docs: quick nav fixes (#5229)

Quick nav fixes

.github/workflows/deploy_docs.yml

@@ -4,11 +4,9 @@ on:
   push:
     branches:
       - main
-      - v0
   pull_request:
     branches:
       - main
-      - v0
   workflow_dispatch:
 
 permissions:
@@ -84,9 +82,9 @@ jobs:
         run: make llms-text
       - name: Build site
         run: |
-          # If this is v0 branch, then we want to download stats. we do this 
+          # If this is main branch, then we want to download stats. we do this 
           # with the env variable DOWNLOAD_STATS=true
-          if [ "${{ github.ref }}" == "refs/heads/v0" ]; then
+          if [ "${{ github.ref }}" == "refs/heads/main" ]; then
             DOWNLOAD_STATS=true make build-docs
           else
             make build-docs
@@ -146,7 +144,7 @@ jobs:
           fi
 
       - name: Configure GitHub Pages
-        if: github.ref == 'refs/heads/v0'
+        if: github.ref == 'refs/heads/main'
         uses: actions/configure-pages@v5
 
       - name: Upload Pages Artifact
@@ -156,6 +154,6 @@ jobs:
           path: ./docs/site/
 
       - name: Deploy to GitHub Pages
-        if: github.ref == 'refs/heads/v0'
+        if: github.ref == 'refs/heads/main'
         id: deployment
         uses: actions/deploy-pages@v4

docs/docs/agents/context.md

@@ -1,17 +1,8 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Context
 
-Agents often require more than a list of messages to function effectively. They need **context**.
+**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.
 
-Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:
+Context includes *any* data outside the message list that can shape behavior. This can be:
 
 - Information passed at runtime, like a `user_id` or API credentials.
 - Internal state updated during a multi-step reasoning process.
@@ -22,18 +13,10 @@ LangGraph provides **three** primary ways to supply context:
 | Type                                                                         | Description                                   | Mutable? | Lifetime                |
 |------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
 | [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
-| [**State**](#state-mutable-context)                                          | dynamic data that can change during execution | ✅        | per run or conversation |
-| [**Long-term Memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
+| [**Short-term memory (State)**](#short-term-memory-mutable-context)          | dynamic data that can change during execution | ✅        | per run or conversation |
+| [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
 
-You can use context to:
-
-- Adjust the system prompt the model sees
-- Feed tools with necessary inputs
-- Track facts during an ongoing conversation
-
-## Providing Runtime Context
-
-Use this when you need to inject data into an agent at runtime.
+## Provide runtime context
 
 ### Config (static context)
 
@@ -44,88 +27,83 @@ Specify configuration using a key called **"configurable"** which is reserved
 for this purpose:
 
 ```python
-agent.invoke(
-    {"messages": [{"role": "user", "content": "hi!"}]},
+graph.invoke( # (1)!
+    {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
     # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
+    config={"configurable": {"user_id": "user_123"}} # (3)!
 )
 ```
 
-### State (mutable context)
+1. This is the invocation of the agent or graph. The `invoke` method runs the underlying graph with the provided input.
+2. This example uses messages as an input, which is common, but your application may use different input structures.
+3. This is where you pass the configuration data. The `config` parameter allows you to provide additional context that the agent can use during its execution.
 
-State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
-
-```python
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-agent = create_react_agent(
-    # Other agent parameters...
-    # highlight-next-line
-    state_schema=CustomState,
-)
-
-agent.invoke({
-    "messages": "hi!",
-    "user_name": "Jane"
-})
-```
-
-!!! tip "Turning on memory"
-
-    Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations.
-    Otherwise, the state is scoped only to a single agent run.
-
-
-
-### Long-Term Memory (cross-conversation context)
-
-For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). For more, see the [Memory guide](../how-tos/memory/add-memory.md).
-
-## Customizing Prompts with Context { #prompts }
-
-Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.
-
-Common use cases:
-
-- Personalization
-- Role or goal customization
-- Conditional behavior (e.g., user is admin)
-
-=== "Using config"
+=== "Agent prompt"
 
     ```python
     from langchain_core.messages import AnyMessage
     from langchain_core.runnables import RunnableConfig
-    from langgraph.prebuilt import create_react_agent
     from langgraph.prebuilt.chat_agent_executor import AgentState
+    from langgraph.prebuilt import create_react_agent
 
-    def prompt(
-        state: AgentState,
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> list[AnyMessage]:
-        # highlight-next-line
+    # highlight-next-line
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
         user_name = config["configurable"].get("user_name")
-        system_msg = f"You are a helpful assistant. User's name is {user_name}"
+        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
         return [{"role": "system", "content": system_msg}] + state["messages"]
 
     agent = create_react_agent(
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[get_weather],
-        # highlight-next-line
         prompt=prompt
     )
 
     agent.invoke(
-        ...,
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
         # highlight-next-line
         config={"configurable": {"user_name": "John Smith"}}
     )
     ```
 
-=== "Using state"
+    * See [Agents](../agents/agents.md) for details.
+
+=== "Workflow node"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+
+    # highlight-next-line
+    def node(state: State, config: RunnableConfig):
+        user_name = config["configurable"].get("user_name")
+        ...
+    ```
+
+    * See [the Graph API](https://langchain-ai.github.io/langgraph/how-tos/graph-api/#add-runtime-configuration) for details.
+
+=== "In a tool"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+
+    @tool
+    # highlight-next-line
+    def get_user_info(config: RunnableConfig) -> str:
+        """Retrieve user information based on user ID."""
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+    ```
+
+    See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.
+
+### Short-term memory (mutable context)
+
+State acts as [short-term memory](../concepts/memory.md) during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
+
+=== "In an agent"
+
+    Example shows how to incorporate state into an agent **prompt**.
+
+    State can also be accessed by the agent's **tools**, which can read or update the state as needed. See [tool calling guide](../how-tos/tool-calling.md#short-term-memory) for details.
 
     ```python
     from langchain_core.messages import AnyMessage
@@ -133,15 +111,14 @@ Common use cases:
     from langgraph.prebuilt import create_react_agent
     from langgraph.prebuilt.chat_agent_executor import AgentState
 
-    class CustomState(AgentState):
-        # highlight-next-line
+    # highlight-next-line
+    class CustomState(AgentState): # (1)!
         user_name: str
 
     def prompt(
         # highlight-next-line
         state: CustomState
     ) -> list[AnyMessage]:
-        # highlight-next-line
         user_name = state["user_name"]
         system_msg = f"You are a helpful assistant. User's name is {user_name}"
         return [{"role": "system", "content": system_msg}] + state["messages"]
@@ -150,87 +127,58 @@ Common use cases:
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[...],
         # highlight-next-line
-        state_schema=CustomState,
-        # highlight-next-line
+        state_schema=CustomState, # (2)!
         prompt=prompt
     )
 
     agent.invoke({
         "messages": "hi!",
-        # highlight-next-line
         "user_name": "John Smith"
     })
     ```
 
-## Accessing Context in Tools { #tools }
-
-Tools can access context through special parameter **annotations**.
-
-* Use `RunnableConfig` for config access
-* Use `Annotated[StateSchema, InjectedState]` for agent state
+    1. Define a custom state schema that extends `AgentState` or `MessagesState`.
+    2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
 
 
-!!! tip 
-
-    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
-
-=== "Using config"
+=== "In a workflow"
 
     ```python
-    def get_user_info(
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = config["configurable"].get("user_id")
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+    from typing_extensions import TypedDict
+    from langchain_core.messages import AnyMessage
+    from langgraph.graph import StateGraph
 
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-    )
+    # highlight-next-line
+    class CustomState(TypedDict): # (1)!
+        messages: list[AnyMessage]
+        extra_field: int
 
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "look up user information"}]},
-        # highlight-next-line
-        config={"configurable": {"user_id": "user_123"}}
-    )
+    # highlight-next-line
+    def node(state: CustomState): # (2)!
+        messages = state["messages"]
+        ...
+        return { # (3)!
+            # highlight-next-line
+            "extra_field": state["extra_field"] + 1
+        }
+
+    builder = StateGraph(State)
+    builder.add_node(node)
+    builder.set_entry_point("node")
+    graph = builder.compile()
     ```
+    
+    1. Define a custom state
+    2. Access the state in any node or tool
+    3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
 
-=== "Using State"
 
-    ```python
-    from typing import Annotated
-    from langgraph.prebuilt import InjectedState
+!!! tip "Turning on memory"
 
-    class CustomState(AgentState):
-        # highlight-next-line
-        user_id: str
+    Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.
 
-    def get_user_info(
-        # highlight-next-line
-        state: Annotated[CustomState, InjectedState]
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = state["user_id"]
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+### Long-term memory (cross-conversation context)
 
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-        # highlight-next-line
-        state_schema=CustomState,
-    )
+For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). 
 
-    agent.invoke({
-        "messages": "look up user information",
-        # highlight-next-line
-        "user_id": "user_123"
-    })
-    ```
-
-### Update Context from Tools
-
-Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](../how-tos/memory/add-memory.md#read-short-term) guide for more information.
+For more information, see the [Memory guide](../how-tos/memory/add-memory.md).

docs/docs/cloud/reference/api/openapi.json

@@ -2601,8 +2601,7 @@
             "description": "Configuration to use for the graph. Useful when graph is configurable and you want to update the assistant's configuration."
           },
           "metadata": {
-            "type": "object",
-            "title": "Metadata",
+            "type": "object",            "title": "Metadata",
             "description": "Metadata to merge with existing assistant metadata."
           },
           "name": {
@@ -2708,6 +2707,13 @@
             "title": "Schedule",
             "description": "The cron schedule to execute this job on."
           },
+          "end_time": {
+            "type": "string",
+            "format": "date-time",
+            "title": "End Time",
+            "description": "The end date to stop running the cron."
+          },
+
           "assistant_id": {
             "anyOf": [
               {
@@ -2814,6 +2820,18 @@
             "description": "The number of results to skip.",
             "default": 0,
             "minimum": 0
+          },
+          "sort_by": {
+            "type": "string",
+            "enum": ["cron_id", "assistant_id", "thread_id", "next_run_date", "end_time", "created_at", "updated_at"],
+            "title": "Sort By",
+            "description": "The field to sort by."
+          },
+          "sort_order": {
+            "type": "string",
+            "enum": ["asc", "desc"],
+            "title": "Sort Order",
+            "description": "The order to sort by."
           }
         },
         "type": "object",

docs/docs/concepts/langgraph_standalone_container.md

@@ -17,6 +17,10 @@ The Standalone Container deployment option is the least restrictive model for de
 | **Where is it hosted?** | n/a | Your cloud |
 | **Who provisions and manages it?** | n/a | You |
 
+!!! warning
+
+      LangGraph Platform should not be deployed in serverless environments.
+
 ## Architecture
 
 ![Standalone Container](./img/langgraph_platform_deployment_architecture.png)

docs/docs/how-tos/tool-calling.md

@@ -507,7 +507,7 @@ def update_user_name(
     new_name: str,
     tool_call_id: Annotated[str, InjectedToolCallId]
 ) -> Command:
-    """Update user name in short-term memory."""
+    """Update user-name in short-term memory."""
     # highlight-next-line
     return Command(update={
         # highlight-next-line

docs/mkdocs.yml

@@ -93,6 +93,14 @@ nav:
     - index.md
     - Quickstarts:
       - Agent: agents/agents.md
+      - LangGraph basics:
+          -  concepts/why-langgraph.md
+          -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
+          -  tutorials/get-started/2-add-tools.md
+          -  tutorials/get-started/3-add-memory.md
+          -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
+          -  tutorials/get-started/5-customize-state.md
+          -  tutorials/get-started/6-time-travel.md
       - Local server: tutorials/langgraph-platform/local-server.md
       - Deployment: cloud/quick_start.md
     - General concepts:
@@ -101,7 +109,7 @@ nav:
         - Workflows & agents: tutorials/workflows.md
       - Agent development: agents/overview.md
       - Workflow orchestration:
-        - Graphs: concepts/low_level.md
+        - Graph API: concepts/low_level.md
         - Subgraphs: concepts/subgraphs.md
         - Runtime: concepts/pregel.md
         - Functional API: concepts/functional_api.md
@@ -153,7 +161,7 @@ nav:
       - Stream outputs: how-tos/streaming.md
       - Use Server API: cloud/how-tos/streaming.md
     - Context:
-      - Use in agent: agents/context.md
+      - Add context: agents/context.md
     - Memory:
       - Add memory: how-tos/memory/add-memory.md
     - Human-in-the-loop:
@@ -174,6 +182,8 @@ nav:
     - MCP:
       - Use MCP: agents/mcp.md
       - Server API: concepts/server-mcp.md
+    - Evaluation:
+        - Basic implementation: agents/evals.md
     - Deployment:
       - Basic deployment: agents/deployment.md
       - Set up your application:
@@ -181,13 +191,12 @@ nav:
         - Use pyproject.toml: cloud/deployment/setup_pyproject.md
         - Use JavaScript: cloud/deployment/setup_javascript.md
         - Use custom Docker: cloud/deployment/custom_docker.md
+        - Rebuild graph at runtime: cloud/deployment/graph_rebuild.md
       - Deploy to production:
          - Cloud SaaS: cloud/deployment/cloud.md
          - Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
          - Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
          - Standalone Container: cloud/deployment/standalone_container.md
-    - Evaluation:
-      - Basic implementation: agents/evals.md
     - Platform capabilities:
       - LangGraph Studio:
         - Quickstart: cloud/how-tos/studio/quick_start.md
@@ -254,14 +263,6 @@ nav:
 
   - Examples:
     - agents/run_agents.md
-    - LangGraph basics:
-      -  concepts/why-langgraph.md
-      -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
-      -  tutorials/get-started/2-add-tools.md
-      -  tutorials/get-started/3-add-memory.md
-      -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
-      -  tutorials/get-started/5-customize-state.md
-      -  tutorials/get-started/6-time-travel.md
     - Template applications: concepts/template_applications.md  # TODO: make tutorial
     - Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb
     - Agent Supervisor: tutorials/multi_agent/agent_supervisor.ipynb
@@ -273,7 +274,6 @@ nav:
         - tutorials/auth/getting_started.md
         - tutorials/auth/resource_auth.md
         - tutorials/auth/add_auth_server.md
-      - Rebuild graph at runtime: cloud/deployment/graph_rebuild.md
       - Use RemoteGraph: how-tos/use-remote-graph.md
       - Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-langgraph-platform.ipynb
         # combine with how-tos/autogen-integration.ipynb

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.5.0"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -15,7 +15,7 @@ dependencies = [
     "langchain-core>=0.1",
     "langgraph-checkpoint>=2.1.0",
     "langgraph-sdk>=0.1.42",
-    "langgraph-prebuilt>=0.5.0rc0",
+    "langgraph-prebuilt>=0.5.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",
 ]

libs/langgraph/tests/test_pregel_async.py

@@ -40,6 +40,7 @@ from langgraph.checkpoint.base import (
     CheckpointTuple,
 )
 from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL, START
 from langgraph.errors import InvalidUpdateError, NodeInterrupt, ParentCommand
 from langgraph.func import entrypoint, task
@@ -106,6 +107,10 @@ async def test_checkpoint_errors() -> None:
         def get_next_version(self, current: Optional[int], channel: None) -> int:
             raise ValueError("Faulty get_next_version")
 
+    class FaultySerializer(JsonPlusSerializer):
+        def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+            raise ValueError("Faulty serializer")
+
     def logic(inp: str) -> str:
         return ""
 
@@ -113,6 +118,18 @@ async def test_checkpoint_errors() -> None:
     builder.add_node("agent", logic)
     builder.add_edge(START, "agent")
 
+    graph = builder.compile(checkpointer=InMemorySaver(serde=FaultySerializer()))
+    with pytest.raises(ValueError, match="Faulty serializer"):
+        await graph.ainvoke("", {"configurable": {"thread_id": "thread-1"}})
+    with pytest.raises(ValueError, match="Faulty serializer"):
+        async for _ in graph.astream("", {"configurable": {"thread_id": "thread-2"}}):
+            pass
+    with pytest.raises(ValueError, match="Faulty serializer"):
+        async for _ in graph.astream_events(
+            "", {"configurable": {"thread_id": "thread-3"}}, version="v2"
+        ):
+            pass
+
     graph = builder.compile(checkpointer=FaultyGetCheckpointer())
     with pytest.raises(ValueError, match="Faulty get_tuple"):
         await graph.ainvoke("", {"configurable": {"thread_id": "thread-1"}})
@@ -171,6 +188,25 @@ async def test_checkpoint_errors() -> None:
         ):
             pass
 
+    def faulty_reducer(a: Any, b: Any) -> Any:
+        raise ValueError("Faulty reducer")
+
+    builder = StateGraph(Annotated[str, faulty_reducer])
+    builder.add_node("agent", logic)
+    builder.add_edge(START, "agent")
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    with pytest.raises(ValueError, match="Faulty reducer"):
+        await graph.ainvoke("", {"configurable": {"thread_id": "thread-1"}})
+    with pytest.raises(ValueError, match="Faulty reducer"):
+        async for _ in graph.astream("", {"configurable": {"thread_id": "thread-2"}}):
+            pass
+    with pytest.raises(ValueError, match="Faulty reducer"):
+        async for _ in graph.astream_events(
+            "", {"configurable": {"thread_id": "thread-3"}}, version="v2"
+        ):
+            pass
+
 
 async def test_py_async_with_cancel_behavior() -> None:
     """This test confirms that in all versions of Python we support, __aexit__

libs/langgraph/uv.lock

@@ -1201,7 +1201,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.5.0"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1423,7 +1423,7 @@ inmem = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.5.1"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -1471,7 +1471,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.71"
+version = "0.1.72"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -448,7 +448,7 @@ def create_react_agent(
 
     if (
         _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools))
-        and len(tool_classes) > 0
+        and len(tool_classes + llm_builtin_tools) > 0
     ):
         model = cast(BaseChatModel, model).bind_tools(tool_classes + llm_builtin_tools)  # type: ignore[operator]
 

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.5.1"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"

libs/prebuilt/uv.lock

@@ -320,7 +320,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.5.0"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -464,7 +464,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.5.1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -511,7 +511,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.71"
+version = "0.1.72"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.85",
+  "version": "0.0.86",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

libs/sdk-js/src/schema.ts

@@ -178,6 +178,9 @@ export interface Cron {
   /** The ID of the cron */
   cron_id: string;
 
+  /** The ID of the assistant */
+  assistant_id: string;
+
   /** The ID of the thread */
   thread_id: Optional<string>;
 
@@ -195,6 +198,15 @@ export interface Cron {
 
   /** The run payload to use for creating new run. */
   payload: Record<string, unknown>;
+
+  /** The user ID of the cron */
+  user_id: Optional<string>;
+
+  /** The next run date of the cron */
+  next_run_date: Optional<string>;
+
+  /** The metadata of the cron */
+  metadata: Record<string, unknown>;
 }
 
 export type DefaultValues = Record<string, unknown>[] | Record<string, unknown>;

libs/sdk-py/langgraph_sdk/schema.py

@@ -319,6 +319,8 @@ class Cron(TypedDict):
 
     cron_id: str
     """The ID of the cron."""
+    assistant_id: str
+    """The ID of the assistant."""
     thread_id: str | None
     """The ID of the thread."""
     end_time: datetime | None
@@ -331,6 +333,12 @@ class Cron(TypedDict):
     """The last time the cron was updated."""
     payload: dict
     """The run payload to use for creating new run."""
+    user_id: str | None
+    """The user ID of the cron."""
+    next_run_date: datetime | None
+    """The next run date of the cron."""
+    metadata: dict
+    """The metadata of the cron."""
 
 
 class RunCreate(TypedDict):

libs/sdk-py/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-sdk"
-version = "0.1.71"
+version = "0.1.72"
 description = "SDK for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/sdk-py/uv.lock

@@ -119,7 +119,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.71"
+version = "0.1.72"
 source = { editable = "." }
 dependencies = [
     { name = "httpx" },