        buffers: list[deque[T]] = [deque() for _ in range(n)]
-        exhausted = [False]
-        error: list[BaseException | None] = [None]
-        lock = asyncio.Lock()
-
-        async def branch(i: int) -> AsyncIterator[T]:
-            buf = buffers[i]
-            while True:
-                if buf:
-                    yield buf.popleft()
-                    continue
-                if exhausted[0]:
-                    if error[0] is not None:
-                        raise error[0]
-                    return
-                async with lock:
-                    if buf or exhausted[0]:
-                        continue
-                    try:
-                        item = await source.__anext__()
-                    except StopAsyncIteration:
-                        exhausted[0] = True
-                        continue
-                    except Exception as e:
-                        error[0] = e
-                        exhausted[0] = True
-                        continue
-                    for b in buffers:
-                        b.append(item)
-
-        return tuple(branch(i) for i in range(n))

libs/langgraph/langgraph/stream/_mux.py

@@ -1,497 +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,
-        )
-        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)
-        self._bind_and_wire(projection)
-        self.extensions.update(projection)
-        owner_name = type(transformer).__name__
-        for key in projection:
-            self._projection_owners[key] = owner_name
-            self._transformer_by_key[key] = transformer
-        if getattr(transformer, "_native", False):
-            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 emit(self, event: ProtocolEvent) -> None:
-        """Append a protocol event directly to the main log.
-
-        Built-in transformers use this for protocol repair events that
-        must appear before the source event they are processing. Direct
-        emission intentionally bypasses the transformer pipeline, but
-        still lets this mux remain the only local sequencing authority.
-        """
-        self._seq += 1
-        event["seq"] = self._seq
-        self._events.push(event)
-
-    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]) -> None:
-        """Bind and wire EventLog / StreamChannel instances in a projection."""
-        for value in projection.values():
-            if isinstance(value, StreamChannel):
-                value._bind(is_async=self._is_async)
-                self._channels.append(value)
-                channel_name = value.name
-
-                def _make_forward(name: str) -> Callable[[Any], None]:
-                    def _forward(item: Any) -> None:
-                        self._forward(name, item)
-
-                    return _forward
-
-                value._wire(_make_forward(channel_name))
-            elif isinstance(value, EventLog):
-                value._bind(is_async=self._is_async)
-                self._logs.append(value)
-
-    def _forward(self, channel_name: str, item: Any) -> None:
-        """Inject a ProtocolEvent for a StreamChannel push.
-
-        Forwarded events bypass the transformer pipeline to avoid
-        infinite recursion (a transformer that pushes to a channel
-        during `process()` would re-trigger itself). These events are
-        visible in the main event log but are not passed through
-        transformers' `process()` methods.
-        """
-        event: ProtocolEvent = {
-            "type": "event",
-            "method": f"custom:{channel_name}",
-            "params": {
-                "namespace": [],
-                "timestamp": int(time.time() * 1000),
-                "data": item,
-            },
-        }
-        self.emit(event)

libs/langgraph/langgraph/stream/_types.py

@@ -1,312 +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
-    node: NotRequired[str]
-    run_id: NotRequired[str]
-    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"]
-    event_id: 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.
-        required_stream_modes: Stream modes the graph must emit for
-            this transformer to have anything to process. Computed as
-            the union across all registered transformers to determine
-            which modes a `GraphStreamer` run requests from the
-            graph. Empty tuple means the transformer consumes only
-            synthetic events (or is purely passive).
-    """
-
-    requires_async: ClassVar[bool] = False
-    scope_exact: ClassVar[bool] = True
-    required_stream_modes: ClassVar[tuple[str, ...]] = ()
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        """Initialize the transformer with its mux's scope.
-
-        Args:
-            scope: The namespace tuple the owning mux is scoped to.
-                `()` for the root, 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

libs/langgraph/langgraph/stream/run_stream.py

@@ -1,471 +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` backing `output` / `interrupted`.
-
-        Resolved lazily off the mux so subclasses don't have to thread
-        it through their constructors. Raises if no `ValuesTransformer`
-        is registered — `output` / `interrupted` / `interrupts` have
-        nothing to return in that case, so failing loudly is better
-        than returning `None` silently.
-        """
-        from langgraph.stream.transformers import ValuesTransformer
-
-        vt = self._mux.transformer_by_key("values")
-        if not isinstance(vt, ValuesTransformer):
-            raise RuntimeError(
-                "No ValuesTransformer is registered on this mux — "
-                "`output`, `interrupted`, and `interrupts` require one. "
-                "Add it to your GraphStreamer subclass's "
-                "`builtin_factories` or pass it via `transformers=`."
-            )
-        return vt
-
-    def __iter__(self) -> Iterator[ProtocolEvent]:
-        """Sync iteration of protocol events on this mux's main log.
-
-        Raises at the EventLog level if the mux is async-bound.
-        """
-        return iter(self._mux._events)
-
-    def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
-        """Async iteration of protocol events on this mux's main log.
-
-        Raises at the EventLog level if the mux is sync-bound.
-        """
-        return self._mux._events.__aiter__()
-
-    def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
-        """Iterate multiple projections round-robin, yielding ``(name, item)``.
-
-        Each turn advances one projection's cursor; when a cursor's
-        buffer is empty, pulling from it drives the pump once, which
-        fans out to every subscribed projection log. Projections whose
-        items aren't consumed on this turn sit in their own buffers
-        only until the next turn reaches them, bounding memory by the
-        skew between projection rates rather than letting any single
-        log grow to the full run length.
-
-        Projections are exhausted independently; a projection that
-        finishes early drops out of the rotation while others
-        continue. The overall iterator ends once all named projections
-        are done.
-
-        Args:
-            *names: Projection keys to interleave. Must match keys in
-                `extensions`.
-
-        Yields:
-            `(name, item)` tuples in round-robin order across the named
-            projections.
-
-        Raises:
-            KeyError: If a name doesn't match a registered projection.
-
-        Example:
-            ```python
-            for name, item in run.interleave("messages", "values"):
-                if name == "messages":
-                    print("msg:", item)
-                else:
-                    print("val:", item)
-            ```
-        """
-        cursors: dict[str, Iterator[Any]] = {
-            name: iter(self.extensions[name]) for name in names
-        }
-        done: set[str] = set()
-        while len(done) < len(cursors):
-            for name, cursor in cursors.items():
-                if name in done:
-                    continue
-                try:
-                    item = next(cursor)
-                except StopIteration:
-                    done.add(name)
-                    continue
-                yield (name, item)
-
-
-class GraphRunStream(BaseRunStream):
-    """Sync run stream with caller-driven pumping.
-
-    The caller's iteration on any projection (`values`, `messages`,
-    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 under the
-                `"values"` key — `output` / `interrupted` / `interrupts`
-                read from it lazily.
-        """
-        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)
-        vt = self._values_transformer
-        if vt.error is not None:
-            raise vt.error
-        return vt._latest
-
-    @property
-    def interrupted(self) -> bool:
-        """Drive the run to completion, then return whether it was interrupted.
-
-        Raises:
-            BaseException: If the run ended with an error.
-        """
-        _drive_until_done(self._pump_next)
-        vt = self._values_transformer
-        if vt.error is not None:
-            raise vt.error
-        return vt._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)
-        vt = self._values_transformer
-        if vt.error is not None:
-            raise vt.error
-        return vt._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 under the
-                `"values"` key — `output` / `interrupted` / `interrupts`
-                read from it lazily.
-        """
-        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
-
-
-class RemoteGraphRunStream(GraphRunStream):
-    """Sync run stream fed by already-normalized remote protocol events."""
-
-    def __init__(self, events: Iterator[ProtocolEvent], mux: StreamMux) -> None:
-        super().__init__(events, mux)
-
-    def _pump_next(self) -> bool:
-        """Pull one remote protocol event and push it through the mux."""
-        if self._exhausted:
-            return False
-        try:
-            event = 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(event)
-        return True
-
-
-class AsyncRemoteGraphRunStream(AsyncGraphRunStream):
-    """Async run stream fed by already-normalized remote protocol events."""
-
-    def __init__(self, events: AsyncIterator[ProtocolEvent], mux: StreamMux) -> None:
-        super().__init__(events, mux)
-
-    async def _apump_next(self) -> bool:
-        """Pull one remote protocol event and push it through the mux."""
-        async with self._pump_cond:
-            if self._exhausted:
-                return False
-            if self._pumping:
-                await self._pump_cond.wait()
-                return not self._exhausted
-            self._pumping = True
-
-        try:
-            try:
-                event = 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(event)
-            return True
-        finally:
-            async with self._pump_cond:
-                self._pumping = False
-                self._pump_cond.notify_all()

libs/langgraph/langgraph/stream/stream_channel.py

@@ -1,109 +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) -> 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.
-        """
-        self.name = name
-        self._log: EventLog[T] = EventLog(maxlen=maxlen)
-        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)

libs/langgraph/langgraph/stream/transformers.py

@@ -1,750 +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,
-    LifecycleCause,
-    LifecycleData,
-    MessagesData,
-)
-
-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
-
-if TYPE_CHECKING:
-    from collections.abc import Awaitable, Callable
-
-    from langgraph.stream._mux import StreamMux
-
-
-logger = logging.getLogger(__name__)
-
-
-SubgraphStatus = Literal["started", "running", "completed", "failed", "interrupted"]
-_TERMINAL_STATUSES: frozenset[SubgraphStatus] = frozenset(
-    {"completed", "failed", "interrupted"}
-)
-
-
-def _is_record(value: Any) -> bool:
-    return isinstance(value, dict)
-
-
-def _to_chat_model_stream_event(event: MessagesData) -> MessagesData:
-    """Convert wire-shaped message fields to ChatModelStream's internal shape."""
-    event_type = event.get("event")
-    converted: dict[str, Any] = dict(event)
-    if (
-        event_type == "message-start"
-        and "message_id" not in converted
-        and isinstance(converted.get("id"), str)
-    ):
-        converted["message_id"] = converted["id"]
-    if (
-        event_type in ("content-block-start", "content-block-delta", "content-block-finish")
-        and "content_block" not in converted
-        and isinstance(converted.get("content"), dict)
-    ):
-        converted["content_block"] = converted["content"]
-    return cast("MessagesData", converted)
-
-
-def _message_event_id(event: MessagesData) -> str | None:
-    raw_id = event.get("id") or event.get("message_id")
-    return str(raw_id) if raw_id is not None else None
-
-
-def _content_block_start_skeleton(content: Any) -> dict[str, Any] | None:
-    """Return a minimal content-block-start payload for a delta/finish block."""
-    if not _is_record(content) or not isinstance(content.get("type"), str):
-        return None
-
-    block_type = content["type"]
-    skeleton: dict[str, Any] = {"type": block_type}
-    if block_type == "text":
-        skeleton["text"] = ""
-    elif block_type == "reasoning":
-        skeleton["reasoning"] = ""
-    elif block_type in ("tool_call", "tool_call_chunk"):
-        skeleton["type"] = "tool_call_chunk"
-        if isinstance(content.get("id"), str):
-            skeleton["id"] = content["id"]
-        if isinstance(content.get("name"), str):
-            skeleton["name"] = content["name"]
-        skeleton["args"] = ""
-    elif block_type in ("server_tool_call", "server_tool_call_chunk"):
-        skeleton["type"] = "server_tool_call_chunk"
-        if isinstance(content.get("id"), str):
-            skeleton["id"] = content["id"]
-        if isinstance(content.get("name"), str):
-            skeleton["name"] = content["name"]
-        skeleton["args"] = ""
-    return skeleton
-
-
-def _copy_event(
-    source: ProtocolEvent,
-    *,
-    method: str,
-    namespace: list[str],
-    data: Any,
-) -> ProtocolEvent:
-    params = {**source["params"], "namespace": namespace, "data": data}
-    return {"type": "event", "method": method, "params": params}
-
-
-def _message_repair_key(event: ProtocolEvent, run_id: str) -> str:
-    namespace_key = "\x1f".join(event["params"]["namespace"])
-    return f"{namespace_key}\x1e{run_id}"
-
-
-def _extract_tool_calls_from_values(data: Any) -> dict[str, dict[str, Any]]:
-    if not _is_record(data):
-        return {}
-    messages = data.get("messages")
-    if not isinstance(messages, list):
-        return {}
-    known: dict[str, dict[str, Any]] = {}
-    for message in messages:
-        if not _is_record(message):
-            continue
-        tool_calls = message.get("tool_calls")
-        if not isinstance(tool_calls, list):
-            continue
-        for tool_call in tool_calls:
-            if not _is_record(tool_call):
-                continue
-            tool_call_id = tool_call.get("id")
-            if not isinstance(tool_call_id, str):
-                continue
-            name = tool_call.get("name")
-            args = tool_call.get("args")
-            known[tool_call_id] = {
-                "tool_name": name if isinstance(name, str) else "",
-                "input": args if _is_record(args) else {},
-            }
-    return known
-
-
-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
-    required_stream_modes = ("values",)
-
-    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 ToolLifecycleTransformer(StreamTransformer):
-    """Repair tool-start events needed for deterministic subagent discovery.
-
-    Some subagent frameworks expose a tool-caused subgraph lifecycle before
-    a LangChain tool callback has emitted the matching `tool-started`
-    frame. Core can infer the missing start from the latest values snapshot
-    (`messages[*].tool_calls`) and emit it before the lifecycle event leaves
-    the mux, keeping remote clients from guessing from values snapshots.
-    """
-
-    scope_exact = False
-    required_stream_modes = ("values", "tools", "lifecycle")
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._known_tool_calls: dict[str, dict[str, Any]] = {}
-        self._emitted_tool_starts: set[str] = set()
-        self._mux: StreamMux | None = None
-
-    def init(self) -> dict[str, Any]:
-        return {}
-
-    def _on_register(self, mux: StreamMux) -> None:
-        self._mux = mux
-
-    def process(self, event: ProtocolEvent) -> bool:
-        method = event["method"]
-        data = event["params"]["data"]
-        if method == "values":
-            self._known_tool_calls.update(_extract_tool_calls_from_values(data))
-            return True
-        if method == "tools" and _is_record(data):
-            if (
-                data.get("event") == "tool-started"
-                and isinstance(data.get("tool_call_id"), str)
-            ):
-                tool_call_id = cast("str", data["tool_call_id"])
-                if tool_call_id in self._emitted_tool_starts:
-                    return False
-                self._emitted_tool_starts.add(tool_call_id)
-            return True
-        if method == "lifecycle":
-            self._emit_missing_tool_started(event)
-        return True
-
-    def _emit_missing_tool_started(self, event: ProtocolEvent) -> None:
-        if self._mux is None:
-            return
-        data = event["params"]["data"]
-        if not _is_record(data) or data.get("event") != "started":
-            return
-        cause = data.get("cause")
-        if not _is_record(cause) or cause.get("type") != "toolCall":
-            return
-        tool_call_id = cause.get("tool_call_id")
-        if not isinstance(tool_call_id, str):
-            return
-        if tool_call_id in self._emitted_tool_starts:
-            return
-        known = self._known_tool_calls.get(tool_call_id)
-        if known is None:
-            return
-
-        self._emitted_tool_starts.add(tool_call_id)
-        namespace = event["params"]["namespace"]
-        self._mux.emit(
-            _copy_event(
-                event,
-                method="tools",
-                namespace=namespace[:-1],
-                data={
-                    "event": "tool-started",
-                    "tool_call_id": tool_call_id,
-                    "tool_name": known["tool_name"],
-                    "input": known["input"],
-                },
-            )
-        )
-
-
-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.
-
-    `scope_exact = False`: matches events at the transformer's own
-    namespace **or** exactly one segment deeper (the chat-model /
-    node's own task ns). Mirrors JS's root-feed filter
-    (`namespaces=[[]], depth=1`) — root accepts depth-0 events plus
-    its own nodes' depth-1 tokens; subgraph mini-muxes accept their
-    own scope plus their internal nodes' tokens. Events deeper than
-    scope + 1 are dropped (the enclosing `SubgraphTransformer` has
-    already forwarded them to the matching child mini-mux).
-    """
-
-    _native = True
-    scope_exact = False
-    required_stream_modes = ("messages",)
-
-    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._started_blocks: dict[str, set[int]] = {}
-        self._mux: StreamMux | None = None
-        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 _on_register(self, mux: StreamMux) -> None:
-        self._mux = mux
-
-    def _bind_pump(self, fn: Callable[[], bool]) -> None:
-        """Wire the sync pull callback. Called by GraphRunStream._wire_request_more."""
-        self._pump_fn = fn
-
-    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
-        """Wire the async pull callback.
-
-        Called by `AsyncGraphRunStream._wire_arequest_more` so each
-        `AsyncChatModelStream` this transformer creates can drive the
-        shared graph pump from its projection cursors.
-        """
-        self._apump_fn = fn
-
-    def _make_stream(
-        self,
-        *,
-        namespace: list[str],
-        node: str | None,
-        message_id: str | None,
-    ) -> ChatModelStream:
-        """Create a ChatModelStream (sync) or AsyncChatModelStream (async).
-
-        Wires whichever pump is bound. Prefers the async pump so nested
-        iteration under `AsyncGraphRunStream` drives the graph forward
-        without a background task. The unwired fallback (no pump bound)
-        is used by unit tests that dispatch events manually.
-        """
-        if self._apump_fn is not None:
-            astream = AsyncChatModelStream(
-                namespace=namespace,
-                node=node,
-                message_id=message_id,
-            )
-            astream.set_arequest_more(self._apump_fn)
-            return astream
-        if self._pump_fn is not None:
-            stream: ChatModelStream = ChatModelStream(
-                namespace=namespace,
-                node=node,
-                message_id=message_id,
-            )
-            stream.set_request_more(self._pump_fn)
-            return stream
-        return AsyncChatModelStream(
-            namespace=namespace,
-            node=node,
-            message_id=message_id,
-        )
-
-    def process(self, event: ProtocolEvent) -> bool:
-        if event["method"] != "messages":
-            return True
-        params = event["params"]
-        # Accept events at our scope or exactly one segment deeper
-        # (the chat-model / node's own task ns). Deeper events belong
-        # to a subgraph and are routed by `SubgraphTransformer`.
-        ns = tuple(params["namespace"])
-        depth = len(self.scope)
-        if ns[:depth] != self.scope:
-            return True
-
-        raw_data = params["data"]
-        metadata: dict[str, Any] = {}
-        if isinstance(raw_data, tuple) and len(raw_data) == 2:
-            payload, raw_metadata = raw_data
-            metadata = raw_metadata if isinstance(raw_metadata, dict) else {}
-        else:
-            payload = raw_data
-        node = params.get("node")
-        if not isinstance(node, str):
-            node = metadata.get("langgraph_node")
-            if not isinstance(node, str):
-                node = None
-        raw_run_id = params.get("run_id", metadata.get("run_id"))
-        run_id = str(raw_run_id) if raw_run_id is not None else ""
-
-        if isinstance(payload, dict) and "event" in payload:
-            self._repair_content_block_lifecycle(
-                event, cast("MessagesData", payload), run_id=run_id
-            )
-            if len(ns) > depth + 1:
-                return True
-            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:
-        stream_event = _to_chat_model_stream_event(event)
-        event_type = event.get("event")
-        if event_type == "message-start":
-            message_id = _message_event_id(event)
-            stream = self._make_stream(
-                namespace=list(self.scope),
-                node=node,
-                message_id=message_id,
-            )
-            self._by_run[run_id or message_id or ""] = stream
-            self._log.push(stream)
-            stream.dispatch(stream_event)
-        elif run_id in self._by_run:
-            stream = self._by_run[run_id]
-            stream.dispatch(stream_event)
-            if event_type == "message-finish":
-                del self._by_run[run_id]
-
-    def _repair_content_block_lifecycle(
-        self,
-        source: ProtocolEvent,
-        event: MessagesData,
-        *,
-        run_id: str,
-    ) -> None:
-        if self._mux is None:
-            return
-        event_type = event.get("event")
-        key = _message_repair_key(source, run_id)
-        if event_type == "message-start":
-            self._started_blocks[key] = set()
-            return
-        if event_type == "content-block-start":
-            index = event.get("index")
-            if isinstance(index, int):
-                self._started_blocks.setdefault(key, set()).add(index)
-            return
-        if event_type in ("content-block-delta", "content-block-finish"):
-            index = event.get("index")
-            if not isinstance(index, int):
-                return
-            started = self._started_blocks.setdefault(key, set())
-            if index in started:
-                return
-            skeleton = _content_block_start_skeleton(event.get("content"))
-            if skeleton is None:
-                return
-            started.add(index)
-            self._mux.emit(
-                _copy_event(
-                    source,
-                    method="messages",
-                    namespace=list(source["params"]["namespace"]),
-                    data={
-                        "event": "content-block-start",
-                        "index": index,
-                        "content": skeleton,
-                    },
-                )
-            )
-        elif event_type == "message-finish":
-            self._started_blocks.pop(key, None)
-
-    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()
-        self._started_blocks.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()
-        self._started_blocks.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.
-
-    Lifecycle fields update in place as events arrive:
-
-    - `path`: the namespace tuple — stable for the life of the handle.
-    - `graph_name` / `cause`: set once from the `started` payload.
-      `cause` is populated by product-specific stream transformers
-      (see `LifecycleCause` in the protocol definition); pregel itself
-      emits no `cause`, so it may be `None` for subgraphs not covered
-      by a product transformer.
-    - `status`: advances `started` → `running` → `completed` /
-      `failed` / `interrupted`.
-    - `error` / `checkpoint`: set on the terminal event when present.
-
-    `.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,
-        cause: LifecycleCause | None = None,
-    ) -> None:
-        super().__init__(mux)
-        self.path: tuple[str, ...] = path
-        self.graph_name: str | None = graph_name
-        self.cause: LifecycleCause | None = cause
-        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 state-machine + dispatcher. At its own `scope` (inherited
-    from `StreamTransformer`, determined by the enclosing mux), it
-    watches for `lifecycle` events at exactly one level deeper to
-    discover direct children. Each discovered child gets its own
-    `SubgraphRunStream` backed by a mini-`StreamMux` — built via
-    `parent_mux.make_child(path)`, so the same factory list produces
-    fresh transformer instances at the child's scope.
-
-    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.
-
-    Lifecycle state for each handle (running / completed / failed /
-    interrupted) is updated in place as events fire. On terminal
-    events, the handle's mini-mux is closed so any subscribed cursors
-    unblock. `finalize` / `fail` handle dangling handles left mid-run.
-
-    Native transformer — `subgraphs` exposes the direct-children log.
-
-    `scope_exact = False`: this transformer sees events at any
-    namespace, because it forwards out-of-scope events to the matching
-    direct-child mini-mux.
-    """
-
-    _native = True
-    scope_exact = False
-    required_stream_modes = ("lifecycle",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._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"])
-        method = event["method"]
-        depth = len(self.scope)
-
-        # 1. On `started` for a direct child (ns depth = mine + 1 and
-        #    ns prefix matches mine), register the handle.
-        if method == "lifecycle" and len(ns) == depth + 1 and ns[:-1] == self.scope:
-            data = cast(LifecycleData, event["params"]["data"])
-            if data.get("event") == "started":
-                self._on_started(ns, data)
-
-        # 2. Forward the event to the matching direct-child mini-mux
-        #    before the status-change step below so that terminal events
-        #    reach the child's log and grandchild transformers *before*
-        #    the child's mini-mux is closed. Prefix-match: ns must start
-        #    with some child's path.
-        direct_child_ns = ns[: depth + 1] if len(ns) > depth else None
-        if direct_child_ns is not None and direct_child_ns in self._by_ns:
-            self._by_ns[direct_child_ns]._mux.push(event)
-
-        # 3. Status change for a direct child (ns = child's path, method
-        #    = lifecycle). Update handle fields, close mini-mux on
-        #    terminal.
-        if (
-            method == "lifecycle"
-            and ns in self._by_ns
-            and len(ns) == depth + 1
-            and ns[:-1] == self.scope
-        ):
-            data = cast(LifecycleData, event["params"]["data"])
-            event_type = data.get("event")
-            if event_type in ("running", "completed", "failed", "interrupted"):
-                self._on_status_change(ns, event_type, data)
-
-        return True
-
-    def _on_started(self, ns: tuple[str, ...], data: LifecycleData) -> None:
-        if ns in self._by_ns:
-            # Duplicate started — ignore.
-            return
-        # `_on_register` is called by the mux during registration, which
-        # happens before any event can be dispatched — so this should
-        # always be set by the time we process an event.
-        assert self._mux is not None, (
-            "SubgraphTransformer processed an event before _on_register; "
-            "transformer registration ordering is broken."
-        )
-        child_mux = self._mux.make_child(ns)
-        handle = SubgraphRunStream(
-            path=ns,
-            mux=child_mux,
-            graph_name=data.get("graph_name"),
-            cause=data.get("cause"),
-        )
-        self._by_ns[ns] = handle
-        self._root_log.push(handle)
-
-    def _on_status_change(
-        self,
-        ns: tuple[str, ...],
-        event_type: SubgraphStatus,
-        data: LifecycleData,
-    ) -> None:
-        handle = self._by_ns[ns]
-        handle.status = event_type
-        err = data.get("error")
-        if err is not None:
-            handle.error = err
-        checkpoint = data.get("checkpoint")
-        if checkpoint is not None:
-            handle.checkpoint = checkpoint
-        if event_type in _TERMINAL_STATUSES:
-            self._close_handle_mux(handle)
-
-    @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`."""
-        for handle in self._by_ns.values():
-            if handle.status not in _TERMINAL_STATUSES:
-                handle.status = "completed"
-            self._close_handle_mux(handle)
-
-    def fail(self, err: BaseException) -> None:
-        """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,
-                    )

libs/langgraph/langgraph/types.py

@@ -116,15 +116,7 @@ def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:
 
 
 StreamMode = Literal[
-    "values",
-    "updates",
-    "checkpoints",
-    "tasks",
-    "debug",
-    "messages",
-    "custom",
-    "lifecycle",
-    "tools",
+    "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
 ]
 """How the stream method should emit outputs.
 
@@ -137,8 +129,6 @@ StreamMode = Literal[
 - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
 - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
 - `"debug"`: Emit `"checkpoints"` and `"tasks"` events for debugging purposes.
-- `"lifecycle"`: Emit subgraph lifecycle events (`started`, `running`, `completed`, `failed`, `interrupted`) with payloads matching `LifecycleData`.
-- `"tools"`: Emit tool-call lifecycle events (`tool-started`, `tool-output-delta`, `tool-finished`, `tool-error`) keyed by `tool_call_id`.
 """
 
 StreamWriter = Callable[[Any], None]

libs/langgraph/pyproject.toml

@@ -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",

libs/langgraph/tests/test_channels.py

@@ -117,3 +117,291 @@ def test_untracked_value() -> None:
     new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
     with pytest.raises(EmptyChannelError):
         new_channel.get()
+
+
+def test_delta_channel_basic_two_steps() -> None:
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.base import DeltaChannelSentinel
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+
+    # Step 1: one message added
+    ch.update([HumanMessage(content="hi", id="h1")])
+    d1 = ch.checkpoint()
+    assert isinstance(d1, DeltaChannelSentinel)
+
+    # Step 2: another message
+    ch.update([AIMessage(content="hello", id="a1")])
+    d2 = ch.checkpoint()
+    assert isinstance(d2, DeltaChannelSentinel)
+
+    # Full accumulated value is preserved in memory
+    assert len(ch.get()) == 2
+    assert ch.get()[0].content == "hi"
+    assert ch.get()[1].content == "hello"
+
+
+def test_delta_channel_from_checkpoint_writes_list() -> None:
+    """from_checkpoint with a flat list of individual writes replays them through the operator."""
+    from langchain_core.messages import AIMessage, HumanMessage
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    # Each element is one write value (as stored in checkpoint_writes)
+    writes = [
+        HumanMessage(content="hi", id="h1"),
+        AIMessage(content="hello", id="a1"),
+        HumanMessage(content="bye", id="h2"),
+    ]
+    ch = spec.from_checkpoint(writes)
+    msgs = ch.get()
+    assert len(msgs) == 3
+    assert msgs[0].content == "hi"
+    assert msgs[1].content == "hello"
+    assert msgs[2].content == "bye"
+
+
+def test_delta_channel_from_checkpoint_backwards_compat() -> None:
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    # Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
+    spec = DeltaChannel(add_messages)
+    old_value = [HumanMessage(content="old", id="h1")]
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == old_value
+
+
+def test_delta_channel_overwrite() -> None:
+    from langchain_core.messages import HumanMessage
+    from langgraph.checkpoint.base import DeltaChannelSentinel
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+    from langgraph.types import Overwrite
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    ch.update([HumanMessage(content="old", id="h1")])
+
+    ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
+    d = ch.checkpoint()
+    assert isinstance(d, DeltaChannelSentinel)
+    # After overwrite, value is reset to only the new message
+    assert len(ch.get()) == 1
+    assert ch.get()[0].content == "new"
+
+
+def test_delta_channel_remove_message_and_replay() -> None:
+    """RemoveMessage must round-trip correctly when writes are replayed."""
+    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(MISSING)
+
+    # Step 1: add two messages
+    ch.update([HumanMessage(content="hi", id="h1")])
+    ch.update([AIMessage(content="hello", id="a1")])
+    assert ch.get() == [
+        HumanMessage(content="hi", id="h1"),
+        AIMessage(content="hello", id="a1"),
+    ]
+
+    # Step 2: remove the AI message
+    ch.update([RemoveMessage(id="a1")])
+    assert ch.get() == [HumanMessage(content="hi", id="h1")]
+
+    # Replay the writes list from scratch — must reproduce the post-remove state
+    writes = [
+        HumanMessage(content="hi", id="h1"),
+        AIMessage(content="hello", id="a1"),
+        RemoveMessage(id="a1"),
+    ]
+    ch2 = spec.from_checkpoint(writes)
+    assert ch2.get() == [HumanMessage(content="hi", id="h1")]
+
+
+def test_delta_channel_update_by_id_and_replay() -> None:
+    """Updating a message by ID must round-trip correctly through writes replay."""
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(MISSING)
+
+    # Step 1: add a message
+    ch.update([HumanMessage(content="original", id="h1")])
+
+    # Step 2: update the same message by ID
+    ch.update([HumanMessage(content="updated", id="h1")])
+    assert ch.get() == [HumanMessage(content="updated", id="h1")]
+
+    # Replay writes — must produce the updated message, not the original
+    writes = [
+        HumanMessage(content="original", id="h1"),
+        HumanMessage(content="updated", id="h1"),
+    ]
+    ch2 = spec.from_checkpoint(writes)
+    assert len(ch2.get()) == 1
+    assert ch2.get()[0].content == "updated"
+
+
+def test_delta_channel_checkpoint_returns_sentinel() -> None:
+    """checkpoint() always returns DeltaChannelSentinel regardless of state."""
+    from langgraph.checkpoint.base import DeltaChannelSentinel
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    assert isinstance(ch.checkpoint(), DeltaChannelSentinel)
+
+    from langchain_core.messages import HumanMessage
+
+    ch.update([HumanMessage(content="hi", id="h1")])
+    assert isinstance(ch.checkpoint(), DeltaChannelSentinel)
+
+
+def test_delta_channel_inmemory_saver_assembles_writes() -> None:
+    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    n = {"v": 0}
+
+    def respond(state: State) -> dict:
+        n["v"] += 1
+        return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "t1"}}
+
+    graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
+    graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
+
+    # get_tuple must return a resolved list (not DeltaChannelSentinel)
+    from langgraph.checkpoint.base import DeltaChannelSentinel
+
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "messages" in saved.checkpoint["channel_values"]
+    assert not isinstance(
+        saved.checkpoint["channel_values"]["messages"], DeltaChannelSentinel
+    )
+    assert isinstance(saved.checkpoint["channel_values"]["messages"], list)
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
+
+
+def _delta_channel_with_type(operator, typ):
+    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
+    from typing import Annotated
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.state import _get_channel
+
+    return _get_channel("_test", Annotated[typ, DeltaChannel(operator)])
+
+
+def test_delta_channel_dict_reducer_fresh_channel() -> None:
+    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    # Should be available (not raise EmptyChannelError) and start empty
+    assert ch.is_available()
+    assert ch.get() == {}
+
+
+def test_delta_channel_dict_reducer_basic_updates() -> None:
+    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
+    from langgraph.checkpoint.base import DeltaChannelSentinel
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+
+    ch.update([{"a": 1}])
+    d1 = ch.checkpoint()
+    assert isinstance(d1, DeltaChannelSentinel)
+
+    ch.update([{"b": 2}])
+    d2 = ch.checkpoint()
+    assert isinstance(d2, DeltaChannelSentinel)
+
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
+    """from_checkpoint with a writes list replays correctly through a dict merge reducer."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    # Each element is one write value (oldest→newest)
+    writes = [{"a": 1}, {"b": 2}, {"c": 3}]
+    ch = spec.from_checkpoint(writes)
+    assert ch.get() == {"a": 1, "b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_with_deletions() -> None:
+    """Dict reducer that treats None values as deletions works end-to-end (deepagents pattern)."""
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
+
+    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
+
+    # Delete file1, add file3
+    ch.update([{"file1.py": None, "file3.py": "content3"}])
+
+    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+    # Confirm writes reconstruction produces the same result
+    writes = [
+        {"file1.py": "content1", "file2.py": "content2"},
+        {"file1.py": None, "file3.py": "content3"},
+    ]
+    spec = _delta_channel_with_type(merge_files, dict)
+    ch2 = spec.from_checkpoint(writes)
+    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}

libs/langgraph/tests/test_delta_channel_benchmark.py

@@ -0,0 +1,372 @@
+"""Benchmark: DeltaChannel vs BinaryOperatorAggregate storage and time.
+
+Run directly:  python tests/test_delta_channel_benchmark.py
+Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
+
+Simulates realistic multi-turn conversations with paragraph-length messages
+(~100 tokens each) scaling up to 1M-token-equivalent histories.
+
+Token estimates: 1 token ≈ 4 chars; each turn ≈ 200 tokens (human + AI).
+A 1M-token conversation ≈ 5,000 turns of realistic messages.
+
+DeltaChannel stores only a zero-byte sentinel in checkpoint_blobs; the actual
+write data lives in checkpoint_writes (already stored there). Reconstruction
+walks the parent chain and replays writes through the operator — O(N) total
+storage vs O(N²) for plain add_messages.
+"""
+
+from __future__ import annotations
+
+import sys
+import time
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.memory import MemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.delta import DeltaChannel
+from langgraph.graph import END, StateGraph
+from langgraph.graph.message import add_messages
+
+try:
+    from langgraph.checkpoint.sqlite import SqliteSaver
+
+    _SQLITE_AVAILABLE = True
+except ImportError:
+    _SQLITE_AVAILABLE = False
+
+try:
+    from langgraph.checkpoint.postgres import PostgresSaver
+
+    _POSTGRES_AVAILABLE = True
+    _POSTGRES_URI = (
+        "postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable"
+    )
+except ImportError:
+    _POSTGRES_AVAILABLE = False
+
+# ---------------------------------------------------------------------------
+# Realistic message payload (~100 tokens / ~400 chars each)
+# ---------------------------------------------------------------------------
+
+_HUMAN_TEMPLATE = (
+    "I need help understanding the implications of {topic} on our system architecture. "
+    "Specifically, I'm concerned about how this interacts with our existing {concern} "
+    "and whether we need to refactor the {component} layer before proceeding."
+)
+
+_AI_TEMPLATE = (
+    "Great question about {topic}. The key insight here is that {concern} introduces "
+    "a subtle ordering dependency that most teams overlook until they hit it in production. "
+    "For your {component} layer specifically, I'd recommend starting with a careful audit "
+    "of the interface boundaries before making any structural changes. This will give you "
+    "a clear picture of the blast radius and let you sequence the migration safely."
+)
+
+_TOPICS = [
+    "distributed tracing",
+    "eventual consistency",
+    "schema migration",
+    "backpressure handling",
+    "idempotency guarantees",
+    "cache invalidation",
+    "connection pooling",
+    "rate limiting",
+    "circuit breaking",
+    "observability pipelines",
+]
+
+_CONCERNS = [
+    "concurrency model",
+    "retry semantics",
+    "state management",
+    "error propagation",
+    "latency budget",
+]
+
+_COMPONENTS = [
+    "persistence",
+    "routing",
+    "ingestion",
+    "aggregation",
+    "serialization",
+]
+
+
+def _human_content(i: int) -> str:
+    return _HUMAN_TEMPLATE.format(
+        topic=_TOPICS[i % len(_TOPICS)],
+        concern=_CONCERNS[i % len(_CONCERNS)],
+        component=_COMPONENTS[i % len(_COMPONENTS)],
+    )
+
+
+def _ai_content(i: int) -> str:
+    return _AI_TEMPLATE.format(
+        topic=_TOPICS[i % len(_TOPICS)],
+        concern=_CONCERNS[i % len(_CONCERNS)],
+        component=_COMPONENTS[i % len(_COMPONENTS)],
+    )
+
+
+# ---------------------------------------------------------------------------
+# State definitions
+# ---------------------------------------------------------------------------
+
+
+class BinaryState(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+class DeltaState(TypedDict):
+    messages: Annotated[list, DeltaChannel(add_messages)]
+
+
+# ---------------------------------------------------------------------------
+# Graph factory
+# ---------------------------------------------------------------------------
+
+
+def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
+    def human_node(state: Any) -> dict:
+        return {}
+
+    def ai_node(state: Any) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
+
+    g = StateGraph(state_cls)
+    g.add_node("human", human_node)
+    g.add_node("ai", ai_node)
+    g.add_edge("human", "ai")
+    g.add_edge("ai", END)
+    g.set_entry_point("human")
+    return g.compile(checkpointer=checkpointer or MemorySaver())
+
+
+# ---------------------------------------------------------------------------
+# Measurement helpers
+# ---------------------------------------------------------------------------
+
+
+def _total_blob_bytes(saver: MemorySaver) -> int:
+    total = 0
+    for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
+        if blob is not None:
+            total += len(blob)
+    return total
+
+
+def _run_turns(
+    n_turns: int,
+    state_cls: type,
+    checkpointer: Any = None,
+) -> tuple[float, float, int]:
+    """Run n_turns conversation turns.
+
+    Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
+    blob_bytes is -1 for savers without in-memory blob stores (e.g. SQLite).
+    Read latency is measured as the time to invoke the graph with no new
+    messages after the full history is built — this forces state rehydration.
+    """
+    graph = _make_graph(state_cls, checkpointer)
+    config = {"configurable": {"thread_id": "bench"}}
+
+    t0 = time.perf_counter()
+    for i in range(n_turns):
+        graph.invoke(
+            {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
+            config,
+        )
+    write_elapsed = time.perf_counter() - t0
+
+    # Measure read/rehydration: get_state forces the channel to rebuild
+    t1 = time.perf_counter()
+    for _ in range(5):
+        graph.get_state(config)
+    read_elapsed = (time.perf_counter() - t1) / 5
+
+    if isinstance(graph.checkpointer, MemorySaver):
+        blob_bytes = _total_blob_bytes(graph.checkpointer)
+    else:
+        blob_bytes = -1
+    return write_elapsed, read_elapsed, blob_bytes
+
+
+def _fmt_bytes(n: int) -> str:
+    if n >= 1_000_000:
+        return f"{n / 1_000_000:.1f} MB"
+    if n >= 1_000:
+        return f"{n / 1_000:.1f} KB"
+    return f"{n} B"
+
+
+def _approx_tokens(n_turns: int) -> str:
+    # ~100 tokens human + ~100 tokens AI per turn
+    tokens = n_turns * 200
+    if tokens >= 1_000_000:
+        return f"~{tokens / 1_000_000:.1f}M tok"
+    if tokens >= 1_000:
+        return f"~{tokens / 1_000:.0f}K tok"
+    return f"~{tokens} tok"
+
+
+# ---------------------------------------------------------------------------
+# Benchmark matrix
+# ---------------------------------------------------------------------------
+
+# Turn counts chosen to demonstrate O(N²) vs O(N) storage growth without running too long.
+# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
+TURN_COUNTS = [10, 25, 50, 100, 500]
+
+
+def _checkpointer_factories() -> list[tuple[str, Any]]:
+    """Return (label, context_manager_or_none) pairs for available checkpointers."""
+    return [("InMemory", None)]
+
+
+def run_benchmark() -> None:
+    print()
+    print(
+        "DeltaChannel vs add_messages (BinaryOperatorAggregate) — checkpoint storage & latency"
+    )
+    print("Simulating realistic multi-turn conversations up to ~1M-token histories")
+    print("(5,000 turns × ~200 tokens/turn ≈ 1M tokens — Claude's full context window)")
+    print()
+
+    checkpointers: list[tuple[str, Any]] = [("InMemory", None)]
+    if _POSTGRES_AVAILABLE:
+        try:
+            import psycopg
+
+            psycopg.connect(_POSTGRES_URI).close()
+            checkpointers.append(("Postgres (recursive CTE)", "postgres"))
+        except Exception:
+            pass
+
+    for cp_label, cp_hint in checkpointers:
+        print(f"--- Checkpointer: {cp_label} ---")
+        _run_benchmark_for_checkpointer(cp_hint)
+
+
+def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
+    import contextlib
+    import tempfile
+
+    @contextlib.contextmanager
+    def _make_saver():
+        if cp_hint is None:
+            yield None
+        elif cp_hint == "postgres":
+            with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
+                saver.setup()
+                with saver._cursor() as cur:
+                    cur.execute("DELETE FROM checkpoints WHERE thread_id = 'bench'")
+                    cur.execute(
+                        "DELETE FROM checkpoint_blobs WHERE thread_id = 'bench'"
+                    )
+                    cur.execute(
+                        "DELETE FROM checkpoint_writes WHERE thread_id = 'bench'"
+                    )
+                yield saver
+        else:
+            with tempfile.NamedTemporaryFile(suffix=".db") as f:
+                with SqliteSaver.from_conn_string(f.name) as saver:
+                    yield saver
+
+    rows = []
+    for turns in TURN_COUNTS:
+        with _make_saver() as saver:
+            b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
+        with _make_saver() as saver:
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt))
+
+    # ── Table 1: Storage ─────────────────────────────────────────────────────
+    W = 70
+    print("Storage (checkpoint blob bytes)")
+    print("=" * W)
+    print(
+        f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}  {'savings':>8}"
+    )
+    print("-" * W)
+    storage_results = []
+    for turns, b_bytes, d_bytes, b_rt, d_rt in rows:
+        if b_bytes < 0:
+            print(
+                f"{turns:>6}  {_approx_tokens(turns):>10}  {'n/a':>12}  {'n/a':>12}  {'n/a':>8}"
+            )
+        else:
+            ratio = b_bytes / d_bytes if d_bytes else float("inf")
+            storage_results.append((turns, b_bytes, d_bytes, ratio))
+            print(
+                f"{turns:>6}  {_approx_tokens(turns):>10}  "
+                f"{_fmt_bytes(b_bytes):>12}  {_fmt_bytes(d_bytes):>12}  "
+                f"{ratio:>7.0f}x"
+            )
+    print("=" * W)
+    print()
+
+    # ── Table 2: Read latency ─────────────────────────────────────────────────
+    print("Read latency (avg of 5 get_state calls)")
+    print("=" * W)
+    print(f"{'turns':>6}  {'ctx size':>10}  {'add_msgs':>12}  {'delta':>12}")
+    print("-" * W)
+    for turns, b_bytes, d_bytes, b_rt, d_rt in rows:
+        print(
+            f"{turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{b_rt * 1000:>10.1f}ms  {d_rt * 1000:>10.1f}ms"
+        )
+    print("=" * W)
+    print()
+
+    if storage_results:
+        turns, b_bytes, d_bytes, ratio = storage_results[-1]
+        b_rt = rows[-1][-2]
+        d_rt = rows[-1][-1]
+        print(
+            f"At {turns} turns: {_fmt_bytes(b_bytes)} → {_fmt_bytes(d_bytes)} ({ratio:.0f}x less storage); "
+            f"read {b_rt * 1000:.1f}ms → {d_rt * 1000:.1f}ms"
+        )
+        print()
+
+    print("Legend:")
+    print("  add_msgs = Annotated[list, add_messages]  — O(N²) storage")
+    print(
+        "  delta    = DeltaChannel(add_messages)     — O(N) storage, full chain replay"
+    )
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Pytest entry point
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+def test_delta_channel_benchmark(capsys: Any) -> None:
+    """Storage grows O(N²) for add_messages, O(N) for DeltaChannel."""
+    with capsys.disabled():
+        run_benchmark()
+
+    # Correctness assertion: DeltaChannel must use less storage at scale.
+    for turns in [25, 50]:
+        _, _, b_bytes = _run_turns(turns, BinaryState)
+        _, _, d_bytes = _run_turns(turns, DeltaState)
+        assert d_bytes < b_bytes, (
+            f"DeltaChannel should use less storage at {turns} turns, "
+            f"got delta={d_bytes} binary={b_bytes}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Script entry point
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    run_benchmark()
+    sys.exit(0)

libs/langgraph/tests/test_graph_callbacks.py

@@ -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 == []

libs/langgraph/tests/test_pregel.py

@@ -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"

libs/langgraph/tests/test_pregel_async.py

@@ -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:

libs/langgraph/tests/test_remote_stream_v2.py

@@ -1,121 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import AsyncIterator, Iterator, Sequence
-from typing import Any, Literal
-
-import pytest
-from langchain_core.runnables import RunnableConfig
-
-from langgraph.pregel.remote import RemoteGraph
-from langgraph.types import All, StreamMode
-
-
-class _FakeRemoteGraph(RemoteGraph):
-    def __init__(self) -> None:
-        super().__init__("agent", url="http://unused")
-        self.last_stream_modes: list[StreamMode] | None = None
-
-    def stream(
-        self,
-        input: dict[str, Any] | Any,
-        config: RunnableConfig | None = None,
-        *,
-        stream_mode: StreamMode | list[StreamMode] | None = None,
-        interrupt_before: All | Sequence[str] | None = None,
-        interrupt_after: All | Sequence[str] | None = None,
-        subgraphs: bool = False,
-        version: Literal["v2"],
-        **kwargs: Any,
-    ) -> Iterator[dict[str, Any]]:
-        assert version == "v2"
-        assert subgraphs is True
-        self.last_stream_modes = (
-            [stream_mode] if isinstance(stream_mode, str) else list(stream_mode or [])
-        )
-        yield {
-            "type": "values",
-            "ns": (),
-            "data": {"value": input["value"] + "A"},
-            "interrupts": (),
-        }
-        yield {
-            "type": "values",
-            "ns": (),
-            "data": {"value": input["value"] + "AB"},
-            "interrupts": (),
-        }
-
-    async def astream(
-        self,
-        input: dict[str, Any] | Any,
-        config: RunnableConfig | None = None,
-        *,
-        stream_mode: StreamMode | list[StreamMode] | None = None,
-        interrupt_before: All | Sequence[str] | None = None,
-        interrupt_after: All | Sequence[str] | None = None,
-        subgraphs: bool = False,
-        version: Literal["v2"],
-        **kwargs: Any,
-    ) -> AsyncIterator[dict[str, Any]]:
-        assert version == "v2"
-        assert subgraphs is True
-        self.last_stream_modes = (
-            [stream_mode] if isinstance(stream_mode, str) else list(stream_mode or [])
-        )
-        yield {
-            "type": "values",
-            "ns": (),
-            "data": {"value": input["value"] + "A"},
-            "interrupts": (),
-        }
-        yield {
-            "type": "values",
-            "ns": (),
-            "data": {"value": input["value"] + "AB"},
-            "interrupts": (),
-        }
-
-
-def test_remote_stream_v2_values_and_output() -> None:
-    remote = _FakeRemoteGraph()
-    run = remote.stream_v2({"value": "x"})
-
-    assert list(run.values) == [{"value": "xA"}, {"value": "xAB"}]
-    assert "values" in (remote.last_stream_modes or [])
-
-
-def test_remote_stream_v2_output_drains_remote_events() -> None:
-    remote = _FakeRemoteGraph()
-    run = remote.stream_v2({"value": "x"})
-
-    assert run.output == {"value": "xAB"}
-
-
-def test_remote_stream_v2_raw_events() -> None:
-    remote = _FakeRemoteGraph()
-    run = remote.stream_v2({"value": "x"})
-
-    events = list(run)
-    assert [event["method"] for event in events] == ["values", "values"]
-    assert [event["seq"] for event in events] == [1, 2]
-
-
-@pytest.mark.anyio
-async def test_remote_astream_v2_values_and_output() -> None:
-    remote = _FakeRemoteGraph()
-    run = await remote.astream_v2({"value": "x"})
-
-    assert [item async for item in run.values] == [
-        {"value": "xA"},
-        {"value": "xAB"},
-    ]
-    assert "values" in (remote.last_stream_modes or [])
-
-
-@pytest.mark.anyio
-async def test_remote_astream_v2_output_drains_remote_events() -> None:
-    remote = _FakeRemoteGraph()
-    run = await remote.astream_v2({"value": "x"})
-
-    assert await run.output() == {"value": "xAB"}
-

libs/langgraph/tests/test_stream_subgraph_transformer.py

@@ -1,628 +0,0 @@
-"""Tests for subgraph lifecycle events and the SubgraphTransformer."""
-
-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,
-    ToolLifecycleTransformer,
-    ValuesTransformer,
-)
-from langgraph.types import interrupt
-
-TS = int(time.time() * 1000)
-
-
-def _lifecycle(
-    event: str,
-    *,
-    namespace: list[str] | None = None,
-    graph_name: str | None = None,
-    cause: dict[str, Any] | None = None,
-    error: str | None = None,
-) -> ProtocolEvent:
-    data: dict[str, Any] = {"event": event}
-    if graph_name is not None:
-        data["graph_name"] = graph_name
-    if cause is not None:
-        data["cause"] = cause
-    if error is not None:
-        data["error"] = error
-    return {
-        "type": "event",
-        "method": "lifecycle",
-        "params": {
-            "namespace": namespace or [],
-            "timestamp": TS,
-            "data": data,
-        },
-    }
-
-
-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,
-    ToolLifecycleTransformer,
-    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:
-        """Return the single root handle after pushing one lifecycle started."""
-        (handle,) = list(transformer._root_log._items)
-        return handle
-
-    def test_root_started_is_ignored(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", graph_name="root"))
-        assert list(transformer._root_log._items) == []
-        assert transformer._by_ns == {}
-
-    def test_child_started_yields_handle(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(
-            _lifecycle(
-                "started",
-                namespace=["task_a:child"],
-                graph_name="child",
-                cause={"type": "toolCall", "tool_call_id": "call_abc"},
-            )
-        )
-
-        handle = self._handle(transformer)
-        assert handle.path == ("task_a:child",)
-        assert handle.graph_name == "child"
-        assert handle.cause == {"type": "toolCall", "tool_call_id": "call_abc"}
-        assert handle.status == "started"
-
-    def test_tool_started_is_synthesized_before_tool_caused_lifecycle(self) -> None:
-        mux, transformer = self._mux()
-        events = iter(mux._events)
-        mux.push(
-            _values(
-                {
-                    "messages": [
-                        {
-                            "tool_calls": [
-                                {
-                                    "id": "call_abc",
-                                    "name": "task",
-                                    "args": {"subagent_type": "researcher"},
-                                }
-                            ]
-                        }
-                    ]
-                },
-                namespace=[],
-            )
-        )
-        mux.push(
-            _lifecycle(
-                "started",
-                namespace=["task:child"],
-                graph_name="child",
-                cause={"type": "toolCall", "tool_call_id": "call_abc"},
-            )
-        )
-
-        mux.close()
-        tool_started, lifecycle_started = list(events)[1:3]
-        assert tool_started["method"] == "tools"
-        assert tool_started["params"]["namespace"] == []
-        assert tool_started["params"]["data"] == {
-            "event": "tool-started",
-            "tool_call_id": "call_abc",
-            "tool_name": "task",
-            "input": {"subagent_type": "researcher"},
-        }
-        assert lifecycle_started["method"] == "lifecycle"
-        assert tool_started["seq"] < lifecycle_started["seq"]
-        assert self._handle(transformer).path == ("task:child",)
-
-    def test_core_golden_trace_uses_js_wire_shape_and_ordering(self) -> None:
-        mux, _transformer = self._mux()
-        events = iter(mux._events)
-        mux.push(
-            _values(
-                {
-                    "messages": [
-                        {
-                            "tool_calls": [
-                                {
-                                    "id": "call_abc",
-                                    "name": "task",
-                                    "args": {"subagent_type": "researcher"},
-                                }
-                            ]
-                        }
-                    ]
-                },
-                namespace=[],
-            )
-        )
-        for data in (
-            {"event": "message-start", "id": "msg-1", "role": "ai"},
-            {
-                "event": "content-block-delta",
-                "index": 0,
-                "content": {"type": "text", "text": "hi"},
-            },
-        ):
-            mux.push(
-                {
-                    "type": "event",
-                    "method": "messages",
-                    "params": {
-                        "namespace": ["call_model:task-1"],
-                        "timestamp": TS,
-                        "data": data,
-                        "run_id": "run-1",
-                    },
-                }
-            )
-        mux.push(
-            _lifecycle(
-                "started",
-                namespace=["task:child"],
-                graph_name="child",
-                cause={"type": "toolCall", "tool_call_id": "call_abc"},
-            )
-        )
-        mux.close()
-
-        trace = [
-            (event["method"], event["params"]["namespace"], event["params"]["data"])
-            for event in events
-        ]
-        assert trace == [
-            (
-                "values",
-                [],
-                {
-                    "messages": [
-                        {
-                            "tool_calls": [
-                                {
-                                    "id": "call_abc",
-                                    "name": "task",
-                                    "args": {"subagent_type": "researcher"},
-                                }
-                            ]
-                        }
-                    ]
-                },
-            ),
-            (
-                "messages",
-                ["call_model:task-1"],
-                {"event": "message-start", "id": "msg-1", "role": "ai"},
-            ),
-            (
-                "messages",
-                ["call_model:task-1"],
-                {
-                    "event": "content-block-start",
-                    "index": 0,
-                    "content": {"type": "text", "text": ""},
-                },
-            ),
-            (
-                "messages",
-                ["call_model:task-1"],
-                {
-                    "event": "content-block-delta",
-                    "index": 0,
-                    "content": {"type": "text", "text": "hi"},
-                },
-            ),
-            (
-                "tools",
-                [],
-                {
-                    "event": "tool-started",
-                    "tool_call_id": "call_abc",
-                    "tool_name": "task",
-                    "input": {"subagent_type": "researcher"},
-                },
-            ),
-            (
-                "lifecycle",
-                ["task:child"],
-                {
-                    "event": "started",
-                    "graph_name": "child",
-                    "cause": {"type": "toolCall", "tool_call_id": "call_abc"},
-                },
-            ),
-        ]
-
-    def test_status_transitions(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        mux.push(_lifecycle("running", namespace=["t:c"]))
-        mux.push(_lifecycle("completed", namespace=["t:c"]))
-
-        handle = self._handle(transformer)
-        assert handle.status == "completed"
-
-    def test_grandchild_surfaces_under_child(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", namespace=["t:child"], graph_name="child"))
-
-        child = self._handle(transformer)
-        _pre_subscribe_handle(child)
-
-        mux.push(
-            _lifecycle(
-                "started",
-                namespace=["t:child", "u:grand"],
-                graph_name="grand",
-            )
-        )
-
-        (grand,) = _handle_subgraphs_items(child)
-        assert grand.path == ("t:child", "u:grand")
-        assert grand.graph_name == "grand"
-
-    def test_failed_stores_error(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        mux.push(_lifecycle("failed", namespace=["t:c"], error="boom"))
-
-        handle = self._handle(transformer)
-        assert handle.status == "failed"
-        assert handle.error == "boom"
-
-    def test_values_routed_into_handle(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-
-        handle = self._handle(transformer)
-        _pre_subscribe_handle(handle)
-
-        mux.push(_values({"value": 1}, namespace=["t:c"]))
-        mux.push(_values({"value": 2}, namespace=["t:c"]))
-
-        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(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        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) == []
-
-    def test_finalize_closes_dangling(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        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(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        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(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        handle = self._handle(transformer)
-
-        mux.fail(RuntimeError("explode"))
-        assert handle.status == "failed"
-        assert handle.error == "explode"
-
-    def test_duplicate_started_ignored(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
-        mux.push(_lifecycle("started", namespace=["t:c"], graph_name="other"))
-
-        handles = list(transformer._root_log._items)
-        assert len(handles) == 1
-        assert handles[0].graph_name == "c"
-
-    def test_non_lifecycle_non_values_passthrough(self) -> None:
-        mux, transformer = self._mux()
-        mux.push(
-            {
-                "type": "event",
-                "method": "messages",
-                "params": {
-                    "namespace": ["t:c"],
-                    "timestamp": TS,
-                    "data": (
-                        {"event": "message-start", "message_id": "m1"},
-                        {"run_id": "m1"},
-                    ),
-                },
-            }
-        )
-        assert list(transformer._root_log._items) == []
-
-
-# ---------------------------------------------------------------------------
-# 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 TestSubgraphCause:
-    """Pregel core emits no `cause`; product transformers populate it."""
-
-    def test_cause_not_populated_by_pregel(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 still encodes `node_name:task_id`
-        # (that's pregel's internal namespace format), but `cause` is now
-        # product-agnostic and must be populated by a stream transformer,
-        # not by pregel itself.
-        assert ":" in child.path[0]
-        node_name, _, task_id = child.path[0].partition(":")
-        assert node_name == "sub"
-        assert task_id  # non-empty
-        assert child.cause is None
-
-
-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"

libs/langgraph/tests/test_time_travel.py

@@ -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 +972,356 @@ 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 +1758,321 @@ 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 +3066,16 @@ 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 +3083,7 @@ 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

libs/langgraph/tests/test_time_travel_async.py

@@ -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

libs/langgraph/tests/test_tool_stream_handler.py

@@ -1,290 +0,0 @@
-"""Tests for StreamToolCallHandler and emit_tool_output_delta.
-
-These tests exercise the langgraph-core piece in isolation — the prebuilt
-`ToolCallTransformer` has its own test file. Here we feed real graphs
-through `Pregel.stream(stream_mode=["tools", ...])` and inspect the raw
-`(ns, mode, payload)` tuples on the `tools` channel.
-"""
-
-from __future__ import annotations
-
-from typing import Annotated, Any
-
-import pytest
-from langchain_core.messages import AIMessage
-from langchain_core.tools import tool
-from langgraph.prebuilt import ToolNode
-from typing_extensions import TypedDict
-
-from langgraph.config import emit_tool_output_delta
-from langgraph.constants import END, START
-from langgraph.graph import StateGraph
-from langgraph.graph.message import add_messages
-
-
-class _State(TypedDict):
-    messages: Annotated[list, add_messages]
-
-
-def _caller_sync(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
-    def caller(state: _State) -> dict:
-        return {
-            "messages": [
-                AIMessage(
-                    content="",
-                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
-                )
-            ]
-        }
-
-    return caller
-
-
-def _caller_async(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
-    async def caller(state: _State) -> dict:
-        return {
-            "messages": [
-                AIMessage(
-                    content="",
-                    tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
-                )
-            ]
-        }
-
-    return caller
-
-
-def _build_graph(caller, tools) -> Any:
-    sg = StateGraph(_State)
-    sg.add_node("caller", caller)
-    sg.add_node("tools", ToolNode(tools))
-    sg.add_edge(START, "caller")
-    sg.add_edge("caller", "tools")
-    sg.add_edge("tools", END)
-    return sg.compile()
-
-
-def _tool_events(stream) -> list[tuple[tuple[str, ...], dict]]:
-    """Collect `(ns, payload)` for every `tools`-mode chunk."""
-    out: list[tuple[tuple[str, ...], dict]] = []
-    for ns, mode, payload in stream:
-        if mode == "tools":
-            out.append((tuple(ns), payload))
-    return out
-
-
-class TestSyncGraphSyncTool:
-    def test_started_finished_cycle(self) -> None:
-        @tool
-        def echo(text: str) -> str:
-            """echo."""
-            return f"echoed:{text}"
-
-        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
-        events = _tool_events(
-            graph.stream(
-                {"messages": []},
-                stream_mode=["tools"],
-                subgraphs=True,
-            )
-        )
-
-        assert [p["event"] for _, p in events] == [
-            "tool-started",
-            "tool-finished",
-        ]
-        assert events[0][1]["tool_call_id"] == "tc1"
-        assert events[0][1]["tool_name"] == "echo"
-        assert events[0][1]["input"] == {"text": "hi"}
-        # ToolNode wraps the return in a ToolMessage.
-        assert events[1][1]["tool_call_id"] == "tc1"
-
-    def test_emit_tool_output_delta_produces_delta_events(self) -> None:
-        @tool
-        def streaming_echo(text: str) -> str:
-            """stream chunks."""
-            for chunk in ("a", "b", "c"):
-                emit_tool_output_delta(chunk)
-            return text
-
-        graph = _build_graph(
-            _caller_sync("streaming_echo", {"text": "x"}), [streaming_echo]
-        )
-        events = _tool_events(
-            graph.stream(
-                {"messages": []},
-                stream_mode=["tools"],
-                subgraphs=True,
-            )
-        )
-
-        deltas = [p["delta"] for _, p in events if p["event"] == "tool-output-delta"]
-        assert deltas == ["a", "b", "c"]
-        # The deltas must be bracketed by started and finished.
-        ordered = [p["event"] for _, p in events]
-        assert ordered[0] == "tool-started"
-        assert ordered[-1] == "tool-finished"
-
-    def test_tool_error_event(self) -> None:
-        @tool
-        def boom() -> str:
-            """raises."""
-            raise ValueError("nope")
-
-        graph = _build_graph(_caller_sync("boom", {}), [boom])
-        events: list[tuple[tuple[str, ...], dict]] = []
-        with pytest.raises(ValueError, match="nope"):
-            for ns, mode, payload in graph.stream(
-                {"messages": []},
-                stream_mode=["tools"],
-                subgraphs=True,
-            ):
-                if mode == "tools":
-                    events.append((tuple(ns), payload))
-
-        kinds = [p["event"] for _, p in events]
-        assert kinds == ["tool-started", "tool-error"]
-        assert events[1][1]["message"] == "nope"
-
-    def test_emit_outside_tool_is_noop(self) -> None:
-        # Called at import time (outside any tool body) — must not raise.
-        emit_tool_output_delta("ignored")
-        emit_tool_output_delta({"any": "payload"})
-
-    def test_no_events_without_tools_mode(self) -> None:
-        @tool
-        def echo(text: str) -> str:
-            """echo."""
-            return text
-
-        graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
-        # No "tools" in stream_mode — handler is not attached and zero
-        # `tools`-method events fire.
-        chunks = list(
-            graph.stream(
-                {"messages": []},
-                stream_mode=["values"],
-                subgraphs=True,
-            )
-        )
-        assert all(
-            not (isinstance(c, tuple) and len(c) == 3 and c[1] == "tools")
-            for c in chunks
-        )
-
-
-class TestAsyncGraphAsyncTool:
-    @pytest.mark.anyio
-    async def test_async_tool_produces_events(self) -> None:
-        @tool
-        async def aecho(text: str) -> str:
-            """async echo."""
-            emit_tool_output_delta(text)
-            return f"got:{text}"
-
-        graph = _build_graph(_caller_async("aecho", {"text": "hi"}), [aecho])
-        events: list[tuple[tuple[str, ...], dict]] = []
-        async for ns, mode, payload in graph.astream(
-            {"messages": []},
-            stream_mode=["tools"],
-            subgraphs=True,
-        ):
-            if mode == "tools":
-                events.append((tuple(ns), payload))
-
-        kinds = [p["event"] for _, p in events]
-        assert kinds == ["tool-started", "tool-output-delta", "tool-finished"]
-        assert events[1][1]["delta"] == "hi"
-
-
-class TestConcurrentToolCalls:
-    def test_parallel_tool_calls_do_not_bleed(self) -> None:
-        @tool
-        def streamer(marker: str) -> str:
-            """emits marker twice."""
-            emit_tool_output_delta(f"{marker}-1")
-            emit_tool_output_delta(f"{marker}-2")
-            return marker
-
-        def caller(state: _State) -> dict:
-            return {
-                "messages": [
-                    AIMessage(
-                        content="",
-                        tool_calls=[
-                            {"name": "streamer", "args": {"marker": "A"}, "id": "a"},
-                            {"name": "streamer", "args": {"marker": "B"}, "id": "b"},
-                        ],
-                    )
-                ]
-            }
-
-        graph = _build_graph(caller, [streamer])
-        events = _tool_events(
-            graph.stream(
-                {"messages": []},
-                stream_mode=["tools"],
-                subgraphs=True,
-            )
-        )
-
-        # Group deltas by tool_call_id.
-        by_id: dict[str, list[str]] = {}
-        for _, p in events:
-            if p["event"] == "tool-output-delta":
-                by_id.setdefault(p["tool_call_id"], []).append(p["delta"])
-        assert by_id["a"] == ["A-1", "A-2"]
-        assert by_id["b"] == ["B-1", "B-2"]
-
-
-class TestSubgraphNamespacePropagation:
-    def test_tool_inside_subgraph_emits_with_subgraph_ns(self) -> None:
-        @tool
-        def inner_tool(text: str) -> str:
-            """inner tool."""
-            return text
-
-        def sub_caller(state: _State) -> dict:
-            return {
-                "messages": [
-                    AIMessage(
-                        content="",
-                        tool_calls=[
-                            {
-                                "name": "inner_tool",
-                                "args": {"text": "x"},
-                                "id": "tc1",
-                            }
-                        ],
-                    )
-                ]
-            }
-
-        inner = StateGraph(_State)
-        inner.add_node("sub_caller", sub_caller)
-        inner.add_node("sub_tools", ToolNode([inner_tool]))
-        inner.add_edge(START, "sub_caller")
-        inner.add_edge("sub_caller", "sub_tools")
-        inner.add_edge("sub_tools", END)
-        inner_graph = inner.compile()
-
-        outer = StateGraph(_State)
-        outer.add_node("sub", inner_graph)
-        outer.add_edge(START, "sub")
-        outer.add_edge("sub", END)
-        graph = outer.compile()
-
-        events = _tool_events(
-            graph.stream(
-                {"messages": []},
-                stream_mode=["tools"],
-                subgraphs=True,
-            )
-        )
-
-        # All `tools` events should carry a non-empty namespace rooted
-        # at the `sub` node.
-        assert events, "expected at least one tools event"
-        for ns, _ in events:
-            assert ns  # non-empty
-            assert ns[0].startswith("sub:")

libs/langgraph/tests/test_utils.py

@@ -427,3 +427,49 @@ def test_callback_manager_copies_configurable_ids_to_tracing_metadata() -> None:
         "thread_id": "th-123",
         "user_id": "uid-1",
     }
+
+
+def test_get_nonlocal_names_cached_by_code_object() -> None:
+    """_get_nonlocal_names caches by code object so repeated calls are cheap."""
+    from langgraph.pregel._utils import _get_nonlocal_names
+
+    x = 1
+
+    def my_func() -> int:
+        return x
+
+    result1 = _get_nonlocal_names(my_func.__code__)
+    result2 = _get_nonlocal_names(my_func.__code__)
+
+    # Same frozenset instance returned (cache hit)
+    assert result1 is result2
+    assert "x" in result1
+
+
+def test_get_function_nonlocals_fast_path_no_freevars() -> None:
+    """Functions with no free variables return [] without AST parsing."""
+    from langgraph.pregel._utils import _get_nonlocal_names, get_function_nonlocals
+
+    cache_info_before = _get_nonlocal_names.cache_info()
+
+    def pure_func(a: int, b: int) -> int:
+        return a + b
+
+    result = get_function_nonlocals(pure_func)
+
+    # Should have returned early without touching the cache
+    assert result == []
+    assert _get_nonlocal_names.cache_info().misses == cache_info_before.misses
+
+
+def test_get_function_nonlocals_returns_closure_values() -> None:
+    """get_function_nonlocals correctly extracts values from closures."""
+    from langgraph.pregel._utils import get_function_nonlocals
+
+    sentinel = object()
+
+    def my_func() -> object:
+        return sentinel
+
+    result = get_function_nonlocals(my_func)
+    assert sentinel in result

libs/langgraph/uv.lock

@@ -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 = "0.0.12"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/5c/51/1157009b6f94e6e58be58fa8b620187d657909a8b36a6bf5b0c52a2711f6/langchain_protocol-0.0.12.tar.gz", hash = "sha256:5e14c434290a705c9510fdb1a83ecf7561a5e6e0dfd053930ade80dba069269f", size = 6408, upload-time = "2026-04-25T01:05:01.489Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/95/82/3431e3061c917439589fa88a6b23c9bc0e154cba0f05d2e895a68c76ff74/langchain_protocol-0.0.12-py3-none-any.whl", hash = "sha256:402b61f42d4139692528cf37226c367bb6efc8ff8165b29380accb0abfece7b2", size = 6639, upload-time = "2026-04-25T01:05:00.487Z" },
+    { url = "https://files.pythonhosted.org/packages/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]

libs/prebuilt/langgraph/prebuilt/__init__.py

@@ -1,7 +1,5 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
 
-from langgraph.prebuilt._tool_call_stream import ToolCallStream
-from langgraph.prebuilt._tool_call_transformer import ToolCallTransformer
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import (
     InjectedState,
@@ -15,8 +13,6 @@ from langgraph.prebuilt.tool_validator import ValidationNode
 __all__ = [
     "create_react_agent",
     "ToolNode",
-    "ToolCallStream",
-    "ToolCallTransformer",
     "tools_condition",
     "ValidationNode",
     "InjectedState",

libs/prebuilt/langgraph/prebuilt/_tool_call_stream.py

@@ -1,117 +0,0 @@
-"""In-process handle for a single tool call's streaming execution.
-
-Mirrors the shape of `ChatModelStream` from langchain-core but simpler —
-a tool has one output channel, no content-block multiplexing. Populated
-by `ToolCallTransformer` as `tool-started` / `tool-output-delta` /
-`tool-finished` / `tool-error` events flow in on the `tools` channel.
-"""
-
-from __future__ import annotations
-
-from collections.abc import AsyncIterator, Iterator
-from typing import Any
-
-from langgraph.stream._event_log import EventLog
-
-
-class ToolCallStream:
-    """Scoped view of a single tool call's lifecycle.
-
-    Yielded on `run.tool_calls` once per `tool-started` event. Fields
-    are populated as events arrive:
-
-    - `tool_call_id`, `tool_name`, `input`: stable from the start event.
-    - `output_deltas`: an `EventLog` of delta chunks. Iterate (sync or
-      async) to consume partial output in arrival order.
-    - `output`: terminal payload from `tool-finished`, or `None` if the
-      call failed or is still in flight.
-    - `error`: terminal error string from `tool-error`, or `None` if the
-      call succeeded or is still in flight.
-    - `completed`: True once a terminal event (`tool-finished` or
-      `tool-error`) has been observed.
-
-    `ToolCallStream` is not meant to be constructed by end users — it's
-    produced by `ToolCallTransformer` as events flow through the mux.
-    """
-
-    def __init__(
-        self,
-        tool_call_id: str,
-        tool_name: str,
-        input: dict[str, Any] | None = None,
-    ) -> None:
-        """Initialize a fresh handle for a tool call.
-
-        Args:
-            tool_call_id: The `tool_call_id` from the AIMessage.
-            tool_name: The tool's name.
-            input: The tool's input arguments (as reported by
-                `on_tool_start`), or `None` if none were captured.
-        """
-        self.tool_call_id = tool_call_id
-        self.tool_name = tool_name
-        self.input = input
-        self._output_deltas: EventLog[Any] = EventLog()
-        self.output: Any = None
-        self.error: str | None = None
-        self.completed = False
-
-    @property
-    def output_deltas(self) -> EventLog[Any]:
-        """The EventLog of streamed `tool-output-delta` payloads.
-
-        Iterate (sync or async depending on how the run was started)
-        to consume partial output in arrival order. The log closes when
-        the tool finishes or errors.
-        """
-        return self._output_deltas
-
-    def _bind(self, *, is_async: bool) -> None:
-        """Bind the deltas log to sync or async iteration.
-
-        Called by `ToolCallTransformer` when constructing this handle so
-        the log matches the enclosing mux's mode.
-        """
-        self._output_deltas._bind(is_async=is_async)
-
-    def _push_delta(self, delta: Any) -> None:
-        self._output_deltas.push(delta)
-
-    def _finish(self, output: Any) -> None:
-        self.output = output
-        self.completed = True
-        self._output_deltas.close()
-
-    def _fail(self, message: str) -> None:
-        self.error = message
-        self.completed = True
-        self._output_deltas.close()
-
-    def __iter__(self) -> Iterator[Any]:
-        """Iterate delta chunks synchronously.
-
-        Equivalent to `iter(self.output_deltas)`. Raises `TypeError` if
-        the underlying log is bound to async mode.
-        """
-        return iter(self._output_deltas)
-
-    def __aiter__(self) -> AsyncIterator[Any]:
-        """Iterate delta chunks asynchronously.
-
-        Equivalent to `aiter(self.output_deltas)`. Raises `TypeError`
-        if the underlying log is bound to sync mode.
-        """
-        return self._output_deltas.__aiter__()
-
-    def __repr__(self) -> str:
-        status = (
-            "completed"
-            if self.completed and self.error is None
-            else "failed"
-            if self.completed
-            else "running"
-        )
-        return (
-            f"ToolCallStream(tool_call_id={self.tool_call_id!r}, "
-            f"tool_name={self.tool_name!r}, status={status})"
-        )

libs/prebuilt/langgraph/prebuilt/_tool_call_transformer.py

@@ -1,128 +0,0 @@
-"""Transformer that projects `tools` channel events into `ToolCallStream`s."""
-
-from __future__ import annotations
-
-from collections.abc import Awaitable, Callable
-from typing import Any
-
-from langgraph.stream._event_log import EventLog
-from langgraph.stream._types import ProtocolEvent, StreamTransformer
-
-from langgraph.prebuilt._tool_call_stream import ToolCallStream
-
-
-class ToolCallTransformer(StreamTransformer):
-    """Project `tools` channel events into `ToolCallStream` handles.
-
-    Each `tool-started` event spawns a `ToolCallStream`, pushed onto
-    `run.tool_calls`. Subsequent `tool-output-delta` events append to
-    that stream's deltas log; `tool-finished` and `tool-error` close it.
-
-    Native transformer — the `tool_calls` projection is exposed as a
-    direct attribute on the run stream.
-
-    `EventLog[ToolCallStream]` is used (not `StreamChannel`) because the
-    live handles are not serializable and should not be auto-forwarded
-    onto the main event log. Wire consumers subscribe to the `tools`
-    channel instead, where the raw protocol events flow through
-    untouched by this transformer (`process` returns `True`).
-
-    Registered explicitly by users at compile time via
-    `builder.compile(transformers=[ToolCallTransformer])` — not a
-    default built-in, so the `tools` channel is user-opt-in.
-    """
-
-    _native = True
-    required_stream_modes = ("tools",)
-
-    def __init__(self, scope: tuple[str, ...] = ()) -> None:
-        super().__init__(scope)
-        self._log: EventLog[ToolCallStream] = EventLog()
-        self._active: dict[str, ToolCallStream] = {}
-        self._is_async = False
-        self._pump_fn: Callable[[], bool] | None = None
-        self._apump_fn: Callable[[], Awaitable[bool]] | None = None
-
-    def init(self) -> dict[str, Any]:
-        return {"tool_calls": self._log}
-
-    def _bind_pump(self, fn: Callable[[], bool]) -> None:
-        """Wire the sync pull callback onto this transformer.
-
-        Called by `StreamMux.bind_pump`. Stored so each new
-        `ToolCallStream` created by `process` can wire its deltas log
-        for pump-driven iteration.
-        """
-        self._pump_fn = fn
-        self._is_async = False
-
-    def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
-        """Async counterpart to `_bind_pump`."""
-        self._apump_fn = fn
-        self._is_async = True
-
-    def _new_stream(
-        self,
-        tool_call_id: str,
-        tool_name: str,
-        tool_input: dict[str, Any] | None,
-    ) -> ToolCallStream:
-        stream = ToolCallStream(tool_call_id, tool_name, tool_input)
-        stream._bind(is_async=self._is_async)
-        if self._apump_fn is not None:
-            stream._output_deltas._arequest_more = self._apump_fn
-        if self._pump_fn is not None:
-            stream._output_deltas._request_more = self._pump_fn
-        return stream
-
-    def process(self, event: ProtocolEvent) -> bool:
-        # Namespace filtering is handled by the mux via `scope_exact`.
-        if event["method"] != "tools":
-            return True
-
-        data = event["params"]["data"]
-        tool_call_id = data.get("tool_call_id")
-        if tool_call_id is None:
-            return True
-        event_type = data.get("event")
-
-        stream: ToolCallStream | None
-        if event_type == "tool-started":
-            stream = self._new_stream(
-                tool_call_id,
-                data.get("tool_name", ""),
-                data.get("input"),
-            )
-            self._active[tool_call_id] = stream
-            self._log.push(stream)
-        elif event_type == "tool-output-delta":
-            stream = self._active.get(tool_call_id)
-            if stream is not None:
-                stream._push_delta(data.get("delta"))
-        elif event_type == "tool-finished":
-            stream = self._active.pop(tool_call_id, None)
-            if stream is not None:
-                stream._finish(data.get("output"))
-        elif event_type == "tool-error":
-            stream = self._active.pop(tool_call_id, None)
-            if stream is not None:
-                stream._fail(data.get("message", ""))
-
-        # Pass-through — wire consumers subscribe to the `tools` channel
-        # directly and reconstruct handles client-side.
-        return True
-
-    def finalize(self) -> None:
-        """Close any still-active tool streams left open at run end."""
-        for stream in self._active.values():
-            if not stream.completed:
-                stream._finish(None)
-        self._active.clear()
-
-    def fail(self, err: BaseException) -> None:
-        """Fail any still-active tool streams when the run errors."""
-        message = str(err)
-        for stream in self._active.values():
-            if not stream.completed:
-                stream._fail(message)
-        self._active.clear()

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -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
@@ -1831,9 +1842,17 @@ def _get_injection_from_type(
     return None
 
 
+# Cache keyed by tool object identity. Stores (tool, result) to keep a strong
+# reference that prevents GC from reusing the id for a different object.
+_INJECTED_ARGS_CACHE: dict[int, tuple[BaseTool, _InjectedArgs]] = {}
+
+
 def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
     """Extract all injected arguments from tool in a single pass.
 
+    Results are cached by tool identity so the expensive type-hint and schema
+    inspection only runs once per unique tool object across ToolNode instances.
+
     This function analyzes both the tool's input schema and function signature
     to identify all arguments that should be injected (state, store, runtime).
 
@@ -1843,6 +1862,11 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
     Returns:
         _InjectedArgs structure containing all detected injections.
     """
+    tool_id = id(tool)
+    entry = _INJECTED_ARGS_CACHE.get(tool_id)
+    if entry is not None and entry[0] is tool:
+        return entry[1]
+
     # Get annotations from both schema and function signature
     full_schema = tool.get_input_schema()
     schema_annotations = get_all_basemodel_annotations(full_schema)
@@ -1859,6 +1883,7 @@ 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 +1898,9 @@ 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
 
@@ -1884,9 +1912,12 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
         if _get_injection_from_type(type_, ToolRuntime):
             runtime_arg = name
 
-    return _InjectedArgs(
+    result = _InjectedArgs(
         state=state_args,
         store=store_arg,
         runtime=runtime_arg,
         all_injected_keys=all_injected_keys,
+        _optional_state_args=_optional_state_args,
     )
+    _INJECTED_ARGS_CACHE[tool_id] = (tool, result)
+    return result

libs/prebuilt/pyproject.toml

@@ -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"

libs/prebuilt/tests/test_injected_state_not_required.py

@@ -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

libs/prebuilt/tests/test_tool_call_transformer.py

@@ -1,306 +0,0 @@
-"""Tests for ToolCallTransformer and the ToolCallStream projection."""
-
-from __future__ import annotations
-
-import time
-from typing import Annotated, Any
-
-import pytest
-from langchain_core.messages import AIMessage
-from langchain_core.tools import tool
-from langgraph.config import emit_tool_output_delta
-from langgraph.constants import END, START
-from langgraph.graph import StateGraph
-from langgraph.graph.message import add_messages
-from langgraph.stream._event_log import EventLog
-from langgraph.stream._mux import StreamMux
-from langgraph.stream._types import ProtocolEvent
-from langgraph.stream.transformers import (
-    MessagesTransformer,
-    SubgraphTransformer,
-    ValuesTransformer,
-)
-from typing_extensions import TypedDict
-
-from langgraph.prebuilt import ToolCallStream, ToolCallTransformer, ToolNode
-
-TS = int(time.time() * 1000)
-
-
-def _tool_event(
-    event: str,
-    tool_call_id: str,
-    *,
-    tool_name: str = "",
-    input: dict[str, Any] | None = None,
-    delta: Any = None,
-    output: Any = None,
-    message: str = "",
-    namespace: list[str] | None = None,
-) -> ProtocolEvent:
-    data: dict[str, Any] = {"event": event, "tool_call_id": tool_call_id}
-    if event == "tool-started":
-        data["tool_name"] = tool_name
-        if input is not None:
-            data["input"] = input
-    elif event == "tool-output-delta":
-        data["delta"] = delta
-    elif event == "tool-finished":
-        data["output"] = output
-    elif event == "tool-error":
-        data["message"] = message
-    return {
-        "type": "event",
-        "method": "tools",
-        "params": {
-            "namespace": namespace or [],
-            "timestamp": TS,
-            "data": data,
-        },
-    }
-
-
-def _subscribe(log: EventLog) -> None:
-    log._subscribed = True
-
-
-def _mux() -> tuple[StreamMux, ToolCallTransformer]:
-    mux = StreamMux(
-        factories=[
-            ValuesTransformer,
-            MessagesTransformer,
-            SubgraphTransformer,
-            ToolCallTransformer,
-        ],
-        is_async=False,
-    )
-    transformer = mux.transformer_by_key("tool_calls")
-    assert isinstance(transformer, ToolCallTransformer)
-    _subscribe(transformer._log)
-    return mux, transformer
-
-
-class TestToolCallTransformerUnit:
-    def test_required_stream_modes_declares_tools(self) -> None:
-        assert ToolCallTransformer.required_stream_modes == ("tools",)
-
-    def test_tool_started_yields_handle(self) -> None:
-        mux, transformer = _mux()
-        mux.push(
-            _tool_event(
-                "tool-started",
-                "tc1",
-                tool_name="echo",
-                input={"text": "hi"},
-            )
-        )
-        handles = list(transformer._log._items)
-        assert len(handles) == 1
-        h = handles[0]
-        assert isinstance(h, ToolCallStream)
-        assert h.tool_call_id == "tc1"
-        assert h.tool_name == "echo"
-        assert h.input == {"text": "hi"}
-        assert h.completed is False
-
-    def test_delta_accumulates_on_active_stream(self) -> None:
-        mux, transformer = _mux()
-        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
-        _subscribe(transformer._active["tc1"]._output_deltas)
-        mux.push(_tool_event("tool-output-delta", "tc1", delta="a"))
-        mux.push(_tool_event("tool-output-delta", "tc1", delta="b"))
-        stream = transformer._active["tc1"]
-        assert list(stream._output_deltas._items) == ["a", "b"]
-
-    def test_finish_closes_stream(self) -> None:
-        mux, transformer = _mux()
-        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
-        stream = transformer._active["tc1"]
-        mux.push(_tool_event("tool-finished", "tc1", output="done"))
-        assert stream.completed is True
-        assert stream.output == "done"
-        assert stream.error is None
-        assert "tc1" not in transformer._active
-
-    def test_error_closes_stream(self) -> None:
-        mux, transformer = _mux()
-        mux.push(_tool_event("tool-started", "tc1", tool_name="boom"))
-        stream = transformer._active["tc1"]
-        mux.push(_tool_event("tool-error", "tc1", message="nope"))
-        assert stream.completed is True
-        assert stream.output is None
-        assert stream.error == "nope"
-        assert "tc1" not in transformer._active
-
-    def test_concurrent_tool_calls_do_not_bleed(self) -> None:
-        mux, transformer = _mux()
-        mux.push(_tool_event("tool-started", "a", tool_name="t"))
-        mux.push(_tool_event("tool-started", "b", tool_name="t"))
-        for tc in ("a", "b"):
-            _subscribe(transformer._active[tc]._output_deltas)
-        mux.push(_tool_event("tool-output-delta", "a", delta="A1"))
-        mux.push(_tool_event("tool-output-delta", "b", delta="B1"))
-        mux.push(_tool_event("tool-output-delta", "a", delta="A2"))
-        assert list(transformer._active["a"]._output_deltas._items) == ["A1", "A2"]
-        assert list(transformer._active["b"]._output_deltas._items) == ["B1"]
-
-    def test_tools_event_passes_through_main_log(self) -> None:
-        mux, transformer = _mux()
-        _subscribe(mux._events)
-        mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
-        kept = [e for e in mux._events._items if e["method"] == "tools"]
-        assert len(kept) == 1
-
-
-# ---------------------------------------------------------------------------
-# End-to-end tests with a real graph
-# ---------------------------------------------------------------------------
-
-
-class _State(TypedDict):
-    messages: Annotated[list, add_messages]
-
-
-def _build_graph(caller, tools):
-    sg = StateGraph(_State)
-    sg.add_node("caller", caller)
-    sg.add_node("tools", ToolNode(tools))
-    sg.add_edge(START, "caller")
-    sg.add_edge("caller", "tools")
-    sg.add_edge("tools", END)
-    return sg.compile()
-
-
-class TestToolCallTransformerEndToEnd:
-    def test_sync_streaming_tool_populates_tool_calls(self) -> None:
-        @tool
-        def streamer(text: str) -> str:
-            """streams chunks."""
-            for chunk in ("one", "two"):
-                emit_tool_output_delta(chunk)
-            return text
-
-        def caller(state: _State) -> dict:
-            return {
-                "messages": [
-                    AIMessage(
-                        content="",
-                        tool_calls=[
-                            {"name": "streamer", "args": {"text": "x"}, "id": "tc1"}
-                        ],
-                    )
-                ]
-            }
-
-        graph = _build_graph(caller, [streamer])
-        run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
-
-        tool_calls: list[ToolCallStream] = []
-        for tc in run.tool_calls:
-            tool_calls.append(tc)
-            deltas = list(tc.output_deltas)
-            assert deltas == ["one", "two"]
-        assert len(tool_calls) == 1
-        tc = tool_calls[0]
-        assert tc.tool_call_id == "tc1"
-        assert tc.tool_name == "streamer"
-        assert tc.completed is True
-        assert tc.error is None
-
-    def test_stream_modes_union_includes_tools(self) -> None:
-        @tool
-        def echo(text: str) -> str:
-            """echo."""
-            return text
-
-        def caller(state: _State) -> dict:
-            return {
-                "messages": [
-                    AIMessage(
-                        content="",
-                        tool_calls=[
-                            {"name": "echo", "args": {"text": "x"}, "id": "tc1"}
-                        ],
-                    )
-                ]
-            }
-
-        graph = _build_graph(caller, [echo])
-        # Without ToolCallTransformer, no tool_calls projection is
-        # exposed and no `tools` events flow through (required_stream_modes
-        # omits it).
-        run_no_tc = graph.stream_v2({"messages": []})
-        assert "tool_calls" not in run_no_tc._mux.extensions  # type: ignore[attr-defined]
-
-        # With ToolCallTransformer, the projection is present.
-        run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
-        assert "tool_calls" in run._mux.extensions  # type: ignore[attr-defined]
-        # Drain so the run closes cleanly.
-        list(run.tool_calls)
-
-    @pytest.mark.anyio
-    async def test_async_streaming_tool_populates_tool_calls(self) -> None:
-        @tool
-        async def astreamer(text: str) -> str:
-            """async streams."""
-            emit_tool_output_delta(text)
-            emit_tool_output_delta(text + "!")
-            return text
-
-        async def caller(state: _State) -> dict:
-            return {
-                "messages": [
-                    AIMessage(
-                        content="",
-                        tool_calls=[
-                            {"name": "astreamer", "args": {"text": "hi"}, "id": "tc1"}
-                        ],
-                    )
-                ]
-            }
-
-        graph = _build_graph(caller, [astreamer])
-        run = await graph.astream_v2(
-            {"messages": []}, transformers=[ToolCallTransformer]
-        )
-
-        collected: list[ToolCallStream] = []
-        async for tc in run.tool_calls:
-            collected.append(tc)
-            deltas = [d async for d in tc.output_deltas]
-            assert deltas == ["hi", "hi!"]
-        assert len(collected) == 1
-        assert collected[0].completed is True
-        assert collected[0].error is None
-
-    def test_tool_error_populates_error_field(self) -> None:
-        @tool
-        def boom() -> str:
-            """raises."""
-            raise ValueError("nope")
-
-        def caller(state: _State) -> dict:
-            return {
-                "messages": [
-                    AIMessage(
-                        content="",
-                        tool_calls=[{"name": "boom", "args": {}, "id": "tc1"}],
-                    )
-                ]
-            }
-
-        graph = _build_graph(caller, [boom])
-        run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
-
-        collected: list[ToolCallStream] = []
-        with pytest.raises(ValueError, match="nope"):
-            for tc in run.tool_calls:
-                collected.append(tc)
-                # Drain deltas so the error field is populated before we
-                # inspect it below.
-                list(tc.output_deltas)
-
-        assert len(collected) == 1
-        assert collected[0].error == "nope"
-        assert collected[0].output is None
-        assert collected[0].completed is True

libs/prebuilt/tests/test_tool_node.py

@@ -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 ---

libs/prebuilt/uv.lock

@@ -249,7 +249,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -261,14 +261,14 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
+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/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
+    { 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" },
@@ -281,7 +281,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "." },
     { name = "langgraph-sdk", editable = "../sdk-py" },
@@ -352,7 +352,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -490,7 +490,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -619,7 +619,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.6.4"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -629,11 +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 = [
-    { 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]]

libs/sdk-py/langgraph_sdk/_async/stream.py

@@ -1,228 +0,0 @@
-"""Async thread-centric streaming primitives."""
-
-from __future__ import annotations
-
-import time
-from collections.abc import AsyncIterator, Mapping, Sequence
-from typing import Any, cast
-
-from langgraph_sdk._async.http import HttpClient
-from langgraph_sdk.protocol import (
-    Channel,
-    Command,
-    CommandMethod,
-    CommandResponse,
-    ErrorResponse,
-    Event,
-    SubscribeParams,
-)
-from langgraph_sdk.schema import QueryParamTypes, StreamPart
-
-
-def _stream_part_to_event(part: StreamPart) -> Event:
-    """Normalize an SSE ``StreamPart`` into a protocol event envelope."""
-    if isinstance(part.data, dict) and part.data.get("type") == "event":
-        event = cast(Event, part.data)
-    else:
-        event = {
-            "type": "event",
-            "method": part.event,
-            "params": {
-                "namespace": [],
-                "timestamp": int(time.time() * 1000),
-                "data": part.data,
-            },
-        }
-    if part.id is not None and "event_id" not in event:
-        event["event_id"] = part.id
-    return event
-
-
-class EventSubscription:
-    """Async iterable handle for a filtered event subscription."""
-
-    def __init__(
-        self,
-        subscription_id: str,
-        params: SubscribeParams,
-        events: AsyncIterator[Event],
-        on_unsubscribe: Any,
-    ) -> None:
-        self.subscription_id = subscription_id
-        self.params = params
-        self._events = events
-        self._on_unsubscribe = on_unsubscribe
-
-    def __aiter__(self) -> AsyncIterator[Event]:
-        return self._events
-
-    async def unsubscribe(self) -> None:
-        await self._on_unsubscribe(self.subscription_id)
-
-
-class ProtocolSseTransport:
-    """SSE transport for the thread-centric protocol."""
-
-    def __init__(
-        self,
-        http: HttpClient,
-        thread_id: str,
-        *,
-        headers: Mapping[str, str] | None = None,
-        params: QueryParamTypes | None = None,
-    ) -> None:
-        self.http = http
-        self.thread_id = thread_id
-        self.headers = headers
-        self.params = params
-        self.commands_path = f"/v2/threads/{thread_id}/commands"
-        self.events_path = f"/v2/threads/{thread_id}/events"
-
-    async def send(self, command: Command) -> CommandResponse | ErrorResponse | None:
-        return await self.http.post(
-            self.commands_path,
-            json=cast(dict[str, Any], command),
-            headers=self.headers,
-            params=self.params,
-        )
-
-    def open_event_stream(self, params: SubscribeParams) -> AsyncIterator[Event]:
-        async def iterate() -> AsyncIterator[Event]:
-            async for part in self.http.stream(
-                self.events_path,
-                "POST",
-                json=cast(dict[str, Any], params),
-                headers=self.headers,
-                params=self.params,
-            ):
-                yield _stream_part_to_event(part)
-
-        return iterate()
-
-
-class RunModule:
-    """Run commands exposed by ``ThreadStream.run``."""
-
-    def __init__(self, stream: ThreadStream) -> None:
-        self._stream = stream
-
-    async def input(self, params: Mapping[str, Any]) -> Any:
-        return await self._stream.command("run.input", dict(params))
-
-
-class InputModule:
-    """Human-input commands exposed by ``ThreadStream.input``."""
-
-    def __init__(self, stream: ThreadStream) -> None:
-        self._stream = stream
-
-    async def respond(self, params: Mapping[str, Any]) -> Any:
-        return await self._stream.command("input.respond", dict(params))
-
-    async def inject(self, params: Mapping[str, Any]) -> Any:
-        return await self._stream.command("input.inject", dict(params))
-
-
-class StateModule:
-    """State commands exposed by ``ThreadStream.state``."""
-
-    def __init__(self, stream: ThreadStream) -> None:
-        self._stream = stream
-
-    async def get(self, params: Mapping[str, Any] | None = None) -> Any:
-        return await self._stream.command("state.get", dict(params or {}))
-
-    async def list_checkpoints(self, params: Mapping[str, Any] | None = None) -> Any:
-        return await self._stream.command("state.listCheckpoints", dict(params or {}))
-
-    async def fork(self, params: Mapping[str, Any]) -> Any:
-        return await self._stream.command("state.fork", dict(params))
-
-
-class AgentModule:
-    """Agent commands exposed by ``ThreadStream.agent``."""
-
-    def __init__(self, stream: ThreadStream) -> None:
-        self._stream = stream
-
-    async def get_tree(self, params: Mapping[str, Any] | None = None) -> Any:
-        return await self._stream.command("agent.getTree", dict(params or {}))
-
-
-class ThreadStream:
-    """High-level async wrapper around a thread protocol transport."""
-
-    def __init__(
-        self,
-        transport: ProtocolSseTransport,
-        *,
-        assistant_id: str,
-        starting_command_id: int = 0,
-    ) -> None:
-        if not assistant_id:
-            raise ValueError("assistant_id is required")
-        self.transport = transport
-        self.assistant_id = assistant_id
-        self._next_command_id = starting_command_id
-        self._next_subscription_id = 0
-        self.run = RunModule(self)
-        self.input = InputModule(self)
-        self.state = StateModule(self)
-        self.agent = AgentModule(self)
-
-    @property
-    def thread_id(self) -> str:
-        return self.transport.thread_id
-
-    def _command_id(self) -> str:
-        self._next_command_id += 1
-        return str(self._next_command_id)
-
-    def _subscription_id(self) -> str:
-        self._next_subscription_id += 1
-        return f"sub-{self._next_subscription_id}"
-
-    async def command(self, method: CommandMethod, params: dict[str, Any]) -> Any:
-        command: Command = {
-            "id": self._command_id(),
-            "method": method,
-            "params": params,
-        }
-        if method == "run.input":
-            command["params"] = {"assistant_id": self.assistant_id, **params}
-        response = await self.transport.send(command)
-        if response is None:
-            return None
-        if "error" in response:
-            raise RuntimeError(response["error"])
-        return response.get("result")
-
-    async def subscribe(
-        self,
-        channels: Sequence[Channel | str] | SubscribeParams,
-        *,
-        namespaces: Sequence[Sequence[str]] | None = None,
-        depth: int | None = None,
-    ) -> EventSubscription:
-        if isinstance(channels, dict):
-            params = SubscribeParams(**channels)
-        else:
-            params = SubscribeParams(channels=list(channels))
-            if namespaces is not None:
-                params["namespaces"] = [list(ns) for ns in namespaces]
-            if depth is not None:
-                params["depth"] = depth
-
-        subscription_id = self._subscription_id()
-        events = self.transport.open_event_stream(params)
-        return EventSubscription(subscription_id, params, events, self._unsubscribe)
-
-    async def _unsubscribe(self, subscription_id: str) -> None:
-        await self.command(
-            "subscription.unsubscribe",
-            {"subscription_id": subscription_id},
-        )
-
-    async def close(self) -> None:
-        return None
-

libs/sdk-py/langgraph_sdk/_async/threads.py

@@ -6,7 +6,6 @@ from collections.abc import AsyncIterator, Mapping, Sequence
 from typing import Any
 
 from langgraph_sdk._async.http import HttpClient
-from langgraph_sdk._async.stream import ProtocolSseTransport, ThreadStream
 from langgraph_sdk.schema import (
     Checkpoint,
     Json,
@@ -43,28 +42,6 @@ class ThreadsClient:
     def __init__(self, http: HttpClient) -> None:
         self.http = http
 
-    def stream(
-        self,
-        thread_id: str,
-        *,
-        assistant_id: str,
-        headers: Mapping[str, str] | None = None,
-        params: QueryParamTypes | None = None,
-    ) -> ThreadStream:
-        """Create a thread-centric protocol stream.
-
-        This mirrors the JavaScript SDK's ``client.threads.stream(...)`` API
-        and is intentionally separate from ``join_stream()``, which follows
-        the older thread event stream endpoint.
-        """
-        transport = ProtocolSseTransport(
-            self.http,
-            thread_id,
-            headers=headers,
-            params=params,
-        )
-        return ThreadStream(transport, assistant_id=assistant_id)
-
     async def get(
         self,
         thread_id: str,

libs/sdk-py/langgraph_sdk/_sync/stream.py

@@ -1,225 +0,0 @@
-"""Synchronous thread-centric streaming primitives."""
-
-from __future__ import annotations
-
-import time
-from collections.abc import Iterator, Mapping, Sequence
-from typing import Any, cast
-
-from langgraph_sdk._sync.http import SyncHttpClient
-from langgraph_sdk.protocol import (
-    Channel,
-    Command,
-    CommandMethod,
-    CommandResponse,
-    ErrorResponse,
-    Event,
-    SubscribeParams,
-)
-from langgraph_sdk.schema import QueryParamTypes, StreamPart
-
-
-def _stream_part_to_event(part: StreamPart) -> Event:
-    """Normalize an SSE ``StreamPart`` into a protocol event envelope."""
-    if isinstance(part.data, dict) and part.data.get("type") == "event":
-        event = cast(Event, part.data)
-    else:
-        event = {
-            "type": "event",
-            "method": part.event,
-            "params": {
-                "namespace": [],
-                "timestamp": int(time.time() * 1000),
-                "data": part.data,
-            },
-        }
-    if part.id is not None and "event_id" not in event:
-        event["event_id"] = part.id
-    return event
-
-
-class SyncEventSubscription:
-    """Iterator handle for a filtered event subscription."""
-
-    def __init__(
-        self,
-        subscription_id: str,
-        params: SubscribeParams,
-        events: Iterator[Event],
-        on_unsubscribe: Any,
-    ) -> None:
-        self.subscription_id = subscription_id
-        self.params = params
-        self._events = events
-        self._on_unsubscribe = on_unsubscribe
-
-    def __iter__(self) -> Iterator[Event]:
-        return self._events
-
-    def unsubscribe(self) -> None:
-        self._on_unsubscribe(self.subscription_id)
-
-
-class SyncProtocolSseTransport:
-    """SSE transport for the thread-centric protocol."""
-
-    def __init__(
-        self,
-        http: SyncHttpClient,
-        thread_id: str,
-        *,
-        headers: Mapping[str, str] | None = None,
-        params: QueryParamTypes | None = None,
-    ) -> None:
-        self.http = http
-        self.thread_id = thread_id
-        self.headers = headers
-        self.params = params
-        self.commands_path = f"/v2/threads/{thread_id}/commands"
-        self.events_path = f"/v2/threads/{thread_id}/events"
-
-    def send(self, command: Command) -> CommandResponse | ErrorResponse | None:
-        return self.http.post(
-            self.commands_path,
-            json=cast(dict[str, Any], command),
-            headers=self.headers,
-            params=self.params,
-        )
-
-    def open_event_stream(self, params: SubscribeParams) -> Iterator[Event]:
-        for part in self.http.stream(
-            self.events_path,
-            "POST",
-            json=cast(dict[str, Any], params),
-            headers=self.headers,
-            params=self.params,
-        ):
-            yield _stream_part_to_event(part)
-
-
-class SyncRunModule:
-    """Run commands exposed by ``SyncThreadStream.run``."""
-
-    def __init__(self, stream: SyncThreadStream) -> None:
-        self._stream = stream
-
-    def input(self, params: Mapping[str, Any]) -> Any:
-        return self._stream.command("run.input", dict(params))
-
-
-class SyncInputModule:
-    """Human-input commands exposed by ``SyncThreadStream.input``."""
-
-    def __init__(self, stream: SyncThreadStream) -> None:
-        self._stream = stream
-
-    def respond(self, params: Mapping[str, Any]) -> Any:
-        return self._stream.command("input.respond", dict(params))
-
-    def inject(self, params: Mapping[str, Any]) -> Any:
-        return self._stream.command("input.inject", dict(params))
-
-
-class SyncStateModule:
-    """State commands exposed by ``SyncThreadStream.state``."""
-
-    def __init__(self, stream: SyncThreadStream) -> None:
-        self._stream = stream
-
-    def get(self, params: Mapping[str, Any] | None = None) -> Any:
-        return self._stream.command("state.get", dict(params or {}))
-
-    def list_checkpoints(self, params: Mapping[str, Any] | None = None) -> Any:
-        return self._stream.command("state.listCheckpoints", dict(params or {}))
-
-    def fork(self, params: Mapping[str, Any]) -> Any:
-        return self._stream.command("state.fork", dict(params))
-
-
-class SyncAgentModule:
-    """Agent commands exposed by ``SyncThreadStream.agent``."""
-
-    def __init__(self, stream: SyncThreadStream) -> None:
-        self._stream = stream
-
-    def get_tree(self, params: Mapping[str, Any] | None = None) -> Any:
-        return self._stream.command("agent.getTree", dict(params or {}))
-
-
-class SyncThreadStream:
-    """High-level sync wrapper around a thread protocol transport."""
-
-    def __init__(
-        self,
-        transport: SyncProtocolSseTransport,
-        *,
-        assistant_id: str,
-        starting_command_id: int = 0,
-    ) -> None:
-        if not assistant_id:
-            raise ValueError("assistant_id is required")
-        self.transport = transport
-        self.assistant_id = assistant_id
-        self._next_command_id = starting_command_id
-        self._next_subscription_id = 0
-        self.run = SyncRunModule(self)
-        self.input = SyncInputModule(self)
-        self.state = SyncStateModule(self)
-        self.agent = SyncAgentModule(self)
-
-    @property
-    def thread_id(self) -> str:
-        return self.transport.thread_id
-
-    def _command_id(self) -> str:
-        self._next_command_id += 1
-        return str(self._next_command_id)
-
-    def _subscription_id(self) -> str:
-        self._next_subscription_id += 1
-        return f"sub-{self._next_subscription_id}"
-
-    def command(self, method: CommandMethod, params: dict[str, Any]) -> Any:
-        command: Command = {
-            "id": self._command_id(),
-            "method": method,
-            "params": params,
-        }
-        if method == "run.input":
-            command["params"] = {"assistant_id": self.assistant_id, **params}
-        response = self.transport.send(command)
-        if response is None:
-            return None
-        if "error" in response:
-            raise RuntimeError(response["error"])
-        return response.get("result")
-
-    def subscribe(
-        self,
-        channels: Sequence[Channel | str] | SubscribeParams,
-        *,
-        namespaces: Sequence[Sequence[str]] | None = None,
-        depth: int | None = None,
-    ) -> SyncEventSubscription:
-        if isinstance(channels, dict):
-            params = SubscribeParams(**channels)
-        else:
-            params = SubscribeParams(channels=list(channels))
-            if namespaces is not None:
-                params["namespaces"] = [list(ns) for ns in namespaces]
-            if depth is not None:
-                params["depth"] = depth
-
-        subscription_id = self._subscription_id()
-        events = self.transport.open_event_stream(params)
-        return SyncEventSubscription(subscription_id, params, events, self._unsubscribe)
-
-    def _unsubscribe(self, subscription_id: str) -> None:
-        self.command(
-            "subscription.unsubscribe",
-            {"subscription_id": subscription_id},
-        )
-
-    def close(self) -> None:
-        return None
-

libs/sdk-py/langgraph_sdk/_sync/threads.py

@@ -6,7 +6,6 @@ from collections.abc import Iterator, Mapping, Sequence
 from typing import Any
 
 from langgraph_sdk._sync.http import SyncHttpClient
-from langgraph_sdk._sync.stream import SyncProtocolSseTransport, SyncThreadStream
 from langgraph_sdk.schema import (
     Checkpoint,
     Json,
@@ -42,28 +41,6 @@ class SyncThreadsClient:
     def __init__(self, http: SyncHttpClient) -> None:
         self.http = http
 
-    def stream(
-        self,
-        thread_id: str,
-        *,
-        assistant_id: str,
-        headers: Mapping[str, str] | None = None,
-        params: QueryParamTypes | None = None,
-    ) -> SyncThreadStream:
-        """Create a thread-centric protocol stream.
-
-        This mirrors the JavaScript SDK's ``client.threads.stream(...)`` API
-        and is intentionally separate from ``join_stream()``, which follows
-        the older thread event stream endpoint.
-        """
-        transport = SyncProtocolSseTransport(
-            self.http,
-            thread_id,
-            headers=headers,
-            params=params,
-        )
-        return SyncThreadStream(transport, assistant_id=assistant_id)
-
     def get(
         self,
         thread_id: str,

libs/sdk-py/langgraph_sdk/client.py

@@ -17,11 +17,6 @@ from langgraph_sdk._async.client import LangGraphClient, get_client
 from langgraph_sdk._async.cron import CronClient
 from langgraph_sdk._async.http import HttpClient, _adecode_json, _aencode_json
 from langgraph_sdk._async.runs import RunsClient
-from langgraph_sdk._async.stream import (
-    EventSubscription,
-    ProtocolSseTransport,
-    ThreadStream,
-)
 from langgraph_sdk._async.store import StoreClient
 from langgraph_sdk._async.threads import ThreadsClient
 from langgraph_sdk._shared.utilities import configure_loopback_transports
@@ -32,11 +27,6 @@ from langgraph_sdk._sync.client import SyncLangGraphClient, get_sync_client
 from langgraph_sdk._sync.cron import SyncCronClient
 from langgraph_sdk._sync.http import SyncHttpClient, _decode_json, _encode_json
 from langgraph_sdk._sync.runs import SyncRunsClient
-from langgraph_sdk._sync.stream import (
-    SyncEventSubscription,
-    SyncProtocolSseTransport,
-    SyncThreadStream,
-)
 from langgraph_sdk._sync.store import SyncStoreClient
 from langgraph_sdk._sync.threads import SyncThreadsClient
 
@@ -45,21 +35,15 @@ __all__ = [
     "CronClient",
     "HttpClient",
     "LangGraphClient",
-    "EventSubscription",
-    "ProtocolSseTransport",
     "RunsClient",
     "StoreClient",
     "SyncAssistantsClient",
     "SyncCronClient",
     "SyncHttpClient",
     "SyncLangGraphClient",
-    "SyncEventSubscription",
-    "SyncProtocolSseTransport",
     "SyncRunsClient",
     "SyncStoreClient",
-    "SyncThreadStream",
     "SyncThreadsClient",
-    "ThreadStream",
     "ThreadsClient",
     "_adecode_json",
     "_aencode_json",

libs/sdk-py/langgraph_sdk/protocol.py

@@ -1,105 +0,0 @@
-"""Typed protocol messages for thread-centric remote streaming.
-
-These shapes mirror the JSON protocol used by the JavaScript SDK's
-``ThreadStream`` layer.  The SDK keeps them lightweight and dependency-free so
-``langgraph`` can build higher-level projections on top without creating a
-package cycle.
-"""
-
-from __future__ import annotations
-
-from typing import Any, Literal
-
-from typing_extensions import NotRequired, TypedDict
-
-Channel = Literal[
-    "values",
-    "updates",
-    "messages",
-    "tools",
-    "custom",
-    "lifecycle",
-    "input",
-    "debug",
-    "checkpoints",
-    "tasks",
-]
-"""Built-in subscribable protocol channels."""
-
-CommandMethod = Literal[
-    "run.input",
-    "subscription.subscribe",
-    "subscription.unsubscribe",
-    "agent.getTree",
-    "input.respond",
-    "input.inject",
-    "state.get",
-    "state.listCheckpoints",
-    "state.fork",
-]
-"""Command methods understood by the thread stream protocol."""
-
-
-class SubscribeParams(TypedDict, total=False):
-    """Filter used when subscribing to thread protocol events."""
-
-    channels: list[Channel | str]
-    namespaces: NotRequired[list[list[str]]]
-    depth: NotRequired[int]
-
-
-class Command(TypedDict):
-    """Command sent to a thread protocol transport."""
-
-    id: str
-    method: CommandMethod
-    params: dict[str, Any]
-
-
-class CommandResponse(TypedDict):
-    """Successful command response."""
-
-    id: str
-    result: Any
-
-
-class ErrorResponse(TypedDict):
-    """Error command response."""
-
-    id: str
-    error: dict[str, Any]
-
-
-class EventParams(TypedDict):
-    """Protocol event parameters."""
-
-    namespace: list[str]
-    timestamp: int
-    data: Any
-    node: NotRequired[str]
-    run_id: NotRequired[str]
-    interrupts: NotRequired[list[Any]]
-
-
-class Event(TypedDict):
-    """Protocol event envelope."""
-
-    type: Literal["event"]
-    method: str
-    params: EventParams
-    event_id: NotRequired[str]
-    seq: NotRequired[int]
-
-
-class RunInputResult(TypedDict, total=False):
-    """Result returned by ``run.input`` commands."""
-
-    run_id: str
-    thread_id: str
-
-
-class SubscribeResult(TypedDict):
-    """Result returned by ``subscription.subscribe`` commands."""
-
-    subscription_id: str
-

libs/sdk-py/tests/test_api_parity.py

@@ -41,7 +41,6 @@ def _normalize_return_annotation(ann: object) -> str:
     s = str(ann)
     s = re.sub(r"\s+", "", s)
     s = s.replace("typing.", "").replace("collections.abc.", "")
-    s = s.replace("SyncThreadStream", "ThreadStream")
     s = re.sub(r"AsyncGenerator\[([^,\]]+)(?:,[^\]]*)?\]", r"Iterator[\1]", s)
     s = re.sub(r"Generator\[([^,\]]+)(?:,[^\]]*)?\]", r"Iterator[\1]", s)
     s = re.sub(r"AsyncIterator\[(.+)\]", r"Iterator[\1]", s)

libs/sdk-py/tests/test_client_exports.py

@@ -10,23 +10,17 @@ from langgraph_sdk import get_sync_client as public_get_sync_client
 from langgraph_sdk.client import (
     AssistantsClient,
     CronClient,
-    EventSubscription,
     HttpClient,
     LangGraphClient,
     RunsClient,
     StoreClient,
-    ProtocolSseTransport,
     SyncAssistantsClient,
     SyncCronClient,
-    SyncEventSubscription,
     SyncHttpClient,
     SyncLangGraphClient,
-    SyncProtocolSseTransport,
     SyncRunsClient,
     SyncStoreClient,
-    SyncThreadStream,
     SyncThreadsClient,
-    ThreadStream,
     ThreadsClient,
     _adecode_json,
     _aencode_json,
@@ -58,9 +52,6 @@ def test_client_exports():
     assert RunsClient is not None
     assert CronClient is not None
     assert StoreClient is not None
-    assert EventSubscription is not None
-    assert ProtocolSseTransport is not None
-    assert ThreadStream is not None
 
     # Resource client classes - Sync
     assert SyncAssistantsClient is not None
@@ -68,9 +59,6 @@ def test_client_exports():
     assert SyncRunsClient is not None
     assert SyncCronClient is not None
     assert SyncStoreClient is not None
-    assert SyncEventSubscription is not None
-    assert SyncProtocolSseTransport is not None
-    assert SyncThreadStream is not None
 
     # Internal utilities (used by tests)
     assert callable(_aencode_json)

libs/sdk-py/tests/test_client_stream.py

@@ -9,14 +9,7 @@ import pytest
 from typing_extensions import assert_type
 
 from langgraph_sdk._shared.utilities import _sse_to_v2_dict
-from langgraph_sdk.client import (
-    HttpClient,
-    SyncHttpClient,
-    SyncThreadStream,
-    SyncThreadsClient,
-    ThreadStream,
-    ThreadsClient,
-)
+from langgraph_sdk.client import HttpClient, SyncHttpClient
 from langgraph_sdk.schema import (
     CheckpointPayload,
     CheckpointsStreamPart,
@@ -161,107 +154,6 @@ def test_sync_http_client_stream_flushes_trailing_event():
     assert parts == [StreamPart(event="foo", data={"bar": 1})]
 
 
-@pytest.mark.asyncio
-async def test_async_threads_stream_sends_commands_and_subscribes() -> None:
-    requests: list[tuple[str, str]] = []
-
-    async def handler(request: httpx.Request) -> httpx.Response:
-        body = await request.aread()
-        requests.append((request.method, request.url.path))
-        if request.url.path == "/v2/threads/thread-1/commands":
-            assert request.method == "POST"
-            payload = httpx.Response(200, content=body).json()
-            if payload["method"] == "run.input":
-                assert payload["params"]["assistant_id"] == "agent"
-                return httpx.Response(200, json={"id": payload["id"], "result": "ok"})
-            if payload["method"] == "subscription.unsubscribe":
-                return httpx.Response(200, json={"id": payload["id"], "result": {}})
-        if request.url.path == "/v2/threads/thread-1/events":
-            assert request.method == "POST"
-            assert httpx.Response(200, content=body).json() == {
-                "channels": ["messages"]
-            }
-            return httpx.Response(
-                200,
-                headers={"Content-Type": "text/event-stream"},
-                content=(
-                    b"id: evt-1\n"
-                    b"event: messages\n"
-                    b'data: {"type":"event","method":"messages","params":{"namespace":[],"timestamp":1,"data":{"event":"message-start","id":"m1"}}}\n\n'
-                ),
-            )
-        raise AssertionError(f"unexpected request: {request.method} {request.url.path}")
-
-    transport = httpx.MockTransport(handler)
-    async with httpx.AsyncClient(
-        transport=transport, base_url="https://example.com"
-    ) as client:
-        threads = ThreadsClient(HttpClient(client))
-        thread = threads.stream("thread-1", assistant_id="agent")
-        assert isinstance(thread, ThreadStream)
-        assert await thread.run.input({"input": {"messages": []}}) == "ok"
-        subscription = await thread.subscribe(["messages"])
-        events = [event async for event in subscription]
-        await subscription.unsubscribe()
-
-    assert events[0]["method"] == "messages"
-    assert events[0]["event_id"] == "evt-1"
-    assert requests == [
-        ("POST", "/v2/threads/thread-1/commands"),
-        ("POST", "/v2/threads/thread-1/events"),
-        ("POST", "/v2/threads/thread-1/commands"),
-    ]
-
-
-def test_sync_threads_stream_sends_commands_and_subscribes() -> None:
-    requests: list[tuple[str, str]] = []
-
-    def handler(request: httpx.Request) -> httpx.Response:
-        body = request.read()
-        requests.append((request.method, request.url.path))
-        if request.url.path == "/v2/threads/thread-1/commands":
-            assert request.method == "POST"
-            payload = httpx.Response(200, content=body).json()
-            if payload["method"] == "run.input":
-                assert payload["params"]["assistant_id"] == "agent"
-                return httpx.Response(200, json={"id": payload["id"], "result": "ok"})
-            if payload["method"] == "subscription.unsubscribe":
-                return httpx.Response(200, json={"id": payload["id"], "result": {}})
-        if request.url.path == "/v2/threads/thread-1/events":
-            assert request.method == "POST"
-            assert httpx.Response(200, content=body).json() == {
-                "channels": ["messages"]
-            }
-            return httpx.Response(
-                200,
-                headers={"Content-Type": "text/event-stream"},
-                content=(
-                    b"id: evt-1\n"
-                    b"event: messages\n"
-                    b'data: {"type":"event","method":"messages","params":{"namespace":[],"timestamp":1,"data":{"event":"message-start","id":"m1"}}}\n\n'
-                ),
-            )
-        raise AssertionError(f"unexpected request: {request.method} {request.url.path}")
-
-    transport = httpx.MockTransport(handler)
-    with httpx.Client(transport=transport, base_url="https://example.com") as client:
-        threads = SyncThreadsClient(SyncHttpClient(client))
-        thread = threads.stream("thread-1", assistant_id="agent")
-        assert isinstance(thread, SyncThreadStream)
-        assert thread.run.input({"input": {"messages": []}}) == "ok"
-        subscription = thread.subscribe(["messages"])
-        events = list(subscription)
-        subscription.unsubscribe()
-
-    assert events[0]["method"] == "messages"
-    assert events[0]["event_id"] == "evt-1"
-    assert requests == [
-        ("POST", "/v2/threads/thread-1/commands"),
-        ("POST", "/v2/threads/thread-1/events"),
-        ("POST", "/v2/threads/thread-1/commands"),
-    ]
-
-
 def test_sync_http_client_stream_recovers_after_disconnect():
     reconnect_path = "/reconnect"
     first_chunks = [

libs/sdk-py/uv.lock

@@ -262,7 +262,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -274,14 +274,14 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
+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/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
+    { 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 +294,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = "==1.3.0a2" },
+    { name = "langchain-core", specifier = ">=1.3.0,<2" },
     { name = "langgraph-checkpoint", editable = "../checkpoint" },
     { name = "langgraph-prebuilt", editable = "../prebuilt" },
     { name = "langgraph-sdk", editable = "." },
@@ -365,7 +365,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.0.1"
+version = "4.0.2"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -413,7 +413,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.9"
+version = "1.0.10"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -534,7 +534,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.7.20"
+version = "0.7.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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     { name = "zstandard" },
 ]
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+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" }
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+    { 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]]

pr_delta_channel.md

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