Update error message for missing dev command

Example screenshot:

![image](https://github.com/user-attachments/assets/8304b08d-bc9b-4cd3-8ff4-ed2471d3d6c7)

.github/workflows/ci.yml

@@ -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

docs/cassettes/pass-config-to-tools_15.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/pass-config-to-tools_16.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/pass-config-to-tools_17.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/pass-config-to-tools_18.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/docs/cloud/reference/api/openapi_control_plane.json

@@ -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.",

docs/docs/cloud/reference/cli.md

@@ -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.

docs/docs/concepts/deployment_options.md

@@ -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:
 

docs/docs/concepts/langgraph_cli.md

@@ -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)

docs/docs/concepts/multi_agent.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()
 

docs/docs/concepts/persistence.md

@@ -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
 

docs/docs/concepts/self_hosted.md

@@ -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

docs/docs/concepts/time-travel.md

@@ -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
 

docs/docs/how-tos/auth/custom_auth.md

@@ -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

docs/docs/how-tos/deploy-self-hosted.md

@@ -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

docs/docs/how-tos/memory/semantic-search.ipynb

@@ -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",

docs/docs/how-tos/persistence_postgres.ipynb

@@ -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."
    ]
   },
   {

docs/docs/troubleshooting/errors/MULTIPLE_SUBGRAPHS.md

@@ -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.

docs/docs/tutorials/auth/resource_auth.md

@@ -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:

docs/docs/tutorials/customer-support/customer-support.ipynb

@@ -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",
@@ -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`\"."
    ]
   },
   {

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

@@ -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(...)`."
                 )

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

@@ -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:

libs/cli/langgraph_cli/cli.py

@@ -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))

libs/cli/langgraph_cli/config.py

@@ -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}. "

libs/cli/pyproject.toml

@@ -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"

libs/cli/tests/unit_tests/test_config.py

@@ -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:

libs/langgraph/langgraph/prebuilt/chat_agent_executor.py

@@ -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):
@@ -633,11 +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,
@@ -647,12 +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"
@@ -668,6 +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`.
@@ -675,6 +783,7 @@ 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,
     )
 
     def route_tool_responses(state: AgentState) -> Literal["agent", "__end__"]:
@@ -682,7 +791,7 @@ def create_react_agent(
             if not isinstance(m, ToolMessage):
                 break
             if m.name in should_return_direct:
-                return "__end__"
+                return END
         return "agent"
 
     if should_return_direct:

libs/langgraph/pyproject.toml

@@ -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"

libs/langgraph/tests/__snapshots__/test_large_cases.ambr

@@ -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
   '''

libs/langgraph/tests/test_prebuilt.py

@@ -32,7 +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
@@ -47,7 +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,
@@ -71,6 +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"
 
@@ -98,6 +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]],
@@ -511,6 +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."""

libs/sdk-js/jest.config.js

@@ -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,
+      },
+    ],
+  },
+};

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.34",
+  "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"

libs/sdk-js/src/client.ts

@@ -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,
@@ -1039,7 +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,

libs/sdk-js/src/index.ts

@@ -17,5 +17,6 @@ export type {
   Checkpoint,
   Interrupt,
 } from "./schema.js";
+export { overrideFetchImplementation } from "./singletons/fetch.js";
 
 export type { OnConflictBehavior, Command } from "./types.js";

libs/sdk-js/src/schema.ts

@@ -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;
+}

libs/sdk-js/src/singletons/fetch.ts

@@ -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
+  );
+};

libs/sdk-js/src/tests/fetch.test.ts

@@ -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();
+    });
+  });
+});

libs/sdk-py/langgraph_sdk/auth/__init__.py

@@ -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.