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feat(docs): Document disabling webhooks (#5535 )
2026-10-16 13:15:32 +02:00 · 2025-07-17 20:05:48 +00:00 · 2025-07-17 20:04:12 +00:00 · 2025-07-17 15:23:00 -04:00 · 2025-07-17 12:22:30 -07:00 · 2025-07-17 12:10:11 -07:00
30 changed files with 411 additions and 625 deletions
@@ -140,6 +140,22 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN

 Your server should extract and validate this token before processing requests.

+## Disable webhooks
+
+As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
+
+```json
+{
+  "http": {
+    "disable_webhooks": true
+  }
+}
+```
+
+This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
+
+For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
+
 ## Test webhooks

 You can test your webhook using online services like:
@@ -409,8 +409,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    | Option                       | Default                   | Description                                                                                                             |
    | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
    | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
-| `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
+    | `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
+    | `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
    | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
    | `--watch`                    |                           | Restart on file changes                                                                                                 |
    | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
@@ -438,8 +438,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    | Option                                                                 | Default                   | Description                                                                                                             |
    | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
    | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
-| <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
+    | <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
+    | <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
    | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
    | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
    | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
@@ -28,6 +28,9 @@ Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_

 If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.

+!!! note 
+    Enabling `DD_API_KEY` (and thus `ddtrace-run`) can override or interfere with other auto-instrumentation solutions (such as OpenTelemetry) that you may have instrumented into your application code.
+
 ## `LANGCHAIN_TRACING_SAMPLING_RATE`

 Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.
@@ -4,6 +4,20 @@

 ---

+## v0.2.95 (2025-07-17)
+- Avoided setting the future if it is already done to prevent redundant operations.
+- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
+
+## v0.2.94 (2025-07-16)
+- Improved performance by omitting pending sends for langgraph versions 0.5 and above.
+- Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.
+
+## v0.2.93 (2025-07-16)
+- Removed the GIN index for run metadata to improve performance.
+
+## v0.2.92 (2025-07-16)
+- Enabled copying functionality for blobs and checkpoints, improving data management flexibility.
+
 ## v0.2.91 (2025-07-16)
 - Reduced writes to the `checkpoint_blobs` table by inlining small values (null, numeric, str, etc.). This means we don't need to store extra values for channels that haven't been updated.

@@ -45,7 +45,7 @@ The first thing you do when you define a graph is define the `State` of the grap

 ### Schema

-The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation.
+The main documented way to specify the schema of a graph is by using a [`TypedDict`](https://docs.python.org/3/library/typing.html#typing.TypedDict). If you want to provide default values in your state, use a [`dataclass`](https://docs.python.org/3/library/dataclasses.html). We also support using a Pydantic [BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state if you want recursive data validation (though note that pydantic is less performant than a `TypedDict` or `dataclass`).

 By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [guide here](../how-tos/graph-api.md#define-input-and-output-schemas) for how to use.

@@ -0,0 +1,17 @@
+# Tracing
+
+Traces are a series of steps that your application takes to go from input to output. Each of these individual steps is represented by a run. You can use [LangSmith](https://smith.langchain.com/) to visualize these execution steps. To use it, [enable tracing for your application](../how-tos/enable-tracing.md). This enables you to do the following:
+
+- [Debug a locally running application](../cloud/how-tos/clone_traces_studio.md).
+- [Evaluate the application performance](../agents/evals.md).
+- [Monitor the application](https://docs.smith.langchain.com/observability/how_to_guides/dashboards).
+
+To get started, sign up for a free account at [LangSmith](https://smith.langchain.com/).
+
+## Learn more
+
+- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md)
+- [LangSmith Observability quickstart](https://docs.smith.langchain.com/observability)
+- [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph)
+- [Tracing conceptual guide](https://docs.smith.langchain.com/observability/concepts#traces)
+
@@ -2,6 +2,11 @@

 The pages in this section provide a conceptual overview and how-tos for the following topics:

+## Agent development
+
+- [Overview](../agents/overview.md): Use prebuilt components to build an agent.
+- [Run an agent](../agents/run_agents.md): Run an agent by providing input, interpreting output, enabling streaming, and controlling execution limits.
+
 ## LangGraph APIs

 - [Graph API](../concepts/low_level.md): Use the Graph API to define workflows using a graph paradigm.
@@ -0,0 +1,16 @@
+# Enable tracing for your application
+
+To enable [tracing](../concepts/tracing.md) for your application, set the following environment variables:
+
+```python
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=<your-api-key>
+```
+
+For more information, see [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph).
+
+## Learn more
+
+- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md)
+- [LangSmith Observability quickstart](https://docs.smith.langchain.com/observability)
+- [Tracing conceptual guide](https://docs.smith.langchain.com/observability/concepts#traces)
@@ -328,14 +328,15 @@ Output of graph invocation: {'a': 'set by node_3'}

 A [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.StateGraph) accepts a `state_schema` argument on initialization that specifies the "shape" of the state that the nodes in the graph can access and update.

-In our examples, we typically use a python-native `TypedDict` for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).
+In our examples, we typically use a python-native `TypedDict` or [`dataclass`](https://docs.python.org/3/library/dataclasses.html) for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).

-Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/). can be used for `state_schema` to add run time validation on **inputs**.
+Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/) can be used for `state_schema` to add run-time validation on **inputs**.

 !!! note "Known Limitations"
    - Currently, the output of the graph will **NOT** be an instance of a pydantic model.
    - Run-time validation only occurs on inputs into nodes, not on the outputs.
    - The validation error trace from pydantic does not show which node the error arises in.
+    - Pydantic's recursive validation can be slow. For performance-sensitive applications, you may want to consider using a `dataclass` instead.

 ```python
 from langgraph.graph import StateGraph, START, END
@@ -103,14 +103,15 @@ nav:
          -  5. Customize state: tutorials/get-started/5-customize-state.md
          -  6. Time travel: tutorials/get-started/6-time-travel.md
      - Run a local server: tutorials/langgraph-platform/local-server.md
-    - Agent development:
+    - General concepts:
      - Workflows & agents: tutorials/workflows.md
-      - Prebuilt components: agents/overview.md
-      - Run an agent: agents/run_agents.md
      - Agent architectures: concepts/agentic_concepts.md

  - Guides:
    - guides/index.md
+    - Agent development:
+      - Overview: agents/overview.md
+      - Run an agent: agents/run_agents.md
    - LangGraph APIs:
      - Graph API:
          - Overview: concepts/low_level.md
@@ -157,8 +158,10 @@ nav:
          - Overview: concepts/mcp.md
          - Use MCP: agents/mcp.md
          - Server API: concepts/server-mcp.md
-      - Evaluation:
-          - Basic implementation: agents/evals.md
+      - Tracing:
+          - Overview: concepts/tracing.md
+          - Enable tracing: how-tos/enable-tracing.md
+          - Evaluate performance: agents/evals.md
    - Platform-only capabilities:
      - LangGraph Platform:
          - Overview: concepts/langgraph_platform.md
@@ -304,7 +304,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -29,7 +29,7 @@ _AIO_ERROR_MSG = (
    "from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver\n"
    "Note: AsyncSqliteSaver requires the aiosqlite package to use.\n"
    "Install with:\n`pip install aiosqlite`\n"
-    "See https://langchain-ai.github.io/langgraph/reference/checkpoints/asyncsqlitesaver"
+    "See https://langchain-ai.github.io/langgraph/reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver"
    "for more information."
 )

@@ -316,7 +316,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -343,10 +343,14 @@ async def _run(

                    # set the results of each operation
                    for fut, result in zip(futs, results):
-                        fut.set_result(result)
+                        # guard against future being done (e.g. cancelled)
+                        if not fut.done():
+                            fut.set_result(result)
                except Exception as e:
                    for fut in futs:
-                        fut.set_exception(e)
+                        # guard against future being done (e.g. cancelled)
+                        if not fut.done():
+                            fut.set_exception(e)
            finally:
                # remove strong ref to store
                del s
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.9"
@@ -155,6 +155,43 @@ async def test_async_batch_store(mocker: MockerFixture) -> None:
    ]


+async def test_async_batch_store_handles_cancellation() -> None:
+    class MockStore(AsyncBatchedBaseStore):
+        def batch(self, ops: Iterable[Op]) -> list[Result]:
+            raise NotImplementedError
+
+        async def abatch(self, ops: Iterable[Op]) -> list[Result]:
+            assert all(isinstance(op, GetOp) for op in ops)
+            return [
+                Item(
+                    value={},
+                    key=getattr(op, "key", ""),
+                    namespace=getattr(op, "namespace", ()),
+                    created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+                    updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+                )
+                for op in ops
+            ]
+
+    store = MockStore()
+
+    # Simulate cancellation
+    task = asyncio.create_task(store.aget(namespace=("a",), key="b"))
+    await asyncio.sleep(0)
+    task.cancel()
+    await asyncio.sleep(0)
+
+    # Cancelling individual queries against the store should not break the store
+    result = await store.aget(namespace=("c",), key="d")
+    assert result == Item(
+        value={},
+        key="d",
+        namespace=("c",),
+        created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+        updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+    )
+
+
 def test_list_namespaces_basic() -> None:
    store = InMemoryStore()

@@ -323,7 +323,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-cli"
-version = "0.3.4"
+version = "0.3.5"
 description = "CLI for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"
@@ -19,7 +19,7 @@ dependencies = [
 [project.optional-dependencies]
 inmem = [
    "langgraph-api>=0.2.67,<0.3.0 ; python_version >= '3.11'",
-    "langgraph-runtime-inmem>=0.3.0,<0.4.0 ; python_version >= '3.11'",
+    "langgraph-runtime-inmem>=0.6.0 ; python_version >= '3.11'",
    "python-dotenv>=0.8.0",
 ]

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 ]

+[[package]]
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+version = "1.2.0"
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+]
+
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+version = "1.5.25"
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 [[package]]
@@ -444,7 +453,7 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "0.3.68"
+version = "0.3.69"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch", marker = "python_full_version >= '3.11'" },
@@ -455,14 +464,14 @@ dependencies = [
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    { name = "typing-extensions", marker = "python_full_version >= '3.11'" },
 ]
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 [[package]]
 name = "langgraph"
-version = "0.5.2"
+version = "0.5.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "langchain-core", marker = "python_full_version >= '3.11'" },
@@ -472,14 +481,14 @@ dependencies = [
    { name = "pydantic", marker = "python_full_version >= '3.11'" },
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 ]

 [[package]]
@@ -974,13 +974,51 @@ class CompiledStateGraph(
                )
                raise InvalidUpdateError(msg)

+        # Create a validated version of _control_branch that has access to available nodes
+        def _control_branch_with_validation(value: Any) -> Sequence[tuple[str, Any]]:
+            if isinstance(value, Send):
+                return ((TASKS, value),)
+            commands: list[Command] = []
+            if isinstance(value, Command):
+                commands.append(value)
+            elif isinstance(value, (list, tuple)):
+                for cmd in value:
+                    if isinstance(cmd, Command):
+                        commands.append(cmd)
+            rtn: list[tuple[str, Any]] = []
+            for command in commands:
+                if command.graph == Command.PARENT:
+                    raise ParentCommand(command)
+                if isinstance(command.goto, Send):
+                    rtn.append((TASKS, command.goto))
+                elif isinstance(command.goto, str):
+                    # Validate that the goto target exists in the graph
+                    if command.goto != END and command.goto not in self.builder.nodes:
+                        raise InvalidUpdateError(
+                            f"Node '{command.goto}' does not exist in the graph"
+                        )
+                    rtn.append((CHANNEL_BRANCH_TO.format(command.goto), None))
+                else:
+                    # Handle sequence of goto targets
+                    for go in command.goto:
+                        if isinstance(go, Send):
+                            rtn.append((TASKS, go))
+                        else:
+                            # Validate that the goto target exists in the graph
+                            if go != END and go not in self.builder.nodes:
+                                raise InvalidUpdateError(
+                                    f"Node '{go}' does not exist in the graph"
+                                )
+                            rtn.append((CHANNEL_BRANCH_TO.format(go), None))
+            return rtn
+
        # state updaters
        write_entries: tuple[ChannelWriteEntry | ChannelWriteTupleEntry, ...] = (
            ChannelWriteTupleEntry(
                mapper=_get_root if output_keys == ["__root__"] else _get_updates
            ),
            ChannelWriteTupleEntry(
-                mapper=_control_branch,
+                mapper=_control_branch_with_validation,
                static=_control_static(node.ends)
                if node is not None and node.ends is not None
                else None,
@@ -1301,7 +1301,7 @@ wheels = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
@@ -52,7 +52,7 @@ lint lint_diff lint_package lint_tests:

 format format_diff:
 	uv run ruff format $(PYTHON_FILES)
-	uv run ruff check --select I --fix $(PYTHON_FILES)
+	uv run ruff check --fix $(PYTHON_FILES)

 spell_check:
 	uv run codespell --toml pyproject.toml
@@ -187,7 +187,7 @@ def _should_bind_tools(
    return False


-def _get_underlying_model(model: LanguageModelLike) -> BaseChatModel:
+def _get_model(model: LanguageModelLike) -> BaseChatModel:
    """Get the underlying model from a RunnableBinding or return the model itself."""
    if isinstance(model, RunnableSequence):
        model = next(
@@ -241,11 +241,8 @@ def _validate_chat_history(
    raise ValueError(error_message)


-DynamicModel = Callable[[StateSchemaType, RunnableConfig], BaseChatModel]
-
-
 def create_react_agent(
-    model: Union[str, LanguageModelLike, DynamicModel],
+    model: Union[str, LanguageModelLike],
    tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
    *,
    prompt: Optional[Prompt] = None,
@@ -435,8 +432,6 @@ def create_react_agent(
        tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])
        tool_classes = list(tool_node.tools_by_name.values())

-    tool_calling_enabled = len(tool_classes) > 0
-
    if isinstance(model, str):
        try:
            from langchain.chat_models import (  # type: ignore[import-not-found]
@@ -446,52 +441,18 @@ def create_react_agent(
            raise ImportError(
                "Please install langchain (`pip install langchain`) to use '<provider>:<model>' string syntax for `model` parameter."
            )
-        model_instance = cast(BaseChatModel, init_chat_model(model))
-    elif isinstance(model, Runnable):
-        model_instance = _get_underlying_model(model)
-    elif callable(model):
-        model_instance = None
-    else:
-        raise TypeError(
-            "Expected `model` to be a string, LanguageModelLike, "
-            f"or callable, got {type(model)}"
-        )

-    # If we have a static model, we'll attempt to bind tools to it.
-    if model_instance:
-        # Apply tool binding for static model
-        if (
-            _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools))
-            and len(tool_classes + llm_builtin_tools) > 0
-        ):
-            model_instance = cast(BaseChatModel, model_instance).bind_tools(
-                tool_classes + llm_builtin_tools
-            )  # type: ignore[operator]
-        static_model: Optional[BaseChatModel] = (
-            _get_prompt_runnable(prompt) | model_instance
-        )
-    else:
-        static_model = None
+        model = cast(BaseChatModel, init_chat_model(model))

-    def _resolve_model(state: StateSchema, config: RunnableConfig) -> BaseChatModel:
-        """Resolve the model, handling both static and dynamic models."""
-        if static_model:
-            return static_model
+    tool_calling_enabled = len(tool_classes) > 0

-        # If we have a dynamic model, we need to resolve it at runtime
-        resolved_model = model(state, config)  # type: ignore[call-arg]
-        # Apply tools binding if needed
-        if (
-            _should_bind_tools(
-                resolved_model, tool_classes, num_builtin=len(llm_builtin_tools)
-            )
-            and len(tool_classes + llm_builtin_tools) > 0
-        ):
-            resolved_model = cast(BaseChatModel, resolved_model).bind_tools(
-                tool_classes + llm_builtin_tools
-            )
+    if (
+        _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools))
+        and len(tool_classes + llm_builtin_tools) > 0
+    ):
+        model = cast(BaseChatModel, model).bind_tools(tool_classes + llm_builtin_tools)  # type: ignore[operator]

-        return cast(BaseChatModel, _get_prompt_runnable(prompt) | resolved_model)
+    model_runnable = _get_prompt_runnable(prompt) | model

    # If any of the tools are configured to return_directly after running,
    # our graph needs to check if these were called
@@ -543,8 +504,7 @@ def create_react_agent(
    # Define the function that calls the model
    def call_model(state: StateSchema, config: RunnableConfig) -> StateSchema:
        state = _get_model_input_state(state)
-        runnable = _resolve_model(state, config)
-        response = runnable.invoke(state, config)
+        response = cast(AIMessage, model_runnable.invoke(state, config))
        # add agent name to the AIMessage
        response.name = name

@@ -562,8 +522,7 @@ def create_react_agent(

    async def acall_model(state: StateSchema, config: RunnableConfig) -> StateSchema:
        state = _get_model_input_state(state)
-        runnable = _resolve_model(state, config)
-        response = await runnable.ainvoke(state, config)
+        response = cast(AIMessage, await model_runnable.ainvoke(state, config))
        # add agent name to the AIMessage
        response.name = name
        if _are_more_steps_needed(state, response):
@@ -602,17 +561,13 @@ def create_react_agent(
    def generate_structured_response(
        state: StateSchema, config: RunnableConfig
    ) -> StateSchema:
-        """Generate a structured response from the model."""
        messages = _get_state_value(state, "messages")
        structured_response_schema = response_format
        if isinstance(response_format, tuple):
            system_prompt, structured_response_schema = response_format
            messages = [SystemMessage(content=system_prompt)] + list(messages)

-        # We need to re-bind structured outputs to the model
-        model_instance_ = _resolve_model(state, config)
-        underlying_model = _get_underlying_model(model_instance_)
-        model_with_structured_output = underlying_model.with_structured_output(
+        model_with_structured_output = _get_model(model).with_structured_output(
            cast(StructuredResponseSchema, structured_response_schema)
        )
        response = model_with_structured_output.invoke(messages, config)
@@ -621,17 +576,13 @@ def create_react_agent(
    async def agenerate_structured_response(
        state: StateSchema, config: RunnableConfig
    ) -> StateSchema:
-        """Generate a structured response from the model."""
        messages = _get_state_value(state, "messages")
        structured_response_schema = response_format
        if isinstance(response_format, tuple):
            system_prompt, structured_response_schema = response_format
            messages = [SystemMessage(content=system_prompt)] + list(messages)

-        # We need to re-bind structured outputs to the model
-        model_instance_ = _resolve_model(state, config)
-        underlying_model = _get_underlying_model(model_instance_)
-        model_with_structured_output = underlying_model.with_structured_output(
+        model_with_structured_output = _get_model(model).with_structured_output(
            cast(StructuredResponseSchema, structured_response_schema)
        )
        response = await model_with_structured_output.ainvoke(messages, config)
@@ -700,7 +651,7 @@ def create_react_agent(
                if post_model_hook is not None:
                    return "post_model_hook"
                tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)
+                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
                    for call in last_message.tool_calls
                ]
                return [Send("tools", [tool_call]) for tool_call in tool_calls]
@@ -783,7 +734,7 @@ def create_react_agent(

            if pending_tool_calls:
                pending_tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)
+                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
                    for call in pending_tool_calls
                ]
                return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
@@ -35,8 +35,7 @@ import asyncio
 import inspect
 import json
 from copy import copy, deepcopy
-from dataclasses import dataclass, replace
-from itertools import repeat
+from dataclasses import replace
 from typing import (
    Any,
    Callable,
@@ -74,7 +73,6 @@ from typing_extensions import Annotated, get_args, get_origin
 from langgraph.errors import GraphBubbleUp
 from langgraph.store.base import BaseStore
 from langgraph.types import Command, Send
-from langgraph.typing import StateLike
 from langgraph.utils.runnable import RunnableCallable

 INVALID_TOOL_NAME_ERROR_TEMPLATE = (
@@ -83,43 +81,6 @@ INVALID_TOOL_NAME_ERROR_TEMPLATE = (
 TOOL_CALL_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."


-@dataclass(frozen=True)
-class ToolResolver:
-    """Encapsulates tool metadata for thread-safe tool execution.
-
-    This class holds all the precomputed metadata needed for tool execution,
-    including tool instances, state injection mappings, and store injection settings.
-    """
-
-    tools_by_name: dict[str, BaseTool]
-    tool_to_state_args: dict[str, dict[str, Optional[str]]]
-    tool_to_store_arg: dict[str, Optional[str]]
-
-    @staticmethod
-    def from_tools(tools: Sequence[Union[BaseTool, Callable]]) -> "ToolResolver":
-        """Create a ToolResolver from a sequence of tools.
-
-        Args:
-            tools: Sequence of tools to process. Can be BaseTool instances or
-                  callables that will be converted to tools.
-
-        Returns:
-            A ToolResolver containing all the metadata for the provided tools.
-        """
-        tools_by_name = {}
-        tool_to_state_args = {}
-        tool_to_store_arg = {}
-
-        for tool in tools:
-            if not isinstance(tool, BaseTool):
-                tool = create_tool(tool)
-            tools_by_name[tool.name] = tool
-            tool_to_state_args[tool.name] = _get_state_args(tool)
-            tool_to_store_arg[tool.name] = _get_store_arg(tool)
-
-        return ToolResolver(tools_by_name, tool_to_state_args, tool_to_store_arg)
-
-
 def msg_content_output(output: Any) -> Union[str, list[dict]]:
    """Convert tool output to valid message content format.

@@ -283,11 +244,8 @@ class ToolNode(RunnableCallable):
    Tool calls can also be passed directly as a list of `ToolCall` dicts.

    Args:
-        tools: Either a sequence of tools that can be invoked by this node, or a
-            callable that returns tools dynamically based on input, config, and store.
-            Static tools can be BaseTool instances or plain functions that will be
-            converted to tools. Dynamic tool providers receive (input, config, store)
-            and should return a sequence of tools for that specific execution.
+        tools: A sequence of tools that can be invoked by this node. Tools can be
+            BaseTool instances or plain functions that will be converted to tools.
        name: The name identifier for this node in the graph. Used for debugging
            and visualization. Defaults to "tools".
        tags: Optional metadata tags to associate with the node for filtering
@@ -358,13 +316,7 @@ class ToolNode(RunnableCallable):

    def __init__(
        self,
-        tools: Union[
-            Sequence[Union[BaseTool, Callable]],
-            Callable[
-                [StateLike, RunnableConfig, Optional[BaseStore]],
-                Sequence[Union[BaseTool, Callable]],
-            ],
-        ],
+        tools: Sequence[Union[BaseTool, Callable]],
        *,
        name: str = "tools",
        tags: Optional[list[str]] = None,
@@ -376,64 +328,24 @@ class ToolNode(RunnableCallable):
        """Initialize the ToolNode with the provided tools and configuration.

        Args:
-            tools: Either a sequence of tools to make available for execution,
-                  or a callable that returns tools dynamically based on input,
-                  config, and store.
+            tools: Sequence of tools to make available for execution.
            name: Node name for graph identification.
            tags: Optional metadata tags.
            handle_tool_errors: Error handling configuration.
            messages_key: State key containing messages.
        """
        super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
+        self.tools_by_name: dict[str, BaseTool] = {}
+        self.tool_to_state_args: dict[str, dict[str, Optional[str]]] = {}
+        self.tool_to_store_arg: dict[str, Optional[str]] = {}
        self.handle_tool_errors = handle_tool_errors
        self.messages_key = messages_key
-
-        if callable(tools):
-            # Dynamic tool provider
-            self._tool_provider_fn = tools
-            self._static_resolver: Optional[ToolResolver] = None
-            # Likely migrate to property and raise a RunTimeError
-            self.tools_by_name: dict[str, BaseTool] = {}
-            self.tool_to_state_args: dict[str, dict[str, Optional[str]]] = {}
-            self.tool_to_store_arg: dict[str, Optional[str]] = {}
-        else:
-            # Static tools
-            self._tool_provider_fn = None
-            self._static_resolver = ToolResolver.from_tools(tools)
-            self.tools_by_name = self._static_resolver.tools_by_name
-            self.tool_to_state_args = self._static_resolver.tool_to_state_args
-            self.tool_to_store_arg = self._static_resolver.tool_to_store_arg
-
-    def _get_resolver(
-        self,
-        input: Union[list[AnyMessage], dict[str, Any], BaseModel],
-        config: Optional[RunnableConfig],
-        store: Optional[BaseStore],
-    ) -> ToolResolver:
-        """Get the appropriate ToolResolver for this execution.
-
-        Args:
-            input: The input to the tool node
-            config: The runnable configuration
-            store: The optional store instance
-
-        Returns:
-            A ToolResolver containing the tools and metadata for this execution.
-
-        Raises:
-            RuntimeError: If no tools are configured.
-        """
-        if self._tool_provider_fn:
-            # Dynamic tools compute fresh for each run
-            # Note: config may be None during _parse_input,
-            # but tool provider should handle this
-            tools = self._tool_provider_fn(input, config, store)
-            return ToolResolver.from_tools(tools)
-        elif self._static_resolver:
-            # Static tools - use cached resolver
-            return self._static_resolver
-        else:
-            raise RuntimeError("ToolNode has no tools configured")
+        for tool_ in tools:
+            if not isinstance(tool_, BaseTool):
+                tool_ = create_tool(tool_)
+            self.tools_by_name[tool_.name] = tool_
+            self.tool_to_state_args[tool_.name] = _get_state_args(tool_)
+            self.tool_to_store_arg[tool_.name] = _get_store_arg(tool_)

    def _func(
        self,
@@ -446,16 +358,12 @@ class ToolNode(RunnableCallable):
        *,
        store: Optional[BaseStore],
    ) -> Any:
-        tool_calls, input_type = self._parse_input(input, store, config)
-        resolver = self._get_resolver(input, config, store)
+        tool_calls, input_type = self._parse_input(input, store)
        config_list = get_config_list(config, len(tool_calls))
        input_types = [input_type] * len(tool_calls)
        with get_executor_for_config(config) as executor:
            outputs = [
-                *executor.map(
-                    lambda args: self._run_one(*args),
-                    list(zip(tool_calls, input_types, config_list, repeat(resolver))),
-                )
+                *executor.map(self._run_one, tool_calls, input_types, config_list)
            ]

        return self._combine_tool_outputs(outputs, input_type)
@@ -471,10 +379,9 @@ class ToolNode(RunnableCallable):
        *,
        store: Optional[BaseStore],
    ) -> Any:
-        tool_calls, input_type = self._parse_input(input, store, config)
-        resolver = self._get_resolver(input, config, store)
+        tool_calls, input_type = self._parse_input(input, store)
        outputs = await asyncio.gather(
-            *(self._arun_one(call, input_type, config, resolver) for call in tool_calls)
+            *(self._arun_one(call, input_type, config) for call in tool_calls)
        )

        return self._combine_tool_outputs(outputs, input_type)
@@ -528,23 +435,20 @@ class ToolNode(RunnableCallable):
        call: ToolCall,
        input_type: Literal["list", "dict", "tool_calls"],
        config: RunnableConfig,
-        resolver: ToolResolver,
    ) -> ToolMessage:
-        if invalid_tool_message := self._validate_tool_call(call, resolver):
+        if invalid_tool_message := self._validate_tool_call(call):
            return invalid_tool_message

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

        # GraphInterrupt is a special exception that will always be raised.
        # 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
+        # (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:
            raise e
        except Exception as e:
@@ -586,23 +490,20 @@ class ToolNode(RunnableCallable):
        call: ToolCall,
        input_type: Literal["list", "dict", "tool_calls"],
        config: RunnableConfig,
-        resolver: ToolResolver,
    ) -> ToolMessage:
-        if invalid_tool_message := self._validate_tool_call(call, resolver):
+        if invalid_tool_message := self._validate_tool_call(call):
            return invalid_tool_message

        try:
            input = {**call, **{"type": "tool_call"}}
-            response = await resolver.tools_by_name[call["name"]].ainvoke(input, config)
+            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:
        # (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
+        # (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:
            raise e
        except Exception as e:
@@ -648,10 +549,7 @@ class ToolNode(RunnableCallable):
            BaseModel,
        ],
        store: Optional[BaseStore],
-        config: RunnableConfig,
    ) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
-        """Parse the input to extract tool calls and determine input type."""
-        input_type: Literal["list", "dict", "tool_calls"]
        if isinstance(input, list):
            if isinstance(input[-1], dict) and input[-1].get("type") == "tool_call":
                input_type = "tool_calls"
@@ -675,22 +573,17 @@ class ToolNode(RunnableCallable):
        except StopIteration:
            raise ValueError("No AIMessage found in input")

-        # For _parse_input, we need to compute the resolver to inject tool args
-        # We pass None for config since we don't have it yet at this stage
-        resolver = self._get_resolver(input, config=config, store=store)
        tool_calls = [
-            self.inject_tool_args(call, input, store, resolver)
+            self.inject_tool_args(call, input, store)
            for call in latest_ai_message.tool_calls
        ]
        return tool_calls, input_type

-    def _validate_tool_call(
-        self, call: ToolCall, resolver: ToolResolver
-    ) -> Optional[ToolMessage]:
-        if (requested_tool := call["name"]) not in resolver.tools_by_name:
+    def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
+        if (requested_tool := call["name"]) not in self.tools_by_name:
            content = INVALID_TOOL_NAME_ERROR_TEMPLATE.format(
                requested_tool=requested_tool,
-                available_tools=", ".join(resolver.tools_by_name.keys()),
+                available_tools=", ".join(self.tools_by_name.keys()),
            )
            return ToolMessage(
                content, name=requested_tool, tool_call_id=call["id"], status="error"
@@ -706,9 +599,8 @@ class ToolNode(RunnableCallable):
            dict[str, Any],
            BaseModel,
        ],
-        resolver: ToolResolver,
    ) -> ToolCall:
-        state_args = resolver.tool_to_state_args[tool_call["name"]]
+        state_args = self.tool_to_state_args[tool_call["name"]]
        if state_args and isinstance(input, list):
            required_fields = list(state_args.values())
            if (
@@ -745,9 +637,9 @@ class ToolNode(RunnableCallable):
        return tool_call

    def _inject_store(
-        self, tool_call: ToolCall, store: Optional[BaseStore], resolver: ToolResolver
+        self, tool_call: ToolCall, store: Optional[BaseStore]
    ) -> ToolCall:
-        store_arg = resolver.tool_to_store_arg[tool_call["name"]]
+        store_arg = self.tool_to_store_arg[tool_call["name"]]
        if not store_arg:
            return tool_call

@@ -772,8 +664,6 @@ class ToolNode(RunnableCallable):
            BaseModel,
        ],
        store: Optional[BaseStore],
-        # TODO(EUGENE): Potentially breaking change?
-        resolver: Optional[ToolResolver] = None,
    ) -> ToolCall:
        """Inject graph state and store into tool call arguments.

@@ -793,8 +683,6 @@ class ToolNode(RunnableCallable):
                Can be a message list, state dictionary, or BaseModel instance.
            store: The persistent store instance to inject into tools requiring storage.
                Will be None if no store is configured for the graph.
-            resolver: The ToolResolver instance containing metadata about available
-                tools, including their state and store injection requirements.

        Returns:
            A new ToolCall dictionary with the same structure as the input but with
@@ -810,17 +698,12 @@ class ToolNode(RunnableCallable):
            The injection is performed on a copy of the tool call to avoid mutating
            the original.
        """
-
-        resolver_ = resolver or self._static_resolver
-
-        if tool_call["name"] not in resolver_.tools_by_name:
+        if tool_call["name"] not in self.tools_by_name:
            return tool_call

        tool_call_copy: ToolCall = copy(tool_call)
-        tool_call_with_state = self._inject_state(tool_call_copy, input, resolver_)
-        tool_call_with_store = self._inject_store(
-            tool_call_with_state, store, resolver_
-        )
+        tool_call_with_state = self._inject_state(tool_call_copy, input)
+        tool_call_with_store = self._inject_store(tool_call_with_state, store)
        return tool_call_with_store

    def _validate_tool_command(
@@ -1,27 +1,5 @@
 import re
-from typing import Any, Sequence, Union
-
-from typing_extensions import Self
-
-
-class FloatBetween(float):
-    def __new__(cls, min_value: float, max_value: float) -> Self:
-        return super().__new__(cls, min_value)
-
-    def __init__(self, min_value: float, max_value: float) -> None:
-        super().__init__()
-        self.min_value = min_value
-        self.max_value = max_value
-
-    def __eq__(self, other: object) -> bool:
-        return (
-            isinstance(other, float)
-            and other >= self.min_value
-            and other <= self.max_value
-        )
-
-    def __hash__(self) -> int:
-        return hash((float(self), self.min_value, self.max_value))
+from typing import Union


 class AnyStr(str):
@@ -38,49 +16,3 @@ class AnyStr(str):

    def __hash__(self) -> int:
        return hash((str(self), self.prefix))
-
-
-class AnyDict(dict):
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, dict) or len(self) != len(other):
-            return False
-        for k, v in self.items():
-            if kk := next((kk for kk in other if kk == k), None):
-                if v == other[kk]:
-                    continue
-                else:
-                    return False
-        else:
-            return True
-
-
-class AnyVersion:
-    def __init__(self) -> None:
-        super().__init__()
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, (str, int, float))
-
-    def __hash__(self) -> int:
-        return hash(str(self))
-
-
-class UnsortedSequence:
-    def __init__(self, *values: Any) -> None:
-        self.seq = values
-
-    def __eq__(self, value: object) -> bool:
-        return (
-            isinstance(value, Sequence)
-            and len(self.seq) == len(value)
-            and all(a in value for a in self.seq)
-        )
-
-    def __hash__(self) -> int:
-        return hash(frozenset(self.seq))
-
-    def __repr__(self) -> str:
-        return repr(self.seq)
@@ -1,31 +1,19 @@
-import asyncio
 import os
 import tempfile
 from collections import defaultdict
 from functools import partial
-from typing import Any, Optional
-
-from langchain_core.runnables import RunnableConfig
+from typing import Optional

 from langgraph.checkpoint.base import (
    ChannelVersions,
    Checkpoint,
    CheckpointMetadata,
-    CheckpointTuple,
    SerializerProtocol,
 )
 from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
 from langgraph.pregel.checkpoint import copy_checkpoint


-class NoopSerializer(SerializerProtocol):
-    def loads_typed(self, data: tuple[str, bytes]) -> Any:
-        return data[1]
-
-    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
-        return "type", obj
-
-
 class MemorySaverAssertImmutable(InMemorySaver):
    storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]

@@ -69,66 +57,3 @@ class MemorySaverAssertImmutable(InMemorySaver):
        )
        # call super to write checkpoint
        return super().put(config, checkpoint, metadata, new_versions)
-
-
-class MemorySaverAssertCheckpointMetadata(InMemorySaver):
-    """This custom checkpointer is for verifying that a run's configurable
-    fields are merged with the previous checkpoint config for each step in
-    the run. This is the desired behavior. Because the checkpointer's (a)put()
-    method is called for each step, the implementation of this checkpointer
-    should produce a side effect that can be asserted.
-    """
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> None:
-        """The implementation of put() merges config["configurable"] (a run's
-        configurable fields) with the metadata field. The state of the
-        checkpoint metadata can be asserted to confirm that the run's
-        configurable fields were merged with the previous checkpoint config.
-        """
-        configurable = config["configurable"].copy()
-
-        # remove checkpoint_id to make testing simpler
-        checkpoint_id = configurable.pop("checkpoint_id", None)
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"]["checkpoint_ns"]
-        self.storage[thread_id][checkpoint_ns].update(
-            {
-                checkpoint["id"]: (
-                    self.serde.dumps_typed(checkpoint),
-                    # merge configurable fields and metadata
-                    self.serde.dumps_typed({**configurable, **metadata}),
-                    checkpoint_id,
-                )
-            }
-        )
-        return {
-            "configurable": {
-                "thread_id": config["configurable"]["thread_id"],
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-
-    async def aput(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        return await asyncio.get_running_loop().run_in_executor(
-            None, self.put, config, checkpoint, metadata, new_versions
-        )
-
-
-class MemorySaverNoPending(InMemorySaver):
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        result = super().get_tuple(config)
-        if result:
-            return CheckpointTuple(result.config, result.checkpoint, result.metadata)
-        return result
@@ -9,33 +9,11 @@ subclassed strings.

 from typing import Any

-from langchain_core.documents import Document
-from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
+from langchain_core.messages import HumanMessage, ToolMessage

 from tests.any_str import AnyStr


-def _AnyIdDocument(**kwargs: Any) -> Document:
-    """Create a document with an id field."""
-    message = Document(**kwargs)
-    message.id = AnyStr()
-    return message
-
-
-def _AnyIdAIMessage(**kwargs: Any) -> AIMessage:
-    """Create ai message with an any id field."""
-    message = AIMessage(**kwargs)
-    message.id = AnyStr()
-    return message
-
-
-def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk:
-    """Create ai message with an any id field."""
-    message = AIMessageChunk(**kwargs)
-    message.id = AnyStr()
-    return message
-
-
 def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage:
    """Create a human message with an any id field."""
    message = HumanMessage(**kwargs)
@@ -5,7 +5,6 @@ from functools import partial
 from typing import (
    Annotated,
    List,
-    Literal,
    Optional,
    Type,
    TypeVar,
@@ -41,7 +40,7 @@ from langgraph.prebuilt.chat_agent_executor import (
    AgentState,
    AgentStatePydantic,
    StateSchemaType,
-    _get_underlying_model,
+    _get_model,
    _should_bind_tools,
    _validate_chat_history,
 )
@@ -471,7 +470,7 @@ def test__infer_handled_types() -> None:


@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
-def test_react_agent_with_structured_response(version: Literal["v1", "v2"]) -> None:
+def test_react_agent_with_structured_response(version: str) -> None:
    class WeatherResponse(BaseModel):
        temperature: float = Field(description="The temperature in fahrenheit")

@@ -1347,7 +1346,7 @@ def test_should_bind_tools(tool_style: str) -> None:

 def test_get_model() -> None:
    model = FakeToolCallingModel(tool_calls=[])
-    assert _get_underlying_model(model) == model
+    assert _get_model(model) == model

    @dec_tool
    def some_tool(some_val: int) -> str:
@@ -1355,18 +1354,18 @@ def test_get_model() -> None:
        return "meow"

    model_with_tools = model.bind_tools([some_tool])
-    assert _get_underlying_model(model_with_tools) == model
+    assert _get_model(model_with_tools) == model

    seq = model | RunnableLambda(lambda message: message)
-    assert _get_underlying_model(seq) == model
+    assert _get_model(seq) == model

    seq_with_tools = model.bind_tools([some_tool]) | RunnableLambda(
        lambda message: message
    )
-    assert _get_underlying_model(seq_with_tools) == model
+    assert _get_model(seq_with_tools) == model

    with pytest.raises(TypeError):
-        _get_underlying_model(RunnableLambda(lambda message: message))
+        _get_model(RunnableLambda(lambda message: message))


 def test_pre_model_hook() -> None:
@@ -1129,49 +1129,3 @@ def test_tool_node_parent_command_with_send():
            graph=Command.PARENT,
        )
    ]
-
-
-async def test_tool_node_dynamic_tools_basic() -> None:
-    """Test basic dynamic tool functionality."""
-
-    @dec_tool()
-    def answer_to_life() -> int:
-        """A very nice dynamic tool."""
-        return 42
-
-    def tool_provider(state, config, store) -> list:
-        """Provide dynamic tools based on state or config."""
-        return [answer_to_life]
-
-    tool_node = ToolNode(tool_provider)
-    result = await tool_node.ainvoke(
-        [
-            {
-                "name": "answer_to_life",
-                "args": {},
-                "id": "1",
-                "type": "tool_call",
-            }
-        ]
-    )
-
-    assert result == {
-        "messages": [
-            ToolMessage(
-                content="42",
-                tool_call_id="1",
-                name="answer_to_life",
-            )
-        ]
-    }
-
-    # Test invoking a tool that's not defined
-    with pytest.raises(ValueError):
-        await tool_node.ainvoke(
-            {
-                "name": "not_available",
-                "args": {},
-                "id": "1",
-                "type": "tool_call",
-            }
-        )
@@ -367,7 +367,7 @@ dev = [

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
    { name = "langchain-core" },
Author	SHA1	Message	Date
open-swe-dev[bot]	290cd4b773	Apply patch	2025-07-17 20:05:48 +00:00
open-swe-dev[bot]	53035d41ea	Apply patch	2025-07-17 20:04:12 +00:00
Eugene YurtsevandGitHub	5717eefa79	feat(docs): Document disabling webhooks (#5535 ) Add information about disabling webhooks	2025-07-17 15:23:00 -04:00
William FH GitHub Sydney Runkle	5978012619	release(cli): Release new CLI version with increased bounds for server (#5565 ) Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>	2025-07-17 12:22:30 -07:00
Eugene YurtsevandGitHub	b447a1c7cf	fix(docs): fix langgraph cli reference (#5563 ) Fix for wrong indentation that completely messes up formatting	2025-07-17 12:10:11 -07:00
open-swe[bot]GitHubopen-swe-dev[bot] <open-swe-dev@users.noreply.github.com>Eugene Yurtsev	f87608a16f	chore(prebuilt): Remove dead code from prebuilt tests (#5555 ) Fixes #5554 This PR removes unused utility classes and functions from the prebuilt tests directory to clean up dead code. Changes include: - Removed unused classes from `libs/prebuilt/tests/any_str.py`: - Deleted FloatBetween, AnyDict, AnyVersion, and UnsortedSequence - Kept only AnyStr class - Removed unused functions from `libs/prebuilt/tests/messages.py`: - Deleted _AnyIdDocument and _AnyIdAIMessageChunk - Kept _AnyIdHumanMessage and _AnyIdToolMessage - Removed unused classes from `libs/prebuilt/tests/memory_assert.py`: - Deleted NoopSerializer, MemorySaverAssertCheckpointMetadata, and MemorySaverNoPending - Kept MemorySaverAssertImmutable Verification: - Manually checked for no remaining references to removed code - Maintained existing import structures - Preserved functionality of the prebuilt test suite The changes reduce code complexity and remove unnecessary utility classes that were not being used in the test suite. --------- Co-authored-by: open-swe-dev[bot] <open-swe-dev@users.noreply.github.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	2025-07-17 18:45:10 +00:00
Lauren Hirata SinghandGitHub	b4eb57da67	docs: Rearrange nav (#5560 ) Move Prebuilts overview and Run an agent to the guides page	2025-07-17 13:47:43 -04:00
Sam CrowderandGitHub	cec3bef7ea	docs: [LangGraph Server Changelog Bot] Changelog updates for new version(s) (#5558 ) Automated changelog update created by the LangGraph Server Changelog Bot. Feel free to merge anytime.	2025-07-17 12:41:13 -04:00
Nuno Campos	71dc92b349	langgraph-checkpoint 2.1.1	2025-07-17 15:04:57 +02:00
jito GitHub Nuno Campos	79b4642e55	fix(docs): broken URL in _AIO_ERROR_MSG for AsyncSqliteSaver (#5483 ) remove unreachable `yield` from unimplemented async methods --------- Co-authored-by: Nuno Campos <nuno@langchain.dev>	2025-07-17 12:26:22 +00:00
Nuno CamposandGitHub	48446bcbd2	chore(docs): Mention dataclass (#5470 )	2025-07-17 14:23:41 +02:00
Nuno CamposandGitHub	cf95c870fe	fix(checkpoint): fix AsyncBatchedBaseStore getting stuck (#5504 )	2025-07-17 12:50:18 +02:00
Nuno CamposandGitHub	a34c38a53d	docs: [LangGraph Server Changelog Bot] Changelog updates for new version(s) (#5547 )	2025-07-17 12:24:24 +02:00
Sam Crowder	db5276ded1	Update changelog via LangGraph Server Changelog Bot	2025-07-16 16:32:19 -07:00
Sam CrowderandGitHub	9c67b9ce4b	docs(docs): add disclaimer about overriding otel with DD_API_KEY (#5538 )	2025-07-16 16:06:54 -07:00
Lauren Hirata SinghandGitHub	08667fe786	docs: More tracing (#5545 ) docs: add more about tracing	2025-07-16 19:06:43 -04:00
Sam CrowderandGitHub	48dabc0538	docs: [LangGraph Server Changelog Bot] Changelog updates for new version(s) (#5540 ) Update changelog via LangGraph Server Changelog Bot	2025-07-16 18:24:46 -04:00
Sam Crowder GitHub Lauren Hirata Singh	d2cc02d789	Update docs/docs/cloud/reference/env_var.md Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>	2025-07-16 14:17:56 -07:00
Lauren Hirata SinghandGitHub	92c66d13ec	docs: add o11y overview (#5542 ) * docs: add o11y overview * add section for enabling tracing	2025-07-16 16:32:11 -04:00
Sam Crowder	7f821deded	remove word tracing	2025-07-16 11:36:16 -07:00
Sam Crowder	12a601c8a3	fix: add disclaimer to the docs about DD_API_KEY overriding app-level tracing	2025-07-16 11:35:48 -07:00
Darren Clark	0d8dfa7bba	fix(checkpoint): fix AsyncBatchedBaseStore getting stuck This commit fixes #5503 Gist of it is: - `asyncio.exception.InvalidStateError` were being raised when the future was cancelled - this exception bubbled up and killed the background task - `AsyncBatchedBaseStore` stopped doing queries because the background task wasn't running anymore This commit adds some "if future is not done" checks to guard against this.	2025-07-14 18:03:51 -04:00
William Fu-Hinthorn	a71eb09488	chore[docs]: Mention dataclass	2025-07-12 14:11:25 -07:00