Log queue size when future cancelled

technically a breaking change, however the old response type was
incorrect.

README.md

@@ -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
 

docs/_scripts/_patch.py

@@ -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

docs/_scripts/prepare_notebooks_for_ci.py

@@ -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)

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 @@
-eNrtVnlsFFUYL1YO8UQa7+i4RgXt7M7sbvdoMLhtYZVSKHSRQ8rm7czbnaGz86bz3mzZ1iZY8QTFKQTUKNCydKVWoBElHmhUUEIIoR5RLiUEjVcFDDResb7ZdmkLGLz9Q3b/efPed/ze73vf0ZBOQB3LSB3UJqsE6kAg9AM3NqR1WG1ATBa0xCGRkJgqn1wRWm3o8u4bJUI0XOhwAE22A5VIOtJkwS6guCPBO+IQYxCDOBVBYnLPOR11tjiYFyaoCqrYVsjwnNOdz9iyUnTn7jqbjhRIVzYDQ91GTwVEoajE2tIUCDBkokiPQcLUSIAwMkOQIjJJZDAgggzCxJNMFCSQLhPIAFWOAwXb6istL0iEimVFUIAhQtbFSkCuMlgnBcG5OK/li8C4Rq9NDN2CwNk5aw8hpReaCuI90DQREBjO+glrkGBLXYRY0GXNIs0SC4giQyTIKDImDIr24bLk7ZaCrGoGCWNBgnFANepsGqUP6kTOkEE/LcPW4iTLE09nkTLBYEh6DNOb9GiSpJbBjIkuqzFbvXWj3i2g6yBpq7e2rPjKOhSte/Z4rewniCJzoUAykn0kUDbh7yChJCP213ioPxMWy/IZkWRIG8iYHKVvJPmnIVTWpyUIRJozi1MSwsRsPyUL1gNBgBphoSogkQbAfD5WK2v5jAijCn1DrfR1qzCTZmZrFYQaCxQ5AVt6tMwNQNMUWQDWuWMuRmpbbzawFpZTj1utpGFpLqnE3BTI4nCUJ2nSqvQ9u9x254Z5LCZAVhWadawCKKQWLXP+av8DDQhV1A7bWxDMlh7ldf1lEDbXlAFhcsUAk0AXJHMN0OMe9wv993VDJXIcmuni8lPd9R72uXPZed7ubx9gGCdVwVwTpRkN20+QfEKllSayi+U8LMevy7KkQDVGJHO118k/q0Os0WIG72uhJomBG1I0InDHtnRv+WmeXJqN5qJUCY2NuTkkGfmM08NUQI2xygTDuwrdzkKnjwmWhdqKe52EThuK9pAOVByl4RiXDX1akAy1CoqtxacNemtvkWVl0XyNrsMc764uigWr5xLRqwZmBWvdpQWhaqfw4jxWUJAhsoRWaMhmLjuPmLsZvgDwUa+nIMKJHt4HPVAUvd4oKICCN+pyAW51QgZmK2/nmRhCMQWuLx7PFgP65NmKDCVmumTmpEDZncVtM9ipKIIIZkMgZqZUpMKWCqhTqs3WjGv6eHXYQtWnBmaaG32C3wVEjx/6I4D3Qy87bvrUDVl6Tlw/Zb38TCe4t6WnGm3pum7hsJzMLze0OFC14PYL7u8u3XnDF7eEPv94WXz3shU1o+EVbmNrdfOSHVNHdNddWdp02+vLlzuStx5jpStediWrk76VXx3Z54ZP8CsWv/2R8OANox6Hby0tiu69ftSwQ43gybrLvh6ct+iD5twLH32hfWvbjnUvXRdJbLt42pzPa1atut7xxeBjP/1YmmeL2W4+9EvwvY5D48rWo+DYT7+CyWTh0dDR2yrHjDqSmFjHFua5V3aNb7rcPXL4zQ+sbJ4/Z1Nn8aDzy25aBb/rdnTmvuNvRpdsXLxz9EN1rs2/cIt2zV8RmD6taX7zz8e31+59fVBOTnd3bs6aC9If++j6b+qnuYf/q35q1d6sXYAxLawU6kDjd/e1HquDhql7S+BM3SNThrMFVxaz6gZNBh+aOGm8Wp3wJZOlwQlYc+MiNFOlzX0AnFOveTISWggM5SS0NtVQFFt+H9bw7/JtYcRhqOtIp9KZykThnJ02zk4bZ6eN/+20keI5zvt3jhv+f2vc8M+K3yGVF8QCd3mCaApN6oIS4p/42+OGz+Pmon7g4UCUd3oFd8RfIDqjQOQ9AufzRv/hcYP+OfcfGTe2Dhref97YP+Ht2y+5Pwyf+Dr+sDzuFUf58aLaJuahBfHEU/n7v9nyzO65o5M1d07Z9Vzupz/ci9880HGQqazYP2bzRbPHfvKG8lZ8U5Xx6uHESrhwyPEx7z1tG/HRiKt2XBM7UNLwbOkjQ4Z25XVErq48unEGW3v5Y3DK9OOvzJpWI234LLU0r3PvZzVTvqz6ds7Wg7nHvi05MLqxcWjneUWF2rVPD5l0oGt23s7Zzmv2PYzLG8zQsiP7Rm5hht/zPQocPrer5Lw9d9iXhA8eU8uXvnvTkpGTJLMRbTy0YOwDu4JNH1y6JxRxz1iX7hxcfNVCz/bghIbE5rUX6h1vtM0pe9+b0zODePkP11bQGeRXEbIQmQ==

docs/docs/cloud/deployment/cloud.md

@@ -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.

docs/docs/cloud/reference/env_var.md

@@ -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.

docs/docs/concepts/bring_your_own_cloud.md

@@ -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).

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/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/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

@@ -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,

docs/docs/tutorials/introduction.ipynb

@@ -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",

docs/docs/tutorials/multi_agent/agent_supervisor.ipynb

@@ -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",

docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb

@@ -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"
    ]
   },
   {

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

@@ -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);
     """,
     """

libs/checkpoint-postgres/pyproject.toml

@@ -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"

libs/checkpoint-postgres/tests/test_store.py

@@ -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()

libs/checkpoint-postgres/tests/test_sync.py

@@ -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()

libs/checkpoint/langgraph/store/base/batch.py

@@ -1,7 +1,18 @@
 import asyncio
 import functools
 import weakref
-from typing import Any, Callable, Iterable, Literal, Optional, TypeVar, Union
+import sys
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    Literal,
+    Optional,
+    TypeVar,
+    Union,
+    AsyncIterator,
+)
+from contextlib import asynccontextmanager
 
 from langgraph.store.base import (
     BaseStore,
@@ -17,9 +28,15 @@ from langgraph.store.base import (
     SearchOp,
     _validate_namespace,
 )
+import asyncio
+import logging
+
+logger = logging.getLogger(__name__)
 
 F = TypeVar("F", bound=Callable)
 
+SUPPORTS_EXC_NOTES = sys.version_info >= (3, 11)
+
 
 def _check_loop(func: F) -> F:
     @functools.wraps(func)
@@ -67,7 +84,8 @@ class AsyncBatchedBaseStore(BaseStore):
     ) -> Optional[Item]:
         fut = self._loop.create_future()
         self._aqueue[fut] = GetOp(namespace, key)
-        return await fut
+        async with _raise_with_queue_stats(self._aqueue):
+            return await fut
 
     async def asearch(
         self,
@@ -81,7 +99,8 @@ class AsyncBatchedBaseStore(BaseStore):
     ) -> list[SearchItem]:
         fut = self._loop.create_future()
         self._aqueue[fut] = SearchOp(namespace_prefix, filter, limit, offset, query)
-        return await fut
+        async with _raise_with_queue_stats(self._aqueue):
+            return await fut
 
     async def aput(
         self,
@@ -93,7 +112,8 @@ class AsyncBatchedBaseStore(BaseStore):
         _validate_namespace(namespace)
         fut = self._loop.create_future()
         self._aqueue[fut] = PutOp(namespace, key, value, index)
-        return await fut
+        async with _raise_with_queue_stats(self._aqueue):
+            return await fut
 
     async def adelete(
         self,
@@ -102,7 +122,8 @@ class AsyncBatchedBaseStore(BaseStore):
     ) -> None:
         fut = self._loop.create_future()
         self._aqueue[fut] = PutOp(namespace, key, None)
-        return await fut
+        async with _raise_with_queue_stats(self._aqueue):
+            return await fut
 
     async def alist_namespaces(
         self,
@@ -127,7 +148,8 @@ class AsyncBatchedBaseStore(BaseStore):
             offset=offset,
         )
         self._aqueue[fut] = op
-        return await fut
+        async with _raise_with_queue_stats(self._aqueue):
+            return await fut
 
     @_check_loop
     def batch(self, ops: Iterable[Op]) -> list[Result]:
@@ -281,3 +303,17 @@ async def _run(
             break
         # remove strong ref to store
         del s
+
+
+@asynccontextmanager
+async def _raise_with_queue_stats(
+    queue: dict[asyncio.Future, Op]
+) -> AsyncIterator[None]:
+    try:
+        yield
+    except asyncio.CancelledError as e:
+        if SUPPORTS_EXC_NOTES:
+            e.add_note(f"Queue size: {len(queue)}")
+        else:
+            logger.warning(f"Queue size: {len(queue)}")
+        raise

libs/langgraph/README.md

@@ -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
 

libs/langgraph/langgraph/graph/state.py

@@ -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.

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):
@@ -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:

libs/langgraph/poetry.lock

@@ -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]

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

@@ -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)

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.33",
+  "version": "0.0.35",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

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 {
@@ -184,7 +186,7 @@ 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 +199,13 @@ 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 +217,7 @@ 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 +230,7 @@ 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,
     });
@@ -817,6 +822,8 @@ 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,

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/types.ts

@@ -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 {
   /**

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.
@@ -510,7 +523,7 @@ class _StoreOn:
         """
         if fn is not None:
             # Used as a plain decorator
-            _register_handler(self._auth, None, None, fn)
+            _register_handler(self._auth, "store", None, fn)
             return fn
 
         # Used with parameters, return a decorator

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

@@ -795,17 +795,8 @@ class StoreGet(typing.TypedDict):
 class StoreSearch(typing.TypedDict):
     """Operation to search for items within a specified namespace hierarchy."""
 
-    namespace_prefix: tuple[str, ...]
-    """Hierarchical path prefix defining the search scope.
-
-    ???+ example "Examples"
-
-        ```python
-        ()  # Search entire store
-        ("documents",)  # Search all documents
-        ("users", "content")  # Search within user content
-        ```
-    """
+    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."""
@@ -823,8 +814,8 @@ class StoreSearch(typing.TypedDict):
 class StoreListNamespaces(typing.TypedDict):
     """Operation to list and filter namespaces in the store."""
 
-    prefix: typing.Optional[tuple[str, ...]]
-    """Optional conditions for filtering namespaces."""
+    namespace: typing.Optional[tuple[str, ...]]
+    """Prefix filter namespaces."""
 
     suffix: typing.Optional[tuple[str, ...]]
     """Optional conditions for filtering namespaces."""

libs/sdk-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-sdk"
-version = "0.1.50"
+version = "0.1.51"
 description = "SDK for interacting with LangGraph API"
 authors = []
 license = "MIT"

poetry.lock

@@ -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"

pyproject.toml

@@ -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"