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feat(docs): add python alt for UI how to (#5593 )
2026-09-30 21:45:08 +02:00 · 2025-07-20 14:00:34 +00:00 · 2025-07-20 13:57:33 +00:00 · 2025-07-20 09:33:17 -04:00 · 2025-07-18 14:54:55 -07:00 · 2025-07-18 10:43:03 -04:00
36 changed files with 564 additions and 401 deletions
@@ -0,0 +1,119 @@
+# Egress for Subscription Metrics and Operational Metadata
+
+> **Important: Self Hosted Only** 
+> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
+> This does not apply to SaaS or Hybrid deployments.
+
+Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
+
+In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
+
+> **Warning**  
+> **This will require egress to `https://beacon.langchain.com` from your network.**
+> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
+
+Generally, data that we send to Beacon can be categorized as follows:
+
+- **Subscription Metrics**
+  - Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
+    - Nodes Executed
+    - Runs Executed
+    - License Key Verification
+- **Operational Metadata**
+  - This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
+
+## Example Payloads
+
+In an effort to maximize transparency, we provide sample payloads here:
+
+### License Verification (If using an Enterprise License)
+
+**Endpoint:**
+
+`POST beacon.langchain.com/v1/beacon/verify`
+
+**Request:**
+
+```json
+{
+  "license": "<YOUR_LICENSE_KEY>"
+}
+```
+
+**Response:**
+
+```json
+{
+  "token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
+}
+```
+
+### Api Key Verification (If using a LangSmith API Key)
+
+**Endpoint:**
+`POST api.smith.langchain.com/auth`
+
+**Request:**
+
+```json
+"Headers": {
+  X-Api-Key: <YOUR_API_KEY>
+}
+```
+
+**Response:**
+
+```json
+{
+  "org_config": {
+    "org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
+    ... // Additional organization details
+  }
+}
+```
+
+### Usage Reporting
+
+**Endpoint:**
+
+`POST beacon.langchain.com/v1/metadata/submit`
+
+**Request:**
+
+```json
+{
+  "license": "<YOUR_LICENSE_KEY>",
+  "from_timestamp": "2025-01-06T09:00:00Z",
+  "to_timestamp": "2025-01-06T10:00:00Z",
+  "tags": {
+    "langgraph.python.version": "0.1.0",
+    "langgraph_api.version": "0.2.0",
+    "langgraph.platform.revision": "abc123",
+    "langgraph.platform.variant": "standard",
+    "langgraph.platform.host": "host-1",
+    "langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
+    "langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
+    "langgraph.platform.plan": "enterprise",
+    "user_app.uses_indexing": "true",
+    "user_app.uses_custom_app": "false",
+    "user_app.uses_custom_auth": "true",
+    "user_app.uses_thread_ttl": "true",
+    "user_app.uses_store_ttl": "false"
+  },
+  "measures": {
+    "langgraph.platform.runs": 150,
+    "langgraph.platform.nodes": 450
+  },
+  "logs": []
+}
+```
+
+**Response:**
+
+```json
+"204 No Content"
+```
+
+## Our Commitment
+
+LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.
@@ -23,6 +23,8 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane

        kubectl get storageclass

+1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
+
 ## Setup

 1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.
@@ -24,6 +24,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../
    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
    1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
+1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.

 ## Kubernetes (Helm)

@@ -30,17 +30,33 @@ export default {

 Next, define your UI components in your `langgraph.json` configuration:

-```json
-{
-  "node_version": "20",
-  "graphs": {
-    "agent": "./src/agent/index.ts:graph"
-  },
-  "ui": {
-    "agent": "./src/agent/ui.tsx"
-  }
-}
-```
+=== "Python agent"
+
+    ```json title="langgraph.json"
+    {
+      "node_version": "20",
+      "graphs": {
+        "agent": "./src/agent.py:graph"
+      },
+      "ui": {
+        "agent": "./src/agent/ui.tsx"
+      }
+    }
+    ```
+
+=== "JS agent"
+
+    ```json title="langgraph.json"
+    {
+      "node_version": "20",
+      "graphs": {
+        "agent": "./src/agent/index.ts:graph"
+      },
+      "ui": {
+        "agent": "./src/agent/ui.tsx"
+      }
+    }
+    ```

 The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.

@@ -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 +4,23 @@

 ---

+## v0.2.96 (2025-07-17)
+- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
+
+## 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.

@@ -10,7 +10,7 @@ search:
 There are two free options for deploying LangGraph applications via the LangGraph Server:

 1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
-1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
+1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more than 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.

 ## Production deployment

@@ -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",
 ]

@@ -30,25 +30,34 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
 ]

+[[package]]
+name = "backports-asyncio-runner"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" }
+wheels = [
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 ]

 [[package]]
@@ -49,6 +49,7 @@ from langgraph.constants import (
    CONFIG_KEY_STORE,
    CONFIG_KEY_TASK_ID,
    EMPTY_SEQ,
+    END,
    ERROR,
    INTERRUPT,
    MISSING,
@@ -288,9 +289,11 @@ def apply_writes(
            elif chan in channels:
                pending_writes_by_channel[chan].append(val)
            else:
-                logger.warning(
-                    f"Task {task.name} with path {task.path} wrote to unknown channel {chan}, ignoring it."
-                )
+                # Skip warning for END channels as they are handled specially by graph termination logic
+                if not (chan == f"branch:to:{END}"):
+                    logger.warning(
+                        f"Task {task.name} with path {task.path} wrote to unknown channel {chan}, ignoring it."
+                    )

    # Apply writes to channels
    updated_channels: set[str] = set()
@@ -1097,3 +1100,4 @@ class LazyAtomicCounter:
                if self._counter is None:
                    self._counter = itertools.count(0).__next__
        return self._counter()
+
@@ -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
@@ -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."""
@@ -39,6 +39,7 @@ from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.graph.state import CompiledStateGraph
 from langgraph.managed import IsLastStep, RemainingSteps
+from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.store.base import BaseStore
 from langgraph.types import Checkpointer, Send
@@ -650,11 +651,17 @@ 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(state_schema or AgentState, config_schema=config_schema)
@@ -733,11 +740,17 @@ 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:
@@ -71,6 +71,7 @@ from pydantic import BaseModel
 from typing_extensions import Annotated, get_args, get_origin

 from langgraph.errors import GraphBubbleUp
+from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.store.base import BaseStore
 from langgraph.types import Command, Send
 from langgraph.utils.runnable import RunnableCallable
@@ -358,7 +359,8 @@ 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:
@@ -379,7 +381,8 @@ 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)
        )
@@ -436,18 +439,19 @@ class ToolNode(RunnableCallable):
        input_type: Literal["list", "dict", "tool_calls"],
        config: RunnableConfig,
    ) -> ToolMessage:
+        """Run a single tool call synchronously."""
        if invalid_tool_message := self._validate_tool_call(call):
            return invalid_tool_message
-
        try:
-            input = {**call, **{"type": "tool_call"}}
-            response = self.tools_by_name[call["name"]].invoke(input, config)
+            call_args = {**call, **{"type": "tool_call"}}
+            response = self.tools_by_name[call["name"]].invoke(call_args, 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
+        # (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
@@ -491,6 +495,7 @@ class ToolNode(RunnableCallable):
        input_type: Literal["list", "dict", "tool_calls"],
        config: RunnableConfig,
    ) -> ToolMessage:
+        """Run a single tool call asynchronously."""
        if invalid_tool_message := self._validate_tool_call(call):
            return invalid_tool_message

@@ -548,16 +553,25 @@ 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):
            if isinstance(input[-1], dict) and input[-1].get("type") == "tool_call":
                input_type = "tool_calls"
-                tool_calls = input
+                tool_calls = cast(list[ToolCall], input)
                return tool_calls, input_type
            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, []):
@@ -573,10 +587,7 @@ 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]:
@@ -618,15 +629,20 @@ class ToolNode(RunnableCallable):
                    required_fields_str = ", ".join(f for f in required_fields if f)
                    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()
            }

@@ -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)
@@ -5,6 +5,7 @@ from functools import partial
 from typing import (
    Annotated,
    List,
+    Literal,
    Optional,
    Type,
    TypeVar,
@@ -509,7 +510,7 @@ class CustomStatePydantic(AgentStatePydantic):
@pytest.mark.parametrize("state_schema", [CustomState, CustomStatePydantic])
 def test_react_agent_update_state(
    sync_checkpointer: BaseCheckpointSaver,
-    version: str,
+    version: Literal["v1", "v2"],
    state_schema: StateSchemaType,
 ) -> None:
    @dec_tool
@@ -557,7 +558,7 @@ def test_react_agent_update_state(
        version=version,
    )
    config = {"configurable": {"thread_id": "1"}}
-    # run until interrpupted
+    # Run until interrupted
    agent.invoke({"messages": [("user", "what's my name")]}, config)
    # supply the value for the interrupt
    response = agent.invoke(Command(resume="Archibald"), config)
@@ -781,8 +782,9 @@ class AgentStateExtraKeyPydantic(AgentStatePydantic):
    "state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
 )
 def test_create_react_agent_inject_vars(
-    version: str, state_schema: StateSchemaType
+    version: Literal["v1", "v2"], state_schema: StateSchemaType
 ) -> None:
+    """Test that the agent can inject state and store into tool functions."""
    store = InMemoryStore()
    namespace = ("test",)
    store.put(namespace, "test_key", {"bar": 3})
@@ -817,15 +819,14 @@ def test_create_react_agent_inject_vars(
    model = FakeToolCallingModel(tool_calls=[[tool_call], []])
    agent = create_react_agent(
        model,
-        [tool1],
+        ToolNode([tool1], handle_tool_errors=False),
        state_schema=state_schema,
        store=store,
        version=version,
    )
-    input_message = HumanMessage("hi")
-    result = agent.invoke({"messages": [input_message], "foo": 2})
+    result = agent.invoke({"messages": [{"role": "user", "content": "hi"}], "foo": 2})
    assert result["messages"] == [
-        input_message,
+        _AnyIdHumanMessage(content="hi"),
        AIMessage(content="hi", tool_calls=[tool_call], id="0"),
        _AnyIdToolMessage(content="6", name="tool1", tool_call_id="some 0"),
        AIMessage("hi-hi-6", id="1"),
@@ -1580,13 +1581,14 @@ def test_create_react_agent_inject_vars_with_post_model_hook(
        "type": "tool_call",
    }

-    def post_model_hook(state: dict) -> None:
-        return
+    def post_model_hook(state: dict) -> dict:
+        """Post model hook is injecting a new foo key."""
+        return {"foo": 2}

    model = FakeToolCallingModel(tool_calls=[[tool_call], []])
    agent = create_react_agent(
        model,
-        [tool1],
+        ToolNode([tool1], handle_tool_errors=False),
        state_schema=state_schema,
        store=store,
        post_model_hook=post_model_hook,
@@ -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[bot]	d790db0dee	Apply patch	2025-07-20 14:00:34 +00:00
open-swe[bot]	f1a0e947a3	Apply patch	2025-07-20 13:57:33 +00:00
Sydney RunkleandGitHub	c61ac946af	feat(docs): add python alt for UI how to (#5593 ) Fixes https://github.com/langchain-ai/langgraph/issues/5311	2025-07-20 09:33:17 -04:00
Andrew NguonlyandGitHub	61676b8db0	docs: Change 'that' to 'than' (#5581 )	2025-07-18 14:54:55 -07:00
langchain-infraandGitHub	3b85e53360	docs: fix egress formatting (#5575 )	2025-07-18 10:43:03 -04:00
infra	250a17d711	docs: fix egress formatting	2025-07-18 09:42:46 -05:00
Eugene YurtsevandGitHub	f63bec8578	chore(prebuilt): restructure tool node and tool injection logic (#5562 ) * Cleaning up the underlying tool injection logic which is happening in multiple locations. * State was being injected into the ToolCall via Send in two places in create react agent and the logic doesn't belong there, the actual injection should be happening inside the ToolNode where there's awareness of what run time parameters the tool accepts. Change is required to unblock: https://github.com/langchain-ai/langgraph/pull/5537	2025-07-18 09:59:14 -04:00
langchain-infraandGitHub	dc0f0c5944	docs: add egress docs for LGP self hosted (#5569 )	2025-07-18 03:14:40 -04:00
infra	777fe692d4	docs: add egress docs for LGP self hosted	2025-07-18 00:40:15 -04:00
infra	fdbe31a3aa	docs: add egress docs for LGP self hosted	2025-07-18 00:28:04 -04:00
Sam CrowderandGitHub	78a9933144	docs: [LangGraph Server Changelog Bot] Changelog updates for new version(s) (#5561 )	2025-07-17 20:45:42 -07:00
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
Sam Crowder	e0699fbdaf	Update changelog via LangGraph Server Changelog Bot	2025-07-17 11:04:52 -07:00
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