Update libs/langgraph/langgraph/graph/state.py

default compiled name
2026-08-27 01:52:25 +02:00 · 2025-08-01 13:16:37 -04:00 · 2025-07-31 22:55:40 -04:00
46 changed files with 231 additions and 873 deletions
@@ -18,9 +18,6 @@ build-prebuilt:
 build-docs: build-prebuilt
 	TARGET_LANGUAGE=python uv run python -m mkdocs build --clean -f mkdocs.yml --strict

-build-docs-js: build-prebuilt
-	TARGET_LANGUAGE=js uv run python -m mkdocs build --clean -f mkdocs.yml --strict
-
 llms-text:
 	uv run python -m _scripts.generate_llms_text docs/llms-full.txt

@@ -188,13 +188,13 @@ REDIRECT_MAP = {
    "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langgraph-platform/custom-docker",
    "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langgraph-platform/graph-rebuild",
    "concepts/langgraph_cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
-    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/hybrid",
-    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted",
-    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/self-hosted#data-plane-only",
+    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted-data-plane",
+    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted-control-plane",
+    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/standalone-container",
    "cloud/deployment/cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
-    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-hybrid",
-    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-full-platform",
-    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-data-plane-only",
+    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-data-plane",
+    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-control-plane",
+    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-standalone-container",
    "concepts/server-mcp.md": "https://docs.langchain.com/langgraph-platform/server-mcp",
    "cloud/how-tos/human_in_the_loop_time_travel.md": "https://docs.langchain.com/langgraph-platform/human-in-the-loop-time-travel",
    "cloud/how-tos/add-human-in-the-loop.md": "https://docs.langchain.com/langgraph-platform/add-human-in-the-loop",
@@ -29,7 +29,7 @@ pip install -U langgraph "langchain[anthropic]"

 !!! info

-    `langchain[anthropic]` is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
+    LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).

 :::

@@ -41,7 +41,7 @@ npm install @langchain/langgraph @langchain/core @langchain/anthropic

 !!! info

-    `@langchain/core` `@langchain/anthropic` are installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).
+    LangChain is installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).

 :::

@@ -35,8 +35,7 @@ LangGraph Platform provides different security defaults:
 - Can be customized with your auth handler

 !!! note "Custom auth"
-
-   Custom auth **is supported** for all plans in LangGraph Platform.
+Custom auth **is supported** for all plans in LangGraph Platform.

 ### Self-Hosted

@@ -51,51 +51,6 @@ For some examples of pitfalls to avoid, see the [Common Pitfalls](./functional_a
 how to structure your code using **tasks** to avoid these issues. The same principles apply to the @[StateGraph (Graph API)][StateGraph].
 :::

-## Durability modes
-
-LangGraph supports three durability modes that allow you to balance performance and data consistency based on your application's requirements. The durability modes, from least to most durable, are as follows:
-
- [`"exit"`](#exit)
- [`"async"`](#async)
- [`"sync"`](#sync)
-
-A higher durability mode add more overhead to the workflow execution.
-
-!!! version-added "Added in v0.6.0"
-
-    Use the `durability` parameter instead of `checkpoint_during` (deprecated in v0.6.0) for persistence policy management:
-    
-    * `durability="async"` replaces `checkpoint_during=True`
-    * `durability="exit"` replaces `checkpoint_during=False`
-    
-    for persistence policy management, with the following mapping:
-
-    * `checkpoint_during=True` -> `durability="async"`
-    * `checkpoint_during=False` -> `durability="exit"`
-
-
-### `"exit"`
-Changes are persisted only when graph execution completes (either successfully or with an error). This provides the best performance for long-running graphs but means intermediate state is not saved, so you cannot recover from mid-execution failures or interrupt the graph execution.
-
-### `"async"`
-Changes are persisted asynchronously while the next step executes. This provides good performance and durability, but there's a small risk that checkpoints might not be written if the process crashes during execution.
-
-### `"sync"`
-Changes are persisted synchronously before the next step starts. This ensures that every checkpoint is written before continuing execution, providing high durability at the cost of some performance overhead.
-
-You can specify the durability mode when calling any graph execution method:
-
-:::python
-
-```python
-graph.stream(
-    {"input": "test"}, 
-    durability="sync"
-)
-```
-
-:::
-
 ## Using tasks in nodes

 If a [node](./low_level.md#nodes) contains multiple operations, you may find it easier to convert each operation into a **task** rather than refactor the operations into individual nodes.
@@ -88,6 +88,8 @@ Typically, all graph nodes communicate with a single schema. This means that the

 It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`.

+See [this guide](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) for more detail.
+
 It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this guide](../how-tos/graph-api.md#define-input-and-output-schemas) for more detail.

 Let's look at an example:
@@ -471,7 +473,7 @@ const builder = new StateGraph(State);

 :::

-Behind the scenes, functions are converted to [RunnableLambda](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html)s, which add batch and async support to your function, along with native tracing and debugging.
+Behind the scenes, functions are converted to [RunnableLambda](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html#langchain_core.runnables.base.RunnableLambda)s, which add batch and async support to your function, along with native tracing and debugging.

 If you add a node to a graph without specifying a name, it will be given a default name equivalent to the function name.

@@ -699,8 +701,7 @@ graph.addConditionalEdges("nodeA", routingFunction, {
 :::

 !!! tip
-
-    Use [`Command`](#command) instead of conditional edges if you want to combine state updates and routing in a single function.
+Use [`Command`](#command) instead of conditional edges if you want to combine state updates and routing in a single function.

 ### Entry Point

@@ -819,6 +820,7 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]:
        return Command(update={"foo": "baz"}, goto="my_other_node")
 ```

+Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.
 :::

 :::js
@@ -858,6 +860,7 @@ builder.addNode("myNode", myNode, {
 });
 ```

+Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.
 :::

 !!! important
@@ -145,7 +145,7 @@ def my_node(state, config):
 By default, if you add custom authorization on your resources, this will also apply to interactions made from the Studio. If you want, you can handle logged-in Studio users differently by checking [is_studio_user()](../../reference/functions/sdk_auth.isStudioUser.html).

 !!! note
-    `is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.
+`is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.

 ```python
 from langgraph_sdk.auth import is_studio_user, Auth
@@ -57,7 +57,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
    return handoff_tool
 ```

-1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool using the @[InjectedState] annotation. 
+1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool using the @[InjectedState][InjectedState] annotation. 
 2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
 3. Name of the agent or node to hand off to.
 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
@@ -17,7 +17,7 @@ Create a `MemorySaver` checkpointer:
 :::python

 ```python
-from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.memory import MemorySaver

 memory = InMemorySaver()
 ```
@@ -447,4 +447,3 @@ const graph = new StateGraph(State)
 ## Next steps

 In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.
-
@@ -2337,7 +2337,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.2"
+version = "0.6.1"
 source = { editable = "../libs/langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -2524,7 +2524,6 @@ docs = [
    { name = "markdown-include" },
    { name = "mkdocs" },
    { name = "mkdocs-exclude" },
-    { name = "mkdocs-exclude-search" },
    { name = "mkdocs-git-committers-plugin-2" },
    { name = "mkdocs-include-markdown-plugin" },
    { name = "mkdocs-material", extra = ["imaging"] },
@@ -2596,7 +2595,6 @@ docs = [
    { name = "markdown-include" },
    { name = "mkdocs" },
    { name = "mkdocs-exclude" },
-    { name = "mkdocs-exclude-search" },
    { name = "mkdocs-git-committers-plugin-2" },
    { name = "mkdocs-include-markdown-plugin", specifier = ">=7.1.6" },
    { name = "mkdocs-material", extras = ["imaging"] },
@@ -2643,7 +2641,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.2"
+version = "0.6.1"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -3032,18 +3030,6 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/54/b5/3a8e289282c9e8d7003f8a2f53d673d4fdaa81d493dc6966092d9985b6fc/mkdocs-exclude-1.0.2.tar.gz", hash = "sha256:ba6fab3c80ddbe3fd31d3e579861fd3124513708271180a5f81846da8c7e2a51", size = 6751, upload-time = "2019-02-20T23:34:12.81Z" }

-[[package]]
-name = "mkdocs-exclude-search"
-version = "0.6.6"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "mkdocs" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/52/8243589d294cf6091c1145896915fe50feea0e91d64d843942d0175770c2/mkdocs-exclude-search-0.6.6.tar.gz", hash = "sha256:3cdff1b9afdc1b227019cd1e124f401453235b92153d60c0e5e651a76be4f044", size = 9501, upload-time = "2023-12-03T22:58:21.259Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/3b/ef/9af45ffb1bdba684a0694922abae0bb771e9777aba005933f838b7f1bcea/mkdocs_exclude_search-0.6.6-py3-none-any.whl", hash = "sha256:2b4b941d1689808db533fe4a6afba75ce76c9bab8b21d4e31efc05fd8c4e0a4f", size = 7821, upload-time = "2023-12-03T22:58:19.355Z" },
-]
-
 [[package]]
 name = "mkdocs-get-deps"
 version = "0.2.0"
@@ -81,9 +81,6 @@ class Checkpoint(TypedDict):
    This keeps track of the versions of the channels that each node has seen.
    Used to determine which nodes to execute next.
    """
-    updated_channels: list[str] | None
-    """The channels that were updated in this checkpoint.
-    """


 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -95,7 +92,6 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
        channel_versions=checkpoint["channel_versions"].copy(),
        versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
        pending_sends=checkpoint.get("pending_sends", []).copy(),
-        updated_channels=checkpoint.get("updated_channels", None),
    )


@@ -441,7 +437,6 @@ def empty_checkpoint() -> Checkpoint:
        channel_versions={},
        versions_seen={},
        pending_sends=[],
-        updated_channels=None,
    )


@@ -475,5 +470,4 @@ def create_checkpoint(
        channel_versions=checkpoint["channel_versions"],
        versions_seen=checkpoint["versions_seen"],
        pending_sends=checkpoint.get("pending_sends", []),
-        updated_channels=None,
    )
@@ -64,21 +64,14 @@ class AsyncBatchedBaseStore(BaseStore):
        super().__init__()
        self._loop = asyncio.get_running_loop()
        self._aqueue: asyncio.Queue[tuple[asyncio.Future, Op]] = asyncio.Queue()
-        self._task: asyncio.Task | None = None
-        self._ensure_task()
+        self._task = self._loop.create_task(_run(self._aqueue, weakref.ref(self)))

    def __del__(self) -> None:
        try:
-            if self._task:
-                self._task.cancel()
+            self._task.cancel()
        except RuntimeError:
            pass

-    def _ensure_task(self) -> None:
-        """Ensure the background processing loop is running."""
-        if self._task is None or self._task.done():
-            self._task = self._loop.create_task(_run(self._aqueue, weakref.ref(self)))
-
    async def aget(
        self,
        namespace: tuple[str, ...],
@@ -86,7 +79,7 @@ class AsyncBatchedBaseStore(BaseStore):
        *,
        refresh_ttl: bool | None = None,
    ) -> Item | None:
-        self._ensure_task()
+        assert not self._task.done()
        fut = self._loop.create_future()
        self._aqueue.put_nowait(
            (
@@ -111,7 +104,7 @@ class AsyncBatchedBaseStore(BaseStore):
        offset: int = 0,
        refresh_ttl: bool | None = None,
    ) -> list[SearchItem]:
-        self._ensure_task()
+        assert not self._task.done()
        fut = self._loop.create_future()
        self._aqueue.put_nowait(
            (
@@ -137,7 +130,7 @@ class AsyncBatchedBaseStore(BaseStore):
        *,
        ttl: float | None | NotProvided = NOT_PROVIDED,
    ) -> None:
-        self._ensure_task()
+        assert not self._task.done()
        _validate_namespace(namespace)
        fut = self._loop.create_future()
        self._aqueue.put_nowait(
@@ -155,7 +148,7 @@ class AsyncBatchedBaseStore(BaseStore):
        namespace: tuple[str, ...],
        key: str,
    ) -> None:
-        self._ensure_task()
+        assert not self._task.done()
        fut = self._loop.create_future()
        self._aqueue.put_nowait((fut, PutOp(namespace, key, None)))
        return await fut
@@ -169,7 +162,7 @@ class AsyncBatchedBaseStore(BaseStore):
        limit: int = 100,
        offset: int = 0,
    ) -> list[tuple[str, ...]]:
-        self._ensure_task()
+        assert not self._task.done()
        fut = self._loop.create_future()
        match_conditions = []
        if prefix:
@@ -34,42 +34,6 @@ class MockAsyncBatchedStore(AsyncBatchedBaseStore):
        return self._store.batch(ops)


-async def test_async_batch_store_resilience() -> None:
-    """Test that AsyncBatchedBaseStore recovers gracefully from task cancellation."""
-    doc = {"foo": "bar"}
-    async_store = MockAsyncBatchedStore()
-
-    await async_store.aput(("foo", "langgraph", "foo"), "bar", doc)
-
-    # Store the original task reference
-    original_task = async_store._task
-    assert original_task is not None
-    assert not original_task.done()
-
-    # Cancel the background task
-    original_task.cancel()
-    await asyncio.sleep(0.01)
-    assert original_task.cancelled()
-
-    # Perform a new operation - this should trigger _ensure_task() to create a new task
-    result = await async_store.asearch(("foo", "langgraph", "foo"))
-    assert len(result) > 0
-    assert result[0].value == doc
-
-    # Verify a new task was created
-    new_task = async_store._task
-    assert new_task is not None
-    assert new_task is not original_task
-    assert not new_task.done()
-
-    # Test that operations continue to work with the new task
-    doc2 = {"baz": "qux"}
-    await async_store.aput(("test", "namespace"), "key", doc2)
-    result2 = await async_store.aget(("test", "namespace"), "key")
-    assert result2 is not None
-    assert result2.value == doc2
-
-
 def test_get_text_at_path() -> None:
    nested_data = {
        "name": "test",
@@ -4,7 +4,6 @@ import asyncio
 import enum
 import inspect
 import sys
-import warnings
 from collections.abc import (
    AsyncIterator,
    Awaitable,
@@ -304,16 +303,6 @@ class RunnableCallable(Runnable):
            if typ != (ANY_TYPE,) and p.annotation not in typ:
                # A specific type is required, but the function annotation does
                # not match the expected type.
-
-                # If this is a config parameter with incorrect typing, emit a warning
-                # because we used to support any type but are moving towards more correct typing
-                if kw == "config" and p.annotation != inspect.Parameter.empty:
-                    warnings.warn(
-                        f"The 'config' parameter should be typed as 'RunnableConfig' or "
-                        f"'RunnableConfig | None', not '{p.annotation}'. ",
-                        UserWarning,
-                        stacklevel=4,
-                    )
                continue

            # If the kwarg is accepted by the function, store the key / runtime attribute to inject
@@ -91,7 +91,7 @@ class GraphInterrupt(GraphBubbleUp):

@deprecated(
    "NodeInterrupt is deprecated. Please use `langgraph.types.interrupt` instead.",
-    category=None,
+    stacklevel=2,
 )
 class NodeInterrupt(GraphInterrupt):
    """Raised by a node to interrupt execution.
@@ -41,16 +41,16 @@ _Writer = Callable[


 def _get_branch_path_input_schema(
-    path: Callable[..., Hashable | Sequence[Hashable]]
-    | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
-    | Runnable[Any, Hashable | Sequence[Hashable]],
+    path: Callable[..., Hashable | list[Hashable]]
+    | Callable[..., Awaitable[Hashable | list[Hashable]]]
+    | Runnable[Any, Hashable | list[Hashable]],
 ) -> type[Any] | None:
    input = None
    # detect input schema annotation in the branch callable
    try:
        callable_: (
-            Callable[..., Hashable | Sequence[Hashable]]
-            | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
+            Callable[..., Hashable | list[Hashable]]
+            | Callable[..., Awaitable[Hashable | list[Hashable]]]
            | None
        ) = None
        if isinstance(path, (RunnableCallable, RunnableLambda)):
@@ -22,11 +22,10 @@ from langchain_core.messages import (
    convert_to_messages,
    message_chunk_to_message,
 )
-from typing_extensions import TypedDict, deprecated
+from typing_extensions import TypedDict

 from langgraph._internal._constants import CONF, CONFIG_KEY_SEND, NS_SEP
 from langgraph.graph.state import StateGraph
-from langgraph.warnings import LangGraphDeprecatedSinceV10

 __all__ = (
    "add_messages",
@@ -234,16 +233,9 @@ def add_messages(
    return merged


-@deprecated(
-    "MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.",
-    category=None,
-)
 class MessageGraph(StateGraph):
    """A StateGraph where every node receives a list of messages as input and returns one or more messages as output.

-    !!! warning "Deprecation"
-        MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.
-
    MessageGraph is a subclass of StateGraph whose entire state is a single, append-only* list of messages.
    Each node in a MessageGraph takes a list of messages as input and returns zero or more
    messages as output. The `add_messages` function is used to merge the output messages from each node
@@ -289,11 +281,6 @@ class MessageGraph(StateGraph):
    """

    def __init__(self) -> None:
-        warnings.warn(
-            "MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.",
-            category=LangGraphDeprecatedSinceV10,
-            stacklevel=2,
-        )
        super().__init__(Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]


@@ -128,6 +128,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            Use this to expose immutable context data to your nodes, like user_id, db_conn, etc.
        input_schema: The schema class that defines the input to the graph.
        output_schema: The schema class that defines the output from the graph.
+        name: The default name to use when compiling the graph.

    !!! warning "`config_schema` Deprecated"
        The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0.
@@ -177,7 +178,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
    managed: dict[str, ManagedValueSpec]
    schemas: dict[type[Any], dict[str, BaseChannel | ManagedValueSpec]]
    waiting_edges: set[tuple[tuple[str, ...], str]]
-
+    name: str = "LangGraph"
    compiled: bool
    state_schema: type[StateT]
    context_schema: type[ContextT] | None
@@ -607,9 +608,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
    def add_conditional_edges(
        self,
        source: str,
-        path: Callable[..., Hashable | Sequence[Hashable]]
-        | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
-        | Runnable[Any, Hashable | Sequence[Hashable]],
+        path: Callable[..., Hashable | list[Hashable]]
+        | Callable[..., Awaitable[Hashable | list[Hashable]]]
+        | Runnable[Any, Hashable | list[Hashable]],
        path_map: dict[Hashable, str] | list[str] | None = None,
    ) -> Self:
        """Add a conditional edge from the starting node to any number of destination nodes.
@@ -710,9 +711,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):

    def set_conditional_entry_point(
        self,
-        path: Callable[..., Hashable | Sequence[Hashable]]
-        | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
-        | Runnable[Any, Hashable | Sequence[Hashable]],
+        path: Callable[..., Hashable | list[Hashable]]
+        | Callable[..., Awaitable[Hashable | list[Hashable]]]
+        | Runnable[Any, Hashable | list[Hashable]],
        path_map: dict[Hashable, str] | list[str] | None = None,
    ) -> Self:
        """Sets a conditional entry point in the graph.
@@ -874,7 +875,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            debug=debug,
            store=store,
            cache=cache,
-            name=name or "LangGraph",
+            name=name or self.name,
        )

        compiled.attach_node(START, None)
@@ -1390,14 +1391,6 @@ def _is_field_managed_value(name: str, typ: type[Any]) -> ManagedValueSpec | Non
            if is_managed_value(decoration):
                return decoration

-    # Handle Required, NotRequired, etc wrapped types by extracting the inner type
-    if (
-        get_origin(typ) is not None
-        and (args := get_args(typ))
-        and (inner_type := args[0])
-    ):
-        return _is_field_managed_value(name, inner_type)
-
    return None


@@ -8,7 +8,7 @@ from typing import (

 from typing_extensions import TypeGuard

-from langgraph._internal._scratchpad import PregelScratchpad
+from langgraph.pregel._scratchpad import PregelScratchpad

 V = TypeVar("V")
 U = TypeVar("U")
@@ -1,7 +1,7 @@
 from typing import Annotated

-from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph.managed.base import ManagedValue
+from langgraph.pregel._scratchpad import PregelScratchpad

 __all__ = ("IsLastStep", "RemainingStepsManager")

@@ -53,7 +53,6 @@ from langgraph._internal._constants import (
    RETURN,
    TASKS,
 )
-from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import EMPTY_SEQ, MISSING
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.topic import Topic
@@ -70,6 +69,7 @@ from langgraph.pregel._call import get_runnable_for_task, identifier
 from langgraph.pregel._io import read_channels
 from langgraph.pregel._log import logger
 from langgraph.pregel._read import INPUT_CACHE_KEY_TYPE, PregelNode
+from langgraph.pregel._scratchpad import PregelScratchpad
 from langgraph.runtime import DEFAULT_RUNTIME, Runtime
 from langgraph.store.base import BaseStore
 from langgraph.types import (
@@ -29,7 +29,6 @@ def create_checkpoint(
    step: int,
    *,
    id: str | None = None,
-    updated_channels: set[str] | None = None,
 ) -> Checkpoint:
    """Create a checkpoint for the given channels."""
    ts = datetime.now(timezone.utc).isoformat()
@@ -50,7 +49,6 @@ def create_checkpoint(
        channel_values=values,
        channel_versions=checkpoint["channel_versions"],
        versions_seen=checkpoint["versions_seen"],
-        updated_channels=None if updated_channels is None else sorted(updated_channels),
    )


@@ -83,5 +81,4 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
        channel_values=checkpoint["channel_values"].copy(),
        channel_versions=checkpoint["channel_versions"].copy(),
        versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
-        updated_channels=checkpoint.get("updated_channels", None),
    )
@@ -48,7 +48,6 @@ from langgraph._internal._constants import (
    PUSH,
    RESUME,
 )
-from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import EMPTY_SEQ, MISSING
 from langgraph.cache.base import BaseCache
 from langgraph.channels.base import BaseChannel
@@ -101,6 +100,7 @@ from langgraph.pregel._io import (
    read_channels,
 )
 from langgraph.pregel._read import PregelNode
+from langgraph.pregel._scratchpad import PregelScratchpad
 from langgraph.pregel._utils import get_new_channel_versions, is_xxh3_128_hexdigest
 from langgraph.pregel.debug import (
    map_debug_checkpoint,
@@ -568,9 +568,7 @@ class PregelLoop:
            if task := tasks.get(tid):
                task.writes.append((k, v))

-    def _first(
-        self, *, input_keys: str | Sequence[str], updated_channels: set[str] | None
-    ) -> set[str] | None:
+    def _first(self, *, input_keys: str | Sequence[str]) -> set[str] | None:
        # resuming from previous checkpoint requires
        # - finding a previous checkpoint
        # - receiving None input (outer graph) or RESUMING flag (subgraph)
@@ -587,6 +585,8 @@ class PregelLoop:
                ),
            )
        )
+        # this can be set only when there are input_writes
+        updated_channels: set[str] | None = None

        # map command to writes
        if isinstance(self.input, Command):
@@ -614,15 +614,13 @@ class PregelLoop:
        if null_writes := [
            w[1:] for w in self.checkpoint_pending_writes if w[0] == NULL_TASK_ID
        ]:
-            null_updated_channels = apply_writes(
+            apply_writes(
                self.checkpoint,
                self.channels,
                [PregelTaskWrites((), INPUT, null_writes, [])],
                self.checkpointer_get_next_version,
                self.trigger_to_nodes,
            )
-            if updated_channels is not None:
-                updated_channels.update(null_updated_channels)
        # proceed past previous checkpoint
        if is_resuming:
            self.checkpoint["versions_seen"].setdefault(INTERRUPT, {})
@@ -650,7 +648,6 @@ class PregelLoop:
                store=None,
                checkpointer=None,
                manager=None,
-                updated_channels=updated_channels,
            )
            # apply input writes
            updated_channels = apply_writes(
@@ -664,7 +661,6 @@ class PregelLoop:
                self.trigger_to_nodes,
            )
            # save input checkpoint
-            self.updated_channels = updated_channels
            self._put_checkpoint({"source": "input"})
        elif CONFIG_KEY_RESUMING not in configurable:
            raise EmptyInputError(f"Received no input for {input_keys}")
@@ -697,7 +693,6 @@ class PregelLoop:
            self.channels if do_checkpoint else None,
            self.step,
            id=self.checkpoint["id"] if exiting else None,
-            updated_channels=self.updated_channels,
        )
        # bail if no checkpointer
        if do_checkpoint and self._checkpointer_put_after_previous is not None:
@@ -1041,12 +1036,7 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
        self.step = self.checkpoint_metadata["step"] + 1
        self.stop = self.step + self.config["recursion_limit"] + 1
        self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
-        self.updated_channels = self._first(
-            input_keys=self.input_keys,
-            updated_channels=set(self.checkpoint.get("updated_channels"))  # type: ignore[arg-type]
-            if self.checkpoint.get("updated_channels")
-            else None,
-        )
+        self.updated_channels = self._first(input_keys=self.input_keys)

        return self

@@ -1222,12 +1212,7 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
        self.step = self.checkpoint_metadata["step"] + 1
        self.stop = self.step + self.config["recursion_limit"] + 1
        self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
-        self.updated_channels = self._first(
-            input_keys=self.input_keys,
-            updated_channels=set(self.checkpoint.get("updated_channels"))  # type: ignore[arg-type]
-            if self.checkpoint.get("updated_channels")
-            else None,
-        )
+        self.updated_channels = self._first(input_keys=self.input_keys)

        return self

@@ -29,51 +29,16 @@ Meta = tuple[tuple[str, ...], dict[str, Any]]

 class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
    """A callback handler that implements stream_mode=messages.
-
-    Collects messages from:
-    (1) chat model stream events; and
-    (2) node outputs.
-    """
+    Collects messages from (1) chat model stream events and (2) node outputs."""

    run_inline = True
-    """We want this callback to run in the main thread to avoid order/locking issues."""
+    """We want this callback to run in the main thread, to avoid order/locking issues."""

-    def __init__(
-        self,
-        stream: Callable[[StreamChunk], None],
-        subgraphs: bool,
-        *,
-        parent_ns: tuple[str, ...] | None = None,
-    ) -> None:
-        """Configure the handler to stream messages from LLMs and nodes.
-
-        Args:
-            stream: A callable that takes a StreamChunk and emits it.
-            subgraphs: Whether to emit messages from subgraphs.
-            parent_ns: The namespace where the handler was created.
-                We keep track of this namespace to allow calls to subgraphs that
-                were explicitly requested as a stream with `messages` mode
-                configured.
-
-        Example:
-            parent_ns is used to handle scenarios where the subgraph is explicitly
-            streamed with `stream_mode="messages"`.
-
-            ```python
-            def parent_graph_node():
-                # This node is in the parent graph.
-                async for event in some_subgraph(..., stream_mode="messages"):
-                    do something with event # <-- these events will be emitted
-                return ...
-
-            parent_graph.invoke(subgraphs=False)
-            ```
-        """
+    def __init__(self, stream: Callable[[StreamChunk], None], subgraphs: bool):
        self.stream = stream
        self.subgraphs = subgraphs
        self.metadata: dict[UUID, Meta] = {}
        self.seen: set[int | str] = set()
-        self.parent_ns = parent_ns

    def _emit(self, meta: Meta, message: BaseMessage, *, dedupe: bool = False) -> None:
        if dedupe and message.id in self.seen:
@@ -135,7 +100,7 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
            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:
+            if not self.subgraphs and len(ns) > 0:
                return
            if tags:
                if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
@@ -30,13 +30,13 @@ from langgraph._internal._constants import (
    RETURN,
 )
 from langgraph._internal._future import chain_future, run_coroutine_threadsafe
-from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import MISSING
 from langgraph.constants import TAG_HIDDEN
 from langgraph.errors import GraphBubbleUp, GraphInterrupt
 from langgraph.pregel._algo import Call
 from langgraph.pregel._executor import Submit
 from langgraph.pregel._retry import arun_with_retry, run_with_retry
+from langgraph.pregel._scratchpad import PregelScratchpad
 from langgraph.types import (
    CachePolicy,
    PregelExecutableTask,
@@ -637,7 +637,8 @@ class Pregel(
        **deprecated_kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
        if (
-            config_type := deprecated_kwargs.get("config_type", MISSING)
+            config_type := deprecated_kwargs.get("config_type"),
+            MISSING,
        ) is not MISSING:
            warnings.warn(
                "`config_type` is deprecated and will be removed. Please use `context_schema` instead.",
@@ -784,8 +785,7 @@ class Pregel(
        return self

    @deprecated(
-        "`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead.",
-        category=None,
+        "`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead."
    )
    def config_schema(self, *, include: Sequence[str] | None = None) -> type[BaseModel]:
        warnings.warn(
@@ -810,8 +810,7 @@ class Pregel(
        return create_model(self.get_name("Config"), field_definitions=fields)

    @deprecated(
-        "`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead.",
-        category=None,
+        "`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead."
    )
    def get_config_jsonschema(
        self, *, include: Sequence[str] | None = None
@@ -2352,6 +2351,7 @@ class Pregel(
        interrupt_before: All | Sequence[str] | None,
        interrupt_after: All | Sequence[str] | None,
        durability: Durability | None = None,
+        checkpoint_during: bool | None = None,
    ) -> tuple[
        set[StreamMode],
        str | Sequence[str],
@@ -2399,6 +2399,15 @@ class Pregel(
            cache: BaseCache | None = config[CONF][CONFIG_KEY_CACHE]
        else:
            cache = self.cache
+        if checkpoint_during is not None:
+            if durability is not None:
+                raise ValueError(
+                    "Cannot use both `checkpoint_during` and `durability` parameters."
+                )
+            elif checkpoint_during:
+                durability = "async"
+            else:
+                durability = "exit"
        if durability is None:
            durability = config.get(CONF, {}).get(CONFIG_KEY_DURABILITY, "async")
        return (
@@ -2471,17 +2480,6 @@ class Pregel(
        Yields:
            The output of each step in the graph. The output shape depends on the stream_mode.
        """
-        if (checkpoint_during := kwargs.get("checkpoint_during")) is not None:
-            warnings.warn(
-                "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-                category=LangGraphDeprecatedSinceV10,
-                stacklevel=2,
-            )
-            if durability is not None:
-                raise ValueError(
-                    "Cannot use both `checkpoint_during` and `durability` parameters. Please use `durability` instead."
-                )
-            durability = "async" if checkpoint_during else "exit"

        if stream_mode is None:
            # if being called as a node in another graph, default to values mode
@@ -2505,6 +2503,14 @@ class Pregel(
            run_id=config.get("run_id"),
        )
        try:
+            deprecated_checkpoint_during = cast(
+                Optional[bool], kwargs.get("checkpoint_during")
+            )
+            if deprecated_checkpoint_during is not None:
+                warnings.warn(
+                    "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+                    category=LangGraphDeprecatedSinceV10,
+                )
            # assign defaults
            (
                stream_modes,
@@ -2523,8 +2529,11 @@ class Pregel(
                interrupt_before=interrupt_before,
                interrupt_after=interrupt_after,
                durability=durability,
+                checkpoint_during=deprecated_checkpoint_during,
            )
-            if checkpointer is None and durability is not None:
+            if checkpointer is None and (
+                durability is not None or deprecated_checkpoint_during is not None
+            ):
                warnings.warn(
                    "`durability` has no effect when no checkpointer is present.",
                )
@@ -2534,13 +2543,8 @@ class Pregel(
                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
            # set up messages stream mode
            if "messages" in stream_modes:
-                ns_ = cast(Optional[str], config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
                run_manager.inheritable_handlers.append(
-                    StreamMessagesHandler(
-                        stream.put,
-                        subgraphs,
-                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
-                    )
+                    StreamMessagesHandler(stream.put, subgraphs)
                )

            # set up custom stream mode
@@ -2566,7 +2570,7 @@ class Pregel(
                    pass

            # set durability mode for subgraphs
-            if durability is not None:
+            if durability is not None or deprecated_checkpoint_during is not None:
                config[CONF][CONFIG_KEY_DURABILITY] = durability_

            runtime = Runtime(
@@ -2737,17 +2741,6 @@ class Pregel(
        Yields:
            The output of each step in the graph. The output shape depends on the stream_mode.
        """
-        if (checkpoint_during := kwargs.get("checkpoint_during")) is not None:
-            warnings.warn(
-                "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-                category=LangGraphDeprecatedSinceV10,
-                stacklevel=2,
-            )
-            if durability is not None:
-                raise ValueError(
-                    "Cannot use both `checkpoint_during` and `durability` parameters. Please use `durability` instead."
-                )
-            durability = "async" if checkpoint_during else "exit"

        if stream_mode is None:
            # if being called as a node in another graph, default to values mode
@@ -2790,6 +2783,14 @@ class Pregel(
            else False
        )
        try:
+            deprecated_checkpoint_during = cast(
+                Optional[bool], kwargs.get("checkpoint_during")
+            )
+            if deprecated_checkpoint_during is not None:
+                warnings.warn(
+                    "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+                    category=LangGraphDeprecatedSinceV10,
+                )
            # assign defaults
            (
                stream_modes,
@@ -2808,8 +2809,11 @@ class Pregel(
                interrupt_before=interrupt_before,
                interrupt_after=interrupt_after,
                durability=durability,
+                checkpoint_during=deprecated_checkpoint_during,
            )
-            if checkpointer is None and durability is not None:
+            if checkpointer is None and (
+                durability is not None or deprecated_checkpoint_during is not None
+            ):
                warnings.warn(
                    "`durability` has no effect when no checkpointer is present.",
                )
@@ -2819,14 +2823,8 @@ class Pregel(
                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
            # set up messages stream mode
            if "messages" in stream_modes:
-                # namespace can be None in a root level graph?
-                ns_ = cast(Optional[str], config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
                run_manager.inheritable_handlers.append(
-                    StreamMessagesHandler(
-                        stream_put,
-                        subgraphs,
-                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
-                    )
+                    StreamMessagesHandler(stream_put, subgraphs)
                )

            # set up custom stream mode
@@ -2867,7 +2865,7 @@ class Pregel(
                    pass

            # set durability mode for subgraphs
-            if durability is not None:
+            if durability is not None or deprecated_checkpoint_during is not None:
                config[CONF][CONFIG_KEY_DURABILITY] = durability_

            runtime = Runtime(
@@ -2992,7 +2990,6 @@ class Pregel(
        output_keys: str | Sequence[str] | None = None,
        interrupt_before: All | Sequence[str] | None = None,
        interrupt_after: All | Sequence[str] | None = None,
-        durability: Durability | None = None,
        **kwargs: Any,
    ) -> dict[str, Any] | Any:
        """Run the graph with a single input and config.
@@ -3007,10 +3004,6 @@ class Pregel(
            output_keys: Optional. The output keys to retrieve from the graph run.
            interrupt_before: Optional. The nodes to interrupt the graph run before.
            interrupt_after: Optional. The nodes to interrupt the graph run after.
-            durability: The durability mode for the graph execution, defaults to "async". Options are:
-                - `"sync"`: Changes are persisted synchronously before the next step starts.
-                - `"async"`: Changes are persisted asynchronously while the next step executes.
-                - `"exit"`: Changes are persisted only when the graph exits.
            **kwargs: Additional keyword arguments to pass to the graph run.

        Returns:
@@ -3034,7 +3027,6 @@ class Pregel(
            output_keys=output_keys,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
-            durability=durability,
            **kwargs,
        ):
            if stream_mode == "values":
@@ -3077,7 +3069,6 @@ class Pregel(
        output_keys: str | Sequence[str] | None = None,
        interrupt_before: All | Sequence[str] | None = None,
        interrupt_after: All | Sequence[str] | None = None,
-        durability: Durability | None = None,
        **kwargs: Any,
    ) -> dict[str, Any] | Any:
        """Asynchronously invoke the graph on a single input.
@@ -3092,10 +3083,6 @@ class Pregel(
            output_keys: Optional. The output keys to include in the result. Default is None.
            interrupt_before: Optional. The nodes to interrupt before. Default is None.
            interrupt_after: Optional. The nodes to interrupt after. Default is None.
-            durability: The durability mode for the graph execution, defaults to "async". Options are:
-                - `"sync"`: Changes are persisted synchronously before the next step starts.
-                - `"async"`: Changes are persisted asynchronously while the next step executes.
-                - `"exit"`: Changes are persisted only when the graph exits.
            **kwargs: Additional keyword arguments.

        Returns:
@@ -3120,7 +3107,6 @@ class Pregel(
            output_keys=output_keys,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
-            durability=durability,
            **kwargs,
        ):
            if stream_mode == "values":
@@ -191,7 +191,10 @@ class Interrupt:
        return cls(value=value, id=xxh3_128_hexdigest(ns.encode()))

    @property
-    @deprecated("`interrupt_id` is deprecated. Use `id` instead.", category=None)
+    @deprecated(
+        "`interrupt_id` is deprecated. Use `id` instead.",
+        stacklevel=2,
+    )
    def interrupt_id(self) -> str:
        warn(
            "`interrupt_id` is deprecated. Use `id` instead.",
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph"
-version = "0.6.4"
+version = "0.6.2"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -175,10 +175,10 @@
  '''
 # ---
 # name: test_prebuilt_tool_chat
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "is_last_step": {"title": "Is Last Step", "type": "boolean"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages", "is_last_step", "remaining_steps"], "title": "AgentState", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.1
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "is_last_step": {"title": "Is Last Step", "type": "boolean"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages", "is_last_step", "remaining_steps"], "title": "AgentState", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.2
  '''
@@ -330,7 +330,6 @@ SAVED_CHECKPOINTS = {
                    "docs": ["doc1", "doc2", "doc3", "doc4"],
                    "answer": "doc1,doc2,doc3,doc4",
                },
-                "updated_channels": None,
            },
            metadata={
                "source": "loop",
@@ -391,7 +390,6 @@ SAVED_CHECKPOINTS = {
                    "docs": ["doc1", "doc2", "doc3", "doc4"],
                    "branch:to:qa": None,
                },
-                "updated_channels": None,
            },
            metadata={
                "source": "loop",
@@ -467,7 +465,6 @@ SAVED_CHECKPOINTS = {
                    "branch:to:retriever_one": None,
                    "docs": ["doc3", "doc4"],
                },
-                "updated_channels": None,
            },
            metadata={
                "source": "loop",
@@ -519,7 +516,6 @@ SAVED_CHECKPOINTS = {
                    "branch:to:analyzer_one": None,
                    "branch:to:retriever_two": None,
                },
-                "updated_channels": None,
            },
            metadata={
                "source": "loop",
@@ -574,7 +570,6 @@ SAVED_CHECKPOINTS = {
                    "query": "what is weather in sf",
                    "branch:to:rewrite_query": None,
                },
-                "updated_channels": None,
            },
            metadata={
                "source": "loop",
@@ -623,7 +618,6 @@ SAVED_CHECKPOINTS = {
                },
                "versions_seen": {"__input__": {}},
                "channel_values": {"__start__": {"query": "what is weather in sf"}},
-                "updated_channels": None,
            },
            metadata={
                "source": "input",
@@ -1,18 +1,11 @@
-from __future__ import annotations
-
-import warnings
-from typing import Any, Optional
-
 import pytest
-from langchain_core.runnables import RunnableConfig
 from pytest_mock import MockerFixture
-from typing_extensions import NotRequired, TypedDict
+from typing_extensions import TypedDict

 from langgraph.channels.last_value import LastValue
 from langgraph.errors import NodeInterrupt
 from langgraph.func import entrypoint, task
 from langgraph.graph import StateGraph
-from langgraph.graph.message import MessageGraph
 from langgraph.pregel import NodeBuilder, Pregel
 from langgraph.types import Interrupt, RetryPolicy
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
@@ -101,6 +94,8 @@ def test_pregel_types_deprecation() -> None:
        from langgraph.pregel.types import StateSnapshot  # noqa: F401


+@pytest.mark.filterwarnings("ignore:`config_schema` is deprecated")
+@pytest.mark.filterwarnings("ignore:`get_config_jsonschema` is deprecated")
 def test_config_schema_deprecation() -> None:
    with pytest.warns(
        LangGraphDeprecatedSinceV10,
@@ -126,6 +121,7 @@ def test_config_schema_deprecation() -> None:
        graph.get_config_jsonschema()


+@pytest.mark.filterwarnings("ignore:`config_schema` is deprecated")
 def test_config_schema_deprecation_on_entrypoint() -> None:
    with pytest.warns(
        LangGraphDeprecatedSinceV10,
@@ -136,15 +132,10 @@ def test_config_schema_deprecation_on_entrypoint() -> None:
        def my_entrypoint(state: PlainState) -> PlainState:
            return state

-    with pytest.warns(
-        LangGraphDeprecatedSinceV10,
-        match="`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead.",
-    ):
        assert my_entrypoint.context_schema == PlainState
        assert my_entrypoint.config_schema() is not None


-@pytest.mark.filterwarnings("ignore:`config_type` is deprecated")
 def test_config_type_deprecation_pregel(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    chain = NodeBuilder().subscribe_only("input").do(add_one).write_to("output")
@@ -168,6 +159,7 @@ def test_config_type_deprecation_pregel(mocker: MockerFixture) -> None:
        assert instance.context_schema == PlainState


+@pytest.mark.filterwarnings("ignore:`interrupt_id` is deprecated. Use `id` instead.")
 def test_interrupt_attributes_deprecation() -> None:
    interrupt = Interrupt(value="question", id="abc")

@@ -178,6 +170,7 @@ def test_interrupt_attributes_deprecation() -> None:
        interrupt.interrupt_id


+@pytest.mark.filterwarnings("ignore:NodeInterrupt is deprecated.")
 def test_node_interrupt_deprecation() -> None:
    with pytest.warns(
        LangGraphDeprecatedSinceV10,
@@ -192,152 +185,3 @@ def test_deprecated_import() -> None:
        match="Importing PREVIOUS from langgraph.constants is deprecated. This constant is now private and should not be used directly.",
    ):
        from langgraph.constants import PREVIOUS  # noqa: F401
-
-
-@pytest.mark.filterwarnings(
-    "ignore:`durability` has no effect when no checkpointer is present"
-)
-def test_checkpoint_during_deprecation_state_graph() -> None:
-    class CheckDurability(TypedDict):
-        durability: NotRequired[str]
-
-    def plain_node(state: CheckDurability, config: RunnableConfig) -> CheckDurability:
-        return {"durability": config["configurable"]["__pregel_durability"]}
-
-    builder = StateGraph(CheckDurability)
-    builder.add_node("plain_node", plain_node)
-    builder.set_entry_point("plain_node")
-    graph = builder.compile()
-
-    with pytest.warns(
-        LangGraphDeprecatedSinceV10,
-        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-    ):
-        result = graph.invoke({}, checkpoint_during=True)
-        assert result["durability"] == "async"
-
-    with pytest.warns(
-        LangGraphDeprecatedSinceV10,
-        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-    ):
-        result = graph.invoke({}, checkpoint_during=False)
-        assert result["durability"] == "exit"
-
-    with pytest.warns(
-        LangGraphDeprecatedSinceV10,
-        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-    ):
-        for chunk in graph.stream({}, checkpoint_during=True):  # type: ignore[arg-type]
-            assert chunk["plain_node"]["durability"] == "async"
-
-    with pytest.warns(
-        LangGraphDeprecatedSinceV10,
-        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-    ):
-        for chunk in graph.stream({}, checkpoint_during=False):  # type: ignore[arg-type]
-            assert chunk["plain_node"]["durability"] == "exit"
-
-
-def test_config_parameter_incorrect_typing() -> None:
-    """Test that a warning is raised when config parameter is typed incorrectly."""
-    builder = StateGraph(PlainState)
-
-    # Test sync function with config: dict
-    with pytest.warns(
-        UserWarning,
-        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*dict.*'. ",
-    ):
-
-        def sync_node_with_dict_config(state: PlainState, config: dict) -> PlainState:
-            return state
-
-        builder.add_node(sync_node_with_dict_config)
-
-    # Test async function with config: dict
-    with pytest.warns(
-        UserWarning,
-        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*dict.*'. ",
-    ):
-
-        async def async_node_with_dict_config(
-            state: PlainState, config: dict
-        ) -> PlainState:
-            return state
-
-        builder.add_node(async_node_with_dict_config)
-
-    # Test with other incorrect types
-    with pytest.warns(
-        UserWarning,
-        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*Any.*'. ",
-    ):
-
-        def sync_node_with_any_config(state: PlainState, config: Any) -> PlainState:
-            return state
-
-        builder.add_node(sync_node_with_any_config)
-
-    with pytest.warns(
-        UserWarning,
-        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*Any.*'. ",
-    ):
-
-        async def async_node_with_any_config(
-            state: PlainState, config: Any
-        ) -> PlainState:
-            return state
-
-        builder.add_node(async_node_with_any_config)
-
-    with warnings.catch_warnings(record=True) as w:
-
-        def node_with_correct_config(
-            state: PlainState, config: RunnableConfig
-        ) -> PlainState:
-            return state
-
-        builder.add_node(node_with_correct_config)
-
-        def node_with_optional_config(
-            state: PlainState,
-            config: Optional[RunnableConfig],  # noqa: UP045
-        ) -> PlainState:
-            return state
-
-        builder.add_node(node_with_optional_config)
-
-        def node_with_untyped_config(state: PlainState, config) -> PlainState:
-            return state
-
-        builder.add_node(node_with_untyped_config)
-
-        async def async_node_with_correct_config(
-            state: PlainState, config: RunnableConfig
-        ) -> PlainState:
-            return state
-
-        builder.add_node(async_node_with_correct_config)
-
-        async def async_node_with_optional_config(
-            state: PlainState,
-            config: Optional[RunnableConfig],  # noqa: UP045
-        ) -> PlainState:
-            return state
-
-        builder.add_node(async_node_with_optional_config)
-
-        async def async_node_with_untyped_config(
-            state: PlainState, config
-        ) -> PlainState:
-            return state
-
-        builder.add_node(async_node_with_untyped_config)
-        assert len(w) == 0
-
-
-def test_message_graph_deprecation() -> None:
-    with pytest.warns(
-        LangGraphDeprecatedSinceV10,
-        match="MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.",
-    ):
-        MessageGraph()
@@ -6,9 +6,7 @@ from dataclasses import replace
 from typing import Annotated, Any, Literal, Optional, Union, cast

 import pytest
-from langchain_core.messages import AIMessage, AnyMessage, ToolCall
 from langchain_core.runnables import RunnableConfig, RunnableMap, RunnablePick
-from langchain_core.tools import tool
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
 from typing_extensions import TypedDict
@@ -20,7 +18,7 @@ from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
-from langgraph.graph.message import MessagesState, add_messages
+from langgraph.graph.message import MessageGraph, MessagesState, add_messages
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import NodeBuilder, Pregel
@@ -2443,7 +2441,7 @@ def test_message_graph(
            return "continue"

    # Define a new graph
-    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
+    workflow = MessageGraph()

    # Define the two nodes we will cycle between
    workflow.add_node("agent", model)
@@ -2489,7 +2487,7 @@ def test_message_graph(
        assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
        assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

-    assert app.invoke([HumanMessage(content="what is weather in sf")]) == [
+    assert app.invoke(HumanMessage(content="what is weather in sf")) == [
        _AnyIdHumanMessage(
            content="what is weather in sf",
        ),
@@ -6437,6 +6435,10 @@ def test_weather_subgraph(
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
+    from langchain_core.messages import AIMessage, ToolCall
+    from langchain_core.tools import tool
+
+    from langgraph.graph import MessagesState

    # setup subgraph

@@ -11,7 +11,7 @@ from typing import (
 )

 import pytest
-from langchain_core.messages import AnyMessage, ToolCall
+from langchain_core.messages import ToolCall
 from langchain_core.runnables import RunnableConfig, RunnablePick
 from pytest_mock import MockerFixture
 from typing_extensions import TypedDict
@@ -21,7 +21,7 @@ from langgraph.channels.last_value import LastValue
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import END, START
-from langgraph.graph.message import add_messages
+from langgraph.graph.message import MessageGraph, add_messages
 from langgraph.graph.state import StateGraph
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import ToolNode
@@ -2117,7 +2117,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
            return "continue"

    # Define a new graph
-    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
+    workflow = MessageGraph()

    # Define the two nodes we will cycle between
    workflow.add_node("agent", model)
@@ -2157,7 +2157,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
    # meaning you can use it as you would any other runnable
    app = workflow.compile()

-    assert await app.ainvoke([HumanMessage(content="what is weather in sf")]) == [
+    assert await app.ainvoke(HumanMessage(content="what is weather in sf")) == [
        _AnyIdHumanMessage(
            content="what is weather in sf",
        ),
@@ -1,27 +0,0 @@
-from typing_extensions import NotRequired, Required, TypedDict
-
-from langgraph.graph import StateGraph
-from langgraph.managed import RemainingSteps
-
-
-class StatePlain(TypedDict):
-    remaining_steps: RemainingSteps
-
-
-class StateNotRequired(TypedDict):
-    remaining_steps: NotRequired[RemainingSteps]
-
-
-class StateRequired(TypedDict):
-    remaining_steps: Required[RemainingSteps]
-
-
-def test_managed_values_recognized() -> None:
-    graph = StateGraph(StatePlain)
-    assert "remaining_steps" in graph.managed
-
-    graph = StateGraph(StateNotRequired)
-    assert "remaining_steps" in graph.managed
-
-    graph = StateGraph(StateRequired)
-    assert "remaining_steps" in graph.managed
@@ -16,7 +16,6 @@ from typing import Annotated, Any, Literal, Optional, Union, get_type_hints

 import pytest
 from langchain_core.language_models import GenericFakeChatModel
-from langchain_core.messages import AnyMessage
 from langchain_core.runnables import (
    RunnableConfig,
    RunnableLambda,
@@ -27,7 +26,7 @@ from langsmith import traceable
 from pydantic import BaseModel, ConfigDict, Field, ValidationError
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
-from typing_extensions import NotRequired, TypedDict
+from typing_extensions import TypedDict

 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.cache.base import BaseCache
@@ -46,7 +45,7 @@ from langgraph.config import get_stream_writer
 from langgraph.errors import GraphRecursionError, InvalidUpdateError, ParentCommand
 from langgraph.func import entrypoint, task
 from langgraph.graph import END, START, StateGraph
-from langgraph.graph.message import MessagesState, add_messages
+from langgraph.graph.message import MessageGraph, MessagesState, add_messages
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import (
    NodeBuilder,
@@ -968,7 +967,6 @@ def test_pending_writes_resume(
                "branch:to:two": AnyVersion(),
            },
            "channel_values": {"value": 6},
-            "updated_channels": ["value"],
        },
        metadata={
            "parents": {},
@@ -1016,7 +1014,6 @@ def test_pending_writes_resume(
                "branch:to:one": None,
                "branch:to:two": None,
            },
-            "updated_channels": ["branch:to:one", "branch:to:two", "value"],
        },
        metadata={
            "parents": {},
@@ -1068,7 +1065,6 @@ def test_pending_writes_resume(
                "__start__": AnyVersion(),
            },
            "channel_values": {"__start__": {"value": 1}},
-            "updated_channels": ["__start__"],
        },
        metadata={
            "parents": {},
@@ -3911,7 +3907,7 @@ def test_remove_message_via_state_update(
 ) -> None:
    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage

-    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
+    workflow = MessageGraph()
    workflow.add_node(
        "chatbot",
        lambda state: [
@@ -3944,7 +3940,7 @@ def test_remove_message_via_state_update(
 def test_remove_message_from_node():
    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage

-    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
+    workflow = MessageGraph()
    workflow.add_node(
        "chatbot",
        lambda state: [
@@ -8266,53 +8262,3 @@ def test_fork_and_update_task_results(sync_checkpointer: BaseCheckpointSaver) ->
            ],
        ],
    ]
-
-
-def test_subgraph_streaming_sync() -> None:
-    """Test subgraph streaming when used as a node in sync version"""
-
-    # Create a fake chat model that returns a simple response
-    model = GenericFakeChatModel(messages=iter(["The weather is sunny today."]))
-
-    # Create a subgraph that uses the fake chat model
-    def call_model_node(state: MessagesState, config: RunnableConfig) -> MessagesState:
-        """Node that calls the model with the last message."""
-        messages = state["messages"]
-        last_message = messages[-1].content if messages else ""
-        response = model.invoke([("user", last_message)], config)
-        return {"messages": [response]}
-
-    # Build the subgraph
-    subgraph = StateGraph(MessagesState)
-    subgraph.add_node("call_model", call_model_node)
-    subgraph.add_edge(START, "call_model")
-    compiled_subgraph = subgraph.compile()
-
-    class SomeCustomState(TypedDict):
-        last_chunk: NotRequired[str]
-        num_chunks: NotRequired[int]
-
-    # Will invoke a subgraph as a function
-    def parent_node(state: SomeCustomState, config: RunnableConfig) -> dict:
-        """Node that runs the subgraph."""
-        msgs = {"messages": [("user", "What is the weather in Tokyo?")]}
-        events = []
-        for event in compiled_subgraph.stream(msgs, config, stream_mode="messages"):
-            events.append(event)
-        ai_msg_chunks = [ai_msg_chunk for ai_msg_chunk, _ in events]
-        return {
-            "last_chunk": ai_msg_chunks[-1],
-            "num_chunks": len(ai_msg_chunks),
-        }
-
-    # Build the main workflow
-    workflow = StateGraph(SomeCustomState)
-    workflow.add_node("subgraph", parent_node)
-    workflow.add_edge(START, "subgraph")
-    compiled_workflow = workflow.compile()
-
-    # Test the basic functionality
-    result = compiled_workflow.invoke({})
-
-    assert result["last_chunk"].content == "today."
-    assert result["num_chunks"] == 9
@@ -26,7 +26,7 @@ from langchain_core.utils.aiter import aclosing
 from pydantic import BaseModel, ConfigDict, Field, ValidationError
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
-from typing_extensions import NotRequired, TypedDict
+from typing_extensions import TypedDict

 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.cache.base import BaseCache
@@ -1908,7 +1908,6 @@ async def test_pending_writes_resume(
                "branch:to:two": AnyVersion(),
            },
            "channel_values": {"value": 6},
-            "updated_channels": ["value"],
        },
        metadata={
            "parents": {},
@@ -1956,7 +1955,6 @@ async def test_pending_writes_resume(
                "branch:to:one": None,
                "branch:to:two": None,
            },
-            "updated_channels": ["branch:to:one", "branch:to:two", "value"],
        },
        metadata={
            "parents": {},
@@ -2004,7 +2002,6 @@ async def test_pending_writes_resume(
                "__start__": AnyVersion(),
            },
            "channel_values": {"__start__": {"value": 1}},
-            "updated_channels": ["__start__"],
        },
        metadata={
            "parents": {},
@@ -9053,57 +9050,3 @@ async def test_fork_and_update_task_results(
            ],
        ],
    ]
-
-
-async def test_subgraph_streaming_async() -> None:
-    """Test subgraph streaming when used as a node in async version"""
-
-    # Create a fake chat model that returns a simple response
-    model = GenericFakeChatModel(messages=iter(["The weather is sunny today."]))
-
-    # Create a subgraph that uses the fake chat model
-    async def call_model_node(
-        state: MessagesState, config: RunnableConfig
-    ) -> MessagesState:
-        """Node that calls the model with the last message."""
-        messages = state["messages"]
-        last_message = messages[-1].content if messages else ""
-        response = await model.ainvoke([("user", last_message)], config)
-        return {"messages": [response]}
-
-    # Build the subgraph
-    subgraph = StateGraph(MessagesState)
-    subgraph.add_node("call_model", call_model_node)
-    subgraph.add_edge(START, "call_model")
-    compiled_subgraph = subgraph.compile()
-
-    class SomeCustomState(TypedDict):
-        last_chunk: NotRequired[str]
-        num_chunks: NotRequired[int]
-
-    # Will invoke a subgraph as a function
-    async def parent_node(state: SomeCustomState, config: RunnableConfig) -> dict:
-        """Node that runs the subgraph."""
-        msgs = {"messages": [("user", "What is the weather in Tokyo?")]}
-        events = []
-        async for event in compiled_subgraph.astream(
-            msgs, config, stream_mode="messages"
-        ):
-            events.append(event)
-        ai_msg_chunks = [ai_msg_chunk for ai_msg_chunk, _ in events]
-        return {
-            "last_chunk": ai_msg_chunks[-1],
-            "num_chunks": len(ai_msg_chunks),
-        }
-
-    # Build the main workflow
-    workflow = StateGraph(SomeCustomState)
-    workflow.add_node("subgraph", parent_node)
-    workflow.add_edge(START, "subgraph")
-    compiled_workflow = workflow.compile()
-
-    # Test the basic functionality
-    result = await compiled_workflow.ainvoke({})
-
-    assert result["last_chunk"].content == "today."
-    assert result["num_chunks"] == 9
@@ -1192,7 +1192,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.4"
+version = "0.6.2"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -1433,7 +1433,7 @@ dev = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.4"
+version = "0.6.2"
 source = { editable = "../prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -0,0 +1,26 @@
+from typing import Any, Literal, TypedDict
+
+from langchain_core.messages import ToolCall
+
+
+class ToolCallWithContext(TypedDict):
+    """ToolCall with additional context for graph state.
+
+    This is an internal data-structure meant to help the ToolNode accept
+    tools calls with additional context (e.g. state) when dispatched using the
+    `Send` API.
+
+    The Send API is used in create_react_agent to be able to distribute the tool
+    calls in parallel and support human-in-the-loop workflows where graph execution
+    may be paused for an indefinite time.
+    """
+
+    tool_call: ToolCall
+    __type: Literal["tool_call_with_context"]
+    """Type to parameterize the payload.
+    
+    Using "__" as a prefix to be defensive against potential name collisions with
+    regular user state.
+    """
+    state: Any
+    """The state is provided as additional context."""
@@ -34,7 +34,7 @@ from langchain_core.runnables import (
 )
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel
-from typing_extensions import Annotated, NotRequired, TypedDict
+from typing_extensions import Annotated, TypedDict

 from langgraph._internal._runnable import RunnableCallable, RunnableLike
 from langgraph._internal._typing import MISSING
@@ -42,7 +42,8 @@ from langgraph.errors import ErrorCode, create_error_message
 from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.graph.state import CompiledStateGraph
-from langgraph.managed import RemainingSteps
+from langgraph.managed import IsLastStep, RemainingSteps
+from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.runtime import Runtime
 from langgraph.store.base import BaseStore
@@ -64,7 +65,9 @@ class AgentState(TypedDict):

    messages: Annotated[Sequence[BaseMessage], add_messages]

-    remaining_steps: NotRequired[RemainingSteps]
+    is_last_step: IsLastStep
+
+    remaining_steps: RemainingSteps


 class AgentStatePydantic(BaseModel):
@@ -473,11 +476,6 @@ def create_react_agent(
        if context_schema is None:
            context_schema = config_schema

-    if len(deprecated_kwargs) > 0:
-        raise TypeError(
-            f"create_react_agent() got unexpected keyword arguments: {deprecated_kwargs}"
-        )
-
    if version not in ("v1", "v2"):
        raise ValueError(
            f"Invalid version {version}. Supported versions are 'v1' and 'v2'."
@@ -573,13 +571,16 @@ def create_react_agent(
            else False
        )
        remaining_steps = _get_state_value(state, "remaining_steps", None)
-        if remaining_steps is not None:
-            if remaining_steps < 1 and all_tools_return_direct:
-                return True
-            elif remaining_steps < 2 and has_tool_calls:
-                return True
-
-        return False
+        is_last_step = _get_state_value(state, "is_last_step", False)
+        return (
+            (remaining_steps is None and is_last_step and has_tool_calls)
+            or (
+                remaining_steps is not None
+                and remaining_steps < 1
+                and all_tools_return_direct
+            )
+            or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)
+        )

    def _get_model_input_state(state: StateSchema) -> StateSchema:
        if pre_model_hook is not None:
@@ -794,11 +795,17 @@ def create_react_agent(
            elif version == "v2":
                if post_model_hook is not None:
                    return "post_model_hook"
-                tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
-                    for call in last_message.tool_calls
+                return [
+                    Send(
+                        "tools",
+                        ToolCallWithContext(
+                            __type="tool_call_with_context",
+                            tool_call=tool_call,
+                            state=state,
+                        ),
+                    )
+                    for tool_call in last_message.tool_calls
                ]
-                return [Send("tools", [tool_call]) for tool_call in tool_calls]

    # Define a new graph
    workflow = StateGraph(
@@ -879,11 +886,17 @@ def create_react_agent(
            ]

            if pending_tool_calls:
-                pending_tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
-                    for call in pending_tool_calls
+                return [
+                    Send(
+                        "tools",
+                        ToolCallWithContext(
+                            __type="tool_call_with_context",
+                            tool_call=tool_call,
+                            state=state,
+                        ),
+                    )
+                    for tool_call in pending_tool_calls
                ]
-                return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
            elif isinstance(messages[-1], ToolMessage):
                return entrypoint
            elif response_format is not None:
@@ -893,13 +906,13 @@ def create_react_agent(

        workflow.add_conditional_edges(
            "post_model_hook",
-            post_model_hook_router,
+            post_model_hook_router,  # type: ignore[arg-type]
            path_map=post_model_hook_paths,
        )

    workflow.add_conditional_edges(
        "agent",
-        should_continue,
+        should_continue,  # type: ignore[arg-type]
        path_map=agent_paths,
    )

@@ -74,6 +74,7 @@ from typing_extensions import Annotated, get_args, get_origin
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.store.base import BaseStore
 from langgraph.types import Command, Send

@@ -360,7 +361,8 @@ class ToolNode(RunnableCallable):
        *,
        store: Optional[BaseStore],
    ) -> Any:
-        tool_calls, input_type = self._parse_input(input, store)
+        tool_calls, input_type = self._parse_input(input)
+        tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
        config_list = get_config_list(config, len(tool_calls))
        input_types = [input_type] * len(tool_calls)
        with get_executor_for_config(config) as executor:
@@ -381,7 +383,8 @@ class ToolNode(RunnableCallable):
        *,
        store: Optional[BaseStore],
    ) -> Any:
-        tool_calls, input_type = self._parse_input(input, store)
+        tool_calls, input_type = self._parse_input(input)
+        tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
        outputs = await asyncio.gather(
            *(self._arun_one(call, input_type, config) for call in tool_calls)
        )
@@ -499,14 +502,13 @@ class ToolNode(RunnableCallable):
            return invalid_tool_message

        try:
-            call_args = {**call, **{"type": "tool_call"}}
-            response = await self.tools_by_name[call["name"]].ainvoke(call_args, config)
+            input = {**call, **{"type": "tool_call"}}
+            response = await self.tools_by_name[call["name"]].ainvoke(input, config)

        # GraphInterrupt is a special exception that will always be raised.
-        # It can be triggered in the following scenarios,
-        # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation most commonly:
-        # (1) a GraphInterrupt is raised inside a tool
-        # (2) a GraphInterrupt is raised inside a graph node for a graph called as a tool
+        # It can be triggered in the following scenarios:
+        # (1) a NodeInterrupt is raised inside a tool
+        # (2) a NodeInterrupt is raised inside a graph node for a graph called as a tool
        # (3) a GraphInterrupt is raised when a subgraph is interrupted inside a graph called as a tool
        # (2 and 3 can happen in a "supervisor w/ tools" multi-agent architecture)
        except GraphBubbleUp as e:
@@ -553,7 +555,6 @@ class ToolNode(RunnableCallable):
            dict[str, Any],
            BaseModel,
        ],
-        store: Optional[BaseStore],
    ) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
        input_type: Literal["list", "dict", "tool_calls"]
        if isinstance(input, list):
@@ -564,6 +565,15 @@ class ToolNode(RunnableCallable):
            else:
                input_type = "list"
                messages = input
+        elif (
+            isinstance(input, dict) and input.get("__type") == "tool_call_with_context"
+        ):
+            # mypy will not be able to type narrow correctly since the signature
+            # for input contains dict[str, Any]. We'd need to type dict[str, Any]
+            # before we can apply correct typing.
+            input = cast(ToolCallWithContext, input)  # type: ignore[assignment]
+            input_type = "tool_calls"
+            return [input["tool_call"]], input_type
        elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
            input_type = "dict"
        elif messages := getattr(input, self.messages_key, []):
@@ -579,10 +589,7 @@ class ToolNode(RunnableCallable):
        except StopIteration:
            raise ValueError("No AIMessage found in input")

-        tool_calls = [
-            self.inject_tool_args(call, input, store)
-            for call in latest_ai_message.tool_calls
-        ]
+        tool_calls = [call for call in latest_ai_message.tool_calls]
        return tool_calls, input_type

    def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
@@ -625,14 +632,19 @@ class ToolNode(RunnableCallable):
                    err_msg += f" State should contain fields {required_fields_str}."
                raise ValueError(err_msg)

-        if isinstance(input, dict):
+        if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
+            state = input["state"]
+        else:
+            state = input
+
+        if isinstance(state, dict):
            tool_state_args = {
-                tool_arg: input[state_field] if state_field else input
+                tool_arg: state[state_field] if state_field else state
                for tool_arg, state_field in state_args.items()
            }
        else:
            tool_state_args = {
-                tool_arg: getattr(input, state_field) if state_field else input
+                tool_arg: getattr(state, state_field) if state_field else state
                for tool_arg, state_field in state_args.items()
            }

@@ -790,6 +802,7 @@ def tools_condition(

    Args:
        state: The current graph state to examine for tool calls. Supported formats:
+            - List of messages (for MessageGraph)
            - Dictionary containing a messages key (for StateGraph)
            - BaseModel instance with a messages attribute
        messages_key: The key or attribute name containing the message list in the state.
@@ -2,7 +2,8 @@
 in a langchain graph. It applies a pydantic schema to tool_calls in the models' outputs,
 and returns a ToolMessage with the validated content. If the schema is not valid, it
 returns a ToolMessage with the error message. The ValidationNode can be used in a
-StateGraph with a "messages" key. If multiple tool calls are requested, they will be run in parallel.
+StateGraph with a "messages" key or in a MessageGraph. If multiple tool calls are
+requested, they will be run in parallel.
 """

 from typing import (
@@ -48,7 +49,7 @@ def _default_format_error(
 class ValidationNode(RunnableCallable):
    """A node that validates all tools requests from the last AIMessage.

-    It can be used either in StateGraph with a "messages" key.
+    It can be used either in StateGraph with a "messages" key or in MessageGraph.

    !!! note

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

 [project]
 name = "langgraph-prebuilt"
-version = "0.6.4"
+version = "0.6.2"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"
@@ -31,11 +31,3 @@ def test_config_schema_deprecation() -> None:
        match="`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead.",
    ):
        assert agent.get_config_jsonschema() is not None
-
-
-def test_extra_kwargs_deprecation() -> None:
-    with pytest.raises(
-        TypeError,
-        match="create_react_agent\(\) got unexpected keyword arguments: \{'extra': 'extra'\}",
-    ):
-        create_react_agent(FakeToolCallingModel(), [], extra="extra")
@@ -24,7 +24,7 @@ from langchain_core.messages import (
    ToolCall,
    ToolMessage,
 )
-from langchain_core.runnables import RunnableConfig, RunnableLambda
+from langchain_core.runnables import RunnableLambda
 from langchain_core.tools import InjectedToolCallId, ToolException
 from langchain_core.tools import tool as dec_tool
 from pydantic import BaseModel, Field
@@ -1236,190 +1236,6 @@ def test_tool_node_stream_writer() -> None:
    ]


-@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
-def test_react_agent_subgraph_streaming_sync(version: Literal["v1", "v2"]) -> None:
-    """Test React agent streaming when used as a subgraph node sync version"""
-
-    @dec_tool
-    def get_weather(city: str) -> str:
-        """Get the weather of a city."""
-        return f"The weather of {city} is sunny."
-
-    # Create a React agent
-    model = FakeToolCallingModel(
-        tool_calls=[
-            [{"args": {"city": "Tokyo"}, "id": "1", "name": "get_weather"}],
-            [],
-        ]
-    )
-
-    agent = create_react_agent(
-        model,
-        tools=[get_weather],
-        prompt="You are a helpful travel assistant.",
-        version=version,
-    )
-
-    # Create a subgraph that uses the React agent as a node
-    def react_agent_node(state: MessagesState, config: RunnableConfig) -> MessagesState:
-        """Node that runs the React agent and collects streaming output."""
-        collected_content = ""
-
-        # Stream the agent output and collect content
-        for msg_chunk, msg_metadata in agent.stream(
-            {"messages": [("user", state["messages"][-1].content)]},
-            config,
-            stream_mode="messages",
-        ):
-            if hasattr(msg_chunk, "content") and msg_chunk.content:
-                collected_content += msg_chunk.content
-
-        return {"messages": [("assistant", collected_content)]}
-
-    # Create the main workflow with the React agent as a subgraph node
-    workflow = StateGraph(MessagesState)
-    workflow.add_node("react_agent", react_agent_node)
-    workflow.add_edge(START, "react_agent")
-    workflow.add_edge("react_agent", "__end__")
-    compiled_workflow = workflow.compile()
-
-    # Test the streaming functionality
-    result = compiled_workflow.invoke(
-        {"messages": [("user", "What is the weather in Tokyo?")]}
-    )
-
-    # Verify the result contains expected structure
-    assert len(result["messages"]) == 2
-    assert result["messages"][0].content == "What is the weather in Tokyo?"
-    assert "assistant" in str(result["messages"][1])
-
-    # Test streaming with subgraphs = True
-    result = compiled_workflow.invoke(
-        {"messages": [("user", "What is the weather in Tokyo?")]},
-        subgraphs=True,
-    )
-    assert len(result["messages"]) == 2
-
-    events = []
-    for event in compiled_workflow.stream(
-        {"messages": [("user", "What is the weather in Tokyo?")]},
-        stream_mode="messages",
-        subgraphs=False,
-    ):
-        events.append(event)
-
-    assert len(events) == 0
-
-    events = []
-    for event in compiled_workflow.stream(
-        {"messages": [("user", "What is the weather in Tokyo?")]},
-        stream_mode="messages",
-        subgraphs=True,
-    ):
-        events.append(event)
-
-    assert len(events) == 3
-    namespace, (msg, metadata) = events[0]
-    # FakeToolCallingModel returns a single AIMessage with tool calls
-    # The content of the AIMessage reflects the input message
-    assert msg.content.startswith("You are a helpful travel assistant")
-    namespace, (msg, metadata) = events[1]  # ToolMessage
-    assert msg.content.startswith("The weather of Tokyo is sunny.")
-
-
-@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
-async def test_react_agent_subgraph_streaming(version: Literal["v1", "v2"]) -> None:
-    """Test React agent streaming when used as a subgraph node."""
-
-    @dec_tool
-    def get_weather(city: str) -> str:
-        """Get the weather of a city."""
-        return f"The weather of {city} is sunny."
-
-    # Create a React agent
-    model = FakeToolCallingModel(
-        tool_calls=[
-            [{"args": {"city": "Tokyo"}, "id": "1", "name": "get_weather"}],
-            [],
-        ]
-    )
-
-    agent = create_react_agent(
-        model,
-        tools=[get_weather],
-        prompt="You are a helpful travel assistant.",
-        version=version,
-    )
-
-    # Create a subgraph that uses the React agent as a node
-    async def react_agent_node(
-        state: MessagesState, config: RunnableConfig
-    ) -> MessagesState:
-        """Node that runs the React agent and collects streaming output."""
-        collected_content = ""
-
-        # Stream the agent output and collect content
-        async for msg_chunk, msg_metadata in agent.astream(
-            {"messages": [("user", state["messages"][-1].content)]},
-            config,
-            stream_mode="messages",
-        ):
-            if hasattr(msg_chunk, "content") and msg_chunk.content:
-                collected_content += msg_chunk.content
-
-        return {"messages": [("assistant", collected_content)]}
-
-    # Create the main workflow with the React agent as a subgraph node
-    workflow = StateGraph(MessagesState)
-    workflow.add_node("react_agent", react_agent_node)
-    workflow.add_edge(START, "react_agent")
-    workflow.add_edge("react_agent", "__end__")
-    compiled_workflow = workflow.compile()
-
-    # Test the streaming functionality
-    result = await compiled_workflow.ainvoke(
-        {"messages": [("user", "What is the weather in Tokyo?")]}
-    )
-
-    # Verify the result contains expected structure
-    assert len(result["messages"]) == 2
-    assert result["messages"][0].content == "What is the weather in Tokyo?"
-    assert "assistant" in str(result["messages"][1])
-
-    # Test streaming with subgraphs = True
-    result = await compiled_workflow.ainvoke(
-        {"messages": [("user", "What is the weather in Tokyo?")]},
-        subgraphs=True,
-    )
-    assert len(result["messages"]) == 2
-
-    events = []
-    async for event in compiled_workflow.astream(
-        {"messages": [("user", "What is the weather in Tokyo?")]},
-        stream_mode="messages",
-        subgraphs=False,
-    ):
-        events.append(event)
-
-    assert len(events) == 0
-
-    events = []
-    async for event in compiled_workflow.astream(
-        {"messages": [("user", "What is the weather in Tokyo?")]},
-        stream_mode="messages",
-        subgraphs=True,
-    ):
-        events.append(event)
-
-    assert len(events) == 3
-    namespace, (msg, metadata) = events[0]
-    # FakeToolCallingModel returns a single AIMessage with tool calls
-    # The content of the AIMessage reflects the input message
-    assert msg.content.startswith("You are a helpful travel assistant")
-    namespace, (msg, metadata) = events[1]  # ToolMessage
-    assert msg.content.startswith("The weather of Tokyo is sunny.")
-
-
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
 def test_tool_node_node_interrupt(
    sync_checkpointer: BaseCheckpointSaver, version: str
@@ -316,7 +316,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.4"
+version = "0.6.2"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -460,7 +460,7 @@ dev = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.4"
+version = "0.6.2"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
Author	SHA1	Message	Date
Eugene YurtsevandGitHub	be30d153e2	Update libs/langgraph/langgraph/graph/state.py	2025-08-01 13:16:37 -04:00
Eugene Yurtsev	6bfaefed78	default compiled name	2025-07-31 22:55:40 -04:00