Update error message for missing dev command

checkpoint-sqlite/postgres: handle calling .list on async checkpointer (#3019 )
FIx docs: Update MULTIPLE_SUBGRAPHS.md (#3016 )
2026-08-18 05:35:43 +02:00 · 2025-01-14 11:28:51 -08:00 · 2025-01-14 19:06:11 +00:00 · 2025-01-14 13:41:50 -05:00 · 2025-01-14 11:53:53 -05:00 · 2025-01-13 19:40:40 -05:00
65 changed files with 2997 additions and 269 deletions
@@ -109,9 +109,31 @@ jobs:
      - name: Build
        run: yarn build

+  test-js:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v3
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+      - name: Install dependencies
+        run: yarn install
+      - name: Run tests
+        run: yarn test
+
  ci_success:
    name: "CI Success"
-    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
+    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test, test-js]
    if: |
      always()
    runs-on: ubuntu-latest
@@ -8,7 +8,7 @@
 ⚡ Building language agents as graphs ⚡

 > [!NOTE]
-> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
+> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).

 ## Overview

@@ -0,0 +1,93 @@
+import functools
+
+from urllib3 import __version__ as urllib3version  # type: ignore[import-untyped]
+from urllib3 import connection  # type: ignore[import-untyped]
+
+
+def _ensure_str(s, encoding="utf-8", errors="strict") -> str:
+    if isinstance(s, str):
+        return s
+
+    if isinstance(s, bytes):
+        return s.decode(encoding, errors)
+    return str(s)
+
+
+# Copied from https://github.com/urllib3/urllib3/blob/1c994dfc8c5d5ecaee8ed3eb585d4785f5febf6e/src/urllib3/connection.py#L231
+def request(self, method, url, body=None, headers=None):
+    """Make the request.
+
+    This function is based on the urllib3 request method, with modifications
+    to handle potential issues when using vcrpy in concurrent workloads.
+
+    Args:
+        self: The HTTPConnection instance.
+        method (str): The HTTP method (e.g., 'GET', 'POST').
+        url (str): The URL for the request.
+        body (Optional[Any]): The body of the request.
+        headers (Optional[dict]): Headers to send with the request.
+
+    Returns:
+        The result of calling the parent request method.
+    """
+    # Update the inner socket's timeout value to send the request.
+    # This only triggers if the connection is re-used.
+    if getattr(self, "sock", None) is not None:
+        self.sock.settimeout(self.timeout)
+
+    if headers is None:
+        headers = {}
+    else:
+        # Avoid modifying the headers passed into .request()
+        headers = headers.copy()
+    if "user-agent" not in (_ensure_str(k.lower()) for k in headers):
+        headers["User-Agent"] = connection._get_default_user_agent()
+    # The above is all the same ^^^
+    # The following is different:
+    return self._parent_request(method, url, body=body, headers=headers)
+
+
+_PATCHED = False
+
+
+def patch_urllib3():
+    """Patch the request method of urllib3 to avoid type errors when using vcrpy.
+
+    In concurrent workloads (such as the tracing background queue), the
+    connection pool can get in a state where an HTTPConnection is created
+    before vcrpy patches the HTTPConnection class. In urllib3 >= 2.0 this isn't
+    a problem since they use the proper super().request(...) syntax, but in older
+    versions, super(HTTPConnection, self).request is used, resulting in a TypeError
+    since self is no longer a subclass of "HTTPConnection" (which at this point
+    is vcr.stubs.VCRConnection).
+
+    This method patches the class to fix the super() syntax to avoid mixed inheritance.
+    In the case of the LangSmith tracing logic, it doesn't really matter since we always
+    exclude cache checks for calls to LangSmith.
+
+    The patch is only applied for urllib3 versions older than 2.0.
+    """
+    global _PATCHED
+    if _PATCHED:
+        return
+    from packaging import version
+
+    if version.parse(urllib3version) >= version.parse("2.0"):
+        _PATCHED = True
+        return
+
+    # Lookup the parent class and its request method
+    parent_class = connection.HTTPConnection.__bases__[0]
+    parent_request = parent_class.request
+
+    def new_request(self, *args, **kwargs):
+        """Handle parent request.
+
+        This method binds the parent's request method to self and then
+        calls our modified request function.
+        """
+        self._parent_request = functools.partial(parent_request, self)
+        return request(self, *args, **kwargs)
+
+    connection.HTTPConnection.request = new_request
+    _PATCHED = True
@@ -43,7 +43,9 @@ NOTEBOOKS_NO_EXECUTION = [
    "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
    "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
-    "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR
+    "docs/docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
+    "docs/docs/tutorials/tot/tot.ipynb",
+    "docs/docs/how-tos/visualization.ipynb"
 ]


@@ -86,6 +88,7 @@ def add_vcr_to_notebook(
 ) -> nbformat.NotebookNode:
    """Inject `with vcr.cassette` into each code cell of the notebook."""

+    uses_langsmith = False
    # Inject VCR context manager into each code cell
    for idx, cell in enumerate(notebook.cells):
        if cell.cell_type != "code":
@@ -120,6 +123,9 @@ def add_vcr_to_notebook(
            f"    {line}" for line in lines
        )

+        if any("hub.pull" in line or "from langsmith import" in line for line in lines):
+            uses_langsmith = True
+
    # Add import statement
    vcr_import_lines = [
        "import nest_asyncio",
@@ -152,6 +158,15 @@ def add_vcr_to_notebook(
        "custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
        "custom_vcr.serializer = 'advanced_compressed'",
    ]
+    if uses_langsmith:
+        vcr_import_lines.extend(
+            # patch urllib3 to handle vcr errors, see more here:
+            # https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
+            "import sys",
+            f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
+            "import _patch as patch_urllib3",
+            "patch_urllib3.patch_urllib3()",
+        )
    import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
    import_cell.pop("id", None)
    notebook.cells.insert(0, import_cell)
@@ -0,0 +1 @@
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@@ -1 +1 @@
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@@ -1 +0,0 @@
-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
@@ -1 +0,0 @@
-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
@@ -11,7 +11,7 @@ LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith

 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...

-1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
 1. In the top-right corner, select `+ New Deployment` to create a new deployment.
 1. In the `Create New Deployment` panel, fill out the required fields.
    1. `Deployment details`
@@ -38,7 +38,7 @@ When [creating a new deployment](#create-new-deployment), a new revision is crea

 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...

-1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
 1. Select an existing deployment to create a new revision for.
 1. In the `Deployment` view, in the top-right corner, select `+ New Revision`.
 1. In the `New Revision` modal, fill out the required fields.
@@ -52,15 +52,15 @@ Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmi
        1. Update the value of existing secrets or environment variables.
 1. Select `Submit`. After a few seconds, the `New Revision` modal will close and the new revision will be queued for deployment.

-## View Build and Deployment Logs
+## View Build and Server Logs

-Build and deployment logs are available for each revision.
+Build and server logs are available for each revision.

-Starting from the `LangGraph Cloud` view...
+Starting from the `LangGraph Platform` view...

 1. Select the desired revision from the `Revisions` table. A panel slides open from the right-hand side and the `Build` tab is selected by default, which displays build logs for the revision.
-1. In the panel, select the `Deploy` tab to view deployment logs for the revision.
-1. Within the `Deploy` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
+1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
+1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.

 ## Interrupt Revision

@@ -69,7 +69,7 @@ Interrupting a revision will stop deployment of the revision.
 !!! warning "Undefined Behavior"
    Interrupted revisions have undefined behavior. This is only useful if you need to deploy a new revision and you already have a revision "stuck" in progress. In the future, this feature may be removed.

-Starting from the `LangGraph Cloud` view...
+Starting from the `LangGraph Platform` view...

 1. Select the menu icon (three dots) on the right-hand side of the row for the desired revision from the `Revisions` table.
 1. Select `Interrupt` from the menu.
@@ -79,13 +79,13 @@ Starting from the `LangGraph Cloud` view...

 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...

-1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
 1. Select the menu icon (three dots) on the right-hand side of the row for the desired deployment and select `Delete`.
 1. A `Confirmation` modal will appear. Select `Delete`.

 ## Deployment Settings

-Starting from the `LangGraph Cloud` view...
+Starting from the `LangGraph Platform` view...

 1. In the top-right corner, select the gear icon (`Deployment Settings`).
 1. Update the `Git Branch` to the desired branch.
@@ -279,6 +279,58 @@
        }
      }
    },
+    "/v1/projects/{project_id}/revisions/{revision_id}/deploy": {
+      "post": {
+        "tags": ["Revisions (v1)"],
+        "summary": "Deploy Revision",
+        "description": "Deploy revision by ID.\n\nThis endpoint redeploys the deployment of a revision without rebuilding the image for the deployment. Redeploying the deployment of a revision may mitigate intermittent issues with a deployment.\n\nThe revision must be in the `DEPLOYED` status and must be the latest revision of the project.",
+        "operationId": "deploy_revision_projects__project_id__revisions__revision_id__deploy_post",
+        "parameters": [
+          {
+            "required": true,
+            "schema": {
+              "type": "string",
+              "format": "uuid",
+              "title": "Project ID"
+            },
+            "name": "project_id",
+            "in": "path"
+          },
+          {
+            "required": true,
+            "schema": {
+              "type": "string",
+              "format": "uuid",
+              "title": "Revision ID"
+            },
+            "name": "revision_id",
+            "in": "path"
+          }
+        ],
+        "responses": {
+          "400": {
+            "description": "Revision is not in DEPLOYED status or revision is not the latest revision for the project.",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "404": {
+            "description": "Revision not found.",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
    "/v1/projects/{project_id}/revisions/{revision_id}/interrupt": {
      "post": {
        "tags": ["Revisions (v1)"],
@@ -319,31 +371,6 @@
      }
    },
    "schemas": {
-      "EnvVar": {
-        "type": "object",
-        "description": "An environment variable or secret.",
-        "properties": {
-          "name": {
-            "type": "string",
-            "description": "Environment variable or secret name.",
-            "required": true
-          },
-          "value": {
-            "type": "string",
-            "description": "Environment variable or secret value.",
-            "required": true
-          },
-          "type": {
-            "type": "string",
-            "enum": [
-              "default",
-              "secret"
-            ],
-            "description": "Field to designate type of the environment variable (default) or secret.",
-            "required": true
-          }
-        }
-      },
      "ContainerSpec": {
        "type": "object",
        "description": "Container specification for a revision's deployment.\n\nIf any field is omitted or set to `null`, the internal default value is used depending on the deployment type (`dev` or `prod`).",
@@ -404,6 +431,42 @@
          }
        }
      },
+      "EnvVar": {
+        "type": "object",
+        "description": "An environment variable or secret.",
+        "properties": {
+          "name": {
+            "type": "string",
+            "description": "Environment variable or secret name.",
+            "required": true
+          },
+          "value": {
+            "type": "string",
+            "description": "Environment variable or secret value.",
+            "required": true
+          },
+          "type": {
+            "type": "string",
+            "enum": [
+              "default",
+              "secret"
+            ],
+            "description": "Field to designate type of the environment variable (default) or secret.",
+            "required": true
+          }
+        }
+      },
+      "ErrorResponse": {
+        "type": "object",
+        "description": "Error response.",
+        "properties": {
+          "detail": {
+            "type": "string",
+            "description": "Error details.",
+            "required": true
+          }
+        }
+      },
      "Project": {
        "type": "object",
        "description": "A project corresponds to a LangGraph Server deployment and the associated LangSmith tracing project.",
@@ -289,4 +289,7 @@ RUN set -ex && \
 RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*

 ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
+```
+
+???+ note "Updating your langgraph.json file"
+    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
@@ -1,6 +1,6 @@
 # Environment Variables

-The LangGraph Cloud API supports specific environment variables for configuring a deployment.
+The LangGraph Cloud Server supports specific environment variables for configuring a deployment.

 ## `LANGCHAIN_TRACING_SAMPLING_RATE`

@@ -10,10 +10,42 @@ See <a href="https://docs.smith.langchain.com/how_to_guides/tracing/sample_trace

 ## `LANGGRAPH_AUTH_TYPE`

-Type of authentication for the LangGraph Cloud API deployment. Valid values: `langsmith`, `noop`.
+Type of authentication for the LangGraph Cloud Server deployment. Valid values: `langsmith`, `noop`.

 For deployments to LangGraph Cloud, this environment variable is set automatically. For local development or deployments where authentication is handled externally (e.g. self-hosted), set this environment variable to `noop`.

+## `LANGSMITH_RUNS_ENDPOINTS`
+
+For [Bring Your Own Cloud (BYOC)](../../concepts/bring_your_own_cloud.md) deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.
+
+Set this environment variable to have a BYOC deployment send traces to a self-hosted LangSmith instance. The value of `LANGSMITH_RUNS_ENDPOINTS` is a JSON string: `{"<SELF_HOSTED_LANGSMITH_HOSTNAME>":"<LANGSMITH_API_KEY>"}`.
+
+`SELF_HOSTED_LANGSMITH_HOSTNAME` is the hostname of the self-hosted LangSmith instance. It must be accessible to the BYOC deployment. `LANGSMITH_API_KEY` is a LangSmith API generated from the self-hosted LangSmith instance.
+
 ## `N_JOBS_PER_WORKER`

 Number of jobs per worker for the LangGraph Cloud task queue. Defaults to `10`.
+
+## `POSTGRES_URI_CUSTOM`
+
+For [Bring Your Own Cloud (BYOC)](../../concepts/bring_your_own_cloud.md) deployments only.
+
+Specify `POSTGRES_URI_CUSTOM` to use an externally managed Postgres instance. The value of `POSTGRES_URI_CUSTOM` must be a valid [Postgres connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
+
+Postgres:
+
+- Version 15.8 or higher.
+- An initial database must be present and the connection URI must reference the database.
+
+Control Plane Functionality:
+
+- If `POSTGRES_URI_CUSTOM` is specified, the LangGraph Control Plane will not provision a database for the server.
+- If `POSTGRES_URI_CUSTOM` is removed, the LangGraph Control Plane will not provision a database for the server and will not delete the externally managed Postgres instance.
+- If `POSTGRES_URI_CUSTOM` is removed, deployment of the revision will not succeed. Once `POSTGRES_URI_CUSTOM` is specified, it must always be set for the lifecycle of the deployment.
+- If the deployment is deleted, the LangGraph Control Plane will not delete the externally managed Postgres instance.
+- The value of `POSTGRES_URI_CUSTOM` can be updated. For example, a password in the URI can be updated.
+
+Database Connectivity:
+
+- The externally managed Postgres instance must be accessible by the LangGraph Server service in the ECS cluster. The BYOC user is responsible for ensuring connectivity.
+- For example, if an AWS RDS Postgres instance is provisioned, it can be provisioned in the same VPC (`langgraph-cloud-vpc`) as the ECS cluster with the `langgraph-cloud-service-sg` security group to ensure connectivity.
@@ -39,6 +39,7 @@ LangChain has no direct access to the resources created in your cloud account, a
            - Read CloudWatch metrics/logs to monitor your instances/push deployment logs
        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonRDSFullAccess.html
            - Provision `RDS` instances for your LangGraph Cloud instances
+            - Alternatively, an externally managed Postgres instance can be used instead of the default `RDS` instance. LangChain does not monitor or manage the externally managed Postgres instance. See details for [`POSTGRES_URI_CUSTOM` environment variable](../cloud/reference/env_var.md#postgres_uri_custom).
    2. Either
        - Tags an existing vpc / subnets as `langgraph-cloud-enabled`
        - Creates a new vpc and subnets and tags them as `langgraph-cloud-enabled`
@@ -50,5 +51,5 @@ LangChain has no direct access to the resources created in your cloud account, a

 Notes for customers using [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting):

- Creation of new LangGraph Cloud projects and revisions currently needs to be done on smith.langchain.com.
- You can however set up the project to trace to your self-hosted LangSmith instance if desired
+- Creation of new LangGraph Cloud projects and revisions currently needs to be done on `smith.langchain.com`.
+- However, you can set up the project to trace to your self-hosted LangSmith instance if desired. See details for [`LANGSMITH_RUNS_ENDPOINTS` environment variable](../cloud/reference/env_var.md#langsmith_runs_endpoints).
@@ -28,6 +28,10 @@ The guide below will explain the differences between the deployment options.

    The Self-Hosted Enterprise version is only available for the **Enterprise** plan.

+!!! warning "Note"
+
+    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
+
 With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.

 You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
@@ -43,6 +47,10 @@ For more information, please see:

    The Self-Hosted Lite version is available for all plans.

+!!! warning "Note"
+
+    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
+
 The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.

 With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
@@ -61,12 +69,11 @@ For more information, please see:

    The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.

-
 The [Cloud SaaS](./langgraph_cloud.md) version of LangGraph Platform is hosted as part of [LangSmith](https://smith.langchain.com/).

 The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.

-This deployment option provides an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
+This deployment option provides access to the LangGraph Platform UI (within LangSmith) and an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.

 For more information, please see:

@@ -81,7 +88,7 @@ For more information, please see:
    The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.


-This combines the best of both worlds for Cloud and Self-Hosted. We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud. This is currently only available on AWS.
+This combines the best of both worlds for Cloud and Self-Hosted. Create your deployments through the LangGraph Platform UI (within LangSmith) and we manage the infrastructure so you don't have to. The infrastructure all runs within your cloud. This is currently only available on AWS.

 For more information please see:

@@ -62,6 +62,9 @@ The server includes all API endpoints for your graph's runs, threads, assistants

 The `langgraph dockerfile` command generates a [Dockerfile](https://docs.docker.com/reference/dockerfile/) that can be used to build images for and deploy instances of the [LangGraph API server](./langgraph_server.md). This is useful if you want to further customize the dockerfile or deploy in a more custom way.

+??? note "Updating your langgraph.json file"
+    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+
 ## Related

 - [LangGraph CLI API Reference](../cloud/reference/cli.md)
@@ -112,7 +112,7 @@ In this architecture, agents are defined as graph nodes. Each agent can communic
 ```python
 from typing import Literal
 from langchain_openai import ChatOpenAI
-from langgraph.graph import StateGraph, MessagesState, START
+from langgraph.graph import StateGraph, MessagesState, START, END

 model = ChatOpenAI()

@@ -147,24 +147,21 @@ In our example, the output of `get_state_history` will look like this:

 ### Replay

-It's also possible to play-back a prior graph execution. If we `invoking` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the graph from a checkpoint that corresponds to the `checkpoint_id`.
+It's also possible to play-back a prior graph execution. If we `invoke` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the previously executed steps _before_ a checkpoint that corresponds to the `checkpoint_id`, and only execute the steps _after_ the checkpoint.

-* `thread_id` is simply the ID of a thread. This is always required.
-* `checkpoint_id` This identifier refers to a specific checkpoint within a thread. 
+* `thread_id` is the ID of a thread.
+* `checkpoint_id` is an identifier that refers to a specific checkpoint within a thread.

 You must pass these when invoking the graph as part of the `configurable` portion of the config:

 ```python
-# {"configurable": {"thread_id": "1"}}  # valid config
-# {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
-
-config = {"configurable": {"thread_id": "1"}}
+config = {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
 graph.invoke(None, config=config)
 ```

-Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
+Importantly, LangGraph knows whether a particular step has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step, but only for the steps _before_ the provided `checkpoint_id`. All of the steps _after_ `checkpoint_id` will be executed (i.e., a new fork), even if they have been executed previously. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).

-![Replay](img/persistence/re_play.jpg)
+![Replay](img/persistence/re_play.png)

 ### Update state

@@ -32,6 +32,10 @@ To use the Self-Hosted Enterprise version, you must acquire a license key that y
 - Build the docker image for [LangGraph Server](./langgraph_server.md) using the [LangGraph CLI](./langgraph_cli.md).
 - Deploy a web server that will run the docker image and pass in the necessary environment variables.

+!!! warning "Note"
+
+    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite or Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
+
 For step-by-step instructions, see [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).

 ## Helm Chart
@@ -17,17 +17,9 @@ We call these debugging techniques **Time Travel**, composed of two key actions:

 ![](./img/human_in_the_loop/replay.png)

-Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
+Replaying allows us to revisit and reproduce an agent's past actions, up to and including a specific step (checkpoint).

-To replay from the current state, simply pass `None` as the input along with a `thread`:
-
-```python
-thread = {"configurable": {"thread_id": "1"}}
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
-```
-
-To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
+To replay actions before a specific checkpoint, start by retrieving all checkpoints for the thread:

 ```python
 all_checkpoints = []
@@ -43,7 +35,7 @@ for event in graph.stream(None, config, stream_mode="values"):
    print(event)
 ```

-The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
+The graph replays previously executed steps _before_ the provided `checkpoint_id` and executes the steps _after_ `checkpoint_id` (i.e., a new fork), even if they have been executed previously.

 ## Forking

@@ -37,7 +37,7 @@ async def authenticate(authorization: str) -> str:
            detail="Invalid token"
        )

-# Optional: Add authorization rules
+# Add authorization rules to actually control access to resources
@my_auth.on
 async def add_owner(
    ctx: Auth.types.AuthContext,
@@ -48,6 +48,13 @@ async def add_owner(
    metadata = value.setdefault("metadata", {})
    metadata.update(filters)
    return filters
+
+# Assumes you organize information in store like (user_id, resource_type, resource_id)
+@my_auth.on.store()
+async def authorize_store(ctx: Auth.types.AuthContext, value: dict):
+    namespace: tuple = value["namespace"]
+    assert namespace[0] == ctx.user.identity, "Not authorized"
+
 ```

 ## 2. Update configuration
@@ -29,6 +29,7 @@ You will eventually need to pass in the following environment variables to the L
 - `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
 - `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
 - `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
+- `LANGCHAIN_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGCHAIN_ENDPOINT` to the hostname of the self-hosted LangSmith instance.


 ## Build the Docker Image
@@ -208,7 +208,7 @@
    "from typing import Optional\n",
    "\n",
    "from langchain.chat_models import init_chat_model\n",
-    "from langchain_core.tools import InjectedToolArg\n",
+    "from langgraph.prebuilt import InjectedStore\n",
    "from langgraph.store.base import BaseStore\n",
    "from typing_extensions import Annotated\n",
    "\n",
@@ -232,7 +232,7 @@
    "    content: str,\n",
    "    *,\n",
    "    memory_id: Optional[uuid.UUID] = None,\n",
-    "    store: Annotated[BaseStore, InjectedToolArg],\n",
+    "    store: Annotated[BaseStore, InjectedStore],\n",
    "):\n",
    "    \"\"\"Upsert a memory in the database.\"\"\"\n",
    "    # The LLM can use this tool to store a new memory\n",
@@ -42,7 +42,10 @@
    "checkpointer = # postgres checkpointer (see examples below)\n",
    "graph = builder.compile(checkpointer=checkpointer)\n",
    "...\n",
-    "```"
+    "```\n",
+    "\n",
+    "!!! info \"Setup\n",
+    "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
   ]
  },
  {
@@ -1,6 +1,6 @@
 # MULTIPLE_SUBGRAPHS

-You are calling the same subgraph multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
+You are calling subgraphs multiple times within a single LangGraph node with checkpointing enabled for each subgraph.

 This is currently not allowed due to internal restrictions on how checkpoint namespacing for subgraphs works.

@@ -9,4 +9,4 @@ This is currently not allowed due to internal restrictions on how checkpoint nam
 The following may help resolve this error:

 - If you don't need to interrupt/resume from a subgraph, pass `checkpointer=False` when compiling it like this: `.compile(checkpointer=False)`
- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.
+- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.
@@ -275,6 +275,13 @@ async def on_assistants(
        status_code=403,
        detail="User lacks the required permissions.",
    )
+
+# Assumes you organize information in store like (user_id, resource_type, resource_id)
+@auth.on.store()
+async def authorize_store(ctx: Auth.types.AuthContext, value: dict):
+    # The "namespace" field for each store item is a tuple you can think of as the directory of an item.
+    namespace: tuple = value["namespace"]
+    assert namespace[0] == ctx.user.identity, "Not authorized"
 ```

 Notice that instead of one global handler, we now have specific handlers for:
@@ -246,7 +246,7 @@
    "\n",
    "Define the (`fetch_user_flight_information`) tool to let the agent see the current user's flight information.  Then define tools to search for flights and manage the passenger's bookings stored in the SQL database.\n",
    "\n",
-    "We the can [access the RunnableConfig](https://python.langchain.com/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
+    "We then can [access the RunnableConfig](https://python.langchain.com/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
    "\n",
    "<div class=\"admonition warning\">\n",
    "    <p class=\"admonition-title\">Compatibility</p>\n",
@@ -444,7 +444,7 @@
    "\n",
    "    # Check the signed-in user actually has this ticket\n",
    "    cursor.execute(\n",
-    "        \"SELECT flight_id FROM tickets WHERE ticket_no = ? AND passenger_id = ?\",\n",
+    "        \"SELECT ticket_no FROM tickets WHERE ticket_no = ? AND passenger_id = ?\",\n",
    "        (ticket_no, passenger_id),\n",
    "    )\n",
    "    current_ticket = cursor.fetchone()\n",
@@ -3423,7 +3423,7 @@
    "\n",
    "#### Utility\n",
    "\n",
-    "Create a function to make an \"entry\" node for each workflow, stating \"the current assistant ix `assistant_name`\"."
+    "Create a function to make an \"entry\" node for each workflow, stating \"the current assistant is `assistant_name`\"."
   ]
  },
  {
@@ -4444,7 +4444,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
  }
 },
 "nbformat": 4,
@@ -1659,7 +1659,7 @@
   "id": "584de971-6b10-4931-986e-cc35f7adbb3d",
   "metadata": {},
   "source": [
-    "Now the graph is complete, since we've provided the final response message! Since state updates simulate a graph step, they even generate corresponding traces. Inspec the [LangSmith trace](https://smith.langchain.com/public/6d72aeb5-3bca-4090-8684-a11d5a36b10c/r) of the `update_state` call above to see what's going on.\n",
+    "Now the graph is complete, since we've provided the final response message! Since state updates simulate a graph step, they even generate corresponding traces. Inspect the [LangSmith trace](https://smith.langchain.com/public/6d72aeb5-3bca-4090-8684-a11d5a36b10c/r) of the `update_state` call above to see what's going on.\n",
    "\n",
    "**Notice** that our new messages are _appended_ to the messages already in the state. Remember how we defined the `State` type?\n",
    "\n",
@@ -135,7 +135,7 @@
    "from typing_extensions import TypedDict\n",
    "\n",
    "from langchain_anthropic import ChatAnthropic\n",
-    "from langgraph.graph import MessagesState\n",
+    "from langgraph.graph import MessagesState, END\n",
    "from langgraph.types import Command\n",
    "\n",
    "\n",
@@ -26,7 +26,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U --quiet langchain-community tiktoken langchain-openai langchainhub chromadb langchain langgraph langchain-text-splitters"
+    "%pip install -U --quiet langchain-community tiktoken langchain-openai langchainhub chromadb langchain langgraph langchain-text-splitters beautifulsoup4"
   ]
  },
  {
@@ -380,6 +380,18 @@ class AsyncPostgresSaver(BasePostgresSaver):
        Yields:
            Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples.
        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncSqliteSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `checkpointer.alist(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
        while True:
            try:
@@ -410,7 +422,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
            if asyncio.get_running_loop() is self.loop:
                raise asyncio.InvalidStateError(
                    "Synchronous calls to AsyncPostgresSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface."
+                    "different thread. From the main thread, use the async interface. "
                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
                    "graph.ainvoke(...)`."
                )
@@ -58,8 +58,6 @@ MIGRATIONS = [
 );""",
    "ALTER TABLE checkpoint_blobs ALTER COLUMN blob DROP not null;",
    """
-    """,
-    """
    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoints_thread_id_idx ON checkpoints(thread_id);
    """,
    """
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.9"
+version = "2.0.10"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 license = "MIT"
@@ -1,5 +1,6 @@
 # type: ignore

+import re
 from contextlib import contextmanager
 from typing import Any, Optional
 from uuid import uuid4
@@ -782,3 +783,10 @@ def test_scores(

        assert len(results) == 1
        assert results[0].score == pytest.approx(similarities[0], abs=1e-3)
+
+
+def test_nonnull_migrations() -> None:
+    _leading_comment_remover = re.compile(r"^/\*.*?\*/")
+    for migration in PostgresStore.MIGRATIONS:
+        statement = _leading_comment_remover.sub("", migration).split()[0]
+        assert statement.strip()
@@ -1,5 +1,6 @@
 # type: ignore

+import re
 from contextlib import contextmanager
 from typing import Any
 from uuid import uuid4
@@ -238,3 +239,10 @@ def test_null_chars(saver_name: str, test_data) -> None:
            list(saver.list(None, filter={"my_key": "abc"}))[0].metadata["my_key"]
            == "abc"
        )
+
+
+def test_nonnull_migrations() -> None:
+    _leading_comment_remover = re.compile(r"^/\*.*?\*/")
+    for migration in PostgresSaver.MIGRATIONS:
+        statement = _leading_comment_remover.sub("", migration).split()[0]
+        assert statement.strip()
@@ -159,7 +159,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
            if asyncio.get_running_loop() is self.loop:
                raise asyncio.InvalidStateError(
                    "Synchronous calls to AsyncSqliteSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface."
+                    "different thread. From the main thread, use the async interface. "
                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
                    "graph.ainvoke(...)`."
                )
@@ -191,6 +191,18 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
        Yields:
            Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples.
        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncSqliteSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `checkpointer.alist(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
        while True:
            try:
@@ -575,6 +575,13 @@ def dev(
    try:
        from langgraph_api.cli import run_server
    except ImportError:
+        py_version_msg = ""
+        if sys.version_info < (3, 11):
+            py_version_msg = (
+                "\n\nNote: The in-mem server requires Python 3.11 or higher to be installed."
+                f" You are currently using Python {sys.version_info.major}.{sys.version_info.minor}."
+                ' Please upgrade your Python version before installing "langgraph-cli[inmem]".'
+            )
        try:
            from importlib import util

@@ -582,16 +589,19 @@ def dev(
                raise click.UsageError(
                    "Required package 'langgraph-api' is not installed.\n"
                    "Please install it with:\n\n"
-                    '    pip install -U "langgraph-cli[inmem]"\n\n'
+                    '    pip install -U "langgraph-cli[inmem]"'
+                    f"{py_version_msg}"
                ) from None
        except ImportError:
            raise click.UsageError(
                "Could not verify package installation. Please ensure Python is up to date and\n"
                "langgraph-cli is installed with the 'inmem' extra: pip install -U \"langgraph-cli[inmem]\""
+                f"{py_version_msg}"
            ) from None
        raise click.UsageError(
            "Could not import run_server. This likely means your installation is incomplete.\n"
            "Please ensure langgraph-cli is installed with the 'inmem' extra: pip install -U \"langgraph-cli[inmem]\""
+            f"{py_version_msg}"
        ) from None

    config_json = langgraph_cli.config.validate_config_file(pathlib.Path(config))
@@ -100,7 +100,7 @@ class Config(TypedDict, total=False):
 def _parse_version(version_str: str) -> tuple[int, int]:
    """Parse a version string into a tuple of (major, minor)."""
    try:
-        major, minor = map(int, version_str.split("."))
+        major, minor = map(int, version_str.split("-")[0].split("."))
        return (major, minor)
    except ValueError:
        raise click.UsageError(f"Invalid version format: {version_str}") from None
@@ -159,7 +159,7 @@ def validate_config(config: Config) -> Config:
    if config.get("python_version"):
        pyversion = config["python_version"]
        if not pyversion.count(".") == 1 or not all(
-            part.isdigit() for part in pyversion.split(".")
+            part.isdigit() for part in pyversion.split("-")[0].split(".")
        ):
            raise click.UsageError(
                f"Invalid Python version format: {pyversion}. "
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.65"
+version = "0.1.67"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"
@@ -91,6 +91,24 @@ def test_validate_config():
        validate_config({"python_version": "3.10"})
    assert "Minimum required version" in str(exc_info.value)

+    config = validate_config(
+        {
+            "python_version": "3.11-bullseye",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    assert config["python_version"] == "3.11-bullseye"
+
+    config = validate_config(
+        {
+            "python_version": "3.12-slim",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    assert config["python_version"] == "3.12-slim"
+

 def test_validate_config_file():
    with tempfile.TemporaryDirectory() as tmpdir:
@@ -8,7 +8,7 @@
 ⚡ Building language agents as graphs ⚡

 > [!NOTE]
-> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
+> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).

 ## Overview

@@ -398,9 +398,11 @@ class StateGraph(Graph):
        return self

    def add_edge(self, start_key: Union[str, list[str]], end_key: str) -> Self:
-        """Adds a directed edge from the start node to the end node.
+        """Adds a directed edge from the start node (or list of start nodes) to the end node.

-        If the graph transitions to the start_key node, it will always transition to the end_key node next.
+        When a single start node is provided, the graph will wait for that node to complete
+        before executing the end node. When multiple start nodes are provided,
+        the graph will wait for ALL of the start nodes to complete before executing the end node.

        Args:
            start_key (Union[str, list[str]]): The key(s) of the start node(s) of the edge.
@@ -1,4 +1,13 @@
-from typing import Callable, Literal, Optional, Sequence, Type, TypeVar, Union, cast
+from typing import (
+    Callable,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)

 from langchain_core.language_models import BaseChatModel, LanguageModelLike
 from langchain_core.messages import AIMessage, BaseMessage, SystemMessage, ToolMessage
@@ -8,11 +17,12 @@ from langchain_core.runnables import (
    RunnableConfig,
 )
 from langchain_core.tools import BaseTool
+from pydantic import BaseModel
 from typing_extensions import Annotated, TypedDict

 from langgraph._api.deprecation import deprecated_parameter
 from langgraph.errors import ErrorCode, create_error_message
-from langgraph.graph import StateGraph
+from langgraph.graph import END, StateGraph
 from langgraph.graph.graph import CompiledGraph
 from langgraph.graph.message import add_messages
 from langgraph.managed import IsLastStep, RemainingSteps
@@ -22,11 +32,14 @@ from langgraph.store.base import BaseStore
 from langgraph.types import Checkpointer
 from langgraph.utils.runnable import RunnableCallable

+StructuredResponse = Union[dict, BaseModel]
+StructuredResponseSchema = Union[dict, type[BaseModel]]
+

 # We create the AgentState that we will pass around
 # This simply involves a list of messages
 # We want steps to return messages to append to the list
-# So we annotate the messages attribute with operator.add
+# So we annotate the messages attribute with `add_messages` reducer
 class AgentState(TypedDict):
    """The state of the agent."""

@@ -36,6 +49,8 @@ class AgentState(TypedDict):

    remaining_steps: RemainingSteps

+    structured_response: StructuredResponse
+

 StateSchema = TypeVar("StateSchema", bound=AgentState)
 StateSchemaType = Type[StateSchema]
@@ -162,6 +177,19 @@ def _should_bind_tools(model: LanguageModelLike, tools: Sequence[BaseTool]) -> b
    return False


+def _get_model(model: LanguageModelLike) -> BaseChatModel:
+    """Get the underlying model from a RunnableBinding or return the model itself."""
+    if isinstance(model, RunnableBinding):
+        model = model.bound
+
+    if not isinstance(model, BaseChatModel):
+        raise TypeError(
+            f"Expected `model` to be a ChatModel or RunnableBinding (e.g. model.bind_tools(...)), got {type(model)}"
+        )
+
+    return model
+
+
 def _validate_chat_history(
    messages: Sequence[BaseMessage],
 ) -> None:
@@ -201,6 +229,9 @@ def create_react_agent(
    state_schema: Optional[StateSchemaType] = None,
    messages_modifier: Optional[MessagesModifier] = None,
    state_modifier: Optional[StateModifier] = None,
+    response_format: Optional[
+        Union[StructuredResponseSchema, tuple[str, StructuredResponseSchema]]
+    ] = None,
    checkpointer: Optional[Checkpointer] = None,
    store: Optional[BaseStore] = None,
    interrupt_before: Optional[list[str]] = None,
@@ -236,6 +267,25 @@ def create_react_agent(
            - str: This is converted to a SystemMessage and added to the beginning of the list of messages in state["messages"].
            - Callable: This function should take in full graph state and the output is then passed to the language model.
            - Runnable: This runnable should take in full graph state and the output is then passed to the language model.
+        response_format: An optional schema for the final agent output.
+
+            If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key.
+            If not provided, `structured_response` will not be present in the output state.
+            Can be passed in as:
+
+                - an OpenAI function/tool schema,
+                - a JSON Schema,
+                - a TypedDict class,
+                - or a Pydantic class.
+                - a tuple (prompt, schema), where schema is one of the above.
+                    The prompt will be used together with the model that is being used to generate the structured response.
+
+            !!! Important
+                `response_format` requires the model to support `.with_structured_output`
+
+            !!! Note
+                The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished.
+                This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/).
        checkpointer: An optional checkpoint saver object. This is used for persisting
            the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation).
        store: An optional store object. This is used for persisting data
@@ -527,9 +577,11 @@ def create_react_agent(
    """

    if state_schema is not None:
-        if missing_keys := {"messages", "is_last_step"} - set(
-            state_schema.__annotations__
-        ):
+        required_keys = {"messages", "remaining_steps"}
+        if response_format is not None:
+            required_keys.add("structured_response")
+
+        if missing_keys := required_keys - set(state_schema.__annotations__):
            raise ValueError(f"Missing required key(s) {missing_keys} in state_schema")

    if isinstance(tools, ToolExecutor):
@@ -554,6 +606,10 @@ def create_react_agent(
    )
    model_runnable = preprocessor | model

+    # If any of the tools are configured to return_directly after running,
+    # our graph needs to check if these were called
+    should_return_direct = {t.name for t in tool_classes if t.return_direct}
+
    # Define the function that calls the model
    def call_model(state: AgentState, config: RunnableConfig) -> AgentState:
        _validate_chat_history(state["messages"])
@@ -629,11 +685,54 @@ def create_react_agent(
        # We return a list, because this will get added to the existing list
        return {"messages": [response]}

+    def generate_structured_response(
+        state: AgentState, config: RunnableConfig
+    ) -> AgentState:
+        # NOTE: we exclude the last message because there is enough information
+        # for the LLM to generate the structured response
+        messages = state["messages"][:-1]
+        structured_response_schema = response_format
+        if isinstance(response_format, tuple):
+            system_prompt, structured_response_schema = response_format
+            messages = [SystemMessage(content=system_prompt)] + list(messages)
+
+        model_with_structured_output = _get_model(model).with_structured_output(
+            cast(StructuredResponseSchema, structured_response_schema)
+        )
+        response = model_with_structured_output.invoke(messages, config)
+        return {"structured_response": response}
+
+    async def agenerate_structured_response(
+        state: AgentState, config: RunnableConfig
+    ) -> AgentState:
+        # NOTE: we exclude the last message because there is enough information
+        # for the LLM to generate the structured response
+        messages = state["messages"][:-1]
+        structured_response_schema = response_format
+        if isinstance(response_format, tuple):
+            system_prompt, structured_response_schema = response_format
+            messages = [SystemMessage(content=system_prompt)] + list(messages)
+
+        model_with_structured_output = _get_model(model).with_structured_output(
+            cast(StructuredResponseSchema, structured_response_schema)
+        )
+        response = await model_with_structured_output.ainvoke(messages, config)
+        return {"structured_response": response}
+
    if not tool_calling_enabled:
        # Define a new graph
        workflow = StateGraph(state_schema or AgentState)
        workflow.add_node("agent", RunnableCallable(call_model, acall_model))
        workflow.set_entry_point("agent")
+        if response_format is not None:
+            workflow.add_node(
+                "generate_structured_response",
+                RunnableCallable(
+                    generate_structured_response, agenerate_structured_response
+                ),
+            )
+            workflow.add_edge("agent", "generate_structured_response")
+
        return workflow.compile(
            checkpointer=checkpointer,
            store=store,
@@ -643,12 +742,12 @@ def create_react_agent(
        )

    # Define the function that determines whether to continue or not
-    def should_continue(state: AgentState) -> Literal["tools", "__end__"]:
+    def should_continue(state: AgentState) -> str:
        messages = state["messages"]
        last_message = messages[-1]
        # If there is no function call, then we finish
        if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
-            return "__end__"
+            return END if response_format is None else "generate_structured_response"
        # Otherwise if there is, we continue
        else:
            return "tools"
@@ -664,6 +763,19 @@ def create_react_agent(
    # This means that this node is the first one called
    workflow.set_entry_point("agent")

+    # Add a structured output node if response_format is provided
+    if response_format is not None:
+        workflow.add_node(
+            "generate_structured_response",
+            RunnableCallable(
+                generate_structured_response, agenerate_structured_response
+            ),
+        )
+        workflow.add_edge("generate_structured_response", END)
+        should_continue_destinations = ["tools", "generate_structured_response"]
+    else:
+        should_continue_destinations = ["tools", END]
+
    # We now add a conditional edge
    workflow.add_conditional_edges(
        # First, we define the start node. We use `agent`.
@@ -671,18 +783,15 @@ def create_react_agent(
        "agent",
        # Next, we pass in the function that will determine which node is called next.
        should_continue,
+        path_map=should_continue_destinations,
    )

-    # If any of the tools are configured to return_directly after running,
-    # our graph needs to check if these were called
-    should_return_direct = {t.name for t in tool_classes if t.return_direct}
-
    def route_tool_responses(state: AgentState) -> Literal["agent", "__end__"]:
        for m in reversed(state["messages"]):
            if not isinstance(m, ToolMessage):
                break
            if m.name in should_return_direct:
-                return "__end__"
+                return END
        return "agent"

    if should_return_direct:
@@ -965,13 +965,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]

 [[package]]
 name = "jinja2"
-version = "3.1.4"
+version = "3.1.5"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
-    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
+    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
+    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
 ]

 [package.dependencies]
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.2.61"
+version = "0.2.62"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -2832,10 +2832,10 @@
  '''
 # ---
 # name: test_prebuilt_tool_chat
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}}, "required": ["messages"], "title": "LangGraphInput", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}, "BaseModel": {"properties": {}, "title": "BaseModel", "type": "object"}}, "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "structured_response": {"anyOf": [{"type": "object"}, {"$ref": "#/$defs/BaseModel"}], "title": "Structured Response"}}, "required": ["messages", "structured_response"], "title": "LangGraphInput", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.1
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}}, "required": ["messages"], "title": "LangGraphOutput", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}, "BaseModel": {"properties": {}, "title": "BaseModel", "type": "object"}}, "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "structured_response": {"anyOf": [{"type": "object"}, {"$ref": "#/$defs/BaseModel"}], "title": "Structured Response"}}, "required": ["messages", "structured_response"], "title": "LangGraphOutput", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.2
  '''
@@ -1,4 +1,5 @@
 import dataclasses
+import inspect
 import json
 from functools import partial
 from typing import (
@@ -31,7 +32,7 @@ from langchain_core.outputs import ChatGeneration, ChatResult
 from langchain_core.runnables import Runnable, RunnableLambda
 from langchain_core.tools import BaseTool, ToolException
 from langchain_core.tools import tool as dec_tool
-from pydantic import BaseModel, ValidationError
+from pydantic import BaseModel, Field, ValidationError
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import ValidationError as ValidationErrorV1
 from typing_extensions import TypedDict
@@ -46,7 +47,11 @@ from langgraph.prebuilt import (
    create_react_agent,
    tools_condition,
 )
-from langgraph.prebuilt.chat_agent_executor import AgentState, _validate_chat_history
+from langgraph.prebuilt.chat_agent_executor import (
+    AgentState,
+    StructuredResponse,
+    _validate_chat_history,
+)
 from langgraph.prebuilt.tool_node import (
    TOOL_CALL_ERROR_TEMPLATE,
    InjectedState,
@@ -70,6 +75,7 @@ pytestmark = pytest.mark.anyio

 class FakeToolCallingModel(BaseChatModel):
    tool_calls: Optional[list[list[ToolCall]]] = None
+    structured_response: Optional[StructuredResponse] = None
    index: int = 0
    tool_style: Literal["openai", "anthropic"] = "openai"

@@ -97,6 +103,14 @@ class FakeToolCallingModel(BaseChatModel):
    def _llm_type(self) -> str:
        return "fake-tool-call-model"

+    def with_structured_output(
+        self, schema: Type[BaseModel]
+    ) -> Runnable[LanguageModelInput, StructuredResponse]:
+        if self.structured_response is None:
+            raise ValueError("Structured response is not set")
+
+        return RunnableLambda(lambda x: self.structured_response)
+
    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
@@ -510,6 +524,34 @@ def test__infer_handled_types() -> None:
        _infer_handled_types(handler)


+@pytest.mark.skipif(
+    not IS_LANGCHAIN_CORE_030_OR_GREATER,
+    reason="Pydantic v1 is required for this test to pass in langchain-core < 0.3",
+)
+def test_react_agent_with_structured_response() -> None:
+    class WeatherResponse(BaseModel):
+        temperature: float = Field(description="The temperature in fahrenheit")
+
+    tool_calls = [[{"args": {}, "id": "1", "name": "get_weather"}], []]
+
+    def get_weather():
+        """Get the weather"""
+        return "The weather is sunny and 75°F."
+
+    expected_structured_response = WeatherResponse(temperature=75)
+    model = FakeToolCallingModel(
+        tool_calls=tool_calls, structured_response=expected_structured_response
+    )
+    for response_format in (WeatherResponse, ("Meow", WeatherResponse)):
+        agent = create_react_agent(
+            model, [get_weather], response_format=response_format
+        )
+        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
+        assert response["structured_response"] == expected_structured_response
+        assert len(response["messages"]) == 4
+        assert response["messages"][-2].content == "The weather is sunny and 75°F."
+
+
 # tools for testing Too
 def tool1(some_val: int, some_other_val: str) -> str:
    """Tool 1 docstring."""
@@ -2040,3 +2082,9 @@ def test__get_state_args() -> None:
        return 0.0

    assert _get_state_args(foo) == {"a": None, "b": "bar"}
+
+
+def test_inspect_react() -> None:
+    model = FakeToolCallingModel(tool_calls=[])
+    agent = create_react_agent(model, [])
+    inspect.getclosurevars(agent.nodes["agent"].bound.func)
@@ -0,0 +1,17 @@
+/** @type {import('jest').Config} */
+export default {
+  preset: 'ts-jest',
+  testEnvironment: 'node',
+  extensionsToTreatAsEsm: ['.ts'],
+  moduleNameMapper: {
+    '^(\\.{1,2}/.*)\\.js$': '$1',
+  },
+  transform: {
+    '^.+\\.tsx?$': [
+      'ts-jest',
+      {
+        useESM: true,
+      },
+    ],
+  },
+};
@@ -1,6 +1,6 @@
 {
  "name": "@langchain/langgraph-sdk",
-  "version": "0.0.33",
+  "version": "0.0.36",
  "description": "Client library for interacting with the LangGraph API",
  "type": "module",
  "packageManager": "yarn@1.22.19",
@@ -9,7 +9,8 @@
    "build": "yarn clean && yarn lc_build --create-entrypoints --pre --tree-shaking",
    "prepublish": "yarn run build",
    "format": "prettier --write src",
-    "lint": "prettier --check src && tsc --noEmit"
+    "lint": "prettier --check src && tsc --noEmit",
+    "test": "NODE_OPTIONS=--experimental-vm-modules jest --testPathIgnorePatterns=\\.int\\.test.ts"
  },
  "main": "index.js",
  "license": "MIT",
@@ -20,12 +21,16 @@
    "uuid": "^9.0.0"
  },
  "devDependencies": {
+    "@jest/globals": "^29.7.0",
    "@langchain/scripts": "^0.1.4",
    "@tsconfig/recommended": "^1.0.2",
+    "@types/jest": "^29.5.12",
    "@types/node": "^20.12.12",
    "@types/uuid": "^9.0.1",
    "concat-md": "^0.5.1",
+    "jest": "^29.7.0",
    "prettier": "^3.2.5",
+    "ts-jest": "^29.1.2",
    "typedoc": "^0.26.1",
    "typedoc-plugin-markdown": "^4.1.0",
    "typescript": "^5.4.5"
@@ -18,6 +18,8 @@ import {
  ListNamespaceResponse,
  Item,
  ThreadStatus,
+  CronCreateResponse,
+  CronCreateForThreadResponse,
 } from "./schema.js";
 import { AsyncCaller, AsyncCallerParams } from "./utils/async_caller.js";
 import {
@@ -35,7 +37,7 @@ import {
 } from "./types.js";
 import { mergeSignals } from "./utils/signals.js";
 import { getEnvironmentVariable } from "./utils/env.js";
-
+import { _getFetchImplementation } from "./singletons/fetch.js";
 /**
 * Get the API key from the environment.
 * Precedence:
@@ -162,7 +164,8 @@ class BaseClient {
      signal?: AbortSignal;
    },
  ): Promise<T> {
-    const response = await this.asyncCaller.fetch(
+    const response = await this.asyncCaller.call(
+      _getFetchImplementation(),
      ...this.prepareFetchOptions(path, options),
    );
    if (response.status === 202 || response.status === 204) {
@@ -184,7 +187,7 @@ export class CronsClient extends BaseClient {
    threadId: string,
    assistantId: string,
    payload?: CronsCreatePayload,
-  ): Promise<Run> {
+  ): Promise<CronCreateForThreadResponse> {
    const json: Record<string, any> = {
      schedule: payload?.schedule,
      input: payload?.input,
@@ -197,10 +200,13 @@ export class CronsClient extends BaseClient {
      multitask_strategy: payload?.multitaskStrategy,
      if_not_exists: payload?.ifNotExists,
    };
-    return this.fetch<Run>(`/threads/${threadId}/runs/crons`, {
-      method: "POST",
-      json,
-    });
+    return this.fetch<CronCreateForThreadResponse>(
+      `/threads/${threadId}/runs/crons`,
+      {
+        method: "POST",
+        json,
+      },
+    );
  }

  /**
@@ -212,7 +218,7 @@ export class CronsClient extends BaseClient {
  async create(
    assistantId: string,
    payload?: CronsCreatePayload,
-  ): Promise<Run> {
+  ): Promise<CronCreateResponse> {
    const json: Record<string, any> = {
      schedule: payload?.schedule,
      input: payload?.input,
@@ -225,7 +231,7 @@ export class CronsClient extends BaseClient {
      multitask_strategy: payload?.multitaskStrategy,
      if_not_exists: payload?.ifNotExists,
    };
-    return this.fetch<Run>(`/runs/crons`, {
+    return this.fetch<CronCreateResponse>(`/runs/crons`, {
      method: "POST",
      json,
    });
@@ -747,7 +753,8 @@ export class RunsClient extends BaseClient {

    const endpoint =
      threadId == null ? `/runs/stream` : `/threads/${threadId}/runs/stream`;
-    const response = await this.asyncCaller.fetch(
+    const response = await this.asyncCaller.call(
+      _getFetchImplementation(),
      ...this.prepareFetchOptions(endpoint, {
        method: "POST",
        json,
@@ -817,6 +824,8 @@ export class RunsClient extends BaseClient {
      command: payload?.command,
      config: payload?.config,
      metadata: payload?.metadata,
+      stream_mode: payload?.streamMode,
+      stream_subgraphs: payload?.streamSubgraphs,
      assistant_id: assistantId,
      interrupt_before: payload?.interruptBefore,
      interrupt_after: payload?.interruptAfter,
@@ -1037,7 +1046,8 @@ export class RunsClient extends BaseClient {
        ? { signal: options }
        : options;

-    const response = await this.asyncCaller.fetch(
+    const response = await this.asyncCaller.call(
+      _getFetchImplementation(),
      ...this.prepareFetchOptions(`/threads/${threadId}/runs/${runId}/stream`, {
        method: "GET",
        timeoutMs: null,
@@ -17,5 +17,6 @@ export type {
  Checkpoint,
  Interrupt,
 } from "./schema.js";
+export { overrideFetchImplementation } from "./singletons/fetch.js";

 export type { OnConflictBehavior, Command } from "./types.js";
@@ -278,3 +278,22 @@ export interface SearchItem extends Item {
 export interface SearchItemsResponse {
  items: SearchItem[];
 }
+
+export interface CronCreateResponse {
+  cron_id: string;
+  assistant_id: string;
+  thread_id: string | undefined;
+  user_id: string;
+  payload: Record<string, unknown>;
+  schedule: string;
+  next_run_date: string;
+  end_time: string | undefined;
+  created_at: string;
+  updated_at: string;
+  metadata: Metadata;
+}
+
+export interface CronCreateForThreadResponse
+  extends Omit<CronCreateResponse, "thread_id"> {
+  thread_id: string;
+}
@@ -0,0 +1,29 @@
+// Wrap the default fetch call due to issues with illegal invocations
+// in some environments:
+// https://stackoverflow.com/questions/69876859/why-does-bind-fix-failed-to-execute-fetch-on-window-illegal-invocation-err
+// @ts-expect-error Broad typing to support a range of fetch implementations
+const DEFAULT_FETCH_IMPLEMENTATION = (...args: any[]) => fetch(...args);
+
+const LANGSMITH_FETCH_IMPLEMENTATION_KEY = Symbol.for(
+  "lg:fetch_implementation",
+);
+
+/**
+ * Overrides the fetch implementation used for LangSmith calls.
+ * You should use this if you need to use an implementation of fetch
+ * other than the default global (e.g. for dealing with proxies).
+ * @param fetch The new fetch function to use.
+ */
+export const overrideFetchImplementation = (fetch: (...args: any[]) => any) => {
+  (globalThis as any)[LANGSMITH_FETCH_IMPLEMENTATION_KEY] = fetch;
+};
+
+/**
+ * @internal
+ */
+export const _getFetchImplementation: () => (...args: any[]) => any = () => {
+  return (
+    (globalThis as any)[LANGSMITH_FETCH_IMPLEMENTATION_KEY] ??
+    DEFAULT_FETCH_IMPLEMENTATION
+  );
+};
@@ -0,0 +1,74 @@
+/* eslint-disable no-process-env */
+/* eslint-disable @typescript-eslint/no-explicit-any */
+import { jest } from "@jest/globals";
+import { Client } from "../client.js";
+import { overrideFetchImplementation } from "../singletons/fetch.js";
+
+describe.each([[""], ["mocked"]])("Client uses %s fetch", (description) => {
+  let globalFetchMock: jest.Mock;
+  let overriddenFetch: jest.Mock;
+  let expectedFetchMock: jest.Mock;
+  let unexpectedFetchMock: jest.Mock;
+
+  beforeEach(() => {
+    globalFetchMock = jest.fn(() =>
+      Promise.resolve({
+        ok: true,
+        json: () =>
+          Promise.resolve({
+            batch_ingest_config: {
+              use_multipart_endpoint: true,
+            },
+          }),
+        text: () => Promise.resolve(""),
+      }),
+    );
+    overriddenFetch = jest.fn(() =>
+      Promise.resolve({
+        ok: true,
+        json: () =>
+          Promise.resolve({
+            batch_ingest_config: {
+              use_multipart_endpoint: true,
+            },
+          }),
+        text: () => Promise.resolve(""),
+      }),
+    );
+    expectedFetchMock =
+      description === "mocked" ? overriddenFetch : globalFetchMock;
+    unexpectedFetchMock =
+      description === "mocked" ? globalFetchMock : overriddenFetch;
+
+    if (description === "mocked") {
+      overrideFetchImplementation(overriddenFetch);
+    } else {
+      overrideFetchImplementation(globalFetchMock);
+    }
+    // Mock global fetch
+    (globalThis as any).fetch = globalFetchMock;
+  });
+
+  afterEach(() => {
+    jest.restoreAllMocks();
+  });
+
+  describe("createRuns", () => {
+    it("should create an example with the given input and generation", async () => {
+      const client = new Client({ apiKey: "test-api-key" });
+
+      const thread = await client.threads.create();
+      expect(expectedFetchMock).toHaveBeenCalledTimes(1);
+      expect(unexpectedFetchMock).not.toHaveBeenCalled();
+
+      jest.clearAllMocks(); // Clear all mocks before the next operation
+
+      // Then clear & run the function
+      await client.runs.create(thread.thread_id, "somegraph", {
+        input: { foo: "bar" },
+      });
+      expect(expectedFetchMock).toHaveBeenCalledTimes(1);
+      expect(unexpectedFetchMock).not.toHaveBeenCalled();
+    });
+  });
+});
@@ -1,5 +1,15 @@
 import { Checkpoint, Config, Metadata } from "./schema.js";

+/**
+ * Stream modes
+ * - "values": Stream only the state values.
+ * - "messages": Stream complete messages.
+ * - "messages-tuple": Stream (message chunk, metadata) tuples.
+ * - "updates": Stream updates to the state.
+ * - "events": Stream events occurring during execution.
+ * - "debug": Stream detailed debug information.
+ * - "custom": Stream custom events.
+ */
 export type StreamMode =
  | "values"
  | "messages"
@@ -140,13 +150,7 @@ interface RunsInvokePayload {

 export interface RunsStreamPayload extends RunsInvokePayload {
  /**
-   * One of `"values"`, `"messages"`, `"updates"` or `"events"`.
-   * - `"values"`: Stream the thread state any time it changes.
-   * - `"messages"`: Stream chat messages from thread state and calls to chat models,
-   *                 token-by-token where possible.
-   * - `"updates"`: Stream the state updates returned by each node.
-   * - `"events"`: Stream all events produced by the run. You can also access these
-   *               afterwards using the `client.runs.listEvents()` method.
+   * One of `"values"`, `"messages"`, `"messages-tuple"`, `"updates"`, `"events"`, `"debug"`, `"custom"`.
   */
  streamMode?: StreamMode | Array<StreamMode>;

@@ -162,7 +166,17 @@ export interface RunsStreamPayload extends RunsInvokePayload {
  feedbackKeys?: string[];
 }

-export interface RunsCreatePayload extends RunsInvokePayload {}
+export interface RunsCreatePayload extends RunsInvokePayload {
+  /**
+   * One of `"values"`, `"messages"`, `"messages-tuple"`, `"updates"`, `"events"`, `"debug"`, `"custom"`.
+   */
+  streamMode?: StreamMode | Array<StreamMode>;
+
+  /**
+   * Stream output from subgraphs. By default, streams only the top graph.
+   */
+  streamSubgraphs?: boolean;
+}

 export interface CronsCreatePayload extends RunsCreatePayload {
  /**
@@ -69,6 +69,10 @@ class Auth:
        async def authorize_thread_create(params: Auth.on.threads.create.value):
            # Allow the allowed user to create a thread
            assert params.get("metadata", {}).get("owner") == "allowed_user"
+
+        @auth.on.store
+        async def authorize_store(ctx: Auth.types.AuthContext, value: Auth.types.on):
+            assert ctx.user.identity in value["namespace"], "Not authorized"
        ```

    ???+ note "Request Processing Flow"
@@ -157,6 +161,15 @@ class Auth:
                # Implement rate limiting for write operations
                return await check_rate_limit(ctx.user.identity)
            ```
+
+            Auth for the `store` resource is a bit different since its structure is developer defined.
+            You typically want to enforce user creds in the namespace. Y
+            ```python
+            @auth.on.store
+            async def check_store_access(ctx: AuthContext, value: Auth.types.on) -> bool:
+                # Assuming you structure your store like (store.aput((user_id, application_context), key, value))
+                assert value["namespace"][0] == ctx.user.identity
+            ```
        """
        # These are accessed by the API. Changes to their names or types is
        # will be considered a breaking change.
@@ -461,6 +474,73 @@ class _CronsOn(
    Search = types.CronsSearch


+class _StoreOn:
+    def __init__(self, auth: Auth) -> None:
+        self._auth = auth
+
+    @typing.overload
+    def __call__(
+        self,
+        *,
+        actions: typing.Optional[
+            typing.Union[
+                typing.Literal["put", "get", "search", "list_namespaces", "delete"],
+                Sequence[
+                    typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+                ],
+            ]
+        ] = None,
+    ) -> Callable[[AHO], AHO]: ...
+
+    @typing.overload
+    def __call__(self, fn: AHO) -> AHO: ...
+
+    def __call__(
+        self,
+        fn: typing.Optional[AHO] = None,
+        *,
+        actions: typing.Optional[
+            typing.Union[
+                typing.Literal["put", "get", "search", "list_namespaces", "delete"],
+                Sequence[
+                    typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+                ],
+            ]
+        ] = None,
+    ) -> typing.Union[AHO, Callable[[AHO], AHO]]:
+        """Register a handler for specific resources and actions.
+
+        Can be used as a decorator or with explicit resource/action parameters:
+
+        @auth.on.store
+        async def handler(): ... # Handle all store ops
+
+        @auth.on.store(actions=("put", "get", "search", "delete"))
+        async def handler(): ... # Handle specific store ops
+
+        @auth.on.store.put
+        async def handler(): ... # Handle store.put ops
+        """
+        if fn is not None:
+            # Used as a plain decorator
+            _register_handler(self._auth, "store", None, fn)
+            return fn
+
+        # Used with parameters, return a decorator
+        def decorator(
+            handler: AHO,
+        ) -> AHO:
+            if isinstance(actions, str):
+                action_list = [actions]
+            else:
+                action_list = list(actions) if actions is not None else ["*"]
+            for action in action_list:
+                _register_handler(self._auth, "store", action, handler)
+            return handler
+
+        return decorator
+
+
 AHO = typing.TypeVar("AHO", bound=_ActionHandler[dict[str, typing.Any]])


@@ -524,6 +604,7 @@ class _On:
        "threads",
        "runs",
        "crons",
+        "store",
        "value",
    )

@@ -532,6 +613,7 @@ class _On:
        self.assistants = _AssistantsOn(auth, "assistants")
        self.threads = _ThreadsOn(auth, "threads")
        self.crons = _CronsOn(auth, "crons")
+        self.store = _StoreOn(auth)
        self.value = dict[str, typing.Any]

    @typing.overload
@@ -5,7 +5,7 @@ request handling in LangGraph. It includes user protocols, authentication contex
 and typed dictionaries for various API operations.

 Note:
-    All typing.TypedDict classes use total=False to make all fields optional by default.
+    All typing.TypedDict classes use total=False to make all fields typing.Optional by default.
 """

 import functools
@@ -157,7 +157,7 @@ class MinimalUserDict(typing.TypedDict, total=False):
    identity: typing_extensions.Required[str]
    """The required unique identifier for the user."""
    display_name: str
-    """The optional display name for the user."""
+    """The typing.Optional display name for the user."""
    is_authenticated: bool
    """Whether the user is authenticated. Defaults to True."""
    permissions: Sequence[str]
@@ -358,11 +358,34 @@ class AuthContext(BaseAuthContext):
    allowing for fine-grained access control decisions.
    """

-    resource: typing.Literal["runs", "threads", "crons", "assistants"]
+    resource: typing.Literal["runs", "threads", "crons", "assistants", "store"]
    """The resource being accessed."""

-    action: typing.Literal["create", "read", "update", "delete", "search", "create_run"]
-    """The action being performed on the resource."""
+    action: typing.Literal[
+        "create",
+        "read",
+        "update",
+        "delete",
+        "search",
+        "create_run",
+        "put",
+        "get",
+        "list_namespaces",
+    ]
+    """The action being performed on the resource.
+    
+    Most resources support the following actions:
+    - create: Create a new resource
+    - read: Read information about a resource
+    - update: Update an existing resource
+    - delete: Delete a resource
+    - search: Search for resources
+
+    The store supports the following actions:
+    - put: Add or update a document in the store
+    - get: Get a document from the store
+    - list_namespaces: List the namespaces in the store
+    """


 class ThreadsCreate(typing.TypedDict, total=False):
@@ -759,6 +782,84 @@ class CronsSearch(typing.TypedDict, total=False):
    """Offset for pagination."""


+class StoreGet(typing.TypedDict):
+    """Operation to retrieve a specific item by its namespace and key."""
+
+    namespace: tuple[str, ...]
+    """Hierarchical path that uniquely identifies the item's location."""
+
+    key: str
+    """Unique identifier for the item within its specific namespace."""
+
+
+class StoreSearch(typing.TypedDict):
+    """Operation to search for items within a specified namespace hierarchy."""
+
+    namespace: tuple[str, ...]
+    """Prefix filter for defining the search scope."""
+
+    filter: typing.Optional[dict[str, typing.Any]]
+    """Key-value pairs for filtering results based on exact matches or comparison operators."""
+
+    limit: int
+    """Maximum number of items to return in the search results."""
+
+    offset: int
+    """Number of matching items to skip for pagination."""
+
+    query: typing.Optional[str]
+    """Naturalj language search query for semantic search capabilities."""
+
+
+class StoreListNamespaces(typing.TypedDict):
+    """Operation to list and filter namespaces in the store."""
+
+    namespace: typing.Optional[tuple[str, ...]]
+    """Prefix filter namespaces."""
+
+    suffix: typing.Optional[tuple[str, ...]]
+    """Optional conditions for filtering namespaces."""
+
+    max_depth: typing.Optional[int]
+    """Maximum depth of namespace hierarchy to return.
+
+    Note:
+        Namespaces deeper than this level will be truncated.
+    """
+
+    limit: int
+    """Maximum number of namespaces to return."""
+
+    offset: int
+    """Number of namespaces to skip for pagination."""
+
+
+class StorePut(typing.TypedDict):
+    """Operation to store, update, or delete an item in the store."""
+
+    namespace: tuple[str, ...]
+    """Hierarchical path that identifies the location of the item."""
+
+    key: str
+    """Unique identifier for the item within its namespace."""
+
+    value: typing.Optional[dict[str, typing.Any]]
+    """The data to store, or None to mark the item for deletion."""
+
+    index: typing.Optional[typing.Union[typing.Literal[False], list[str]]]
+    """Optional index configuration for full-text search."""
+
+
+class StoreDelete(typing.TypedDict):
+    """Operation to delete an item from the store."""
+
+    namespace: tuple[str, ...]
+    """Hierarchical path that uniquely identifies the item's location."""
+
+    key: str
+    """Unique identifier for the item within its specific namespace."""
+
+
 class on:
    """Namespace for type definitions of different API operations.

@@ -894,6 +995,38 @@ class on:

            value = CronsSearch

+    class store:
+        """Types for store-related operations."""
+
+        value = typing.Union[
+            StoreGet, StoreSearch, StoreListNamespaces, StorePut, StoreDelete
+        ]
+
+        class put:
+            """Type for store put parameters."""
+
+            value = StorePut
+
+        class get:
+            """Type for store get parameters."""
+
+            value = StoreGet
+
+        class search:
+            """Type for store search parameters."""
+
+            value = StoreSearch
+
+        class delete:
+            """Type for store delete parameters."""
+
+            value = StoreDelete
+
+        class list_namespaces:
+            """Type for store list namespaces parameters."""
+
+            value = StoreListNamespaces
+

 __all__ = [
    "on",
@@ -909,4 +1042,9 @@ __all__ = [
    "AssistantsUpdate",
    "AssistantsDelete",
    "AssistantsSearch",
+    "StoreGet",
+    "StoreSearch",
+    "StoreListNamespaces",
+    "StorePut",
+    "StoreDelete",
 ]
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-sdk"
-version = "0.1.49"
+version = "0.1.51"
 description = "SDK for interacting with LangGraph API"
 authors = []
 license = "MIT"
@@ -2251,13 +2251,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]

 [[package]]
 name = "jinja2"
-version = "3.1.4"
+version = "3.1.5"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
-    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
+    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
+    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
 ]

 [package.dependencies]
@@ -2862,21 +2862,21 @@ adal = ["adal (>=1.0.2)"]

 [[package]]
 name = "langchain"
-version = "0.3.9"
+version = "0.3.14"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain-0.3.9-py3-none-any.whl", hash = "sha256:ade5a1fee2f94f2e976a6c387f97d62cc7f0b9f26cfe0132a41d2bda761e1045"},
-    {file = "langchain-0.3.9.tar.gz", hash = "sha256:4950c4ad627d0aa95ce6bda7de453e22059b7e7836b562a8f781fb0b05d7294c"},
+    {file = "langchain-0.3.14-py3-none-any.whl", hash = "sha256:5df9031702f7fe6c956e84256b4639a46d5d03a75be1ca4c1bc9479b358061a2"},
+    {file = "langchain-0.3.14.tar.gz", hash = "sha256:4a5ae817b5832fa0e1fcadc5353fbf74bebd2f8e550294d4dc039f651ddcd3d1"},
 ]

 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
-langchain-core = ">=0.3.21,<0.4.0"
-langchain-text-splitters = ">=0.3.0,<0.4.0"
-langsmith = ">=0.1.17,<0.2.0"
+langchain-core = ">=0.3.29,<0.4.0"
+langchain-text-splitters = ">=0.3.3,<0.4.0"
+langsmith = ">=0.1.17,<0.3"
 numpy = [
    {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
    {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
@@ -2906,45 +2906,46 @@ pydantic = ">=2.7.4,<3.0.0"

 [[package]]
 name = "langchain-community"
-version = "0.3.1"
+version = "0.3.14"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_community-0.3.1-py3-none-any.whl", hash = "sha256:627eb26c16417764762ac47dd0d3005109f750f40242a88bb8f2958b798bcf90"},
-    {file = "langchain_community-0.3.1.tar.gz", hash = "sha256:c964a70628f266a61647e58f2f0434db633d4287a729f100a81dd8b0654aec93"},
+    {file = "langchain_community-0.3.14-py3-none-any.whl", hash = "sha256:cc02a0abad0551edef3e565dff643386a5b2ee45b933b6d883d4a935b9649f3c"},
+    {file = "langchain_community-0.3.14.tar.gz", hash = "sha256:d8ba0fe2dbb5795bff707684b712baa5ee379227194610af415ccdfdefda0479"},
 ]

 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain = ">=0.3.1,<0.4.0"
-langchain-core = ">=0.3.6,<0.4.0"
-langsmith = ">=0.1.125,<0.2.0"
+httpx-sse = ">=0.4.0,<0.5.0"
+langchain = ">=0.3.14,<0.4.0"
+langchain-core = ">=0.3.29,<0.4.0"
+langsmith = ">=0.1.125,<0.3"
 numpy = [
-    {version = ">=1,<2", markers = "python_version < \"3.12\""},
-    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
+    {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
 ]
 pydantic-settings = ">=2.4.0,<3.0.0"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
-tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"

 [[package]]
 name = "langchain-core"
-version = "0.3.23"
+version = "0.3.29"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_core-0.3.23-py3-none-any.whl", hash = "sha256:550c0b996990830fa6515a71a1192a8a0343367999afc36d4ede14222941e420"},
-    {file = "langchain_core-0.3.23.tar.gz", hash = "sha256:f9e175e3b82063cc3b160c2ca2b155832e1c6f915312e1204828f97d4aabf6e1"},
+    {file = "langchain_core-0.3.29-py3-none-any.whl", hash = "sha256:817db1474871611a81105594a3e4d11704949661008e455a10e38ca9ff601a1a"},
+    {file = "langchain_core-0.3.29.tar.gz", hash = "sha256:773d6aeeb612e7ce3d996c0be403433d8c6a91e77bbb7a7461c13e15cfbe5b06"},
 ]

 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.125,<0.2.0"
+langsmith = ">=0.1.125,<0.3"
 packaging = ">=23.2,<25"
 pydantic = [
    {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@@ -3021,21 +3022,21 @@ tiktoken = ">=0.7,<1"

 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.0"
+version = "0.3.5"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_text_splitters-0.3.0-py3-none-any.whl", hash = "sha256:e84243e45eaff16e5b776cd9c81b6d07c55c010ebcb1965deb3d1792b7358e83"},
-    {file = "langchain_text_splitters-0.3.0.tar.gz", hash = "sha256:f9fe0b4d244db1d6de211e7343d4abc4aa90295aa22e1f0c89e51f33c55cd7ce"},
+    {file = "langchain_text_splitters-0.3.5-py3-none-any.whl", hash = "sha256:8c9b059827438c5fa8f327b4df857e307828a5ec815163c9b5c9569a3e82c8ee"},
+    {file = "langchain_text_splitters-0.3.5.tar.gz", hash = "sha256:11cb7ca3694e5bdd342bc16d3875b7f7381651d4a53cbb91d34f22412ae16443"},
 ]

 [package.dependencies]
-langchain-core = ">=0.3.0,<0.4.0"
+langchain-core = ">=0.3.29,<0.4.0"

 [[package]]
 name = "langgraph"
-version = "0.2.59"
+version = "0.2.61"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
@@ -3053,7 +3054,7 @@ url = "libs/langgraph"

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.8"
+version = "2.0.9"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3087,7 +3088,7 @@ pymongo = ">=4.9.0,<4.10.0"

 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.8"
+version = "2.0.9"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3123,7 +3124,7 @@ url = "libs/checkpoint-sqlite"

 [[package]]
 name = "langgraph-sdk"
-version = "0.1.43"
+version = "0.1.49"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3140,23 +3141,28 @@ url = "libs/sdk-py"

 [[package]]
 name = "langsmith"
-version = "0.1.129"
+version = "0.2.10"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
-python-versions = "<4.0,>=3.8.1"
+python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langsmith-0.1.129-py3-none-any.whl", hash = "sha256:31393fbbb17d6be5b99b9b22d530450094fab23c6c37281a6a6efb2143d05347"},
-    {file = "langsmith-0.1.129.tar.gz", hash = "sha256:6c3ba66471bef41b9f87da247cc0b493268b3f54656f73648a256a205261b6a0"},
+    {file = "langsmith-0.2.10-py3-none-any.whl", hash = "sha256:b02f2f174189ff72e54c88b1aa63343defd6f0f676c396a690c63a4b6495dcc2"},
+    {file = "langsmith-0.2.10.tar.gz", hash = "sha256:153c7b3ccbd823528ff5bec84801e7e50a164e388919fc583252df5b27dd7830"},
 ]

 [package.dependencies]
 httpx = ">=0.23.0,<1"
-orjson = ">=3.9.14,<4.0.0"
+orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
 pydantic = [
    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
+
+[package.extras]
+compression = ["zstandard (>=0.23.0,<0.24.0)"]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]

 [[package]]
 name = "loguru"
@@ -5965,6 +5971,20 @@ requests = ">=2.0.0"
 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]

+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
+    {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
+]
+
+[package.dependencies]
+requests = ">=2.0.1,<3.0.0"
+
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"
@@ -7485,4 +7505,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "367f5fb480a8fa5d8ab1c0964a1e9450dbb28e6998097e7536966e7a5fe30c90"
+content-hash = "981f40de9c31530b17537a089651f9e51901b945fbc01b43ac33a466c8a7d9eb"
@@ -42,7 +42,7 @@ langchain-fireworks = "^0.2.0"
 langchain-community = "^0.3.0"
 langchain-experimental = "^0.3.2"
 langgraph-checkpoint-mongodb = "^0.1.0"
-langsmith = "^0.1.129"
+langsmith = "^0.2.0"
 chromadb = "^0.5.5"
 gpt4all = "^2.8.2"
 scikit-learn = "^1.5.2"
				`@@ -0,0 +1 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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