Log queue size when future cancelled

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

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

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docs/cassettes/pass-config-to-tools_16.msgpack.zlib

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

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docs/cassettes/pass-config-to-tools_18.msgpack.zlib

@@ -1 +0,0 @@
-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

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

@@ -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/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/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/package.json

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

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-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.