fix: missing pgvector in CLI (#3165)

Fixes https://github.com/langchain-ai/langgraph/issues/3164

.github/workflows/codespell.yml

@@ -21,7 +21,7 @@
     
           - name: Install Dependencies
             run: |
-              pip install toml codespell jupytext
+              pip install toml codespell==2.3.0 jupytext
     
           - name: Extract Ignore Words List
             run: |

.github/workflows/deploy_docs.yml

@@ -85,7 +85,8 @@ jobs:
           if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
             echo "Running link check on all HTML files matching notebooks in docs directory..."
             poetry run pytest -v \
-              --check-links-ignore "https://(api|web|docs|academy)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
               --check-links-ignore "http://localhost:8123/.*" \
@@ -106,7 +107,8 @@ jobs:
             if [ -n "${CHANGED_FILES}" ]; then
               echo "Running link check on HTML files matching changed notebook files..."
               poetry run pytest -v \
-                --check-links-ignore "https://(api|web|docs|academy)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "https://academy\.langchain\.com/.*" \
                 --check-links-ignore "http://localhost:8123/.*" \
                 --check-links-ignore "http://localhost:2024.*" \
                 --check-links-ignore "http://127.0.0.1:.*" \

README.md

@@ -12,25 +12,48 @@
 
 ## Overview
 
-[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
-[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+### Why use LangGraph?
 
-To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+LangGraph provides fine-grained control over both the flow and state of your
+agent applications. It implements a central
+[persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/),
+enabling features that are common to most agent architectures:
 
-### Key Features
+- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
 
-- **Cycles and Branching**: Implement loops and conditionals in your apps.
-- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
-- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
-- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### LangGraph Platform
 
-LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
 Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
 
 - **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
@@ -47,9 +70,7 @@ pip install -U langgraph
 
 ## Example
 
-One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
-
-Let's take a look at a simple example of an agent that can use a search tool.
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
 pip install langchain-anthropic
@@ -66,10 +87,72 @@ export LANGSMITH_TRACING=true
 export LANGSMITH_API_KEY=lsv2_sk_...
 ```
 
-```python
-from typing import Annotated, Literal, TypedDict
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
+
+<details open>
+  <summary>High-level implementation</summary>
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+app = create_react_agent(model, tools, checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
+> [!TIP]
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
+
+<details>
+<summary>Low-level implementation</summary>
+
+```python
+from typing import Literal
 
-from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +174,7 @@ tools = [search]
 
 tool_node = ToolNode(tools)
 
-model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,92 +228,102 @@ checkpointer = MemorySaver()
 # Note that we're (optionally) passing the memory when compiling the graph
 app = workflow.compile(checkpointer=checkpointer)
 
-# Use the Runnable
+# Use the agent
 final_state = app.invoke(
-    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
     config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 
-```
-"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
-```
+<b>Step-by-step Breakdown</b>:
 
-Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/modules/agents/tools/custom_tools">here</a>.
+  </li>
+</ul>
+</details>
 
-```python
-final_state = app.invoke(
-    {"messages": [HumanMessage(content="what about ny")]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+<details>
+<summary>Initialize graph with state.</summary>
 
-```
-"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
-```
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
 
-### Step-by-step Breakdown
+<details>
+<summary>Define graph nodes.</summary>
 
-1. <details>
-    <summary>Initialize the model and tools.</summary>
+There are two main nodes we need:
 
-    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
-    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-   </details>
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
 
-2. <details>
-    <summary>Initialize graph with state.</summary>
+<details>
+<summary>Define entry point and graph edges.</summary>
 
-    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
-   </details>
+First, we need to set the entry point for graph execution - <code>agent</code> node.
 
-3. <details>
-    <summary>Define graph nodes.</summary>
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
 
-    There are two main nodes we need:
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
 
-      - The `agent` node: responsible for deciding what (if any) actions to take.
-      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
-   </details>
+<details>
+<summary>Compile the graph.</summary>
 
-4. <details>
-    <summary>Define entry point and graph edges.</summary>
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/v0.2/docs/concepts/#runnable-interface">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
 
-      First, we need to set the entry point for graph execution - `agent` node.
+<details>
+<summary>Execute the graph.</summary>
 
-      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
-
-      - Conditional edge: after the agent is called, we should either:
-        - a. Run tools if the agent said to take an action, OR
-        - b. Finish (respond to the user) if the agent did not ask to run tools
-      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
-   </details>
-
-5. <details>
-    <summary>Compile the graph.</summary>
-
-    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
-    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
-    </details>
-
-6. <details>
-   <summary>Execute the graph.</summary>
-
-    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
-    2. The `"agent"` node executes, invoking the chat model.
-    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
-    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
-
-        - If `AIMessage` has `tool_calls`, `"tools"` node executes
-        - The `"agent"` node executes again and returns `AIMessage`
-
-    5. Execution progresses to the special `END` value and outputs the final state.
-    And as a result, we get a list of all our chat messages as output.
-   </details>
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
 
+</details>
 
 ## Documentation
 

docs/_scripts/_patch.py

@@ -31,7 +31,7 @@ def request(self, method, url, body=None, headers=None):
         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.
+    # This only triggers if the connection is reused.
     if getattr(self, "sock", None) is not None:
         self.sock.settimeout(self.timeout)
 
@@ -90,4 +90,4 @@ def patch_urllib3():
         return request(self, *args, **kwargs)
 
     connection.HTTPConnection.request = new_request
-    _PATCHED = True
+    _PATCHED = True

docs/cassettes/create-react-agent-structured-output_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/create-react-agent-structured-output_d1386f99-ffd1-4b36-86ec-cabb3357d929.msgpack.zlib

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docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib

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docs/cassettes/introduction_df6d81bb-e674-44ef-a46e-17b1a8d2381a.msgpack.zlib

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docs/cassettes/introduction_effb95d9-b7d5-40c5-9253-253d193b23b2.msgpack.zlib

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docs/cassettes/introduction_faa345c6-38a2-42e8-9035-9cf56f7bb5b1.msgpack.zlib

@@ -1 +0,0 @@
-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

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

@@ -561,6 +561,16 @@
           },
           "resource": {
             "$ref": "#/components/schemas/ResourceService"
+          },
+          "status": {
+            "type": "string",
+            "enum": [
+              "AWAITING_DATABASE",
+              "READY",
+              "AWAITING_DELETE",
+              "UNKNOWN"
+            ],
+            "description": "Deployment status of the project.\n\nNon-terminal statuses: `AWAITING_DATABASE`, `AWAITING_DELETE`. All other statuses are terminal."
           }
         }
       },

docs/docs/cloud/reference/cli.md

@@ -4,20 +4,26 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 ## Installation
 
-1. Ensure that Docker is installed (e.g. `docker --version`).
-2. Install the `langgraph-cli` package:
- 
-    === "pip"
-        ```bash    
-        pip install langgraph-cli
-        ```
+1.  Ensure that Docker is installed (e.g. `docker --version`).
+2.  Install the CLI package:
 
-    === "Homebrew (MacOS only)"
+    === "Python"
         ```bash
+        pip install langgraph-cli
+
+        # Install via Homebrew
         brew install langgraph-cli
         ```
- 
-3. Run the command `langgraph --help` to confirm that the CLI is installed.
+
+    === "JS"
+        ```bash
+        npx @langchain/langgraph-cli
+
+        # Install globally, will be available as `langgraphjs`
+        npm install -g @langchain/langgraph-cli
+        ```
+
+3.  Run the command `langgraph --help` or `npx @langchain/langgraph-cli --help` to confirm that the CLI is working correctly.
 
 [](){#langgraph.json}
 
@@ -25,17 +31,6 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 The LangGraph CLI requires a JSON configuration file with the following keys:
 
-| Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `dependencies`     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                  |
-| `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
-| `auth`             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                      |
-| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| `store`            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul>                                                                  |
-| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-
 <div class="admonition tip">
     <p class="admonition-title">Note</p>
     <p>
@@ -43,253 +38,418 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     </p>
 </div>
 
+=== "Python"
+
+    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`dependencies`</span>     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                                                     |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`auth`</span>             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`python_version`</span>   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+    | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+
+=== "JS"
+
+    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and creates an instance of `StateGraph` / `CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+
 ### Examples
 
-#### Basic Configuration
+=== "Python"
+    
+    #### Basic Configuration
 
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "chat": "./chat/graph.py:graph"
-  }
-}
-```
-
-#### Adding semantic search to the store
-
-All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
-
-The `fields` configuration determines which parts of your documents to embed:
-
-- If omitted or set to `["$"]`, the entire document will be embedded
-- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
-- Documents missing specified fields will still be stored but won't have embeddings for those fields
-- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "memory_agent": "./agent/graph.py:graph"
-  },
-  "store": {
-    "index": {
-      "embed": "openai:text-embedding-3-small",
-      "dims": 1536,
-      "fields": ["$"]
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      }
     }
-  }
-}
-```
+    ```
 
-!!! note "Common model dimensions"
-        - openai:text-embedding-3-large: 3072
-        - openai:text-embedding-3-small: 1536
-        - openai:text-embedding-ada-002: 1536
-        - cohere:embed-english-v3.0: 1024
-        - cohere:embed-english-light-v3.0: 384
-        - cohere:embed-multilingual-v3.0: 1024
-        - cohere:embed-multilingual-light-v3.0: 384
+    #### Adding semantic search to the store
 
-#### Semantic search with a custom embedding function
+    All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
 
-If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+    The `fields` configuration determines which parts of your documents to embed:
 
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "memory_agent": "./agent/graph.py:graph"
-  },
-  "store": {
-    "index": {
-      "embed": "./embeddings.py:embed_texts",
-      "dims": 768,
-      "fields": ["text", "summary"]
-    }
-  }
-}
-```
+    - If omitted or set to `["$"]`, the entire document will be embedded
+    - To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
+    - Documents missing specified fields will still be stored but won't have embeddings for those fields
+    - You can still override which fields to embed on a specific item at `put` time using the `index` parameter
 
-The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
-
-```python
-# embeddings.py
-def embed_texts(texts: list[str]) -> list[list[float]]:
-    """Custom embedding function for semantic search."""
-    # Implementation using your preferred embedding model
-    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
-```
-
-#### Adding custom authentication
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "chat": "./chat/graph.py:graph"
-  },
-  "auth": {
-    "path": "./auth.py:auth",
-    "openapi": {
-      "securitySchemes": {
-        "apiKeyAuth": {
-          "type": "apiKey",
-          "in": "header",
-          "name": "X-API-Key"
-        }
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
       },
-      "security": [
-        {"apiKeyAuth": []}
-      ]
-    },
-    "disable_studio_auth": false
-  }
-}
-```
+      "store": {
+        "index": {
+          "embed": "openai:text-embedding-3-small",
+          "dims": 1536,
+          "fields": ["$"]
+        }
+      }
+    }
+    ```
+
+    !!! note "Common model dimensions" 
+        - `openai:text-embedding-3-large`: 3072 
+        - `openai:text-embedding-3-small`: 1536 
+        - `openai:text-embedding-ada-002`: 1536 
+        - `cohere:embed-english-v3.0`: 1024 
+        - `cohere:embed-english-light-v3.0`: 384 
+        - `cohere:embed-multilingual-v3.0`: 1024 
+        - `cohere:embed-multilingual-light-v3.0`: 384
+
+    #### Semantic search with a custom embedding function
+
+    If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
+      },
+      "store": {
+        "index": {
+          "embed": "./embeddings.py:embed_texts",
+          "dims": 768,
+          "fields": ["text", "summary"]
+        }
+      }
+    }
+    ```
+
+    The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
+
+    ```python
+    # embeddings.py
+    def embed_texts(texts: list[str]) -> list[list[float]]:
+        """Custom embedding function for semantic search."""
+        # Implementation using your preferred embedding model
+        return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
+    ```
+
+    #### Adding custom authentication
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      },
+      "auth": {
+        "path": "./auth.py:auth",
+        "openapi": {
+          "securitySchemes": {
+            "apiKeyAuth": {
+              "type": "apiKey",
+              "in": "header",
+              "name": "X-API-Key"
+            }
+          },
+          "security": [{ "apiKeyAuth": [] }]
+        },
+        "disable_studio_auth": false
+      }
+    }
+    ```
+
+    See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
+
+
+=== "JS"
+    
+    #### Basic Configuration
+
+    ```json
+    {
+      "graphs": {
+        "chat": "./src/graph.ts:graph"
+      }
+    }
+    ```
 
-See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
 
 ## Commands
 
-The base command for the LangGraph CLI is `langgraph`.
-
 **Usage**
 
-```
-langgraph [OPTIONS] COMMAND [ARGS]
-```
+=== "Python"
+
+    The base command for the LangGraph CLI is `langgraph`.
+
+    ```
+    langgraph [OPTIONS] COMMAND [ARGS]
+    ```
+=== "JS"
+
+    The base command for the LangGraph.js CLI is `langgraphjs`. 
+
+    ```
+    npx @langchain/langgraph-cli [OPTIONS] COMMAND [ARGS]
+    ```
+
+    We recommend using `npx` to always use the latest version of the CLI.
 
 ### `dev`
 
-Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+=== "Python"
 
-!!! note "Python only"
+    Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
 
-    Currently, the CLI only supports Python >= 3.11.
-    JS support is coming soon.
+    !!! note
 
-**Installation**
+        Currently, the CLI only supports Python >= 3.11.
 
-This command requires the "inmem" extra to be installed:
+    **Installation**
 
-```bash
-pip install -U "langgraph-cli[inmem]"
-```
+    This command requires the "inmem" extra to be installed:
 
-**Usage**
+    ```bash
+    pip install -U "langgraph-cli[inmem]"
+    ```
 
-```
-langgraph dev [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph dev [OPTIONS]
+    ```
 
-| Option                        | Default          | Description                                                                         |
-| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
-| `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
-| `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
-| `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
-| `--no-reload`                 |                  | Disable auto-reload                                                                 |
-| `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
-| `--no-browser`                |                  | Disable automatic browser opening                                                   |
-| `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
-| `--help`                      |                  | Display command documentation                                                       |
+    **Options**
+
+    | Option                        | Default          | Description                                                                         |
+    | ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+    | `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+    | `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+    | `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
+    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--help`                      |                  | Display command documentation                                                       |
+
+
+=== "JS"
+
+    Run LangGraph API server in development mode with hot reloading capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli dev [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option                        | Default          | Description                                                                         |
+    | ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+    | `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+    | `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+    | `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
+    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--help`                      |                  | Display command documentation                                                       |
 
 ### `build`
 
-Build LangGraph Cloud API server Docker image.
+=== "Python"
 
-**Usage**
+    Build LangGraph Cloud API server Docker image.
 
-```
-langgraph build [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph build [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+    | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+    | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+    | `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
+    | `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+    | `--help`             |                  | Display command documentation.                                                                                               |
+
+=== "JS"
+
+    Build LangGraph Cloud API server Docker image.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli build [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+    | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+    | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+    | `--no-pull`          |                  | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                      |
+    | `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+    | `--help`             |                  | Display command documentation.                                                                                               |
 
-| Option               | Default          | Description                                                                                                                  |
-| -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
-| `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
-| `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
-| `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
-| `--help`             |                  | Display command documentation.                                                                                               |
 
 ### `up`
 
-Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+=== "Python"
 
-**Usage**
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
 
-```
-langgraph up [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph up [OPTIONS]
+    ```
 
-| Option                       | Default                   | Description                                                                                                             |
-| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
-| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
-| `--watch`                    |                           | Restart on file changes                                                                                                 |
-| `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
-| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
-| `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
-| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
-| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
-| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
-| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
-| `--help`                     |                           | Display command documentation.                                                                                          |
+    **Options**
+
+    | Option                       | Default                   | Description                                                                                                             |
+    | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+    | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+    | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
+    | `--watch`                    |                           | Restart on file changes                                                                                                 |
+    | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
+    | `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
+    | `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
+    | `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+    | `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+    | `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+    | `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
+    | `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
+    | `--help`                     |                           | Display command documentation.                                                                                          |
+
+=== "JS"
+
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli up [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option                                                                 | Default                   | Description                                                                                                             |
+    | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+    | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
+    | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
+    | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+    | <span style="white-space: nowrap;">`-d, --docker-compose FILE`</span>  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+    | <span style="white-space: nowrap;">`-p, --port INTEGER`</span>         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+    | <span style="white-space: nowrap;">`--no-pull`</span>                  |                           | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                 |
+    | <span style="white-space: nowrap;">`--recreate`</span>                 |                           | Recreate containers even if their configuration and image haven't changed                                               |
+    | <span style="white-space: nowrap;">`--help`</span>                     |                           | Display command documentation.                                                                                          |
 
 ### `dockerfile`
 
-Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
+=== "Python"
 
-**Usage**
+    Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
 
-```
-langgraph dockerfile [OPTIONS] SAVE_PATH
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph dockerfile [OPTIONS] SAVE_PATH
+    ```
 
-| Option              | Default          | Description                                                                                                     |
-| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
-| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
-| `--help`            |                  | Show this message and exit.                                                                                     |
+    **Options**
 
-Example:
+    | Option              | Default          | Description                                                                                                     |
+    | ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+    | `--help`            |                  | Show this message and exit.                                                                                     |
 
-```bash
-langgraph dockerfile -c langgraph.json Dockerfile
-```
+    Example:
 
-This generates a Dockerfile that looks similar to:
+    ```bash
+    langgraph dockerfile -c langgraph.json Dockerfile
+    ```
 
-```dockerfile
-FROM langchain/langgraph-api:3.11
+    This generates a Dockerfile that looks similar to:
 
-ADD ./pipconf.txt /pipconfig.txt
+    ```dockerfile
+    FROM langchain/langgraph-api:3.11
 
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
+    ADD ./pipconf.txt /pipconfig.txt
 
-ADD ./graphs /deps/__outer_graphs/src
-RUN set -ex && \
-    for line in '[project]' \
-                'name = "graphs"' \
-                'version = "0.1"' \
-                '[tool.setuptools.package-data]' \
-                '"*" = ["**/*"]'; do \
-        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
-    done
+    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
 
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+    ADD ./graphs /deps/__outer_graphs/src
+    RUN set -ex && \
+        for line in '[project]' \
+                    'name = "graphs"' \
+                    'version = "0.1"' \
+                    '[tool.setuptools.package-data]' \
+                    '"*" = ["**/*"]'; do \
+            echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
+        done
 
-ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
+    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
-???+ note "Updating your langgraph.json file"
-    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+    ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
+    ```
+
+    ???+ note "Updating your langgraph.json file"
+         The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+
+=== "JS"
+
+    Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli dockerfile [OPTIONS] SAVE_PATH
+    ```
+
+    **Options**
+
+    | Option              | Default          | Description                                                                                                     |
+    | ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+    | `--help`            |                  | Show this message and exit.                                                                                     |
+
+    Example:
+
+    ```bash
+    npx @langchain/langgraph-cli dockerfile -c langgraph.json Dockerfile
+    ```
+
+    This generates a Dockerfile that looks similar to:
+
+    ```dockerfile
+    FROM langchain/langgraphjs-api:20
+    
+    ADD . /deps/agent
+    
+    RUN cd /deps/agent && yarn install
+    
+    ENV LANGSERVE_GRAPHS='{"agent":"./src/react_agent/graph.ts:graph"}'
+    
+    WORKDIR /deps/agent
+    
+    RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts
+    ```
+
+    ???+ note "Updating your langgraph.json file"
+         The `npx @langchain/langgraph-cli dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.

docs/docs/concepts/deployment_options.md

@@ -57,6 +57,7 @@ With a Self-Hosted Lite deployment, you are responsible for managing the infrast
 
 You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
 
+[Cron jobs](../cloud/how-tos/cron_jobs.md) are not available for Self-Hosted Lite deployments.
 
 For more information, please see:
 

docs/docs/concepts/high_level.md

@@ -1,58 +1,26 @@
 # Why LangGraph?
 
-LLMs are extremely powerful, particularly when connected to other systems such as a retriever or APIs. This is why many LLM applications use a control flow of steps before and / or after LLM calls. As an example [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the response. Often a control flow of steps before and / or after an LLM is called a "chain." Chains are a popular paradigm for programming with LLMs and offer a high degree of reliability; the same set of steps runs with each chain invocation.
+## LLM applications
 
-However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent gives an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
+LLMs make it possible to embed intelligence into a new class of applications. There are many patterns for building applications that use LLMs. [Workflows](https://www.anthropic.com/research/building-effective-agents) have scaffolding of predefined code paths around LLM calls. LLMs can direct the control flow through these predefined code paths, which some consider to be an "[agentic system](https://www.anthropic.com/research/building-effective-agents)". In other cases, it's possible to remove this scaffolding, creating autonomous agents that can [plan](https://huyenchip.com/2025/01/07/agents.html), take actions via [tool calls](https://python.langchain.com/docs/concepts/tool_calling/), and directly respond [to the feedback from their own actions](https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models/) with further actions.
 
-- Using an LLM to route between two potential paths
-- Using an LLM to decide which of many tools to call
-- Using an LLM to decide whether the generated answer is sufficient or more work is need
+![Agent Workflow](img/agent_workflow.png)
 
-There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which give an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
+## What LangGraph provides
 
-![Agent Types](img/agent_types.png)
+LangGraph provides low-level supporting infrastructure that sits underneath *any* workflow or agent. It does not abstract prompts or architecture, and provides three central benefits:
 
-Several concepts are utilized in many agent architectures:
+### Persistence
 
-- [Tool calling](agentic_concepts.md#tool-calling): this is often how LLMs make decisions
-- Action taking: often times, the LLMs' outputs are used as the input to an action
-- [Memory](agentic_concepts.md#memory): reliable systems need to have knowledge of things that occurred
-- [Planning](agentic_concepts.md#planning): planning steps (either explicit or implicit) are useful for ensuring that the LLM, when making decisions, makes them in the highest fidelity way.
+LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), which offers a number of benefits:
 
-## Challenges
+- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LangGraph persists arbitrary aspects of your application's state, supporting memory of conversations and other updates within and across user interactions;
+- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Because state is checkpointed, execution can be interrupted and resumed, allowing for decisions, validation, and corrections via human input.
 
-In practice, there is often a trade-off between control and reliability. As we give LLMs more control, the application often become less reliable. This can be due to factors such as LLM non-determinism and / or errors in selecting tools (or steps) that the agent uses (takes).
+### Streaming
 
-![Agent Challenge](img/challenge.png)
+LangGraph also provides support for [streaming](../how-tos/index.md#streaming) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](../how-tos/stream-updates.ipynb)) and [tokens from LLM calls](../how-tos/streaming-tokens.ipynb) embedded in an application.
 
-## Core Principles
+### Debugging and Deployment
 
-The motivation of LangGraph is to help bend the curve, preserving higher reliability as we give the agent more control over the application. We'll outline a few specific pillars of LangGraph that make it well suited for building reliable agents. 
-
-![Langgraph](img/langgraph.png)
-
-**Controllability**
-
-LangGraph gives the developer a high degree of [control](../how-tos/index.md#controllability) by expressing the flow of the application as a set of nodes and edges. All nodes can access and modify a common state (memory). The control flow of the application can set using edges that connect nodes, either deterministically or via conditional logic. 
-
-**Persistence**
-
-LangGraph gives the developer many options for [persisting](../how-tos/index.md#persistence) graph state using short-term or long-term (e.g., via a database) memory. 
-
-**Human-in-the-Loop**
-
-The persistence layer enables several different [human-in-the-loop](../how-tos/index.md#human-in-the-loop) interaction patterns with agents; for example, it's possible to pause an agent, review its state, edit it state, and approve a follow-up step. 
-
-**Streaming**
-
-LangGraph comes with first class support for [streaming](../how-tos/index.md#streaming), which can expose state to the user (or developer) over the course of agent execution. LangGraph supports streaming of both events ([like a tool call being taken](../how-tos/stream-updates.ipynb)) as well as of [tokens that an LLM may emit](../how-tos/streaming-tokens.ipynb).
-
-## Debugging
-
-Once you've built a graph, you often want to test and debug it. [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file) is a specialized IDE for visualization and debugging of LangGraph applications.
-
-![Langgraph Studio](img/lg_studio.png)
-
-## Deployment
-
-Once you have confidence in your LangGraph application, many developers want an easy path to deployment. [LangGraph Platform](../concepts/index.md#langgraph-platform) offers a range of options for deploying LangGraph graphs.
+LangGraph provides an easy onramp for testing, debugging, and deploying applications via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). This includes [Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/), an IDE that enables visualization, interaction, and debugging of workflows or agents. This also includes numerous [options](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for deployment. 

docs/docs/concepts/low_level.md

@@ -359,6 +359,25 @@ Use `Command` when you need to **both** update the graph state **and** route to
 
 Use [conditional edges](#conditional-edges) to route between nodes conditionally without updating the state.
 
+### Navigating to a node in a parent graph
+
+If you are using [subgraphs](#subgraphs), you might want to navigate from a node a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:
+
+```python
+def my_node(state: State) -> Command[Literal["my_other_node"]]:
+    return Command(
+        update={"foo": "bar"},
+        goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph
+        graph=Command.PARENT
+    )
+```
+
+!!! note
+
+    Setting `graph` to `Command.PARENT` will navigate to the closest parent graph.
+
+This is particularly useful when implementing [multi-agent handoffs](./multi_agent.md#handoffs).
+
 ### Using inside tools
 
 A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. To update the graph state from the tool, you can return `Command(update={"my_custom_key": "foo", "messages": [...]})` from the tool:

docs/docs/how-tos/create-react-agent-hitl.ipynb

@@ -23,12 +23,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>            \n",
@@ -368,7 +368,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/create-react-agent-memory.ipynb

@@ -5,7 +5,7 @@
    "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
    "metadata": {},
    "source": [
-    "# How to add memory to the prebuilt ReAct agent\n",
+    "# How to add thread-level memory to a ReAct Agent\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",
@@ -28,12 +28,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>\n",
@@ -285,7 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/create-react-agent-structured-output.ipynb

@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
+   "metadata": {},
+   "source": [
+    "# How to return structured output from the prebuilt ReAct agent\n",
+    "\n",
+    "!!! info \"Prerequisites\"\n",
+    "    This guide assumes familiarity with the following:\n",
+    "    \n",
+    "    - [Agent Architectures](../../concepts/agentic_concepts/)\n",
+    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
+    "    - [Tools](https://python.langchain.com/docs/concepts/tools/)\n",
+    "    - [Structured Output](https://python.langchain.com/docs/concepts/structured_outputs/)\n",
+    "\n",
+    "To return structured output from the prebuilt ReAct agent you can provide a `response_format` parameter with the desired output schema to [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent]:\n",
+    "\n",
+    "```python\n",
+    "class ResponseFormat(BaseModel):\n",
+    "    \"\"\"Respond to the user in this format.\"\"\"\n",
+    "    my_special_output: str\n",
+    "\n",
+    "\n",
+    "graph = create_react_agent(\n",
+    "    model,\n",
+    "    tools=tools,\n",
+    "    # specify the schema for the structured output using `response_format` parameter\n",
+    "    response_format=ResponseFormat\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "Prebuilt ReAct makes an additional LLM call at the end of the ReAct loop to produce a structured output response. Please see [this guide](../react-agent-structured-output) to learn about other  strategies for returning structured outputs from a tool-calling agent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87a00ce9",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
+   "metadata": {},
+   "source": [
+    "## Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we initialize the model we want to use.\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+    "\n",
+    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
+    "\n",
+    "from typing import Literal\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "# Define the structured output schema\n",
+    "\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class WeatherResponse(BaseModel):\n",
+    "    \"\"\"Respond to the user in this format.\"\"\"\n",
+    "\n",
+    "    conditions: str = Field(description=\"Weather conditions\")\n",
+    "\n",
+    "\n",
+    "# Define the graph\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "graph = create_react_agent(\n",
+    "    model,\n",
+    "    tools=tools,\n",
+    "    # specify the schema for the structured output using `response_format` parameter\n",
+    "    response_format=WeatherResponse,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00407425-506d-4ffd-9c86-987921d8c844",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "Let's now test our agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
+    "response = graph.invoke(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50e273a0-fbdb-4eee-89ca-580fbfb52daf",
+   "metadata": {},
+   "source": [
+    "You can see that the agent output contains a `structured_response` key with the structured output conforming to the specified `WeatherResponse` schema, in addition to the message history under `messages` key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "300748d4-0ed2-470d-8dbc-7c14231e73b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WeatherResponse(conditions='cloudy')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response[\"structured_response\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd9e3487-2cec-44cf-9472-0a51eebeddff",
+   "metadata": {},
+   "source": [
+    "### Customizing prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a608548d-77fc-4d7a-845c-32ae9ec0489a",
+   "metadata": {},
+   "source": [
+    "You might need to further customize the second LLM call for the structured output generation and provide a system prompt. To do so, you can pass a tuple (prompt, schema):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d1386f99-ffd1-4b36-86ec-cabb3357d929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = create_react_agent(\n",
+    "    model,\n",
+    "    tools=tools,\n",
+    "    # specify both the system prompt and the schema for the structured output\n",
+    "    response_format=(\"Always return capitalized weather conditions\", WeatherResponse),\n",
+    ")\n",
+    "\n",
+    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
+    "response = graph.invoke(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91f34991-b406-4fd2-a776-4dd03e3dc3dd",
+   "metadata": {},
+   "source": [
+    "You can verify that the structured response now contains a capitalized value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ba43a67f-127c-45e7-982c-a8210d97a3ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WeatherResponse(conditions='Cloudy')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response[\"structured_response\"]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/create-react-agent-system-prompt.ipynb

@@ -14,7 +14,7 @@
     "        This guide assumes familiarity with the following:\n",
     "        <ul>\n",
     "            <li>            \n",
-    "                <a href=\"https://python.langchain.com/v0.1/docs/modules/model_io/concepts/#systemmessage\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/messages/#systemmessage\">\n",
     "                    SystemMessage\n",
     "                </a>\n",
     "            </li>\n",
@@ -24,12 +24,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>\n",
@@ -223,7 +223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/create-react-agent.ipynb

@@ -5,7 +5,7 @@
    "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
    "metadata": {},
    "source": [
-    "# How to use the prebuilt ReAct agent"
+    "# How to use the pre-built ReAct agent"
    ]
   },
   {
@@ -24,12 +24,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>\n",
@@ -292,7 +292,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/cross-thread-persistence.ipynb

@@ -64,18 +64,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "aa2c64a7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ANTHROPIC_API_KEY:  ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -86,7 +78,8 @@
     "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
     "\n",
     "\n",
-    "_set_env(\"ANTHROPIC_API_KEY\")"
+    "_set_env(\"ANTHROPIC_API_KEY\")\n",
+    "_set_env(\"OPENAI_API_KEY\")"
    ]
   },
   {
@@ -356,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

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

@@ -25,8 +25,20 @@ If you would like to deploy LangGraph Cloud on Kubernetes, you can use this [Hel
 
 You will eventually need to pass in the following environment variables to the LangGraph Deploy server:
 
-- `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs.
-- `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
+- `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs. The value of `REDIS_URI` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
+
+    !!! Note "Shared Redis Instance"
+        Multiple self-hosted deployments can share the same Redis instance. For example, for `Deployment A`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/1` and for `Deployment B`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/2`.
+
+        `1` and `2` are different database numbers within the same instance, but `<hostname_1>` is shared. **The same database number cannot be used for separate deployments**.
+
+- `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics. The value of `DATABASE_URI` must be a valid [Postgres connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
+
+    !!! Note "Shared Postgres Instance"
+        Multiple self-hosted deployments can share the same Postgres instance. For example, for `Deployment A`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_1>?host=<hostname_1>` and for `Deployment B`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_2>?host=<hostname_1>`.
+
+        `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
+
 - `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
 - `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
 - `LANGCHAIN_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGCHAIN_ENDPOINT` to the hostname of the self-hosted LangSmith instance.

docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb

@@ -591,7 +591,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/index.md

@@ -22,10 +22,10 @@ These how-to guides show how to achieve that controllability.
 
 ### Persistence
 
-[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
+[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (per-thread persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
 
 - [How to add thread-level persistence to your graph](persistence.ipynb)
-- [How to add thread-level persistence to subgraphs](subgraph-persistence.ipynb)
+- [How to add thread-level persistence to a subgraph](subgraph-persistence.ipynb)
 - [How to add cross-thread persistence to your graph](cross-thread-persistence.ipynb)
 - [How to use Postgres checkpointer for persistence](persistence_postgres.ipynb)
 - [How to use MongoDB checkpointer for persistence](persistence_mongodb.ipynb)
@@ -83,7 +83,10 @@ Other methods:
 
 ### Tool calling
 
-[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
+[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of
+[chat model](https://python.langchain.com/docs/concepts/chat_models/) API that accepts
+tool schemas, along with messages, as input and returns invocations of those tools as
+part of the output message.
 
 These how-to guides show common patterns for tool calling with LangGraph:
 
@@ -98,7 +101,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
 
 [Subgraphs](../concepts/low_level.md#subgraphs) allow you to reuse an existing graph from another graph. These how-to guides show how to use subgraphs:
 
-- [How to add and use subgraphs](subgraph.ipynb)
+- [How to use subgraphs](subgraph.ipynb)
 - [How to view and update state in subgraphs](subgraphs-manage-state.ipynb)
 - [How to transform inputs and outputs of a subgraph](subgraph-transform-state.ipynb)
 
@@ -114,7 +117,7 @@ See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for i
 
 ### State Management
 
-- [How to use Pydantic model as state](state-model.ipynb)
+- [How to use Pydantic model as graph state](state-model.ipynb)
 - [How to define input/output schema for your graph](input_output_schema.ipynb)
 - [How to pass private state between nodes inside the graph](pass_private_state.ipynb)
 
@@ -124,7 +127,7 @@ See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for i
 - [How to visualize your graph](visualization.ipynb)
 - [How to add runtime configuration to your graph](configuration.ipynb)
 - [How to add node retries](node-retries.ipynb)
-- [How to force function calling agent to structure output](react-agent-structured-output.ipynb)
+- [How to force tool-calling agent to structure output](react-agent-structured-output.ipynb)
 - [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
 - [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
 - [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
@@ -137,13 +140,18 @@ One of the big benefits of LangGraph is that you can easily create your own agen
 
 These guides show how to use the prebuilt ReAct agent:
 
-- [How to create a ReAct agent](create-react-agent.ipynb)
-- [How to add memory to a ReAct agent](create-react-agent-memory.ipynb)
+- [How to use the pre-built ReAct agent](create-react-agent.ipynb)
+- [How to add thread-level memory to a ReAct Agent](create-react-agent-memory.ipynb)
 - [How to add a custom system prompt to a ReAct agent](create-react-agent-system-prompt.ipynb)
 - [How to add human-in-the-loop processes to a ReAct agent](create-react-agent-hitl.ipynb)
-- [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb)
+- [How to return structured output from a ReAct agent](create-react-agent-structured-output.ipynb)
 - [How to add semantic search for long-term memory to a ReAct agent](memory/semantic-search.ipynb#using-in-create-react-agent)
 
+Interested in further customizing the ReAct agent? This guide provides an
+overview of its underlying implementation to help you customize for your own needs:
+
+- [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb)
+
 ## LangGraph Platform
 
 This section includes how-to guides for LangGraph Platform.
@@ -187,11 +195,17 @@ LangGraph applications can be deployed using LangGraph Cloud, which provides a r
 
 [Assistants](../concepts/assistants.md) is a configured instance of a template.
 
+See [SDK Reference](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient)
+for supported endpoints and other details.
+
 - [How to configure agents](../cloud/how-tos/configuration_cloud.md)
 - [How to version assistants](../cloud/how-tos/assistant_versioning.md)
 
 ### Threads
 
+See [SDK Reference](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.client.ThreadsClient)
+for supported endpoints and other details.
+
 - [How to copy threads](../cloud/how-tos/copy_threads.md)
 - [How to check status of your threads](../cloud/how-tos/check_thread_status.md)
 

docs/docs/how-tos/persistence.ipynb

@@ -78,7 +78,7 @@
    "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
    "metadata": {},
    "source": [
-    "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)"
+    "Next, we need to set API key for Anthropic (the LLM we will use)."
    ]
   },
   {
@@ -378,7 +378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/persistence_postgres.ipynb

@@ -44,7 +44,8 @@
     "...\n",
     "```\n",
     "\n",
-    "!!! info \"Setup\n",
+    "!!! info \"Setup\"",
+    "\n",
     "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
    ]
   },

docs/docs/how-tos/react-agent-from-scratch.ipynb

@@ -6,37 +6,15 @@
    "source": [
     "# How to create a ReAct agent from scratch\n",
     "\n",
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Prerequisites</p>\n",
-    "    <p>\n",
-    "        This guide assumes familiarity with the following:\n",
-    "        <ul>\n",
-    "            <li>\n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling-agent\">\n",
-    "                    Tool calling agent\n",
-    "                </a>\n",
-    "            </li>                \n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models\">\n",
-    "                    Chat Models\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#messages\">\n",
-    "                    Messages\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/\">\n",
-    "                    LangGraph Glossary\n",
-    "                </a>\n",
-    "            </li>\n",
-    "        </ul>\n",
-    "    </p>\n",
-    "</div> \n",
+    "!!! info \"Prerequisites\"\n",
+    "    This guide assumes familiarity with the following:\n",
+    "    \n",
+    "    - [Tool calling agent](../../concepts/agentic_concepts/#tool-calling-agent)\n",
+    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
+    "    - [Messages](https://python.langchain.com/docs/concepts/messages/)\n",
+    "    - [LangGraph Glossary](../../concepts/low_level/)\n",
     "\n",
-    "\n",
-    "Using the prebuilt ReAct agent ([create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent)) is a great way to get started, but sometimes you might want more control and customization. In those cases, you can create a custom ReAct agent. This guide shows how to implement ReAct agent from scratch using LangGraph.\n",
+    "Using the prebuilt ReAct agent [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] is a great way to get started, but sometimes you might want more control and customization. In those cases, you can create a custom ReAct agent. This guide shows how to implement ReAct agent from scratch using LangGraph.\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -375,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/react-agent-structured-output.ipynb

@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How to return structured output with a ReAct style agent\n",
+    "# How to force tool-calling agent to structure output\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",

docs/docs/how-tos/state-model.ipynb

@@ -5,7 +5,7 @@
    "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
    "metadata": {},
    "source": [
-    "# How to use Pydantic model as state\n",
+    "# How to use Pydantic model as graph state\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",

docs/docs/how-tos/subgraph-persistence.ipynb

@@ -5,7 +5,7 @@
    "id": "176e8dbb-1a0a-49ce-a10e-2417e8ea17a0",
    "metadata": {},
    "source": [
-    "# How to add thread-level persistence to subgraphs"
+    "# How to add thread-level persistence to a subgraph"
    ]
   },
   {

docs/docs/how-tos/subgraph.ipynb

@@ -9,7 +9,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How to add and use subgraphs\n",
+    "# How to use subgraphs\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",

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

@@ -217,7 +217,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll be using a small chat model from Anthropic in our example. To use chat models with tool calling, we need to first ensure that the model is aware of the available tools. We do this by calling `.bind_tools` method on `ChatAnthropic` moodel"
+    "We'll be using a small chat model from Anthropic in our example. To use chat models with tool calling, we need to first ensure that the model is aware of the available tools. We do this by calling `.bind_tools` method on `ChatAnthropic` model"
    ]
   },
   {

docs/docs/reference/func.md

@@ -0,0 +1,5 @@
+::: langgraph.func
+    options:
+      members:
+        - task
+        - entrypoint

docs/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md

@@ -6,7 +6,7 @@ support it.
 One way this can occur is if you are using a [fanout](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/)
 or other parallel execution in your graph and you have defined a graph like this:
 
-```python
+```python hl_lines="2"
 class State(TypedDict):
     some_key: str
 
@@ -31,7 +31,7 @@ there is uncertainty around how to update the internal state.
 
 To get around this, you can define a reducer that combines multiple values:
 
-```python
+```python hl_lines="5-6"
 import operator
 from typing import Annotated
 

docs/docs/tutorials/index.md

@@ -9,6 +9,7 @@ New to LangGraph or LLM app development? Read this material to get up and runnin
 ## Get Started 🚀 {#quick-start}
 
 - [LangGraph Quickstart](introduction.ipynb): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
+- [LangGraph Cheatsheet For Common Workflows](workflows.ipynb): Overview of the most common workflows and agent architectures in LangGraph.
 - [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
 - [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
 - [Deploy with LangGraph Cloud Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.

docs/docs/tutorials/langgraph-platform/local-server.md

@@ -250,4 +250,4 @@ Access detailed documentation for development and API usage:
 
 - **[LangGraph Server API Reference](../../cloud/reference/api/api_ref.html)**: Explore the LangGraph Server API documentation.  
 - **[Python SDK Reference](../../cloud/reference/sdk/python_sdk_ref.md)**: Explore the Python SDK API Reference.
-- **[JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md)**: Explore the Python SDK API Reference.
+- **[JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md)**: Explore the JS/TS SDK API Reference.

docs/docs/tutorials/multi_agent/agent_supervisor.ipynb

@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "f04c6778-403b-4b49-9b93-678e910d5cec",
    "metadata": {},
    "outputs": [],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "df2bd80b-c477-4d74-8faa-1c0548622239",
    "metadata": {},
    "outputs": [],
@@ -162,7 +162,11 @@
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
     "\n",
     "\n",
-    "def supervisor_node(state: MessagesState) -> Command[Literal[*members, \"__end__\"]]:\n",
+    "class State(MessagesState):\n",
+    "    next: str\n",
+    "\n",
+    "\n",
+    "def supervisor_node(state: State) -> Command[Literal[*members, \"__end__\"]]:\n",
     "    messages = [\n",
     "        {\"role\": \"system\", \"content\": system_prompt},\n",
     "    ] + state[\"messages\"]\n",
@@ -171,7 +175,7 @@
     "    if goto == \"FINISH\":\n",
     "        goto = END\n",
     "\n",
-    "    return Command(goto=goto)"
+    "    return Command(goto=goto, update={\"next\": goto})"
    ]
   },
   {
@@ -201,7 +205,7 @@
     ")\n",
     "\n",
     "\n",
-    "def research_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def research_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = research_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -217,7 +221,7 @@
     "code_agent = create_react_agent(llm, tools=[python_repl_tool])\n",
     "\n",
     "\n",
-    "def code_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def code_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = code_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -229,7 +233,7 @@
     "    )\n",
     "\n",
     "\n",
-    "builder = StateGraph(MessagesState)\n",
+    "builder = StateGraph(State)\n",
     "builder.add_edge(START, \"supervisor\")\n",
     "builder.add_node(\"supervisor\", supervisor_node)\n",
     "builder.add_node(\"researcher\", research_node)\n",

docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb

@@ -293,6 +293,10 @@
     "from langchain_core.messages import HumanMessage, trim_messages\n",
     "\n",
     "\n",
+    "class State(MessagesState):\n",
+    "    next: str\n",
+    "\n",
+    "\n",
     "def make_supervisor_node(llm: BaseChatModel, members: list[str]) -> str:\n",
     "    options = [\"FINISH\"] + members\n",
     "    system_prompt = (\n",
@@ -308,7 +312,7 @@
     "\n",
     "        next: Literal[*options]\n",
     "\n",
-    "    def supervisor_node(state: MessagesState) -> Command[Literal[*members, \"__end__\"]]:\n",
+    "    def supervisor_node(state: State) -> Command[Literal[*members, \"__end__\"]]:\n",
     "        \"\"\"An LLM-based router.\"\"\"\n",
     "        messages = [\n",
     "            {\"role\": \"system\", \"content\": system_prompt},\n",
@@ -318,7 +322,7 @@
     "        if goto == \"FINISH\":\n",
     "            goto = END\n",
     "\n",
-    "        return Command(goto=goto)\n",
+    "        return Command(goto=goto, update={\"next\": goto})\n",
     "\n",
     "    return supervisor_node"
    ]
@@ -358,7 +362,7 @@
     "search_agent = create_react_agent(llm, tools=[tavily_tool])\n",
     "\n",
     "\n",
-    "def search_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def search_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = search_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -374,7 +378,7 @@
     "web_scraper_agent = create_react_agent(llm, tools=[scrape_webpages])\n",
     "\n",
     "\n",
-    "def web_scraper_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def web_scraper_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = web_scraper_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -410,7 +414,7 @@
    },
    "outputs": [],
    "source": [
-    "research_builder = StateGraph(MessagesState)\n",
+    "research_builder = StateGraph(State)\n",
     "research_builder.add_node(\"supervisor\", research_supervisor_node)\n",
     "research_builder.add_node(\"search\", search_node)\n",
     "research_builder.add_node(\"web_scraper\", web_scraper_node)\n",
@@ -528,7 +532,7 @@
     ")\n",
     "\n",
     "\n",
-    "def doc_writing_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def doc_writing_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = doc_writer_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -551,7 +555,7 @@
     ")\n",
     "\n",
     "\n",
-    "def note_taking_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def note_taking_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = note_taking_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -569,7 +573,7 @@
     ")\n",
     "\n",
     "\n",
-    "def chart_generating_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def chart_generating_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = chart_generating_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -610,7 +614,7 @@
    "outputs": [],
    "source": [
     "# Create the graph here\n",
-    "paper_writing_builder = StateGraph(MessagesState)\n",
+    "paper_writing_builder = StateGraph(State)\n",
     "paper_writing_builder.add_node(\"supervisor\", doc_writing_supervisor_node)\n",
     "paper_writing_builder.add_node(\"doc_writer\", doc_writing_node)\n",
     "paper_writing_builder.add_node(\"note_taker\", note_taking_node)\n",
@@ -730,7 +734,7 @@
    },
    "outputs": [],
    "source": [
-    "def call_research_team(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def call_research_team(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    response = research_graph.invoke({\"messages\": state[\"messages\"][-1]})\n",
     "    return Command(\n",
     "        update={\n",
@@ -744,7 +748,7 @@
     "    )\n",
     "\n",
     "\n",
-    "def call_paper_writing_team(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def call_paper_writing_team(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    response = paper_writing_graph.invoke({\"messages\": state[\"messages\"][-1]})\n",
     "    return Command(\n",
     "        update={\n",
@@ -759,7 +763,7 @@
     "\n",
     "\n",
     "# Define the graph.\n",
-    "super_builder = StateGraph(MessagesState)\n",
+    "super_builder = StateGraph(State)\n",
     "super_builder.add_node(\"supervisor\", teams_supervisor_node)\n",
     "super_builder.add_node(\"research_team\", call_research_team)\n",
     "super_builder.add_node(\"writing_team\", call_paper_writing_team)\n",

docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb

@@ -130,36 +130,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "72d233ca-1dbf-4b43-b680-b3bf39e3691f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m System Message \u001b[0m================================\n",
-      "\n",
-      "You are a helpful assistant.\n",
-      "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain import hub\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "# Get the prompt to use - you can modify this!\n",
-    "prompt = hub.pull(\"ih/ih-react-agent-executor\")\n",
-    "prompt.pretty_print()\n",
-    "\n",
     "# Choose the LLM that will drive the agent\n",
     "llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
+    "prompt = \"You are a helpful assistant.\"\n",
     "agent_executor = create_react_agent(llm, tools, state_modifier=prompt)"
    ]
   },
@@ -546,7 +529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/mkdocs.yml

@@ -20,6 +20,7 @@ theme:
     - content.action.edit
     - content.tooltips
     - header.autohide
+    - navigation.indexes
     - navigation.expand
     - navigation.footer
     - navigation.instant
@@ -182,6 +183,7 @@ nav:
               - how-tos/create-react-agent-memory.ipynb
               - how-tos/create-react-agent-system-prompt.ipynb
               - how-tos/create-react-agent-hitl.ipynb
+              - how-tos/create-react-agent-structured-output.ipynb
               - how-tos/react-agent-from-scratch.ipynb
         - LangGraph Platform:
             - LangGraph Platform: how-tos#langgraph-platform
@@ -294,6 +296,7 @@ nav:
         - Quick Start:
           - Quick Start: tutorials#quick-start
           - tutorials/introduction.ipynb
+          - tutorials/workflows.ipynb
           - tutorials/langgraph-platform/local-server.md
           - cloud/quick_start.md
         - Chatbots:
@@ -368,6 +371,7 @@ nav:
       - Errors: reference/errors.md
       - Types: reference/types.md
       - Constants: reference/constants.md
+      - Functional API: reference/func.md
     - LangGraph Platform:
       - Server API: "cloud/reference/api/api_ref.md"
       - CLI: "cloud/reference/cli.md"

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

@@ -39,14 +39,14 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
     """Asynchronous Postgres-backed store with optional vector search using pgvector.
 
     !!! example "Examples"
-        Basic setup and key-value storage:
+        Basic setup and usage:
         ```python
         from langgraph.store.postgres import AsyncPostgresStore
 
-        async with AsyncPostgresStore.from_conn_string(
-            "postgresql://user:pass@localhost:5432/dbname"
-        ) as store:
-            await store.setup()
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        async with AsyncPostgresStore.from_conn_string(conn_string) as store:
+            await store.setup()  # Run migrations. Done once
 
             # Store and retrieve data
             await store.aput(("users", "123"), "prefs", {"theme": "dark"})
@@ -58,38 +58,41 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
         from langchain.embeddings import init_embeddings
         from langgraph.store.postgres import AsyncPostgresStore
 
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
         async with AsyncPostgresStore.from_conn_string(
-            "postgresql://user:pass@localhost:5432/dbname",
+            conn_string,
             index={
                 "dims": 1536,
                 "embed": init_embeddings("openai:text-embedding-3-small"),
                 "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
             }
         ) as store:
-            await store.setup()  # Do this once to run migrations
+            await store.setup()  # Run migrations. Done once
 
             # Store documents
             await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
             await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
-            # Don't index the following
-            await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)
+            await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index
 
             # Search by similarity
-            results = await store.asearch(("docs",), query="python programming")
+            results = await store.asearch(("docs",), "programming guides", limit=2)
         ```
 
         Using connection pooling for better performance:
         ```python
         from langgraph.store.postgres import AsyncPostgresStore, PoolConfig
 
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
         async with AsyncPostgresStore.from_conn_string(
-            "postgresql://user:pass@localhost:5432/dbname",
+            conn_string,
             pool_config=PoolConfig(
                 min_size=5,
                 max_size=20
             )
         ) as store:
-            await store.setup()
+            await store.setup()  # Run migrations. Done once
             # Use store with connection pooling...
         ```
 
@@ -102,7 +105,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
     Note:
         Semantic search is disabled by default. You can enable it by providing an `index` configuration
         when creating the store. Without this configuration, all `index` arguments passed to
-        `put` or `aput`will have no effect.
+        `put` or `aput` will have no effect.
     """
 
     __slots__ = (

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

@@ -536,18 +536,35 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
     """Postgres-backed store with optional vector search using pgvector.
 
     !!! example "Examples"
-        Basic setup and key-value storage:
+        Basic setup and usage:
+        ```python
+        from langgraph.store.postgres import PostgresStore
+        from psycopg import Connection
+
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        # Using direct connection
+        with Connection.connect(conn_string) as conn:
+            store = PostgresStore(conn)
+            store.setup() # Run migrations. Done once
+
+            # Store and retrieve data
+            store.put(("users", "123"), "prefs", {"theme": "dark"})
+            item = store.get(("users", "123"), "prefs")
+        ```
+
+        Or using the convenient from_conn_string helper:
         ```python
         from langgraph.store.postgres import PostgresStore
 
-        store = PostgresStore(
-            connection_string="postgresql://user:pass@localhost:5432/dbname"
-        )
-        store.setup()
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
 
-        # Store and retrieve data
-        store.put(("users", "123"), "prefs", {"theme": "dark"})
-        item = store.get(("users", "123"), "prefs")
+        with PostgresStore.from_conn_string(conn_string) as store:
+            store.setup()
+
+            # Store and retrieve data
+            store.put(("users", "123"), "prefs", {"theme": "dark"})
+            item = store.get(("users", "123"), "prefs")
         ```
 
         Vector search using LangChain embeddings:
@@ -555,23 +572,25 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
         from langchain.embeddings import init_embeddings
         from langgraph.store.postgres import PostgresStore
 
-        store = PostgresStore(
-            connection_string="postgresql://user:pass@localhost:5432/dbname",
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        with PostgresStore.from_conn_string(
+            conn_string,
             index={
                 "dims": 1536,
                 "embed": init_embeddings("openai:text-embedding-3-small"),
                 "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
             }
-        )
-        store.setup() # Do this once to run migrations
+        ) as store:
+            store.setup() # Do this once to run migrations
 
-        # Store documents
-        store.put(("docs",), "doc1", {"text": "Python tutorial"})
-        store.put(("docs",), "doc2", {"text": "TypeScript guide"})
-        store.put(("docs",), "doc2", {"text": "Other guide"}, index=False) # don't index
+            # Store documents
+            store.put(("docs",), "doc1", {"text": "Python tutorial"})
+            store.put(("docs",), "doc2", {"text": "TypeScript guide"})
+            store.put(("docs",), "doc2", {"text": "Other guide"}, index=False) # don't index
 
-        # Search by similarity
-        results = store.search(("docs",), query="python programming")
+            # Search by similarity
+            results = store.search(("docs",), "programming guides", limit=2)
         ```
 
     Note:

libs/cli/langgraph_cli/cli.py

@@ -605,6 +605,11 @@ def dev(
         ) from None
 
     config_json = langgraph_cli.config.validate_config_file(pathlib.Path(config))
+    if config_json.get("node_version"):
+        raise click.UsageError(
+            "In-mem server for JS graphs is not supported in this version of the LangGraph CLI. Please use `npx @langchain/langgraph-cli` instead."
+        ) from None
+
     cwd = os.getcwd()
     sys.path.append(cwd)
     dependencies = config_json.get("dependencies", [])

libs/cli/langgraph_cli/config.py

@@ -330,7 +330,7 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
                 rfile = resolved / "requirements.txt"
                 pip_reqs.append(
                     (
-                        rfile.relative_to(config_path.parent),
+                        rfile.relative_to(config_path.parent).as_posix(),
                         f"{container_path}/requirements.txt",
                     )
                 )
@@ -469,10 +469,11 @@ def node_config_to_docker(config_path: pathlib.Path, config: Config, base_image:
         except OSError:
             return False
 
-    npm, yarn, pnpm = [
+    npm, yarn, pnpm, bun = [
         test_file("package-lock.json"),
         test_file("yarn.lock"),
         test_file("pnpm-lock.yaml"),
+        test_file("bun.lockb"),
     ]
 
     if yarn:
@@ -481,6 +482,8 @@ def node_config_to_docker(config_path: pathlib.Path, config: Config, base_image:
         install_cmd = "pnpm i --frozen-lockfile"
     elif npm:
         install_cmd = "npm ci"
+    elif bun:
+        install_cmd = "bun i"
     else:
         install_cmd = "npm i"
     store_config = config.get("store")

libs/cli/langgraph_cli/docker.py

@@ -170,13 +170,14 @@ def compose_as_dict(
     # Add Postgres service before langgraph-api if it is needed
     if include_db:
         services["langgraph-postgres"] = {
-            "image": "postgres:16",
+            "image": "pgvector/pgvector:pg16",
             "ports": ['"5433:5432"'],
             "environment": {
                 "POSTGRES_DB": "postgres",
                 "POSTGRES_USER": "postgres",
                 "POSTGRES_PASSWORD": "postgres",
             },
+            "command": ["postgres", "-c", "shared_preload_libraries=vector"],
             "volumes": ["langgraph-data:/var/lib/postgresql/data"],
             "healthcheck": {
                 "test": "pg_isready -U postgres",

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.67"
+version = "0.1.69"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

libs/cli/tests/unit_tests/cli/test_cli.py

@@ -79,13 +79,17 @@ services:
             timeout: 1s
             retries: 5
     langgraph-postgres:
-        image: postgres:16
+        image: pgvector/pgvector:pg16
         ports:
             - "5433:5432"
         environment:
             POSTGRES_DB: postgres
             POSTGRES_USER: postgres
             POSTGRES_PASSWORD: postgres
+        command:
+            - postgres
+            - -c
+            - shared_preload_libraries=vector
         volumes:
             - langgraph-data:/var/lib/postgresql/data
         healthcheck:

libs/cli/tests/unit_tests/test_docker.py

@@ -115,13 +115,17 @@ services:
             timeout: 1s
             retries: 5
     langgraph-postgres:
-        image: postgres:16
+        image: pgvector/pgvector:pg16
         ports:
             - "5433:5432"
         environment:
             POSTGRES_DB: postgres
             POSTGRES_USER: postgres
             POSTGRES_PASSWORD: postgres
+        command:
+            - postgres
+            - -c
+            - shared_preload_libraries=vector
         volumes:
             - langgraph-data:/var/lib/postgresql/data
         healthcheck:

libs/langgraph/README.md

@@ -12,25 +12,48 @@
 
 ## Overview
 
-[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
-[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+### Why use LangGraph?
 
-To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+LangGraph provides fine-grained control over both the flow and state of your
+agent applications. It implements a central
+[persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/),
+enabling features that are common to most agent architectures:
 
-### Key Features
+- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
 
-- **Cycles and Branching**: Implement loops and conditionals in your apps.
-- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
-- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
-- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### LangGraph Platform
 
-LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
 Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
 
 - **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
@@ -47,9 +70,7 @@ pip install -U langgraph
 
 ## Example
 
-One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
-
-Let's take a look at a simple example of an agent that can use a search tool.
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
 pip install langchain-anthropic
@@ -66,10 +87,72 @@ export LANGSMITH_TRACING=true
 export LANGSMITH_API_KEY=lsv2_sk_...
 ```
 
-```python
-from typing import Annotated, Literal, TypedDict
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
+
+<details open>
+  <summary>High-level implementation</summary>
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+app = create_react_agent(model, tools, checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
+> [!TIP]
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
+
+<details>
+<summary>Low-level implementation</summary>
+
+```python
+from typing import Literal
 
-from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +174,7 @@ tools = [search]
 
 tool_node = ToolNode(tools)
 
-model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,92 +228,102 @@ checkpointer = MemorySaver()
 # Note that we're (optionally) passing the memory when compiling the graph
 app = workflow.compile(checkpointer=checkpointer)
 
-# Use the Runnable
+# Use the agent
 final_state = app.invoke(
-    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
     config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 
-```
-"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
-```
+<b>Step-by-step Breakdown</b>:
 
-Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/modules/agents/tools/custom_tools">here</a>.
+  </li>
+</ul>
+</details>
 
-```python
-final_state = app.invoke(
-    {"messages": [HumanMessage(content="what about ny")]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+<details>
+<summary>Initialize graph with state.</summary>
 
-```
-"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
-```
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
 
-### Step-by-step Breakdown
+<details>
+<summary>Define graph nodes.</summary>
 
-1. <details>
-    <summary>Initialize the model and tools.</summary>
+There are two main nodes we need:
 
-    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
-    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-   </details>
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
 
-2. <details>
-    <summary>Initialize graph with state.</summary>
+<details>
+<summary>Define entry point and graph edges.</summary>
 
-    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
-   </details>
+First, we need to set the entry point for graph execution - <code>agent</code> node.
 
-3. <details>
-    <summary>Define graph nodes.</summary>
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
 
-    There are two main nodes we need:
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
 
-      - The `agent` node: responsible for deciding what (if any) actions to take.
-      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
-   </details>
+<details>
+<summary>Compile the graph.</summary>
 
-4. <details>
-    <summary>Define entry point and graph edges.</summary>
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/v0.2/docs/concepts/#runnable-interface">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
 
-      First, we need to set the entry point for graph execution - `agent` node.
+<details>
+<summary>Execute the graph.</summary>
 
-      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
-
-      - Conditional edge: after the agent is called, we should either:
-        - a. Run tools if the agent said to take an action, OR
-        - b. Finish (respond to the user) if the agent did not ask to run tools
-      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
-   </details>
-
-5. <details>
-    <summary>Compile the graph.</summary>
-
-    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
-    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
-    </details>
-
-6. <details>
-   <summary>Execute the graph.</summary>
-
-    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
-    2. The `"agent"` node executes, invoking the chat model.
-    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
-    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
-
-        - If `AIMessage` has `tool_calls`, `"tools"` node executes
-        - The `"agent"` node executes again and returns `AIMessage`
-
-    5. Execution progresses to the special `END` value and outputs the final state.
-    And as a result, we get a list of all our chat messages as output.
-   </details>
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
 
+</details>
 
 ## Documentation
 

libs/langgraph/langgraph/constants.py

@@ -23,6 +23,7 @@ END = sys.intern("__end__")
 """The last (maybe virtual) node in graph-style Pregel."""
 SELF = sys.intern("__self__")
 """The implicit branch that handles each node's Control values."""
+PREVIOUS = sys.intern("__previous__")
 
 # --- Reserved write keys ---
 INPUT = sys.intern("__input__")
@@ -78,6 +79,8 @@ CONFIG_KEY_NODE_FINISHED = sys.intern("__pregel_node_finished")
 # holds a callback to be called when a node is finished
 CONFIG_KEY_SCRATCHPAD = sys.intern("__pregel_scratchpad")
 # holds a mutable dict for temporary storage scoped to the current task
+CONFIG_KEY_PREVIOUS = sys.intern("__pregel_previous")
+# holds the previous return value from a stateful Pregel graph.
 
 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")

libs/langgraph/langgraph/func/__init__.py

@@ -1,104 +1,136 @@
 import asyncio
-import concurrent
 import concurrent.futures
 import functools
 import inspect
-import types
+from dataclasses import dataclass
 from typing import (
     Any,
     Awaitable,
     Callable,
+    Generic,
     Optional,
     TypeVar,
     Union,
+    get_args,
+    get_origin,
     overload,
 )
 
-from typing_extensions import ParamSpec
-
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.checkpoint.base import BaseCheckpointSaver
-from langgraph.constants import CONF, END, START, TAG_HIDDEN
+from langgraph.constants import END, PREVIOUS, START, TAG_HIDDEN
 from langgraph.pregel import Pregel
-from langgraph.pregel.call import get_runnable_for_func
+from langgraph.pregel.call import (
+    P,
+    SyncAsyncFuture,
+    T,
+    call,
+    get_runnable_for_entrypoint,
+)
 from langgraph.pregel.read import PregelNode
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
 from langgraph.store.base import BaseStore
-from langgraph.types import RetryPolicy, StreamMode, StreamWriter
-
-P = ParamSpec("P")
-P1 = TypeVar("P1")
-T = TypeVar("T")
-
-
-def call(
-    func: Callable[P, T],
-    *args: Any,
-    retry: Optional[RetryPolicy] = None,
-    **kwargs: Any,
-) -> concurrent.futures.Future[T]:
-    from langgraph.constants import CONFIG_KEY_CALL
-    from langgraph.utils.config import get_config
-
-    config = get_config()
-    impl = config[CONF][CONFIG_KEY_CALL]
-    fut = impl(func, (args, kwargs), retry=retry, callbacks=config["callbacks"])
-    return fut
+from langgraph.types import _DC_KWARGS, RetryPolicy, StreamMode, StreamWriter
 
 
 @overload
 def task(
-    *, retry: Optional[RetryPolicy] = None
-) -> Callable[[Callable[P, Awaitable[T]]], Callable[P, asyncio.Future[T]]]: ...
-
-
-@overload
-def task(  # type: ignore[overload-cannot-match]
-    *, retry: Optional[RetryPolicy] = None
-) -> Callable[[Callable[P, T]], Callable[P, concurrent.futures.Future[T]]]: ...
+    *, name: Optional[str] = None, retry: Optional[RetryPolicy] = None
+) -> Callable[[Callable[P, T]], Callable[P, SyncAsyncFuture[T]]]: ...
 
 
 @overload
 def task(
     __func_or_none__: Callable[P, T],
-) -> Callable[P, concurrent.futures.Future[T]]: ...
-
-
-@overload
-def task(
-    __func_or_none__: Callable[P, Awaitable[T]],
-) -> Callable[P, asyncio.Future[T]]: ...
+) -> Callable[P, SyncAsyncFuture[T]]: ...
 
 
 def task(
     __func_or_none__: Optional[Union[Callable[P, T], Callable[P, Awaitable[T]]]] = None,
     *,
+    name: Optional[str] = None,
     retry: Optional[RetryPolicy] = None,
 ) -> Union[
-    Callable[[Callable[P, Awaitable[T]]], Callable[P, asyncio.Future[T]]],
-    Callable[[Callable[P, T]], Callable[P, concurrent.futures.Future[T]]],
-    Callable[P, asyncio.Future[T]],
-    Callable[P, concurrent.futures.Future[T]],
+    Callable[[Callable[P, T]], Callable[P, SyncAsyncFuture[T]]],
+    Callable[P, SyncAsyncFuture[T]],
 ]:
+    """Define a LangGraph task using the `task` decorator.
+
+    !!! warning "Beta"
+        The Functional API is currently in beta and is subject to change.
+
+    !!! important "Requires python 3.11 or higher for async functions"
+        The `task` decorator supports both sync and async functions. To use async
+        functions, ensure that you are using Python 3.11 or higher.
+
+    Tasks can only be called from within an [entrypoint][langgraph.func.entrypoint] or
+    from within a StateGraph. A task can be called like a regular function with the
+    following differences:
+
+    - When a checkpointer is enabled, the function inputs and outputs must be serializable.
+    - The decorated function can only be called from within an entrypoint or StateGraph.
+    - Calling the function produces a future. This makes it easy to parallelize tasks.
+
+    Args:
+        retry: An optional retry policy to use for the task in case of a failure.
+
+    Returns:
+        A callable function when used as a decorator.
+
+    Example: Sync Task
+        ```python
+        from langgraph.func import entrypoint, task
+
+        @task
+        def add_one(a: int) -> int:
+            return a + 1
+
+        @entrypoint()
+        def add_one(numbers: list[int]) -> list[int]:
+            futures = [add_one(n) for n in numbers]
+            results = [f.result() for f in futures]
+            return results
+
+        # Call the entrypoint
+        add_one.invoke([1, 2, 3])  # Returns [2, 3, 4]
+        ```
+
+    Example: Async Task
+        ```python
+        import asyncio
+        from langgraph.func import entrypoint, task
+
+        @task
+        async def add_one(a: int) -> int:
+            return a + 1
+
+        @entrypoint()
+        async def add_one(numbers: list[int]) -> list[int]:
+            futures = [add_one(n) for n in numbers]
+            return asyncio.gather(*futures)
+
+        # Call the entrypoint
+        await add_one.ainvoke([1, 2, 3])  # Returns [2, 3, 4]
+        ```
+    """
+
     def decorator(
         func: Union[Callable[P, Awaitable[T]], Callable[P, T]],
-    ) -> Callable[P, concurrent.futures.Future[T]]:
-        if asyncio.iscoroutinefunction(func):
+    ) -> Union[
+        Callable[P, concurrent.futures.Future[T]], Callable[P, asyncio.Future[T]]
+    ]:
+        if name is not None:
+            if hasattr(func, "__func__"):
+                # handle class methods
+                func.__func__.__name__ = name
+            else:
+                # handle regular functions / partials / callable classes, etc.
+                func.__name__ = name
 
-            @functools.wraps(func)
-            async def _tick(__allargs__: tuple) -> T:
-                return await func(*__allargs__[0], **__allargs__[1])
-
-        else:
-
-            @functools.wraps(func)
-            def _tick(__allargs__: tuple) -> T:
-                return func(*__allargs__[0], **__allargs__[1])
-
-        return functools.update_wrapper(
-            functools.partial(call, _tick, retry=retry), func
-        )
+        call_func = functools.partial(call, func, retry=retry)
+        object.__setattr__(call_func, "_is_pregel_task", True)
+        return functools.update_wrapper(call_func, func)
 
     if __func_or_none__ is not None:
         return decorator(__func_or_none__)
@@ -106,51 +138,497 @@ def task(
     return decorator
 
 
-def entrypoint(
-    *,
-    checkpointer: Optional[BaseCheckpointSaver] = None,
-    store: Optional[BaseStore] = None,
-) -> Callable[[types.FunctionType], Pregel]:
-    def _imp(func: types.FunctionType) -> Pregel:
+R = TypeVar("R")
+S = TypeVar("S")
+
+
+# The decorator was wrapped in a class to support the `final` attribute.
+# In this form, the `final` attribute should play nicely with IDE autocompletion,
+# and type checking tools.
+# In addition, we'll be able to surface this information in the API Reference.
+class entrypoint:
+    """Define a LangGraph workflow using the `entrypoint` decorator.
+
+    !!! warning "Beta"
+        The Functional API is currently in beta and is subject to change.
+
+
+    ### Function signature
+
+    The decorated function must accept a **single parameter**, which serves as the input
+    to the function. This input parameter can be of any type. Use a dictionary
+    to pass **multiple parameters** to the function.
+
+    ### Injectable parameters
+
+    The decorated function can request access to additional parameters
+    that will be injected automatically at run time. These parameters include:
+
+    | Parameter        | Description                                                                                        |
+    |------------------|----------------------------------------------------------------------------------------------------|
+    | **`store`**      | An instance of [BaseStore][langgraph.store.base.BaseStore]. Useful for long-term memory.           |
+    | **`writer`**     | A [StreamWriter][langgraph.types.StreamWriter] instance for writing custom data to a stream.       |
+    | **`config`**     | A configuration object (aka RunnableConfig) that holds run-time configuration values.              |
+    | **`previous`**   | The previous return value for the given thread (available only when a checkpointer is provided).   |
+
+    The entrypoint decorator can be applied to sync functions, async functions,
+    generator functions, and async generator functions.
+
+    ### State management
+
+    The **`previous`** parameter can be used to access the return value of the previous
+    invocation of the entrypoint on the same thread id. This value is only available
+    when a checkpointer is provided.
+
+    If you want **`previous`** to be different from the return value, you can use the
+    `entrypoint.final` object to return a value while saving a different value to the
+    checkpoint.
+
+    ### Generator functions
+
+    In generator functions, `yield` is used as a shorthand for writing
+    to the `custom` channel using the `writer` parameter (i.e., `writer(chunk)`).
+
+    The value of `previous` will be the list of the values yielded during the previous
+    run for the given thread id, unless an `entrypoint.final` was yielded.
+
+    If an `entrypoint.final` object is yielded, the value of `previous` will be the
+    value the `save` attribute of the `entrypoint.final` object.
+
+    When executing an entrypoint created from a generator function, expect the following
+    behavior:
+    - stream_mode is set to 'custom' by default, and streaming will not stream the
+      return value
+    - add a `values` or `updates` stream_mode to stream the return value (if needed)
+    - using `invoke` will return the return value of the entrypoint
+
+    Args:
+        checkpointer: Specify a checkpointer to create a workflow that can persist
+            its state across runs.
+        store: A generalized key-value store. Some implementations may support
+            semantic search capabilities through an optional `index` configuration.
+        config_schema: Specifies the schema for the configuration object that will be
+            passed to the workflow.
+
+    Example: Using entrypoint and tasks
+        ```python
+        import time
+
+        from langgraph.func import entrypoint, task
+        from langgraph.types import interrupt, Command
+        from langgraph.checkpoint.memory import MemorySaver
+
+        @task
+        def compose_essay(topic: str) -> str:
+            time.sleep(1.0)  # Simulate slow operation
+            return f"An essay about {topic}"
+
+        @entrypoint(checkpointer=MemorySaver())
+        def review_workflow(topic: str) -> dict:
+            \"\"\"Manages the workflow for generating and reviewing an essay.
+
+            The workflow includes:
+            1. Generating an essay about the given topic.
+            2. Interrupting the workflow for human review of the generated essay.
+
+            Upon resuming the workflow, compose_essay task will not be re-executed
+            as its result is cached by the checkpointer.
+
+            Args:
+                topic (str): The subject of the essay.
+
+            Returns:
+                dict: A dictionary containing the generated essay and the human review.
+            \"\"\"
+            essay_future = compose_essay(topic)
+            essay = essay_future.result()
+            human_review = interrupt({
+                \"question\": \"Please provide a review\",
+                \"essay\": essay
+            })
+            return {
+                \"essay\": essay,
+                \"review\": human_review,
+            }
+
+        # Example configuration for the workflow
+        config = {
+            \"configurable\": {
+                \"thread_id\": \"some_thread\"
+            }
+        }
+
+        # Topic for the essay
+        topic = \"cats\"
+
+        # Stream the workflow to generate the essay and await human review
+        for result in review_workflow.stream(topic, config):
+            print(result)
+
+        # Example human review provided after the interrupt
+        human_review = \"This essay is great.\"
+
+        # Resume the workflow with the provided human review
+        for result in review_workflow.stream(Command(resume=human_review), config):
+            print(result)
+        ```
+
+    Example: Accessing the previous return value
+        When a checkpointer is enabled the function can access the previous return value
+        of the previous invocation on the same thread id.
+
+        ```python
+        from langgraph.checkpoint.memory import MemorySaver
+        from langgraph.func import entrypoint
+
+        @entrypoint(checkpointer=MemorySaver())
+        def my_workflow(input_data: str, previous: Optional[str] = None) -> str:
+            return "world"
+
+        config = {
+            "configurable": {
+                "thread_id": "some_thread"
+            }
+        }
+        my_workflow.invoke("hello")
+        ```
+
+    Example: Using entrypoint.final to save a value
+        The `entrypoint.final` object allows you to return a value while saving
+        a different value to the checkpoint. This value will be accessible
+        in the next invocation of the entrypoint via the `previous` parameter, as
+        long as the same thread id is used.
+
+        ```python
+        from langgraph.checkpoint.memory import MemorySaver
+        from langgraph.func import entrypoint
+
+        @entrypoint(checkpointer=MemorySaver())
+        def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]:
+            previous = previous or 0
+            # This will return the previous value to the caller, saving
+            # 2 * number to the checkpoint, which will be used in the next invocation
+            # for the `previous` parameter.
+            return entrypoint.final(value=previous, save=2 * number)
+
+        config = {
+            "configurable": {
+                "thread_id": "some_thread"
+            }
+        }
+
+        my_workflow.invoke(3, config)  # 0 (previous was None)
+        my_workflow.invoke(1, config)  # 6 (previous was 3 * 2 from the previous invocation)
+        ```
+
+    Example: Using a generator entrypoint
+        You can decorate a generator function with the `entrypoint` decorator.
+
+        ```python
+        from langgraph.checkpoint.memory import MemorySaver
+        from langgraph.func import entrypoint
+
+        @entrypoint(checkpointer=MemorySaver())
+        def workflow(inputs: dict):
+            yield "hello"
+            yield "world"
+
+        config = {
+            "configurable": {
+                "thread_id": "1"
+            }
+        }
+
+        for result in workflow.stream({}, config):
+            print(result)
+        ```
+
+        This will print:
+        ```pycon
+        hello
+        world
+        ```
+
+    """
+
+    def __init__(
+        self,
+        checkpointer: Optional[BaseCheckpointSaver] = None,
+        store: Optional[BaseStore] = None,
+        config_schema: Optional[type[Any]] = None,
+    ) -> None:
+        """Initialize the entrypoint decorator."""
+        self.checkpointer = checkpointer
+        self.store = store
+        self.config_schema = config_schema
+
+    @dataclass(**_DC_KWARGS)
+    class final(Generic[R, S]):
+        """A primitive that can be returned from an entrypoint.
+
+        This primitive allows to save a value to the checkpointer distinct from the
+        return value from the entrypoint.
+
+        Example: Decoupling the return value and the save value
+            ```python
+            from langgraph.checkpoint.memory import MemorySaver
+            from langgraph.func import entrypoint
+
+            @entrypoint(checkpointer=MemorySaver())
+            def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]:
+                previous = previous or 0
+                # This will return the previous value to the caller, saving
+                # 2 * number to the checkpoint, which will be used in the next invocation
+                # for the `previous` parameter.
+                return entrypoint.final(value=previous, save=2 * number)
+
+            config = {
+                "configurable": {
+                    "thread_id": "1"
+                }
+            }
+
+            my_workflow.invoke(3, config)  # 0 (previous was None)
+            my_workflow.invoke(1, config)  # 6 (previous was 3 * 2 from the previous invocation)
+            ```
+        """
+
+        value: R
+        """Value to return. A value will always be returned even if it is None."""
+        save: S
+        """The value for the state for the next checkpoint. 
+
+        A value will always be saved even if it is None.
+        """
+
+    def __call__(self, func: Callable[..., Any]) -> Pregel:
+        """Convert a function into a Pregel graph.
+
+        Args:
+            func: The function to convert. Support both sync and async functions, as well
+                   as generator and async generator functions.
+
+        Returns:
+            A Pregel graph.
+        """
+        # wrap generators in a function that writes to StreamWriter
         if inspect.isgeneratorfunction(func):
+            original_sig = inspect.signature(func)
+            # Check if original signature has a writer argument with a matching type.
+            # If not, we'll inject it into the decorator, but not pass it
+            # to the wrapped function.
+            if "writer" in original_sig.parameters:
 
-            def gen_wrapper(*args: Any, writer: StreamWriter, **kwargs: Any) -> Any:
-                for chunk in func(*args, **kwargs):
-                    writer(chunk)
+                @functools.wraps(func)
+                def gen_wrapper(*args: Any, writer: StreamWriter, **kwargs: Any) -> Any:
+                    final_: Optional[entrypoint.final] = None
+                    chunks = []
+                    for chunk in func(*args, writer=writer, **kwargs):
+                        if isinstance(chunk, entrypoint.final):
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding multiple entrypoint.final "
+                                    "objects is not allowed."
+                                )
+                            else:
+                                final_ = chunk
+                        else:
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding a value after a entrypoint.final "
+                                    "object is not allowed."
+                                )
+                            writer(chunk)
+                            chunks.append(chunk)
 
-            bound = get_runnable_for_func(gen_wrapper)
+                    return final_ if final_ else chunks
+            else:
+
+                @functools.wraps(func)
+                def gen_wrapper(*args: Any, writer: StreamWriter, **kwargs: Any) -> Any:
+                    final_: Optional[entrypoint.final] = None
+                    chunks = []
+                    # Do not pass the writer argument to the wrapped function
+                    # as it does not have a matching parameter
+                    for chunk in func(*args, **kwargs):
+                        if isinstance(chunk, entrypoint.final):
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding multiple entrypoint.final "
+                                    "objects is not allowed."
+                                )
+                            else:
+                                final_ = chunk
+                        else:
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding a value after a entrypoint.final "
+                                    "object is not allowed."
+                                )
+                            writer(chunk)
+                            chunks.append(chunk)
+                    return final_ if final_ else chunks
+
+                # Create a new parameter for the writer argument
+                extra_param = inspect.Parameter(
+                    "writer",
+                    inspect.Parameter.KEYWORD_ONLY,
+                    # The extra argument is a keyword-only argument
+                    default=lambda _: None,
+                )
+                # Update the function's signature to include the extra argument
+                new_params = list(original_sig.parameters.values()) + [extra_param]
+                new_sig = original_sig.replace(parameters=new_params)
+                # Update the signature of the wrapper function
+                gen_wrapper.__signature__ = new_sig  # type: ignore
+
+            bound = get_runnable_for_entrypoint(gen_wrapper)
             stream_mode: StreamMode = "custom"
         elif inspect.isasyncgenfunction(func):
+            original_sig = inspect.signature(func)
+            # Check if original signature has a writer argument with a matching type.
+            # If not, we'll inject it into the decorator, but not pass it
+            # to the wrapped function.
+            if "writer" in original_sig.parameters:
 
-            async def agen_wrapper(
-                *args: Any, writer: StreamWriter, **kwargs: Any
-            ) -> Any:
-                async for chunk in func(*args, **kwargs):
-                    writer(chunk)
+                @functools.wraps(func)
+                async def agen_wrapper(
+                    *args: Any, writer: StreamWriter, **kwargs: Any
+                ) -> Any:
+                    final_: Optional[entrypoint.final] = None
+                    chunks = []
+                    async for chunk in func(*args, writer=writer, **kwargs):
+                        if isinstance(chunk, entrypoint.final):
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding multiple entrypoint.final objects is not allowed."
+                                )
+                            else:
+                                final_ = chunk
+                        else:
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding a value after a entrypoint.final object is not allowed."
+                                )
+                            writer(chunk)
+                            chunks.append(chunk)
 
-            bound = get_runnable_for_func(agen_wrapper)
+                    return final_ if final_ else chunks
+            else:
+
+                @functools.wraps(func)
+                async def agen_wrapper(
+                    *args: Any, writer: StreamWriter, **kwargs: Any
+                ) -> Any:
+                    final_: Optional[entrypoint.final] = None
+                    chunks = []
+                    async for chunk in func(*args, **kwargs):
+                        if isinstance(chunk, entrypoint.final):
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding multiple entrypoint.final objects is not allowed."
+                                )
+                            else:
+                                final_ = chunk
+                        else:
+                            if final_ is not None:
+                                raise RuntimeError(
+                                    "Yielding a value after a entrypoint.final object is not allowed."
+                                )
+                            writer(chunk)
+                            chunks.append(chunk)
+
+                    return final_ if final_ else chunks
+
+                # Create a new parameter for the writer argument
+                extra_param = inspect.Parameter(
+                    "writer",
+                    inspect.Parameter.KEYWORD_ONLY,
+                    # The extra argument is a keyword-only argument
+                    default=lambda _: None,
+                )
+                # Update the function's signature to include the extra argument
+                new_params = list(original_sig.parameters.values()) + [extra_param]
+                new_sig = original_sig.replace(parameters=new_params)
+                # Update the signature of the wrapper function
+                agen_wrapper.__signature__ = new_sig  # type: ignore
+
+            bound = get_runnable_for_entrypoint(agen_wrapper)
             stream_mode = "custom"
         else:
-            bound = get_runnable_for_func(func)
+            bound = get_runnable_for_entrypoint(func)
             stream_mode = "updates"
 
+        # get input and output types
+        sig = inspect.signature(func)
+        first_parameter_name = next(iter(sig.parameters.keys()), None)
+        if not first_parameter_name:
+            raise ValueError("Entrypoint function must have at least one parameter")
+        input_type = (
+            sig.parameters[first_parameter_name].annotation
+            if sig.parameters[first_parameter_name].annotation
+            is not inspect.Signature.empty
+            else Any
+        )
+
+        def _pluck_return_value(value: Any) -> Any:
+            """Extract the return_ value the entrypoint.final object or passthrough."""
+            return value.value if isinstance(value, entrypoint.final) else value
+
+        def _pluck_save_value(value: Any) -> Any:
+            """Get save value from the entrypoint.final object or passthrough."""
+            return value.save if isinstance(value, entrypoint.final) else value
+
+        output_type, save_type = Any, Any
+        if sig.return_annotation is not inspect.Signature.empty:
+            # User does not parameterize entrypoint.final properly
+            if (
+                sig.return_annotation is entrypoint.final
+            ):  # Un-parameterized entrypoint.final
+                output_type = save_type = Any
+            else:
+                origin = get_origin(sig.return_annotation)
+                if origin is entrypoint.final:
+                    type_annotations = get_args(sig.return_annotation)
+                    if len(type_annotations) != 2:
+                        raise TypeError(
+                            "Please an annotation for both the return_ and "
+                            "the save values."
+                            "For example, `-> entrypoint.final[int, str]` would assign a "
+                            "return_ a type of `int` and save the type `str`."
+                        )
+                    output_type, save_type = get_args(sig.return_annotation)
+                else:
+                    output_type = save_type = sig.return_annotation
+
         return Pregel(
             nodes={
                 func.__name__: PregelNode(
                     bound=bound,
                     triggers=[START],
                     channels=[START],
-                    writers=[ChannelWrite([ChannelWriteEntry(END)], tags=[TAG_HIDDEN])],
+                    writers=[
+                        ChannelWrite(
+                            [
+                                ChannelWriteEntry(END, mapper=_pluck_return_value),
+                                ChannelWriteEntry(PREVIOUS, mapper=_pluck_save_value),
+                            ],
+                            tags=[TAG_HIDDEN],
+                        )
+                    ],
                 )
             },
-            channels={START: EphemeralValue(Any), END: LastValue(Any, END)},
+            channels={
+                START: EphemeralValue(input_type),
+                END: LastValue(output_type, END),
+                PREVIOUS: LastValue(save_type, PREVIOUS),
+            },
             input_channels=START,
             output_channels=END,
             stream_channels=END,
             stream_mode=stream_mode,
             stream_eager=True,
-            checkpointer=checkpointer,
-            store=store,
+            checkpointer=self.checkpointer,
+            store=self.store,
+            config_type=self.config_schema,
         )
-
-    return _imp

libs/langgraph/langgraph/graph/state.py

@@ -379,14 +379,27 @@ class StateGraph(Graph):
                     if input_hint := hints.get(first_parameter_name):
                         if isinstance(input_hint, type) and get_type_hints(input_hint):
                             input = input_hint
-                if (
-                    (rtn := hints.get("return"))
-                    and get_origin(rtn) is Command
-                    and (rargs := get_args(rtn))
-                    and get_origin(rargs[0]) is Literal
-                    and (vals := get_args(rargs[0]))
-                ):
-                    ends = vals
+                if rtn := hints.get("return"):
+                    # Handle Union types
+                    rtn_origin = get_origin(rtn)
+                    if rtn_origin is Union:
+                        rtn_args = get_args(rtn)
+                        # Look for Command in the union
+                        for arg in rtn_args:
+                            arg_origin = get_origin(arg)
+                            if arg_origin is Command:
+                                rtn = arg
+                                rtn_origin = arg_origin
+                                break
+
+                    # Check if it's a Command type
+                    if (
+                        rtn_origin is Command
+                        and (rargs := get_args(rtn))
+                        and get_origin(rargs[0]) is Literal
+                        and (vals := get_args(rargs[0]))
+                    ):
+                        ends = vals
         except (TypeError, StopIteration):
             pass
         if input is not None:

libs/langgraph/langgraph/prebuilt/chat_agent_executor.py

@@ -1,3 +1,4 @@
+import inspect
 from typing import (
     Callable,
     Literal,
@@ -91,6 +92,12 @@ def _get_state_modifier_runnable(
             lambda state: [state_modifier] + state["messages"],
             name=STATE_MODIFIER_RUNNABLE_NAME,
         )
+    elif inspect.iscoroutinefunction(state_modifier):
+        state_modifier_runnable = RunnableCallable(
+            None,
+            state_modifier,
+            name=STATE_MODIFIER_RUNNABLE_NAME,
+        )
     elif callable(state_modifier):
         state_modifier_runnable = RunnableCallable(
             state_modifier,
@@ -635,7 +642,7 @@ def create_react_agent(
         if (
             (
                 "remaining_steps" not in state
-                and state["is_last_step"]
+                and state.get("is_last_step", False)
                 and has_tool_calls
             )
             or (
@@ -672,7 +679,7 @@ def create_react_agent(
         if (
             (
                 "remaining_steps" not in state
-                and state["is_last_step"]
+                and state.get("is_last_step", False)
                 and has_tool_calls
             )
             or (

libs/langgraph/langgraph/pregel/__init__.py

@@ -1831,6 +1831,12 @@ class Pregel(PregelProtocol):
                 interrupt_after=interrupt_after,
                 debug=debug,
             )
+            # set up subgraph checkpointing
+            if self.checkpointer is True:
+                ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
+                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = NS_SEP.join(
+                    part.split(NS_END)[0] for part in ns.split(NS_SEP)
+                )
             # set up messages stream mode
             if "messages" in stream_modes:
                 run_manager.inheritable_handlers.append(

libs/langgraph/langgraph/pregel/algo.py

@@ -1,3 +1,5 @@
+import functools
+import itertools
 import sys
 from collections import defaultdict, deque
 from functools import partial
@@ -37,6 +39,7 @@ from langgraph.constants import (
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_NS,
     CONFIG_KEY_CHECKPOINTER,
+    CONFIG_KEY_PREVIOUS,
     CONFIG_KEY_READ,
     CONFIG_KEY_SCRATCHPAD,
     CONFIG_KEY_SEND,
@@ -45,11 +48,11 @@ from langgraph.constants import (
     EMPTY_SEQ,
     ERROR,
     INTERRUPT,
-    MISSING,
     NO_WRITES,
     NS_END,
     NS_SEP,
     NULL_TASK_ID,
+    PREVIOUS,
     PULL,
     PUSH,
     RESERVED,
@@ -61,7 +64,7 @@ from langgraph.constants import (
 )
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
 from langgraph.managed.base import ManagedValueMapping
-from langgraph.pregel.call import get_runnable_for_func
+from langgraph.pregel.call import get_runnable_for_task
 from langgraph.pregel.io import read_channel, read_channels
 from langgraph.pregel.log import logger
 from langgraph.pregel.manager import ChannelsManager
@@ -323,7 +326,7 @@ def apply_writes(
 @overload
 def prepare_next_tasks(
     checkpoint: Checkpoint,
-    pending_writes: Sequence[PendingWrite],
+    pending_writes: list[PendingWrite],
     processes: Mapping[str, PregelNode],
     channels: Mapping[str, BaseChannel],
     managed: ManagedValueMapping,
@@ -340,7 +343,7 @@ def prepare_next_tasks(
 @overload
 def prepare_next_tasks(
     checkpoint: Checkpoint,
-    pending_writes: Sequence[PendingWrite],
+    pending_writes: list[PendingWrite],
     processes: Mapping[str, PregelNode],
     channels: Mapping[str, BaseChannel],
     managed: ManagedValueMapping,
@@ -356,7 +359,7 @@ def prepare_next_tasks(
 
 def prepare_next_tasks(
     checkpoint: Checkpoint,
-    pending_writes: Sequence[PendingWrite],
+    pending_writes: list[PendingWrite],
     processes: Mapping[str, PregelNode],
     channels: Mapping[str, BaseChannel],
     managed: ManagedValueMapping,
@@ -417,7 +420,7 @@ def prepare_single_task(
     task_id_checksum: Optional[str],
     *,
     checkpoint: Checkpoint,
-    pending_writes: Sequence[PendingWrite],
+    pending_writes: list[PendingWrite],
     processes: Mapping[str, PregelNode],
     channels: Mapping[str, BaseChannel],
     managed: ManagedValueMapping,
@@ -438,7 +441,7 @@ def prepare_single_task(
         # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
         task_path_t = cast(tuple[str, tuple, int, str, Call], task_path)
         call = task_path_t[-1]
-        proc_ = get_runnable_for_func(call.func)
+        proc_ = get_runnable_for_task(call.func)
         name = proc_.name
         if name is None:
             raise ValueError("`call` functions must have a `__name__` attribute")
@@ -616,6 +619,9 @@ def prepare_single_task(
                                 pending_writes,
                                 task_id,
                             ),
+                            CONFIG_KEY_PREVIOUS: checkpoint["channel_values"].get(
+                                PREVIOUS, None
+                            ),
                         },
                     ),
                     triggers,
@@ -737,6 +743,9 @@ def prepare_single_task(
                                     pending_writes,
                                     task_id,
                                 ),
+                                CONFIG_KEY_PREVIOUS: checkpoint["channel_values"].get(
+                                    PREVIOUS, None
+                                ),
                             },
                         ),
                         triggers,
@@ -751,23 +760,27 @@ def prepare_single_task(
 
 
 def _scratchpad(
-    pending_writes: Sequence[PendingWrite],
+    pending_writes: list[PendingWrite],
     task_id: str,
 ) -> PregelScratchpad:
+    null_resume_write = next(
+        (w for w in pending_writes if w[0] == NULL_TASK_ID and w[1] == RESUME), None
+    )
+    # using itertools.count as an atomic counter (+= 1 is not thread-safe)
     return PregelScratchpad(
         # call
-        call_counter=0,
+        call_counter=itertools.count(0).__next__,
         # interrupt
-        interrupt_counter=-1,
+        interrupt_counter=itertools.count(0).__next__,
         resume=next(
             (w[2] for w in pending_writes if w[0] == task_id and w[1] == RESUME), []
         ),
-        null_resume=next(
-            (w[2] for w in pending_writes if w[0] == NULL_TASK_ID and w[1] == RESUME),
-            MISSING,
-        ),
+        null_resume=null_resume_write[2] if null_resume_write is not None else None,
+        _consume_null_resume=functools.partial(pending_writes.remove, null_resume_write)
+        if null_resume_write is not None
+        else lambda: None,
         # subgraph
-        subgraph_counter=0,
+        subgraph_counter=itertools.count(0).__next__,
     )
 
 

libs/langgraph/langgraph/pregel/call.py

@@ -1,12 +1,25 @@
 """Utility to convert a user provided function into a Runnable with a ChannelWrite."""
 
+import concurrent.futures
+import functools
+import inspect
 import sys
 import types
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Generator, Generic, Optional, TypeVar, cast
 
-from langgraph.constants import RETURN
+from langchain_core.runnables import Runnable
+from typing_extensions import ParamSpec
+
+from langgraph.constants import CONF, CONFIG_KEY_CALL, RETURN, TAG_HIDDEN
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
-from langgraph.utils.runnable import RunnableSeq, coerce_to_runnable
+from langgraph.types import RetryPolicy
+from langgraph.utils.config import get_config
+from langgraph.utils.runnable import (
+    RunnableCallable,
+    RunnableSeq,
+    is_async_callable,
+    run_in_executor,
+)
 
 ##
 # Utilities borrowed from cloudpickle.
@@ -107,18 +120,114 @@ def _lookup_module_and_qualname(
     return module, name
 
 
-def get_runnable_for_func(func: Callable[..., Any]) -> RunnableSeq:
-    if func in CACHE:
-        return CACHE[func]
+def _explode_args_trace_inputs(
+    sig: inspect.Signature, input: tuple[tuple[Any, ...], dict[str, Any]]
+) -> dict[str, Any]:
+    args, kwargs = input
+    bound = sig.bind_partial(*args, **kwargs)
+    bound.apply_defaults()
+    arguments = dict(bound.arguments)
+    arguments.pop("self", None)
+    arguments.pop("cls", None)
+    for param_name, param in sig.parameters.items():
+        if param.kind == inspect.Parameter.VAR_KEYWORD:
+            # Update with the **kwargs, and remove the original entry
+            # This is to help flatten out keyword arguments
+            if param_name in arguments:
+                arguments.update(arguments.pop(param_name))
+    return arguments
+
+
+def get_runnable_for_entrypoint(func: Callable[..., Any]) -> RunnableSeq:
+    key = (func, False)
+    if key in CACHE:
+        return CACHE[key]
     else:
+        if is_async_callable(func):
+            run = RunnableCallable(
+                None, func, name=func.__name__, trace=False, recurse=False
+            )
+        else:
+            afunc = functools.update_wrapper(
+                functools.partial(run_in_executor, None, func), func
+            )
+            run = RunnableCallable(
+                func,
+                afunc,
+                name=func.__name__,
+                trace=False,
+                recurse=False,
+            )
+        if not _lookup_module_and_qualname(func):
+            return run
+        return CACHE.setdefault(key, run)
+
+
+def get_runnable_for_task(func: Callable[..., Any]) -> RunnableSeq:
+    key = (func, True)
+    if key in CACHE:
+        return CACHE[key]
+    else:
+        if hasattr(func, "__name__"):
+            name = func.__name__
+        elif hasattr(func, "func"):
+            name = func.func.__name__
+        elif hasattr(func, "__class__"):
+            name = func.__class__.__name__
+        else:
+            name = str(func)
+
+        if is_async_callable(func):
+            run = RunnableCallable(
+                None,
+                func,
+                explode_args=True,
+                name=name,
+                trace=False,
+                recurse=False,
+            )
+        else:
+            run = RunnableCallable(
+                func,
+                functools.wraps(func)(functools.partial(run_in_executor, None, func)),
+                explode_args=True,
+                name=name,
+                trace=False,
+                recurse=False,
+            )
         seq = RunnableSeq(
-            coerce_to_runnable(func, name=None, trace=False),
-            ChannelWrite([ChannelWriteEntry(RETURN)]),
-            name=func.__name__,
+            run,
+            ChannelWrite([ChannelWriteEntry(RETURN)], tags=[TAG_HIDDEN]),
+            name=name,
+            trace_inputs=functools.partial(
+                _explode_args_trace_inputs, inspect.signature(func)
+            ),
         )
         if not _lookup_module_and_qualname(func):
             return seq
-        return CACHE.setdefault(func, seq)
+        return CACHE.setdefault(key, seq)
 
 
-CACHE: dict[Callable[..., Any], RunnableSeq] = {}
+CACHE: dict[tuple[Callable[..., Any], bool], Runnable] = {}
+
+
+P = ParamSpec("P")
+P1 = TypeVar("P1")
+T = TypeVar("T")
+
+
+class SyncAsyncFuture(Generic[T], concurrent.futures.Future[T]):
+    def __await__(self) -> Generator[T, None, T]:
+        yield cast(T, ...)
+
+
+def call(
+    func: Callable[P, T],
+    *args: Any,
+    retry: Optional[RetryPolicy] = None,
+    **kwargs: Any,
+) -> SyncAsyncFuture[T]:
+    config = get_config()
+    impl = config[CONF][CONFIG_KEY_CALL]
+    fut = impl(func, (args, kwargs), retry=retry, callbacks=config["callbacks"])
+    return fut

libs/langgraph/langgraph/pregel/executor.py

@@ -1,6 +1,5 @@
 import asyncio
 import concurrent.futures
-import sys
 import time
 from contextlib import ExitStack
 from contextvars import copy_context
@@ -22,6 +21,7 @@ from langchain_core.runnables.config import get_executor_for_config
 from typing_extensions import ParamSpec
 
 from langgraph.errors import GraphBubbleUp
+from langgraph.utils.future import CONTEXT_NOT_SUPPORTED, run_coroutine_threadsafe
 
 P = ParamSpec("P")
 T = TypeVar("T")
@@ -132,8 +132,7 @@ class AsyncBackgroundExecutor(AsyncContextManager):
       ignoring CancelledError"""
 
     def __init__(self, config: RunnableConfig) -> None:
-        self.context_not_supported = sys.version_info < (3, 11)
-        self.tasks: dict[asyncio.Task, tuple[bool, bool]] = {}
+        self.tasks: dict[asyncio.Future, tuple[bool, bool]] = {}
         self.sentinel = object()
         self.loop = asyncio.get_running_loop()
         if max_concurrency := config.get("max_concurrency"):
@@ -150,23 +149,23 @@ class AsyncBackgroundExecutor(AsyncContextManager):
         __name__: Optional[str] = None,
         __cancel_on_exit__: bool = False,
         __reraise_on_exit__: bool = True,
-        __next_tick__: bool = False,
+        __next_tick__: bool = False,  # noop in async (always True)
         **kwargs: P.kwargs,
-    ) -> asyncio.Task[T]:
+    ) -> asyncio.Future[T]:
         coro = cast(Coroutine[None, None, T], fn(*args, **kwargs))
         if self.semaphore:
             coro = gated(self.semaphore, coro)
-        if __next_tick__:
-            coro = anext_tick(coro)
-        if self.context_not_supported:
-            task = self.loop.create_task(coro, name=__name__)
+        if CONTEXT_NOT_SUPPORTED:
+            task = run_coroutine_threadsafe(coro, self.loop, name=__name__)
         else:
-            task = self.loop.create_task(coro, name=__name__, context=copy_context())
+            task = run_coroutine_threadsafe(
+                coro, self.loop, name=__name__, context=copy_context()
+            )
         self.tasks[task] = (__cancel_on_exit__, __reraise_on_exit__)
         task.add_done_callback(self.done)
         return task
 
-    def done(self, task: asyncio.Task) -> None:
+    def done(self, task: asyncio.Future) -> None:
         try:
             if exc := task.exception():
                 # This exception is an interruption signal, not an error
@@ -219,9 +218,3 @@ def next_tick(fn: Callable[P, T], *args: P.args, **kwargs: P.kwargs) -> T:
     """A function that yields control to other threads before running another function."""
     time.sleep(0)
     return fn(*args, **kwargs)
-
-
-async def anext_tick(coro: Coroutine[None, None, T]) -> T:
-    """A coroutine that yields control to event loop before running another coroutine."""
-    await asyncio.sleep(0)
-    return await coro

libs/langgraph/langgraph/pregel/io.py

@@ -89,7 +89,7 @@ def map_command(
                 raise TypeError(
                     f"In Command.goto, expected Send/str, got {type(send).__name__}"
                 )
-    if cmd.resume:
+    if cmd.resume is not None:
         if isinstance(cmd.resume, dict) and all(is_task_id(k) for k in cmd.resume):
             for tid, resume in cmd.resume.items():
                 existing: list[Any] = next(

libs/langgraph/langgraph/pregel/loop.py

@@ -54,7 +54,6 @@ from langgraph.constants import (
     ERROR,
     INPUT,
     INTERRUPT,
-    MISSING,
     NS_SEP,
     NULL_TASK_ID,
     PUSH,
@@ -229,20 +228,23 @@ class PregelLoop(LoopProtocol):
         if self.stream is not None and CONFIG_KEY_STREAM in config[CONF]:
             self.stream = DuplexStream(self.stream, config[CONF][CONFIG_KEY_STREAM])
         scratchpad: Optional[PregelScratchpad] = config[CONF].get(CONFIG_KEY_SCRATCHPAD)
-        if not self.config[CONF].get(CONFIG_KEY_DELEGATE) and scratchpad is not None:
-            if scratchpad["subgraph_counter"]:
+        if not self.config[CONF].get(CONFIG_KEY_DELEGATE) and isinstance(
+            scratchpad, PregelScratchpad
+        ):
+            # if count is > 0, append to checkpoint_ns
+            # if count is 0, leave as is
+            if cnt := scratchpad.subgraph_counter():
                 self.config = patch_configurable(
                     self.config,
                     {
                         CONFIG_KEY_CHECKPOINT_NS: NS_SEP.join(
                             (
                                 config[CONF][CONFIG_KEY_CHECKPOINT_NS],
-                                str(scratchpad["subgraph_counter"]),
+                                str(cnt),
                             )
                         )
                     },
                 )
-            scratchpad["subgraph_counter"] += 1
         if not self.is_nested and config[CONF].get(CONFIG_KEY_CHECKPOINT_NS):
             self.config = patch_configurable(
                 self.config,
@@ -345,11 +347,11 @@ class PregelLoop(LoopProtocol):
                 (PUSH, task.path, write_idx, task.id, call),
                 None,
                 checkpoint=self.checkpoint,
-                pending_writes=[(task.id, *w) for w in task.writes],
+                pending_writes=self.checkpoint_pending_writes,
                 processes=self.nodes,
                 channels=self.channels,
                 managed=self.managed,
-                config=self.config,
+                config=task.config,
                 step=self.step,
                 for_execution=True,
                 store=self.store,
@@ -563,9 +565,14 @@ class PregelLoop(LoopProtocol):
         )
 
         # take resume value from parent
-        if scratchpad := configurable.get(CONFIG_KEY_SCRATCHPAD):
-            if scratchpad["null_resume"] is not MISSING:
-                self.put_writes(NULL_TASK_ID, [(RESUME, scratchpad["null_resume"])])
+        if scratchpad := cast(
+            Optional[PregelScratchpad], configurable.get(CONFIG_KEY_SCRATCHPAD)
+        ):
+            if (
+                isinstance(scratchpad, PregelScratchpad)
+                and scratchpad.null_resume is not None
+            ):
+                self.put_writes(NULL_TASK_ID, [(RESUME, scratchpad.null_resume)])
         # map command to writes
         if isinstance(self.input, Command):
             if self.input.resume is not None and not self.checkpointer:
@@ -1084,6 +1091,6 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
             return await exit_task
         except asyncio.CancelledError as e:
             # Bubble up the exit task upon cancellation to permit the API
-            # consumer to await it before e.g., re-using the DB connection.
+            # consumer to await it before e.g., reusing the DB connection.
             e.args = (*e.args, exit_task)
             raise

libs/langgraph/langgraph/pregel/runner.py

@@ -1,5 +1,6 @@
 import asyncio
 import concurrent.futures
+import threading
 import time
 from functools import partial
 from typing import (
@@ -7,11 +8,13 @@ from typing import (
     AsyncIterator,
     Awaitable,
     Callable,
+    Generic,
     Iterable,
     Iterator,
     Optional,
     Sequence,
     Type,
+    TypeVar,
     Union,
     cast,
 )
@@ -36,9 +39,60 @@ from langgraph.errors import GraphBubbleUp, GraphInterrupt
 from langgraph.pregel.algo import Call
 from langgraph.pregel.executor import Submit
 from langgraph.pregel.retry import arun_with_retry, run_with_retry
-from langgraph.types import PregelExecutableTask, RetryPolicy
+from langgraph.types import PregelExecutableTask, PregelScratchpad, RetryPolicy
 from langgraph.utils.future import chain_future
 
+F = TypeVar("F", concurrent.futures.Future, asyncio.Future)
+E = TypeVar("E", threading.Event, asyncio.Event)
+
+
+class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
+    event: E
+    callback: Callable[[PregelExecutableTask, Optional[BaseException]], None]
+    counter: int
+    done: set[F]
+    lock: threading.Lock
+
+    def __init__(
+        self,
+        event: E,
+        callback: Callable[[PregelExecutableTask, Optional[BaseException]], None],
+        future_type: Type[F],
+        # used for generic typing, newer py supports FutureDict[...](...)
+    ) -> None:
+        super().__init__()
+        self.lock = threading.Lock()
+        self.event = event
+        self.callback = callback
+        self.counter = 0
+        self.done: set[F] = set()
+
+    def __setitem__(
+        self,
+        key: F,
+        value: Optional[PregelExecutableTask],
+    ) -> None:
+        super().__setitem__(key, value)  # type: ignore[index]
+        if value is not None:
+            with self.lock:
+                self.event.clear()
+                self.counter += 1
+            key.add_done_callback(partial(self.on_done, value))
+
+    def on_done(
+        self,
+        task: PregelExecutableTask,
+        fut: F,
+    ) -> None:
+        try:
+            self.callback(task, _exception(fut))
+        finally:
+            with self.lock:
+                self.done.add(fut)
+                self.counter -= 1
+                if self.counter == 0 or _should_stop_others(self.done):
+                    self.event.set()
+
 
 class PregelRunner:
     """Responsible for executing a set of Pregel tasks concurrently, committing
@@ -81,8 +135,7 @@ class PregelRunner:
                 return task.config[CONF][CONFIG_KEY_SEND](writes)
 
             # schedule PUSH tasks, collect futures
-            scratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
-            scratchpad.setdefault("call_counter", 0)
+            scratchpad: PregelScratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
             rtn: dict[int, Optional[concurrent.futures.Future]] = {}
             for idx, w in enumerate(writes):
                 # bail if not a PUSH write
@@ -90,9 +143,9 @@ class PregelRunner:
                     continue
                 # schedule the next task, if the callback returns one
                 wcall = calls[idx] if calls else None
-                cnt = scratchpad["call_counter"]
-                scratchpad["call_counter"] += 1
-                if next_task := self.schedule_task(task, cnt, wcall):
+                if next_task := self.schedule_task(
+                    task, scratchpad.call_counter(), wcall
+                ):
                     if fut := next(
                         (
                             f
@@ -138,7 +191,6 @@ class PregelRunner:
                             # updates from this tick are committed/streamed first
                             __next_tick__=True,
                         )
-                        fut.add_done_callback(partial(self.commit, next_task))
                         futures[fut] = next_task
                         rtn[idx] = fut
             return [rtn.get(i) for i in range(len(writes))]
@@ -151,17 +203,24 @@ class PregelRunner:
             retry: Optional[RetryPolicy] = None,
             callbacks: Callbacks = None,
         ) -> concurrent.futures.Future[Any]:
+            if asyncio.iscoroutinefunction(func):
+                raise RuntimeError("In an sync context async tasks cannot be called")
             (fut,) = writer(
                 task,
                 [(PUSH, None)],
                 calls=[Call(func, input, retry=retry, callbacks=callbacks)],
             )
             assert fut is not None, "writer did not return a future for call"
-            return fut
+            # return a chained future to ensure commit() callback is called
+            # before the returned future is resolved, to ensure stream order etc
+            return chain_future(fut, concurrent.futures.Future())
 
         tasks = tuple(tasks)
-        futures: dict[concurrent.futures.Future, Optional[PregelExecutableTask]] = {}
-        done_futures: set[concurrent.futures.Future] = set()
+        futures = FuturesDict(
+            callback=self.commit,
+            event=threading.Event(),
+            future_type=concurrent.futures.Future,
+        )
         # give control back to the caller
         yield
         # fast path if single task with no timeout and no waiter
@@ -178,12 +237,12 @@ class PregelRunner:
                 )
                 self.commit(t, None)
             except Exception as exc:
-                self.commit(t, None, exc)
+                self.commit(t, exc)
                 if reraise and futures:
                     # will be re-raised after futures are done
                     fut: concurrent.futures.Future = concurrent.futures.Future()
                     fut.set_exception(exc)
-                    done_futures.add(fut)
+                    futures.done.add(fut)
                 elif reraise:
                     raise
             if not futures:  # maybe `t` schuduled another task
@@ -206,7 +265,6 @@ class PregelRunner:
                     },
                     __reraise_on_exit__=reraise,
                 )
-                fut.add_done_callback(partial(self.commit, t))
                 futures[fut] = t
         # execute tasks, and wait for one to fail or all to finish.
         # each task is independent from all other concurrent tasks
@@ -226,9 +284,6 @@ class PregelRunner:
                     # waiter task finished, schedule another
                     if inflight and get_waiter is not None:
                         futures[get_waiter()] = None
-                else:
-                    # store for panic check
-                    done_futures.add(fut)
             else:
                 # remove references to loop vars
                 del fut, task
@@ -237,13 +292,15 @@ class PregelRunner:
                 break
             # give control back to the caller
             yield
-        # wait for pending done callbacks
-        # if a 2nd future finishes while `wait` is returning, it's possible
-        # that done callbacks for the 2nd future aren't called until next tick
-        time.sleep(0)
+        # wait for done callbacks
+        futures.event.wait(
+            timeout=(max(0, end_time - time.monotonic()) if end_time else None)
+        )
+        # give control back to the caller
+        yield
         # panic on failure or timeout
         _panic_or_proceed(
-            done_futures.union(f for f, t in futures.items() if t is not None),
+            futures.done.union(f for f, t in futures.items() if t is not None),
             panic=reraise,
         )
 
@@ -266,8 +323,7 @@ class PregelRunner:
                 return task.config[CONF][CONFIG_KEY_SEND](writes)
 
             # schedule PUSH tasks, collect futures
-            scratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
-            scratchpad.setdefault("call_counter", 0)
+            scratchpad: PregelScratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
             rtn: dict[int, Optional[asyncio.Future]] = {}
             for idx, w in enumerate(writes):
                 # bail if not a PUSH write
@@ -275,9 +331,9 @@ class PregelRunner:
                     continue
                 # schedule the next task, if the callback returns one
                 wcall = calls[idx] if calls is not None else None
-                cnt = scratchpad["call_counter"]
-                scratchpad["call_counter"] += 1
-                if next_task := self.schedule_task(task, cnt, wcall):
+                if next_task := self.schedule_task(
+                    task, scratchpad.call_counter(), wcall
+                ):
                     # if the parent task was retried,
                     # the next task might already be running
                     if fut := next(
@@ -293,7 +349,7 @@ class PregelRunner:
                         rtn[idx] = fut
                     elif next_task.writes:
                         # if it already ran, return the result
-                        fut = asyncio.Future()
+                        fut = asyncio.Future(loop=loop)
                         ret = next(
                             (v for c, v in next_task.writes if c == RETURN), MISSING
                         )
@@ -331,7 +387,6 @@ class PregelRunner:
                                 __next_tick__=True,
                             ),
                         )
-                        fut.add_done_callback(partial(self.commit, next_task))
                         futures[fut] = next_task
                         rtn[idx] = fut
             return [rtn.get(i) for i in range(len(writes))]
@@ -350,17 +405,36 @@ class PregelRunner:
                 calls=[Call(func, input, retry=retry, callbacks=callbacks)],
             )
             assert fut is not None, "writer did not return a future for call"
-            if asyncio.iscoroutinefunction(func):
-                return fut
-            # adapted from asyncio.run_coroutine_threadsafe
-            sfut: concurrent.futures.Future = concurrent.futures.Future()
-            loop.call_soon_threadsafe(chain_future, fut, sfut)
-            return sfut
+            # return a chained future to ensure commit() callback is called
+            # before the returned future is resolved, to ensure stream order etc
+            try:
+                in_async = asyncio.current_task() is not None
+            except RuntimeError:
+                in_async = False
+            # if in async context return an async future
+            # otherwise return a chained sync future
+            if in_async:
+                if isinstance(fut, asyncio.Task):
+                    sfut: Union[asyncio.Future[Any], concurrent.futures.Future[Any]] = (
+                        asyncio.Future(loop=loop)
+                    )
+                    loop.call_soon_threadsafe(chain_future, fut, sfut)
+                    return sfut
+                else:
+                    # already wrapped in a future
+                    return fut
+            else:
+                sfut = concurrent.futures.Future()
+                loop.call_soon_threadsafe(chain_future, fut, sfut)
+                return sfut
 
         loop = asyncio.get_event_loop()
         tasks = tuple(tasks)
-        futures: dict[asyncio.Future, Optional[PregelExecutableTask]] = {}
-        done_futures: set[asyncio.Future] = set()
+        futures = FuturesDict(
+            callback=self.commit,
+            event=asyncio.Event(),
+            future_type=asyncio.Future,
+        )
         # give control back to the caller
         yield
         # fast path if single task with no waiter and no timeout
@@ -378,12 +452,12 @@ class PregelRunner:
                 )
                 self.commit(t, None)
             except Exception as exc:
-                self.commit(t, None, exc)
+                self.commit(t, exc)
                 if reraise and futures:
                     # will be re-raised after futures are done
                     fut: asyncio.Future = loop.create_future()
                     fut.set_exception(exc)
-                    done_futures.add(fut)
+                    futures.done.add(fut)
                 elif reraise:
                     raise
             if not futures:  # maybe `t` schuduled another task
@@ -412,7 +486,6 @@ class PregelRunner:
                         __reraise_on_exit__=reraise,
                     ),
                 )
-                fut.add_done_callback(partial(self.commit, t))
                 futures[fut] = t
         # execute tasks, and wait for one to fail or all to finish.
         # each task is independent from all other concurrent tasks
@@ -432,9 +505,6 @@ class PregelRunner:
                     # waiter task finished, schedule another
                     if inflight and get_waiter is not None:
                         futures[get_waiter()] = None
-                else:
-                    # store for panic check
-                    done_futures.add(fut)
             else:
                 # remove references to loop vars
                 del fut, task
@@ -443,16 +513,19 @@ class PregelRunner:
                 break
             # give control back to the caller
             yield
-        # wait for pending done callbacks
-        # if a 2nd future finishes while `wait` is returning, it's possible
-        # that done callbacks for the 2nd future aren't called until next tick
-        await asyncio.sleep(0)
+        # wait for done callbacks
+        await asyncio.wait_for(
+            futures.event.wait(),
+            timeout=(max(0, end_time - loop.time()) if end_time else None),
+        )
+        # give control back to the caller
+        yield
         # cancel waiter task
         for fut in futures:
             fut.cancel()
         # panic on failure or timeout
         _panic_or_proceed(
-            done_futures.union(f for f, t in futures.items() if t is not None),
+            futures.done.union(f for f, t in futures.items() if t is not None),
             timeout_exc_cls=asyncio.TimeoutError,
             panic=reraise,
         )
@@ -460,11 +533,8 @@ class PregelRunner:
     def commit(
         self,
         task: PregelExecutableTask,
-        fut: Union[None, concurrent.futures.Future[Any], asyncio.Future[Any]],
-        exception: Optional[BaseException] = None,
+        exception: Optional[BaseException],
     ) -> None:
-        if fut is not None:
-            exception = _exception(fut)
         if isinstance(exception, asyncio.CancelledError):
             # for cancelled tasks, also save error in task,
             # so loop can finish super-step
@@ -495,7 +565,7 @@ class PregelRunner:
 
 
 def _should_stop_others(
-    done: Union[set[concurrent.futures.Future[Any]], set[asyncio.Future[Any]]],
+    done: set[F],
 ) -> bool:
     """Check if any task failed, if so, cancel all other tasks.
     GraphInterrupts are not considered failures."""

libs/langgraph/langgraph/types.py

@@ -16,10 +16,11 @@ from typing import (
     TypeVar,
     Union,
     cast,
+    get_type_hints,
 )
 
 from langchain_core.runnables import Runnable, RunnableConfig
-from typing_extensions import Self, TypedDict
+from typing_extensions import Self
 
 from langgraph.checkpoint.base import BaseCheckpointSaver, CheckpointMetadata
 
@@ -289,6 +290,8 @@ class Command(Generic[N], ToolOutputMixin):
             for t in self.update
         ):
             return self.update
+        elif hints := get_type_hints(type(self.update)):
+            return [(k, getattr(self.update, k)) for k in hints]
         elif self.update is not None:
             return [("__root__", self.update)]
         else:
@@ -339,15 +342,25 @@ class LoopProtocol:
         self.stop = stop
 
 
-class PregelScratchpad(TypedDict):
+@dataclasses.dataclass(**{**_DC_KWARGS, "frozen": False})
+class PregelScratchpad:
     # call
-    call_counter: int
+    call_counter: Callable[[], int]
     # interrupt
-    interrupt_counter: int
+    interrupt_counter: Callable[[], int]
     resume: list[Any]
-    null_resume: Any
+    null_resume: Optional[Any]
+    _consume_null_resume: Callable[[], None]
     # subgraph
-    subgraph_counter: int
+    subgraph_counter: Callable[[], int]
+
+    def consume_null_resume(self) -> Any:
+        if self.null_resume is not None:
+            value = self.null_resume
+            self._consume_null_resume()
+            self.null_resume = None
+            return value
+        raise ValueError("No null resume to consume")
 
 
 def interrupt(value: Any) -> Any:
@@ -449,7 +462,6 @@ def interrupt(value: Any) -> Any:
         CONFIG_KEY_CHECKPOINT_NS,
         CONFIG_KEY_SCRATCHPAD,
         CONFIG_KEY_SEND,
-        MISSING,
         NS_SEP,
         RESUME,
     )
@@ -459,19 +471,17 @@ def interrupt(value: Any) -> Any:
     conf = get_config()["configurable"]
     # track interrupt index
     scratchpad: PregelScratchpad = conf[CONFIG_KEY_SCRATCHPAD]
-    scratchpad["interrupt_counter"] += 1
-    idx = scratchpad["interrupt_counter"]
+    idx = scratchpad.interrupt_counter()
     # find previous resume values
-    if scratchpad["resume"]:
-        if idx < len(scratchpad["resume"]):
-            return scratchpad["resume"][idx]
+    if scratchpad.resume:
+        if idx < len(scratchpad.resume):
+            return scratchpad.resume[idx]
     # find current resume value
-    if scratchpad["null_resume"] is not MISSING:
-        assert len(scratchpad["resume"]) == idx, (scratchpad["resume"], idx)
-        v = scratchpad["null_resume"]
-        scratchpad["null_resume"] = MISSING
-        scratchpad["resume"].append(v)
-        conf[CONFIG_KEY_SEND]([(RESUME, scratchpad["resume"])])
+    if scratchpad.null_resume is not None:
+        assert len(scratchpad.resume) == idx, (scratchpad.resume, idx)
+        v = scratchpad.consume_null_resume()
+        scratchpad.resume.append(v)
+        conf[CONFIG_KEY_SEND]([(RESUME, scratchpad.resume)])
         return v
     # no resume value found
     raise GraphInterrupt(

libs/langgraph/langgraph/utils/config.py

@@ -23,9 +23,11 @@ from langgraph.constants import (
     CONFIG_KEY_CHECKPOINT_ID,
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_NS,
+    CONFIG_KEY_STORE,
     NS_END,
     NS_SEP,
 )
+from langgraph.store.base import BaseStore
 
 
 def recast_checkpoint_ns(ns: str) -> str:
@@ -332,4 +334,9 @@ def get_config() -> RunnableConfig:
     if var_config := var_child_runnable_config.get():
         return var_config
     else:
-        raise RuntimeError("Called get_configurable outside of a runnable context")
+        raise RuntimeError("Called get_config outside of a runnable context")
+
+
+def get_store() -> BaseStore:
+    config = get_config()
+    return config[CONF][CONFIG_KEY_STORE]

libs/langgraph/langgraph/utils/future.py

@@ -1,9 +1,16 @@
 import asyncio
 import concurrent.futures
-from typing import Union
+import contextvars
+import inspect
+import sys
+import types
+from typing import Awaitable, Coroutine, Generator, Optional, TypeVar, Union, cast
 
+T = TypeVar("T")
 AnyFuture = Union[asyncio.Future, concurrent.futures.Future]
 
+CONTEXT_NOT_SUPPORTED = sys.version_info < (3, 11)
+
 
 def _get_loop(fut: asyncio.Future) -> asyncio.AbstractEventLoop:
     # Tries to call Future.get_loop() if it's available.
@@ -52,10 +59,11 @@ def _copy_future_state(source: AnyFuture, dest: asyncio.Future) -> None:
 
     The other Future may be a concurrent.futures.Future.
     """
+    if dest.done():
+        return
     assert source.done()
     if dest.cancelled():
         return
-    assert not dest.done()
     if source.cancelled():
         dest.cancel()
     else:
@@ -112,13 +120,85 @@ def _chain_future(source: AnyFuture, destination: AnyFuture) -> None:
     source.add_done_callback(_call_set_state)
 
 
-def chain_future(source: AnyFuture, destination: concurrent.futures.Future) -> None:
+def chain_future(source: AnyFuture, destination: AnyFuture) -> AnyFuture:
     # adapted from asyncio.run_coroutine_threadsafe
     try:
         _chain_future(source, destination)
+        return destination
     except (SystemExit, KeyboardInterrupt):
         raise
     except BaseException as exc:
-        if destination.set_running_or_notify_cancel():
+        if isinstance(destination, concurrent.futures.Future):
+            if destination.set_running_or_notify_cancel():
+                destination.set_exception(exc)
+        else:
             destination.set_exception(exc)
         raise
+
+
+def _ensure_future(
+    coro_or_future: Union[Coroutine[None, None, T], Awaitable[T]],
+    *,
+    loop: asyncio.AbstractEventLoop,
+    name: Optional[str] = None,
+    context: Optional[contextvars.Context] = None,
+) -> asyncio.Task[T]:
+    called_wrap_awaitable = False
+    if not asyncio.iscoroutine(coro_or_future):
+        if inspect.isawaitable(coro_or_future):
+            coro_or_future = cast(
+                Coroutine[None, None, T], _wrap_awaitable(coro_or_future)
+            )
+            called_wrap_awaitable = True
+        else:
+            raise TypeError(
+                "An asyncio.Future, a coroutine or an awaitable is required."
+                f" Got {type(coro_or_future).__name__} instead."
+            )
+
+    try:
+        if CONTEXT_NOT_SUPPORTED:
+            return loop.create_task(coro_or_future, name=name)
+        else:
+            return loop.create_task(coro_or_future, name=name, context=context)
+    except RuntimeError:
+        if not called_wrap_awaitable:
+            coro_or_future.close()
+        raise
+
+
+@types.coroutine
+def _wrap_awaitable(awaitable: Awaitable[T]) -> Generator[None, None, T]:
+    """Helper for asyncio.ensure_future().
+
+    Wraps awaitable (an object with __await__) into a coroutine
+    that will later be wrapped in a Task by ensure_future().
+    """
+    return (yield from awaitable.__await__())
+
+
+def run_coroutine_threadsafe(
+    coro: Coroutine[None, None, T],
+    loop: asyncio.AbstractEventLoop,
+    name: Optional[str] = None,
+    context: Optional[contextvars.Context] = None,
+) -> asyncio.Future[T]:
+    """Submit a coroutine object to a given event loop.
+
+    Return a asyncio.Future to access the result.
+    """
+    future: asyncio.Future[T] = asyncio.Future(loop=loop)
+
+    def callback() -> None:
+        try:
+            chain_future(
+                _ensure_future(coro, loop=loop, name=name, context=context), future
+            )
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            future.set_exception(exc)
+            raise
+
+    loop.call_soon_threadsafe(callback, context=context)
+    return future

libs/langgraph/langgraph/utils/runnable.py

@@ -34,7 +34,12 @@ from langchain_core.runnables.utils import Input
 from langchain_core.tracers._streaming import _StreamingCallbackHandler
 from typing_extensions import TypeGuard
 
-from langgraph.constants import CONF, CONFIG_KEY_STORE, CONFIG_KEY_STREAM_WRITER
+from langgraph.constants import (
+    CONF,
+    CONFIG_KEY_PREVIOUS,
+    CONFIG_KEY_STORE,
+    CONFIG_KEY_STREAM_WRITER,
+)
 from langgraph.store.base import BaseStore
 from langgraph.types import StreamWriter
 from langgraph.utils.config import (
@@ -58,6 +63,10 @@ class StrEnum(str, enum.Enum):
     """A string enum."""
 
 
+# Special type to denote any type is accepted
+ANY_TYPE = object()
+
+
 ASYNCIO_ACCEPTS_CONTEXT = sys.version_info >= (3, 11)
 
 KWARGS_CONFIG_KEYS: tuple[tuple[str, tuple[Any, ...], str, Any], ...] = (
@@ -73,6 +82,12 @@ KWARGS_CONFIG_KEYS: tuple[tuple[str, tuple[Any, ...], str, Any], ...] = (
         CONFIG_KEY_STORE,
         inspect.Parameter.empty,
     ),
+    (
+        sys.intern("previous"),
+        (ANY_TYPE,),
+        CONFIG_KEY_PREVIOUS,
+        inspect.Parameter.empty,
+    ),
 )
 """List of kwargs that can be passed to functions, and their corresponding
 config keys, default values and type annotations.
@@ -105,6 +120,7 @@ class RunnableCallable(Runnable):
         tags: Optional[Sequence[str]] = None,
         trace: bool = True,
         recurse: bool = True,
+        explode_args: bool = False,
         **kwargs: Any,
     ) -> None:
         self.name = name
@@ -126,6 +142,7 @@ class RunnableCallable(Runnable):
         self.kwargs = kwargs
         self.trace = trace
         self.recurse = recurse
+        self.explode_args = explode_args
         # check signature
         if func is None and afunc is None:
             raise ValueError("At least one of func or afunc must be provided.")
@@ -135,9 +152,12 @@ class RunnableCallable(Runnable):
         self.func_accepts: dict[str, bool] = {}
         for kw, typ, _, _ in KWARGS_CONFIG_KEYS:
             p = params.get(kw)
-            self.func_accepts[kw] = (
-                p is not None and p.annotation in typ and p.kind in VALID_KINDS
-            )
+            if typ == (ANY_TYPE,):
+                self.func_accepts[kw] = p is not None and p.kind in VALID_KINDS
+            else:
+                self.func_accepts[kw] = (
+                    p is not None and p.annotation in typ and p.kind in VALID_KINDS
+                )
 
     def __repr__(self) -> str:
         repr_args = {
@@ -158,20 +178,29 @@ class RunnableCallable(Runnable):
             )
         if config is None:
             config = ensure_config()
-        kwargs = {**self.kwargs, **kwargs}
+        if self.explode_args:
+            args, _kwargs = input
+            kwargs = {**self.kwargs, **_kwargs, **kwargs}
+        else:
+            args = (input,)
+            kwargs = {**self.kwargs, **kwargs}
         if self.func_accepts_config:
             kwargs["config"] = config
         _conf = config[CONF]
-        for kw, _, ck, defv in KWARGS_CONFIG_KEYS:
+        for kw, _, config_key, default_value in KWARGS_CONFIG_KEYS:
             if not self.func_accepts[kw]:
                 continue
 
-            if defv is inspect.Parameter.empty and kw not in kwargs and ck not in _conf:
+            if (
+                default_value is inspect.Parameter.empty
+                and kw not in kwargs
+                and config_key not in _conf
+            ):
                 raise ValueError(
-                    f"Missing required config key '{ck}' for '{self.name}'."
+                    f"Missing required config key '{config_key}' for '{self.name}'."
                 )
             elif kwargs.get(kw) is None:
-                kwargs[kw] = _conf.get(ck, defv)
+                kwargs[kw] = _conf.get(config_key, default_value)
 
         context = copy_context()
         if self.trace:
@@ -186,7 +215,7 @@ class RunnableCallable(Runnable):
                 child_config = patch_config(config, callbacks=run_manager.get_child())
                 context = copy_context()
                 context.run(_set_config_context, child_config)
-                ret = context.run(self.func, input, **kwargs)
+                ret = context.run(self.func, *args, **kwargs)
             except BaseException as e:
                 run_manager.on_chain_error(e)
                 raise
@@ -194,7 +223,7 @@ class RunnableCallable(Runnable):
                 run_manager.on_chain_end(ret)
         else:
             context.run(_set_config_context, config)
-            ret = context.run(self.func, input, **kwargs)
+            ret = context.run(self.func, *args, **kwargs)
         if isinstance(ret, Runnable) and self.recurse:
             return ret.invoke(input, config)
         return ret
@@ -206,20 +235,29 @@ class RunnableCallable(Runnable):
             return self.invoke(input, config)
         if config is None:
             config = ensure_config()
-        kwargs = {**self.kwargs, **kwargs}
+        if self.explode_args:
+            args, _kwargs = input
+            kwargs = {**self.kwargs, **_kwargs, **kwargs}
+        else:
+            args = (input,)
+            kwargs = {**self.kwargs, **kwargs}
         if self.func_accepts_config:
             kwargs["config"] = config
         _conf = config[CONF]
-        for kw, _, ck, defv in KWARGS_CONFIG_KEYS:
+        for kw, _, config_key, default_value in KWARGS_CONFIG_KEYS:
             if not self.func_accepts[kw]:
                 continue
 
-            if defv is inspect.Parameter.empty and kw not in kwargs and ck not in _conf:
+            if (
+                default_value is inspect.Parameter.empty
+                and kw not in kwargs
+                and config_key not in _conf
+            ):
                 raise ValueError(
-                    f"Missing required config key '{ck}' for '{self.name}'."
+                    f"Missing required config key '{config_key}' for '{self.name}'."
                 )
             elif kwargs.get(kw) is None:
-                kwargs[kw] = _conf.get(ck, defv)
+                kwargs[kw] = _conf.get(config_key, default_value)
         context = copy_context()
         if self.trace:
             callback_manager = get_async_callback_manager_for_config(config, self.tags)
@@ -232,7 +270,7 @@ class RunnableCallable(Runnable):
             try:
                 child_config = patch_config(config, callbacks=run_manager.get_child())
                 context.run(_set_config_context, child_config)
-                coro = cast(Coroutine[None, None, Any], self.afunc(input, **kwargs))
+                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
                 if ASYNCIO_ACCEPTS_CONTEXT:
                     ret = await asyncio.create_task(coro, context=context)
                 else:
@@ -245,10 +283,10 @@ class RunnableCallable(Runnable):
         else:
             context.run(_set_config_context, config)
             if ASYNCIO_ACCEPTS_CONTEXT:
-                coro = cast(Coroutine[None, None, Any], self.afunc(input, **kwargs))
+                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
                 ret = await asyncio.create_task(coro, context=context)
             else:
-                ret = await self.afunc(input, **kwargs)
+                ret = await self.afunc(*args, **kwargs)
         if isinstance(ret, Runnable) and self.recurse:
             return await ret.ainvoke(input, config)
         return ret
@@ -321,6 +359,7 @@ class RunnableSeq(Runnable):
         self,
         *steps: RunnableLike,
         name: Optional[str] = None,
+        trace_inputs: Optional[Callable[[Any], Any]] = None,
     ) -> None:
         """Create a new RunnableSeq.
 
@@ -345,6 +384,7 @@ class RunnableSeq(Runnable):
             )
         self.steps = steps_flat
         self.name = name
+        self.trace_inputs = trace_inputs
 
     def __or__(
         self,
@@ -406,7 +446,7 @@ class RunnableSeq(Runnable):
         # start the root run
         run_manager = callback_manager.on_chain_start(
             None,
-            input,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
             name=config.get("run_name") or self.get_name(),
             run_id=config.pop("run_id", None),
         )
@@ -416,7 +456,7 @@ class RunnableSeq(Runnable):
             for i, step in enumerate(self.steps):
                 # mark each step as a child run
                 config = patch_config(
-                    config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
+                    config, callbacks=run_manager.get_child(f"seq:step:{i + 1}")
                 )
                 if i == 0:
                     input = step.invoke(input, config, **kwargs)
@@ -443,7 +483,7 @@ class RunnableSeq(Runnable):
         # start the root run
         run_manager = await callback_manager.on_chain_start(
             None,
-            input,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
             name=config.get("run_name") or self.get_name(),
             run_id=config.pop("run_id", None),
         )
@@ -453,7 +493,7 @@ class RunnableSeq(Runnable):
             for i, step in enumerate(self.steps):
                 # mark each step as a child run
                 config = patch_config(
-                    config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
+                    config, callbacks=run_manager.get_child(f"seq:step:{i + 1}")
                 )
                 if i == 0:
                     input = await step.ainvoke(input, config, **kwargs)
@@ -480,7 +520,7 @@ class RunnableSeq(Runnable):
         # start the root run
         run_manager = callback_manager.on_chain_start(
             None,
-            input,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
             name=config.get("run_name") or self.get_name(),
             run_id=config.pop("run_id", None),
         )
@@ -493,7 +533,7 @@ class RunnableSeq(Runnable):
             for idx, step in enumerate(self.steps):
                 config = patch_config(
                     config,
-                    callbacks=run_manager.get_child(f"seq:step:{idx+1}"),
+                    callbacks=run_manager.get_child(f"seq:step:{idx + 1}"),
                 )
                 if idx == 0:
                     iterator = step.stream(input, config, **kwargs)
@@ -543,7 +583,7 @@ class RunnableSeq(Runnable):
         # start the root run
         run_manager = await callback_manager.on_chain_start(
             None,
-            input,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
             name=config.get("run_name") or self.get_name(),
             run_id=config.pop("run_id", None),
         )
@@ -557,7 +597,7 @@ class RunnableSeq(Runnable):
                 for idx, step in enumerate(self.steps):
                     config = patch_config(
                         config,
-                        callbacks=run_manager.get_child(f"seq:step:{idx+1}"),
+                        callbacks=run_manager.get_child(f"seq:step:{idx + 1}"),
                     )
                     if idx == 0:
                         aiterator = step.astream(input, config, **kwargs)

libs/langgraph/poetry.lock

@@ -1324,14 +1324,14 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.25"
+version = "0.3.30"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main", "dev"]
 files = [
-    {file = "langchain_core-0.3.25-py3-none-any.whl", hash = "sha256:e10581c6c74ba16bdc6fdf16b00cced2aa447cc4024ed19746a1232918edde38"},
-    {file = "langchain_core-0.3.25.tar.gz", hash = "sha256:fdb8df41e5cdd928c0c2551ebbde1cea770ee3c64598395367ad77ddf9acbae7"},
+    {file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
+    {file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
 ]
 
 [package.dependencies]

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.2.62"
+version = "0.2.67"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"

libs/langgraph/tests/__snapshots__/test_pregel_async.ambr

@@ -1,334 +1,4 @@
 # serializer version: 1
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class[memory]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class[postgres_aio]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class[postgres_aio_pipe]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class[postgres_aio_pool]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class[sqlite_aio]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.1
-  dict({
-    '$defs': dict({
-      'InnerObject': dict({
-        'properties': dict({
-          'yo': dict({
-            'title': 'Yo',
-            'type': 'integer',
-          }),
-        }),
-        'required': list([
-          'yo',
-        ]),
-        'title': 'InnerObject',
-        'type': 'object',
-      }),
-    }),
-    'properties': dict({
-      'answer': dict({
-        'anyOf': list([
-          dict({
-            'type': 'string',
-          }),
-          dict({
-            'type': 'null',
-          }),
-        ]),
-        'default': None,
-        'title': 'Answer',
-      }),
-      'docs': dict({
-        'items': dict({
-          'type': 'string',
-        }),
-        'title': 'Docs',
-        'type': 'array',
-      }),
-      'inner': dict({
-        '$ref': '#/$defs/InnerObject',
-      }),
-      'query': dict({
-        'title': 'Query',
-        'type': 'string',
-      }),
-    }),
-    'required': list([
-      'query',
-      'inner',
-      'docs',
-    ]),
-    'title': 'State',
-    'type': 'object',
-  })
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.2
-  dict({
-    '$defs': dict({
-      'InnerObject': dict({
-        'properties': dict({
-          'yo': dict({
-            'title': 'Yo',
-            'type': 'integer',
-          }),
-        }),
-        'required': list([
-          'yo',
-        ]),
-        'title': 'InnerObject',
-        'type': 'object',
-      }),
-    }),
-    'properties': dict({
-      'answer': dict({
-        'anyOf': list([
-          dict({
-            'type': 'string',
-          }),
-          dict({
-            'type': 'null',
-          }),
-        ]),
-        'default': None,
-        'title': 'Answer',
-      }),
-      'docs': dict({
-        'items': dict({
-          'type': 'string',
-        }),
-        'title': 'Docs',
-        'type': 'array',
-      }),
-      'inner': dict({
-        '$ref': '#/$defs/InnerObject',
-      }),
-      'query': dict({
-        'title': 'Query',
-        'type': 'string',
-      }),
-    }),
-    'required': list([
-      'query',
-      'inner',
-      'docs',
-    ]),
-    'title': 'State',
-    'type': 'object',
-  })
-# ---
 # name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2[memory]
   '''
   graph TD;
@@ -1055,253 +725,6 @@
     'type': 'object',
   })
 # ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[memory]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[postgres_aio]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[postgres_aio_pipe]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[postgres_aio_pool]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[sqlite_aio]
-  '''
-                +-----------+              
-                | __start__ |              
-                +-----------+              
-                      *                    
-                      *                    
-                      *                    
-              +---------------+            
-              | rewrite_query |            
-              +---------------+            
-               ***         ...             
-              *               .            
-            **                 ...         
-  +--------------+                .        
-  | analyzer_one |                .        
-  +--------------+                .        
-          *                       .        
-          *                       .        
-          *                       .        
-  +---------------+      +---------------+ 
-  | retriever_one |      | retriever_two | 
-  +---------------+      +---------------+ 
-               ***         ***             
-                  *       *                
-                   **   **                 
-                   +----+                  
-                   | qa |                  
-                   +----+                  
-                      *                    
-                      *                    
-                      *                    
-                +---------+                
-                | __end__ |                
-                +---------+                
-  '''
-# ---
-# name: test_nested_graph
-  '''
-  +-----------+  
-  | __start__ |  
-  +-----------+  
-        *        
-        *        
-        *        
-    +-------+    
-    | inner |    
-    +-------+    
-        *        
-        *        
-        *        
-    +------+     
-    | side |     
-    +------+     
-        *        
-        *        
-        *        
-   +---------+   
-   | __end__ |   
-   +---------+   
-  '''
-# ---
 # name: test_send_react_interrupt_control[memory]
   '''
   %%{init: {'flowchart': {'curve': 'linear'}}}%%
@@ -1392,128 +815,3 @@
   
   '''
 # ---
-# name: test_weather_subgraph[memory]
-  '''
-  %%{init: {'flowchart': {'curve': 'linear'}}}%%
-  graph TD;
-  	__start__([<p>__start__</p>]):::first
-  	router_node(router_node)
-  	normal_llm_node(normal_llm_node)
-  	weather_graph_model_node(model_node)
-  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
-  	__end__([<p>__end__</p>]):::last
-  	__start__ --> router_node;
-  	normal_llm_node --> __end__;
-  	weather_graph_weather_node --> __end__;
-  	router_node -.-> normal_llm_node;
-  	router_node -.-> weather_graph_model_node;
-  	router_node -.-> __end__;
-  	subgraph weather_graph
-  	weather_graph_model_node --> weather_graph_weather_node;
-  	end
-  	classDef default fill:#f2f0ff,line-height:1.2
-  	classDef first fill-opacity:0
-  	classDef last fill:#bfb6fc
-  
-  '''
-# ---
-# name: test_weather_subgraph[postgres_aio]
-  '''
-  %%{init: {'flowchart': {'curve': 'linear'}}}%%
-  graph TD;
-  	__start__([<p>__start__</p>]):::first
-  	router_node(router_node)
-  	normal_llm_node(normal_llm_node)
-  	weather_graph_model_node(model_node)
-  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
-  	__end__([<p>__end__</p>]):::last
-  	__start__ --> router_node;
-  	normal_llm_node --> __end__;
-  	weather_graph_weather_node --> __end__;
-  	router_node -.-> normal_llm_node;
-  	router_node -.-> weather_graph_model_node;
-  	router_node -.-> __end__;
-  	subgraph weather_graph
-  	weather_graph_model_node --> weather_graph_weather_node;
-  	end
-  	classDef default fill:#f2f0ff,line-height:1.2
-  	classDef first fill-opacity:0
-  	classDef last fill:#bfb6fc
-  
-  '''
-# ---
-# name: test_weather_subgraph[postgres_aio_pipe]
-  '''
-  %%{init: {'flowchart': {'curve': 'linear'}}}%%
-  graph TD;
-  	__start__([<p>__start__</p>]):::first
-  	router_node(router_node)
-  	normal_llm_node(normal_llm_node)
-  	weather_graph_model_node(model_node)
-  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
-  	__end__([<p>__end__</p>]):::last
-  	__start__ --> router_node;
-  	normal_llm_node --> __end__;
-  	weather_graph_weather_node --> __end__;
-  	router_node -.-> normal_llm_node;
-  	router_node -.-> weather_graph_model_node;
-  	router_node -.-> __end__;
-  	subgraph weather_graph
-  	weather_graph_model_node --> weather_graph_weather_node;
-  	end
-  	classDef default fill:#f2f0ff,line-height:1.2
-  	classDef first fill-opacity:0
-  	classDef last fill:#bfb6fc
-  
-  '''
-# ---
-# name: test_weather_subgraph[postgres_aio_pool]
-  '''
-  %%{init: {'flowchart': {'curve': 'linear'}}}%%
-  graph TD;
-  	__start__([<p>__start__</p>]):::first
-  	router_node(router_node)
-  	normal_llm_node(normal_llm_node)
-  	weather_graph_model_node(model_node)
-  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
-  	__end__([<p>__end__</p>]):::last
-  	__start__ --> router_node;
-  	normal_llm_node --> __end__;
-  	weather_graph_weather_node --> __end__;
-  	router_node -.-> normal_llm_node;
-  	router_node -.-> weather_graph_model_node;
-  	router_node -.-> __end__;
-  	subgraph weather_graph
-  	weather_graph_model_node --> weather_graph_weather_node;
-  	end
-  	classDef default fill:#f2f0ff,line-height:1.2
-  	classDef first fill-opacity:0
-  	classDef last fill:#bfb6fc
-  
-  '''
-# ---
-# name: test_weather_subgraph[sqlite_aio]
-  '''
-  %%{init: {'flowchart': {'curve': 'linear'}}}%%
-  graph TD;
-  	__start__([<p>__start__</p>]):::first
-  	router_node(router_node)
-  	normal_llm_node(normal_llm_node)
-  	weather_graph_model_node(model_node)
-  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
-  	__end__([<p>__end__</p>]):::last
-  	__start__ --> router_node;
-  	normal_llm_node --> __end__;
-  	weather_graph_weather_node --> __end__;
-  	router_node -.-> normal_llm_node;
-  	router_node -.-> weather_graph_model_node;
-  	router_node -.-> __end__;
-  	subgraph weather_graph
-  	weather_graph_model_node --> weather_graph_weather_node;
-  	end
-  	classDef default fill:#f2f0ff,line-height:1.2
-  	classDef first fill-opacity:0
-  	classDef last fill:#bfb6fc
-  
-  '''
-# ---

libs/langgraph/tests/test_large_cases.py

@@ -5425,7 +5425,7 @@ def test_in_one_fan_out_out_one_graph_state() -> None:
         docs: Annotated[list[str], sorted_add]
 
     def rewrite_query(data: State) -> State:
-        return {"query": f'query: {data["query"]}'}
+        return {"query": f"query: {data['query']}"}
 
     def retriever_one(data: State) -> State:
         # timer ensures stream output order is stable
@@ -7277,6 +7277,9 @@ def test_send_dedupe_on_resume(
             setattr(self, "__name__", name)
 
         def __call__(self, state):
+            time.sleep(0)
+            # sleep makes it more likely to trigger edge case where 1st task
+            # finishes before 2nd is registered in futures dict
             self.ticks += 1
             update = (
                 [self.name]

libs/langgraph/tests/test_prebuilt.py

@@ -346,6 +346,50 @@ def test_state_modifier_with_store():
     assert response["messages"][-1].content == "foo-hi"
 
 
+async def test_state_modifier_with_store_async():
+    async def add(a: int, b: int):
+        """Adds a and b"""
+        return a + b
+
+    in_memory_store = InMemoryStore()
+    await in_memory_store.aput(
+        ("memories", "1"), "user_name", {"data": "User name is Alice"}
+    )
+    await in_memory_store.aput(
+        ("memories", "2"), "user_name", {"data": "User name is Bob"}
+    )
+
+    async def modify(state, config, *, store):
+        user_id = config["configurable"]["user_id"]
+        system_str = (await store.aget(("memories", user_id), "user_name")).value[
+            "data"
+        ]
+        return [SystemMessage(system_str)] + state["messages"]
+
+    async def modify_no_store(state, config):
+        return SystemMessage("foo") + state["messages"]
+
+    model = FakeToolCallingModel()
+
+    # test state modifier that uses store works
+    agent = create_react_agent(
+        model, [add], state_modifier=modify, store=in_memory_store
+    )
+    response = await agent.ainvoke(
+        {"messages": [("user", "hi")]}, {"configurable": {"user_id": "1"}}
+    )
+    assert response["messages"][-1].content == "User name is Alice-hi"
+
+    # test state modifier that doesn't use store works
+    agent = create_react_agent(
+        model, [add], state_modifier=modify_no_store, store=in_memory_store
+    )
+    response = await agent.ainvoke(
+        {"messages": [("user", "hi")]}, {"configurable": {"user_id": "2"}}
+    )
+    assert response["messages"][-1].content == "foo-hi"
+
+
 @pytest.mark.parametrize("tool_style", ["openai", "anthropic"])
 def test_model_with_tools(tool_style: str):
     model = FakeToolCallingModel(tool_style=tool_style)
@@ -2088,3 +2132,82 @@ def test_inspect_react() -> None:
     model = FakeToolCallingModel(tool_calls=[])
     agent = create_react_agent(model, [])
     inspect.getclosurevars(agent.nodes["agent"].bound.func)
+
+
+def test_react_with_subgraph_tools() -> None:
+    class State(TypedDict):
+        a: int
+        b: int
+
+    class Output(TypedDict):
+        result: int
+
+    # Define the subgraphs
+    def add(state):
+        return {"result": state["a"] + state["b"]}
+
+    add_subgraph = (
+        StateGraph(State, output=Output).add_node(add).add_edge(START, "add").compile()
+    )
+
+    def multiply(state):
+        return {"result": state["a"] * state["b"]}
+
+    multiply_subgraph = (
+        StateGraph(State, output=Output)
+        .add_node(multiply)
+        .add_edge(START, "multiply")
+        .compile()
+    )
+
+    multiply_subgraph.invoke({"a": 2, "b": 3})
+
+    # Add subgraphs as tools
+
+    def addition(a: int, b: int):
+        """Add two numbers"""
+        return add_subgraph.invoke({"a": a, "b": b})["result"]
+
+    def multiplication(a: int, b: int):
+        """Multiply two numbers"""
+        return multiply_subgraph.invoke({"a": a, "b": b})["result"]
+
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [
+                {"args": {"a": 2, "b": 3}, "id": "1", "name": "addition"},
+                {"args": {"a": 2, "b": 3}, "id": "2", "name": "multiplication"},
+            ],
+            [],
+        ]
+    )
+    checkpointer = MemorySaver()
+    tool_node = ToolNode([addition, multiplication], handle_tool_errors=False)
+    agent = create_react_agent(model, tool_node, checkpointer=checkpointer)
+    result = agent.invoke(
+        {"messages": [HumanMessage(content="What's 2 + 3 and 2 * 3?")]},
+        config={"configurable": {"thread_id": "1"}},
+    )
+    assert result["messages"] == [
+        _AnyIdHumanMessage(content="What's 2 + 3 and 2 * 3?"),
+        AIMessage(
+            content="What's 2 + 3 and 2 * 3?",
+            id="0",
+            tool_calls=[
+                ToolCall(name="addition", args={"a": 2, "b": 3}, id="1"),
+                ToolCall(name="multiplication", args={"a": 2, "b": 3}, id="2"),
+            ],
+        ),
+        ToolMessage(
+            content="5", name="addition", tool_call_id="1", id=result["messages"][2].id
+        ),
+        ToolMessage(
+            content="6",
+            name="multiplication",
+            tool_call_id="2",
+            id=result["messages"][3].id,
+        ),
+        AIMessage(
+            content="What's 2 + 3 and 2 * 3?-What's 2 + 3 and 2 * 3?-5-6", id="1"
+        ),
+    ]

libs/langgraph/tests/test_pregel.py

@@ -1,4 +1,5 @@
 import enum
+import functools
 import json
 import logging
 import operator
@@ -9,6 +10,7 @@ import warnings
 from collections import Counter, deque
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
+from dataclasses import dataclass
 from random import randrange
 from typing import (
     Annotated,
@@ -82,6 +84,8 @@ from tests.messages import (
     _AnyIdToolMessage,
 )
 
+pytestmark = pytest.mark.anyio
+
 logger = logging.getLogger(__name__)
 
 
@@ -1436,6 +1440,9 @@ def test_imp_task(request: pytest.FixtureRequest, checkpointer_name: str) -> Non
     checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
     mapper_calls = 0
 
+    class Config:
+        model: str
+
     @task()
     def mapper(input: int) -> str:
         nonlocal mapper_calls
@@ -1443,13 +1450,57 @@ def test_imp_task(request: pytest.FixtureRequest, checkpointer_name: str) -> Non
         time.sleep(input / 100)
         return str(input) * 2
 
-    @entrypoint(checkpointer=checkpointer)
+    @entrypoint(checkpointer=checkpointer, config_schema=Config)
     def graph(input: list[int]) -> list[str]:
         futures = [mapper(i) for i in input]
         mapped = [f.result() for f in futures]
         answer = interrupt("question")
         return [m + answer for m in mapped]
 
+    assert graph.get_input_jsonschema() == {
+        "type": "array",
+        "items": {"type": "integer"},
+        "title": "LangGraphInput",
+    }
+    assert graph.get_output_jsonschema() == {
+        "type": "array",
+        "items": {"type": "string"},
+        "title": "LangGraphOutput",
+    }
+    assert graph.get_config_jsonschema() == {
+        "$defs": {
+            "Configurable": {
+                "properties": {
+                    "model": {"default": None, "title": "Model", "type": "string"},
+                    "checkpoint_id": {
+                        "anyOf": [{"type": "string"}, {"type": "null"}],
+                        "default": None,
+                        "description": "Pass to fetch a past checkpoint. If None, fetches the latest checkpoint.",
+                        "title": "Checkpoint ID",
+                    },
+                    "checkpoint_ns": {
+                        "default": "",
+                        "description": 'Checkpoint namespace. Denotes the path to the subgraph node the checkpoint originates from, separated by `|` character, e.g. `"child|grandchild"`. Defaults to "" (root graph).',
+                        "title": "Checkpoint NS",
+                        "type": "string",
+                    },
+                    "thread_id": {
+                        "default": "",
+                        "title": "Thread ID",
+                        "type": "string",
+                    },
+                },
+                "title": "Configurable",
+                "type": "object",
+            }
+        },
+        "properties": {
+            "configurable": {"$ref": "#/$defs/Configurable", "default": None}
+        },
+        "title": "LangGraphConfig",
+        "type": "object",
+    }
+
     thread1 = {"configurable": {"thread_id": "1"}}
     assert [*graph.stream([0, 1], thread1)] == [
         {"mapper": "00"},
@@ -1474,9 +1525,77 @@ def test_imp_task(request: pytest.FixtureRequest, checkpointer_name: str) -> Non
     assert mapper_calls == 2
 
 
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_imp_nested(
+    request: pytest.FixtureRequest, checkpointer_name: str, snapshot: SnapshotAssertion
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    def mynode(input: list[str]) -> list[str]:
+        return [it + "a" for it in input]
+
+    builder = StateGraph(list[str])
+    builder.add_node(mynode)
+    builder.add_edge(START, "mynode")
+    add_a = builder.compile()
+
+    @task
+    def submapper(input: int) -> str:
+        time.sleep(input / 100)
+        return str(input)
+
+    @task()
+    def mapper(input: int) -> str:
+        sub = submapper(input)
+        time.sleep(input / 100)
+        return sub.result() * 2
+
+    @entrypoint(checkpointer=checkpointer)
+    def graph(input: list[int]) -> list[str]:
+        futures = [mapper(i) for i in input]
+        mapped = [f.result() for f in futures]
+        answer = interrupt("question")
+        final = [m + answer for m in mapped]
+        return add_a.invoke(final)
+
+    assert graph.get_input_jsonschema() == {
+        "type": "array",
+        "items": {"type": "integer"},
+        "title": "LangGraphInput",
+    }
+    assert graph.get_output_jsonschema() == {
+        "type": "array",
+        "items": {"type": "string"},
+        "title": "LangGraphOutput",
+    }
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert [*graph.stream([0, 1], thread1)] == [
+        {"submapper": "0"},
+        {"mapper": "00"},
+        {"submapper": "1"},
+        {"mapper": "11"},
+        {
+            "__interrupt__": (
+                Interrupt(
+                    value="question",
+                    resumable=True,
+                    ns=[AnyStr("graph:")],
+                    when="during",
+                ),
+            )
+        },
+    ]
+
+    assert graph.invoke(Command(resume="answer"), thread1) == [
+        "00answera",
+        "11answera",
+    ]
+
+
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
 def test_imp_stream_order(
-    request: pytest.FixtureRequest, checkpointer_name: str
+    request: pytest.FixtureRequest, checkpointer_name: str, snapshot: SnapshotAssertion
 ) -> None:
     checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
 
@@ -4902,6 +5021,85 @@ def test_interrupt_loop(request: pytest.FixtureRequest, checkpointer_name: str):
     ]
 
 
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_interrupt_functional(
+    request: pytest.FixtureRequest, checkpointer_name: str, snapshot: SnapshotAssertion
+) -> None:
+    checkpointer: BaseCheckpointSaver = request.getfixturevalue(
+        f"checkpointer_{checkpointer_name}"
+    )
+
+    @task
+    def foo(state: dict) -> dict:
+        return {"a": state["a"] + "foo"}
+
+    @task
+    def bar(state: dict) -> dict:
+        return {"a": state["a"] + "bar", "b": state["b"]}
+
+    @entrypoint(checkpointer=checkpointer)
+    def graph(inputs: dict) -> dict:
+        fut_foo = foo(inputs)
+        value = interrupt("Provide value for bar:")
+        bar_input = {**fut_foo.result(), "b": value}
+        fut_bar = bar(bar_input)
+        return fut_bar.result()
+
+    config = {"configurable": {"thread_id": "1"}}
+    # First run, interrupted at bar
+    graph.invoke({"a": ""}, config)
+    # Resume with an answer
+    res = graph.invoke(Command(resume="bar"), config)
+    assert res == {"a": "foobar", "b": "bar"}
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_interrupt_task_functional(
+    request: pytest.FixtureRequest, checkpointer_name: str, snapshot: SnapshotAssertion
+) -> None:
+    checkpointer: BaseCheckpointSaver = request.getfixturevalue(
+        f"checkpointer_{checkpointer_name}"
+    )
+
+    @task
+    def foo(state: dict) -> dict:
+        return {"a": state["a"] + "foo"}
+
+    @task
+    def bar(state: dict) -> dict:
+        value = interrupt("Provide value for bar:")
+        return {"a": state["a"] + value}
+
+    @entrypoint(checkpointer=checkpointer)
+    def graph(inputs: dict) -> dict:
+        fut_foo = foo(inputs)
+        fut_bar = bar(fut_foo.result())
+        return fut_bar.result()
+
+    config = {"configurable": {"thread_id": "1"}}
+    # First run, interrupted at bar
+    assert not graph.invoke({"a": ""}, config)
+    # Resume with an answer
+    res = graph.invoke(Command(resume="bar"), config)
+    assert res == {"a": "foobar"}
+
+    # Test that we can interrupt the same task multiple times
+    config = {"configurable": {"thread_id": "2"}}
+
+    @entrypoint(checkpointer=checkpointer)
+    def graph(inputs: dict) -> dict:
+        foo_result = foo(inputs).result()
+        bar_result = bar(foo_result).result()
+        baz_result = bar(bar_result).result()
+        return baz_result
+
+    # First run, interrupted at bar
+    assert not graph.invoke({"a": ""}, config)
+    # Provide resumes
+    assert not graph.invoke(Command(resume="bar"), config)
+    assert graph.invoke(Command(resume="baz"), config) == {"a": "foobarbaz"}
+
+
 def test_root_mixed_return() -> None:
     def my_node(state: list[str]):
         return [Command(update=["a"]), ["b"]]
@@ -4930,6 +5128,35 @@ def test_dict_mixed_return() -> None:
     assert graph.invoke({"foo": ""}) == {"foo": "ab"}
 
 
+def test_command_pydantic_dataclass() -> None:
+    from pydantic import BaseModel
+
+    class PydanticState(BaseModel):
+        foo: str
+
+    @dataclass
+    class DataclassState:
+        foo: str
+
+    for State in (PydanticState, DataclassState):
+
+        def node_a(state) -> Command[Literal["node_b"]]:
+            return Command(
+                update=State(foo="foo"),
+                goto="node_b",
+            )
+
+        def node_b(state):
+            return {"foo": state.foo + "bar"}
+
+        builder = StateGraph(State)
+        builder.add_edge(START, "node_a")
+        builder.add_node(node_a)
+        builder.add_node(node_b)
+        graph = builder.compile()
+        assert graph.invoke(State(foo="")) == {"foo": "foobar"}
+
+
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
 def test_command_with_static_breakpoints(
     request: pytest.FixtureRequest, checkpointer_name: str
@@ -5322,7 +5549,9 @@ def test_multiple_updates() -> None:
 
 
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
-def test_falsy_return_from_task(request: pytest.FixtureRequest, checkpointer_name: str):
+def test_falsy_return_from_task(
+    request: pytest.FixtureRequest, checkpointer_name: str, snapshot: SnapshotAssertion
+):
     """Test with a falsy return from a task."""
     checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
 
@@ -5342,10 +5571,10 @@ def test_falsy_return_from_task(request: pytest.FixtureRequest, checkpointer_nam
 
 
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
-def test_multiple_interrupts_imperative(
-    request: pytest.FixtureRequest, checkpointer_name: str
+def test_multiple_interrupts_functional(
+    request: pytest.FixtureRequest, checkpointer_name: str, snapshot: SnapshotAssertion
 ):
-    """Test multiple interrupts with an imperative API."""
+    """Test multiple interrupts with functional API."""
     checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
 
     counter = 0
@@ -5526,3 +5755,562 @@ def test_sync_streaming_with_functional_api() -> None:
     delta = arrival_times[1] - arrival_times[0]
     # Delta cannot be less than 10 ms if it is streaming as results are generated.
     assert delta > time_delay
+
+
+def test_entrypoint_without_checkpointer() -> None:
+    """Test no checkpointer."""
+    states = []
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Test without previous
+    @entrypoint()
+    def foo(inputs: Any) -> Any:
+        states.append(inputs)
+        return inputs
+
+    assert foo.invoke({"a": "1"}, config) == {"a": "1"}
+
+    @entrypoint()
+    def foo(inputs: Any, *, previous: Any) -> Any:
+        states.append(previous)
+        return {"previous": previous, "current": inputs}
+
+    assert foo.invoke({"a": "1"}, config) == {"current": {"a": "1"}, "previous": None}
+    assert foo.invoke({"a": "1"}, config) == {"current": {"a": "1"}, "previous": None}
+
+
+def test_entrypoint_stateful() -> None:
+    """Test stateful entrypoint invoke."""
+
+    # Test invoke
+    states = []
+
+    @entrypoint(checkpointer=MemorySaver())
+    def foo(inputs, *, previous: Any) -> Any:
+        states.append(previous)
+        return {"previous": previous, "current": inputs}
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    assert foo.invoke({"a": "1"}, config) == {"current": {"a": "1"}, "previous": None}
+    assert foo.invoke({"a": "2"}, config) == {
+        "current": {"a": "2"},
+        "previous": {"current": {"a": "1"}, "previous": None},
+    }
+    assert foo.invoke({"a": "3"}, config) == {
+        "current": {"a": "3"},
+        "previous": {
+            "current": {"a": "2"},
+            "previous": {"current": {"a": "1"}, "previous": None},
+        },
+    }
+    assert states == [
+        None,
+        {"current": {"a": "1"}, "previous": None},
+        {"current": {"a": "2"}, "previous": {"current": {"a": "1"}, "previous": None}},
+    ]
+
+    # Test stream
+    @entrypoint(checkpointer=MemorySaver())
+    def foo(inputs, *, previous: Any) -> Any:
+        return {"previous": previous, "current": inputs}
+
+    config = {"configurable": {"thread_id": "1"}}
+    items = [item for item in foo.stream({"a": "1"}, config)]
+    assert items == [{"foo": {"current": {"a": "1"}, "previous": None}}]
+
+
+def test_entrypoint_from_sync_generator() -> None:
+    """@entrypoint does not support sync generators."""
+    previous_return_values = []
+
+    @entrypoint(checkpointer=MemorySaver())
+    def foo(inputs, previous=None) -> Any:
+        previous_return_values.append(previous)
+        yield "a"
+        yield "b"
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    assert foo.invoke({"a": "1"}, config) == ["a", "b"]
+    assert previous_return_values == [None]
+    assert foo.invoke({"a": "2"}, config) == ["a", "b"]
+    assert previous_return_values == [None, ["a", "b"]]
+
+
+def test_entrypoint_request_stream_writer() -> None:
+    """Test using a stream writer with an entrypoint."""
+
+    @entrypoint(checkpointer=MemorySaver())
+    def foo(inputs, writer: StreamWriter) -> Any:
+        writer("a")
+        yield "b"
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Different invocations
+    # Are any of these confusing or unexpected?
+    assert list(foo.invoke({}, config)) == ["b"]
+    assert list(foo.stream({}, config)) == ["a", "b"]
+
+    # Stream modes
+    assert list(foo.stream({}, config, stream_mode=["updates"])) == [
+        ("updates", {"foo": ["b"]})
+    ]
+    assert list(foo.stream({}, config, stream_mode=["values"])) == [("values", ["b"])]
+    assert list(foo.stream({}, config, stream_mode=["custom"])) == [
+        (
+            "custom",
+            "a",
+        ),
+        (
+            "custom",
+            "b",
+        ),
+    ]
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_multiple_subgraphs(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class State(TypedDict):
+        a: int
+        b: int
+
+    class Output(TypedDict):
+        result: int
+
+    # Define the subgraphs
+    def add(state):
+        return {"result": state["a"] + state["b"]}
+
+    add_subgraph = (
+        StateGraph(State, output=Output).add_node(add).add_edge(START, "add").compile()
+    )
+
+    def multiply(state):
+        return {"result": state["a"] * state["b"]}
+
+    multiply_subgraph = (
+        StateGraph(State, output=Output)
+        .add_node(multiply)
+        .add_edge(START, "multiply")
+        .compile()
+    )
+
+    # Test calling the same subgraph multiple times
+    def call_same_subgraph(state):
+        result = add_subgraph.invoke(state)
+        another_result = add_subgraph.invoke({"a": result["result"], "b": 10})
+        return another_result
+
+    parent_call_same_subgraph = (
+        StateGraph(State, output=Output)
+        .add_node(call_same_subgraph)
+        .add_edge(START, "call_same_subgraph")
+        .compile(checkpointer=checkpointer)
+    )
+    config = {"configurable": {"thread_id": "1"}}
+    assert parent_call_same_subgraph.invoke({"a": 2, "b": 3}, config) == {"result": 15}
+
+    # Test calling multiple subgraphs
+    class Output(TypedDict):
+        add_result: int
+        multiply_result: int
+
+    def call_multiple_subgraphs(state):
+        add_result = add_subgraph.invoke(state)
+        multiply_result = multiply_subgraph.invoke(state)
+        return {
+            "add_result": add_result["result"],
+            "multiply_result": multiply_result["result"],
+        }
+
+    parent_call_multiple_subgraphs = (
+        StateGraph(State, output=Output)
+        .add_node(call_multiple_subgraphs)
+        .add_edge(START, "call_multiple_subgraphs")
+        .compile(checkpointer=checkpointer)
+    )
+    config = {"configurable": {"thread_id": "2"}}
+    assert parent_call_multiple_subgraphs.invoke({"a": 2, "b": 3}, config) == {
+        "add_result": 5,
+        "multiply_result": 6,
+    }
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_multiple_subgraphs_functional(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    # Define addition subgraph
+    @entrypoint()
+    def add(inputs):
+        a, b = inputs
+        return a + b
+
+    # Define multiplication subgraph using tasks
+    @task
+    def multiply_task(a, b):
+        return a * b
+
+    @entrypoint()
+    def multiply(inputs):
+        return multiply_task(*inputs).result()
+
+    # Test calling the same subgraph multiple times
+    @task
+    def call_same_subgraph(a, b):
+        result = add.invoke([a, b])
+        another_result = add.invoke([result, 10])
+        return another_result
+
+    @entrypoint(checkpointer=checkpointer)
+    def parent_call_same_subgraph(inputs):
+        return call_same_subgraph(*inputs).result()
+
+    config = {"configurable": {"thread_id": "1"}}
+    assert parent_call_same_subgraph.invoke([2, 3], config) == 15
+
+    # Test calling multiple subgraphs
+    @task
+    def call_multiple_subgraphs(a, b):
+        add_result = add.invoke([a, b])
+        multiply_result = multiply.invoke([a, b])
+        return [add_result, multiply_result]
+
+    @entrypoint(checkpointer=checkpointer)
+    def parent_call_multiple_subgraphs(inputs):
+        return call_multiple_subgraphs(*inputs).result()
+
+    config = {"configurable": {"thread_id": "2"}}
+    assert parent_call_multiple_subgraphs.invoke([2, 3], config) == [5, 6]
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_multiple_subgraphs_mixed(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class State(TypedDict):
+        a: int
+        b: int
+
+    class Output(TypedDict):
+        result: int
+
+    # Define the subgraphs
+    def add(state):
+        return {"result": state["a"] + state["b"]}
+
+    add_subgraph = (
+        StateGraph(State, output=Output).add_node(add).add_edge(START, "add").compile()
+    )
+
+    def multiply(state):
+        return {"result": state["a"] * state["b"]}
+
+    multiply_subgraph = (
+        StateGraph(State, output=Output)
+        .add_node(multiply)
+        .add_edge(START, "multiply")
+        .compile()
+    )
+
+    # Test calling the same subgraph multiple times
+    @task
+    def call_same_subgraph(a, b):
+        result = add_subgraph.invoke({"a": a, "b": b})["result"]
+        another_result = add_subgraph.invoke({"a": result, "b": 10})["result"]
+        return another_result
+
+    @entrypoint(checkpointer=checkpointer)
+    def parent_call_same_subgraph(inputs):
+        return call_same_subgraph(*inputs).result()
+
+    config = {"configurable": {"thread_id": "1"}}
+    assert parent_call_same_subgraph.invoke([2, 3], config) == 15
+
+    # Test calling multiple subgraphs
+    @task
+    def call_multiple_subgraphs(a, b):
+        add_result = add_subgraph.invoke({"a": a, "b": b})["result"]
+        multiply_result = multiply_subgraph.invoke({"a": a, "b": b})["result"]
+        return [add_result, multiply_result]
+
+    @entrypoint(checkpointer=checkpointer)
+    def parent_call_multiple_subgraphs(inputs):
+        return call_multiple_subgraphs(*inputs).result()
+
+    config = {"configurable": {"thread_id": "2"}}
+    assert parent_call_multiple_subgraphs.invoke([2, 3], config) == [5, 6]
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_multiple_subgraphs_mixed_checkpointer(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class SubgraphState(TypedDict):
+        sub_counter: Annotated[int, operator.add]
+
+    def subgraph_node(state):
+        return {"sub_counter": 2}
+
+    sub_graph_1 = (
+        StateGraph(SubgraphState)
+        .add_node(subgraph_node)
+        .add_edge(START, "subgraph_node")
+        .compile(checkpointer=True)
+    )
+
+    class OtherSubgraphState(TypedDict):
+        other_sub_counter: Annotated[int, operator.add]
+
+    def other_subgraph_node(state):
+        return {"other_sub_counter": 3}
+
+    sub_graph_2 = (
+        StateGraph(OtherSubgraphState)
+        .add_node(other_subgraph_node)
+        .add_edge(START, "other_subgraph_node")
+        .compile()
+    )
+
+    class ParentState(TypedDict):
+        parent_counter: int
+
+    def parent_node(state):
+        result = sub_graph_1.invoke({"sub_counter": state["parent_counter"]})
+        other_result = sub_graph_2.invoke({"other_sub_counter": result["sub_counter"]})
+        return {"parent_counter": other_result["other_sub_counter"]}
+
+    parent_graph = (
+        StateGraph(ParentState)
+        .add_node(parent_node)
+        .add_edge(START, "parent_node")
+        .compile(checkpointer=checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+    assert parent_graph.invoke({"parent_counter": 0}, config) == {"parent_counter": 5}
+    assert parent_graph.invoke({"parent_counter": 0}, config) == {"parent_counter": 7}
+    config = {"configurable": {"thread_id": "2"}}
+    assert [
+        c
+        for c in parent_graph.stream(
+            {"parent_counter": 0}, config, subgraphs=True, stream_mode="updates"
+        )
+    ] == [
+        (("parent_node",), {"subgraph_node": {"sub_counter": 2}}),
+        (
+            (AnyStr("parent_node:"), "1"),
+            {"other_subgraph_node": {"other_sub_counter": 3}},
+        ),
+        ((), {"parent_node": {"parent_counter": 5}}),
+    ]
+    assert [
+        c
+        for c in parent_graph.stream(
+            {"parent_counter": 0}, config, subgraphs=True, stream_mode="updates"
+        )
+    ] == [
+        (("parent_node",), {"subgraph_node": {"sub_counter": 2}}),
+        (
+            (AnyStr("parent_node:"), "1"),
+            {"other_subgraph_node": {"other_sub_counter": 3}},
+        ),
+        ((), {"parent_node": {"parent_counter": 7}}),
+    ]
+
+
+def test_entrypoint_output_schema_with_return_and_save() -> None:
+    """Test output schema inference with entrypoint.final."""
+
+    # Un-parameterized entrypoint.final is interpreted as entrypoint.final[Any, Any]
+    @entrypoint()
+    def foo2(inputs, *, previous: Any) -> entrypoint.final:
+        return entrypoint.final(value="foo", save=1)
+
+    assert foo2.get_output_schema().model_json_schema() == {
+        "title": "LangGraphOutput",
+    }
+
+    @entrypoint()
+    def foo(inputs, *, previous: Any) -> entrypoint.final[str, int]:
+        return entrypoint.final(value="foo", save=1)
+
+    assert foo.get_output_schema().model_json_schema() == {
+        "title": "LangGraphOutput",
+        "type": "string",
+    }
+
+    with pytest.raises(TypeError):
+        # Raise an exception on an improperly parameterized entrypoint.final
+        # User is attempting to parameterize in this case, so we'll offer
+        # a bit of help if it's not done correctly.
+        @entrypoint()
+        def foo(inputs, *, previous: Any) -> entrypoint.final[int]:
+            return entrypoint.final(value=1, save=1)  # type: ignore
+
+    @entrypoint()
+    def foo(inputs, *, previous: Any) -> Generator[int, None, None]:
+        yield 1
+
+    assert foo.get_output_schema().model_json_schema() == {
+        "items": {
+            "type": "integer",
+        },
+        "title": "LangGraphOutput",
+        "type": "array",
+    }
+
+
+def test_entrypoint_with_return_and_save() -> None:
+    """Test entrypoint with return and save."""
+    previous_ = None
+
+    @entrypoint(checkpointer=MemorySaver())
+    def foo(msg: str, *, previous: Any) -> entrypoint.final[int, list[str]]:
+        nonlocal previous_
+        previous_ = previous
+        previous = previous or []
+        return entrypoint.final(value=len(previous), save=previous + [msg])
+
+    assert foo.get_output_schema().model_json_schema() == {
+        "title": "LangGraphOutput",
+        "type": "integer",
+    }
+
+    config = {"configurable": {"thread_id": "1"}}
+    assert foo.invoke("hello", config) == 0
+    assert previous_ is None
+    assert foo.invoke("goodbye", config) == 1
+    assert previous_ == ["hello"]
+    assert foo.invoke("definitely", config) == 2
+    assert previous_ == ["hello", "goodbye"]
+
+
+def test_entrypoint_generator_with_return_and_save() -> None:
+    """Verify that generators produce expected results."""
+    previous_ = None
+
+    @entrypoint(checkpointer=MemorySaver())
+    def workflow(inputs: dict, *, previous: Any):
+        nonlocal previous_
+        previous_ = previous
+
+        yield "hello"
+        yield "world"
+        yield entrypoint.final(value="!", save="saved value")
+
+    assert list(workflow.stream({}, {"configurable": {"thread_id": "0"}})) == [
+        "hello",
+        "world",
+    ]
+    assert list(
+        workflow.stream({}, {"configurable": {"thread_id": "0"}}, stream_mode="updates")
+    ) == [
+        {
+            "workflow": "!",
+        }
+    ]
+
+    assert workflow.invoke({}, {"configurable": {"thread_id": "1"}}) == "!"
+    assert previous_ is None
+
+    # 2nd time around previous is set
+    assert workflow.invoke({}, {"configurable": {"thread_id": "1"}}) == "!"
+    assert previous_ == "saved value"
+
+    # Test with another thread
+    assert workflow.invoke({}, {"configurable": {"thread_id": "2"}}) == "!"
+    assert previous_ is None
+
+
+async def test_entrypoint_async_generator_with_return_and_save() -> None:
+    """Verify that generators produce expected results."""
+    previous_ = None
+
+    @entrypoint(checkpointer=MemorySaver())
+    async def workflow(inputs: dict, *, previous: Any):
+        nonlocal previous_
+        previous_ = previous
+
+        yield "hello"
+        yield "world"
+        yield entrypoint.final(value="!", save="saved value")
+
+    assert [
+        c async for c in workflow.astream({}, {"configurable": {"thread_id": "0"}})
+    ] == [
+        "hello",
+        "world",
+    ]
+
+    assert await workflow.ainvoke({}, {"configurable": {"thread_id": "1"}}) == "!"
+
+    assert previous_ is None
+
+    # 2nd time around previous is set
+    assert await workflow.ainvoke({}, {"configurable": {"thread_id": "1"}}) == "!"
+    assert previous_ == "saved value"
+
+    # Test with another thread
+    assert await workflow.ainvoke({}, {"configurable": {"thread_id": "2"}}) == "!"
+    assert previous_ is None
+
+
+def test_named_tasks_functional() -> None:
+    class Foo:
+        def foo(self, value: str) -> dict:
+            return value + "foo"
+
+    f = Foo()
+
+    # class method task
+    foo = task(f.foo, name="custom_foo")
+
+    # regular function task
+    @task(name="custom_bar")
+    def bar(value: str) -> dict:
+        return value + "|bar"
+
+    def baz(update: str, value: str) -> dict:
+        return value + f"|{update}"
+
+    # partial function task (unnamed)
+    baz_task = task(functools.partial(baz, "baz"))
+    # partial function task (named_)
+    custom_baz_task = task(functools.partial(baz, "custom_baz"), name="custom_baz")
+
+    class Qux:
+        def __call__(self, value: str) -> dict:
+            return value + "|qux"
+
+    qux_task = task(Qux(), name="qux")
+
+    @entrypoint()
+    def workflow(inputs: dict) -> dict:
+        fut_foo = foo(inputs)
+        fut_bar = bar(fut_foo.result())
+        fut_baz = baz_task(fut_bar.result())
+        fut_custom_baz = custom_baz_task(fut_baz.result())
+        fut_qux = qux_task(fut_custom_baz.result())
+        return fut_qux.result()
+
+    assert list(workflow.stream("", stream_mode="updates")) == [
+        {"custom_foo": "foo"},
+        {"custom_bar": "foo|bar"},
+        {"baz": "foo|bar|baz"},
+        {"custom_baz": "foo|bar|baz|custom_baz"},
+        {"qux": "foo|bar|baz|custom_baz|qux"},
+        {"workflow": "foo|bar|baz|custom_baz|qux"},
+    ]

libs/langgraph/tests/test_pregel_async.py

@@ -1,4 +1,5 @@
 import asyncio
+import functools
 import logging
 import operator
 import random
@@ -1132,7 +1133,8 @@ async def test_node_not_cancelled_on_other_node_interrupted(
         assert awhiles == 1
 
 
-async def test_step_timeout_on_stream_hang() -> None:
+@pytest.mark.parametrize("stream_hang_s", [0.3, 0.6])
+async def test_step_timeout_on_stream_hang(stream_hang_s: float) -> None:
     inner_task_cancelled = False
 
     async def awhile(input: Any) -> None:
@@ -1157,7 +1159,7 @@ async def test_step_timeout_on_stream_hang() -> None:
     with pytest.raises(asyncio.TimeoutError):
         async for chunk in graph.astream(1, stream_mode="updates"):
             assert chunk == {"alittlewhile": {"alittlewhile": "1"}}
-            await asyncio.sleep(0.6)
+            await asyncio.sleep(stream_hang_s)
 
     assert inner_task_cancelled
 
@@ -2474,7 +2476,12 @@ async def test_imp_task(checkpointer_name: str) -> None:
         assert mapper_calls == 2
         assert len(tracer.runs) == 1
         assert len(tracer.runs[0].child_runs) == 1
-        assert tracer.runs[0].child_runs[0].name == "graph"
+        entrypoint_run = tracer.runs[0].child_runs[0]
+        assert entrypoint_run.name == "graph"
+        mapper_runs = [r for r in entrypoint_run.child_runs if r.name == "mapper"]
+        assert len(mapper_runs) == 2
+        assert any(r.inputs == {"input": 0} for r in mapper_runs)
+        assert any(r.inputs == {"input": 1} for r in mapper_runs)
 
         assert await graph.ainvoke(Command(resume="answer"), thread1) == [
             "00answer",
@@ -2483,6 +2490,71 @@ async def test_imp_task(checkpointer_name: str) -> None:
         assert mapper_calls == 2
 
 
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_imp_nested(checkpointer_name: str) -> None:
+    async def mynode(input: list[str]) -> list[str]:
+        return [it + "a" for it in input]
+
+    builder = StateGraph(list[str])
+    builder.add_node(mynode)
+    builder.add_edge(START, "mynode")
+    add_a = builder.compile()
+
+    @task
+    def submapper(input: int) -> str:
+        return str(input)
+
+    @task
+    async def mapper(input: int) -> str:
+        await asyncio.sleep(input / 100)
+        return await submapper(input) * 2
+
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        @entrypoint(checkpointer=checkpointer)
+        async def graph(input: list[int]) -> list[str]:
+            futures = [mapper(i) for i in input]
+            mapped = await asyncio.gather(*futures)
+            answer = interrupt("question")
+            final = [m + answer for m in mapped]
+            return await add_a.ainvoke(final)
+
+        assert graph.get_input_jsonschema() == {
+            "type": "array",
+            "items": {"type": "integer"},
+            "title": "LangGraphInput",
+        }
+        assert graph.get_output_jsonschema() == {
+            "type": "array",
+            "items": {"type": "string"},
+            "title": "LangGraphOutput",
+        }
+
+        thread1 = {"configurable": {"thread_id": "1"}}
+        assert [c async for c in graph.astream([0, 1], thread1)] == [
+            {"submapper": "0"},
+            {"mapper": "00"},
+            {"submapper": "1"},
+            {"mapper": "11"},
+            {
+                "__interrupt__": (
+                    Interrupt(
+                        value="question",
+                        resumable=True,
+                        ns=[AnyStr("graph:")],
+                        when="during",
+                    ),
+                )
+            },
+        ]
+
+        assert await graph.ainvoke(Command(resume="answer"), thread1) == [
+            "00answera",
+            "11answera",
+        ]
+
+
 @NEEDS_CONTEXTVARS
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
 async def test_imp_task_cancel(checkpointer_name: str) -> None:
@@ -6281,6 +6353,63 @@ async def test_interrupt_loop(checkpointer_name: str):
         ]
 
 
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_interrupt_functional(checkpointer_name: str) -> None:
+    @task
+    async def foo(state: dict) -> dict:
+        return {"a": state["a"] + "foo"}
+
+    @task
+    async def bar(state: dict) -> dict:
+        return {"a": state["a"] + "bar", "b": state["b"]}
+
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        @entrypoint(checkpointer=checkpointer)
+        async def graph(inputs: dict) -> dict:
+            foo_result = await foo(inputs)
+            value = interrupt("Provide value for bar:")
+            bar_input = {**foo_result, "b": value}
+            bar_result = await bar(bar_input)
+            return bar_result
+
+        config = {"configurable": {"thread_id": "1"}}
+        # First run, interrupted at bar
+        await graph.ainvoke({"a": ""}, config)
+        # Resume with an answer
+        res = await graph.ainvoke(Command(resume="bar"), config)
+        assert res == {"a": "foobar", "b": "bar"}
+
+
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_interrupt_task_functional(checkpointer_name: str) -> None:
+    @task
+    async def foo(state: dict) -> dict:
+        return {"a": state["a"] + "foo"}
+
+    @task
+    async def bar(state: dict) -> dict:
+        value = interrupt("Provide value for bar:")
+        return {"a": state["a"] + value}
+
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        @entrypoint(checkpointer=checkpointer)
+        async def graph(inputs: dict) -> dict:
+            foo_result = await foo(inputs)
+            bar_result = await bar(foo_result)
+            return bar_result
+
+        config = {"configurable": {"thread_id": "1"}}
+        # First run, interrupted at bar
+        await graph.ainvoke({"a": ""}, config)
+        # Resume with an answer
+        res = await graph.ainvoke(Command(resume="bar"), config)
+        assert res == {"a": "foobar"}
+
+
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
 async def test_command_with_static_breakpoints(checkpointer_name: str) -> None:
     """Test that we can use Command to resume and update with static breakpoints."""
@@ -6684,8 +6813,8 @@ async def test_falsy_return_from_task(checkpointer_name: str) -> None:
 
 @NEEDS_CONTEXTVARS
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
-async def test_multiple_interrupts_imperative(checkpointer_name: str) -> None:
-    """Test multiple interrupts with an imperative API."""
+async def test_multiple_interrupts_functional(checkpointer_name: str) -> None:
+    """Test multiple interrupts with functional API."""
     from langgraph.func import entrypoint, task
 
     counter = 0
@@ -6871,3 +7000,378 @@ async def test_async_streaming_with_functional_api() -> None:
     delta = arrival_times[1] - arrival_times[0]
     # Delta cannot be less than 10 ms if it is streaming as results are generated.
     assert delta > time_delay
+
+
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_multiple_subgraphs(checkpointer_name: str) -> None:
+    class State(TypedDict):
+        a: int
+        b: int
+
+    class Output(TypedDict):
+        result: int
+
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+        # Define the subgraphs
+        async def add(state):
+            return {"result": state["a"] + state["b"]}
+
+        add_subgraph = (
+            StateGraph(State, output=Output)
+            .add_node(add)
+            .add_edge(START, "add")
+            .compile()
+        )
+
+        async def multiply(state):
+            return {"result": state["a"] * state["b"]}
+
+        multiply_subgraph = (
+            StateGraph(State, output=Output)
+            .add_node(multiply)
+            .add_edge(START, "multiply")
+            .compile()
+        )
+
+        # Test calling the same subgraph multiple times
+        async def call_same_subgraph(state):
+            result = await add_subgraph.ainvoke(state)
+            another_result = await add_subgraph.ainvoke(
+                {"a": result["result"], "b": 10}
+            )
+            return another_result
+
+        parent_call_same_subgraph = (
+            StateGraph(State, output=Output)
+            .add_node(call_same_subgraph)
+            .add_edge(START, "call_same_subgraph")
+            .compile(checkpointer=checkpointer)
+        )
+        config = {"configurable": {"thread_id": "1"}}
+        assert await parent_call_same_subgraph.ainvoke({"a": 2, "b": 3}, config) == {
+            "result": 15
+        }
+
+        # Test calling multiple subgraphs
+        class Output(TypedDict):
+            add_result: int
+            multiply_result: int
+
+        async def call_multiple_subgraphs(state):
+            add_result = await add_subgraph.ainvoke(state)
+            multiply_result = await multiply_subgraph.ainvoke(state)
+            return {
+                "add_result": add_result["result"],
+                "multiply_result": multiply_result["result"],
+            }
+
+        parent_call_multiple_subgraphs = (
+            StateGraph(State, output=Output)
+            .add_node(call_multiple_subgraphs)
+            .add_edge(START, "call_multiple_subgraphs")
+            .compile(checkpointer=checkpointer)
+        )
+        config = {"configurable": {"thread_id": "2"}}
+        assert await parent_call_multiple_subgraphs.ainvoke(
+            {"a": 2, "b": 3}, config
+        ) == {
+            "add_result": 5,
+            "multiply_result": 6,
+        }
+
+
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_multiple_subgraphs_functional(checkpointer_name: str) -> None:
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+        # Define addition subgraph
+        @entrypoint()
+        async def add(inputs):
+            a, b = inputs
+            return a + b
+
+        # Define multiplication subgraph using tasks
+        @task
+        async def multiply_task(a, b):
+            return a * b
+
+        @entrypoint()
+        async def multiply(inputs):
+            return await multiply_task(*inputs)
+
+        # Test calling the same subgraph multiple times
+        @task
+        async def call_same_subgraph(a, b):
+            result = await add.ainvoke([a, b])
+            another_result = await add.ainvoke([result, 10])
+            return another_result
+
+        @entrypoint(checkpointer=checkpointer)
+        async def parent_call_same_subgraph(inputs):
+            return await call_same_subgraph(*inputs)
+
+        config = {"configurable": {"thread_id": "1"}}
+        assert await parent_call_same_subgraph.ainvoke([2, 3], config) == 15
+
+        # Test calling multiple subgraphs
+        @task
+        async def call_multiple_subgraphs(a, b):
+            add_result = await add.ainvoke([a, b])
+            multiply_result = await multiply.ainvoke([a, b])
+            return [add_result, multiply_result]
+
+        @entrypoint(checkpointer=checkpointer)
+        async def parent_call_multiple_subgraphs(inputs):
+            return await call_multiple_subgraphs(*inputs)
+
+        config = {"configurable": {"thread_id": "2"}}
+        assert await parent_call_multiple_subgraphs.ainvoke([2, 3], config) == [5, 6]
+
+
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_multiple_subgraphs_mixed(checkpointer_name: str) -> None:
+    class State(TypedDict):
+        a: int
+        b: int
+
+    class Output(TypedDict):
+        result: int
+
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+        # Define the subgraphs
+        async def add(state):
+            return {"result": state["a"] + state["b"]}
+
+        add_subgraph = (
+            StateGraph(State, output=Output)
+            .add_node(add)
+            .add_edge(START, "add")
+            .compile()
+        )
+
+        async def multiply(state):
+            return {"result": state["a"] * state["b"]}
+
+        multiply_subgraph = (
+            StateGraph(State, output=Output)
+            .add_node(multiply)
+            .add_edge(START, "multiply")
+            .compile()
+        )
+
+        # Test calling the same subgraph multiple times
+        @task
+        async def call_same_subgraph(a, b):
+            result = (await add_subgraph.ainvoke({"a": a, "b": b}))["result"]
+            another_result = (await add_subgraph.ainvoke({"a": result, "b": 10}))[
+                "result"
+            ]
+            return another_result
+
+        @entrypoint(checkpointer=checkpointer)
+        async def parent_call_same_subgraph(inputs):
+            return await call_same_subgraph(*inputs)
+
+        config = {"configurable": {"thread_id": "1"}}
+        assert await parent_call_same_subgraph.ainvoke([2, 3], config) == 15
+
+        # Test calling multiple subgraphs
+        @task
+        async def call_multiple_subgraphs(a, b):
+            add_result = (await add_subgraph.ainvoke({"a": a, "b": b}))["result"]
+            multiply_result = (await multiply_subgraph.ainvoke({"a": a, "b": b}))[
+                "result"
+            ]
+            return [add_result, multiply_result]
+
+        @entrypoint(checkpointer=checkpointer)
+        async def parent_call_multiple_subgraphs(inputs):
+            return await call_multiple_subgraphs(*inputs)
+
+        config = {"configurable": {"thread_id": "2"}}
+        assert await parent_call_multiple_subgraphs.ainvoke([2, 3], config) == [5, 6]
+
+
+@NEEDS_CONTEXTVARS
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_multiple_subgraphs_mixed_checkpointer(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        class SubgraphState(TypedDict):
+            sub_counter: Annotated[int, operator.add]
+
+        async def subgraph_node(state):
+            return {"sub_counter": 2}
+
+        sub_graph_1 = (
+            StateGraph(SubgraphState)
+            .add_node(subgraph_node)
+            .add_edge(START, "subgraph_node")
+            .compile(checkpointer=True)
+        )
+
+        class OtherSubgraphState(TypedDict):
+            other_sub_counter: Annotated[int, operator.add]
+
+        async def other_subgraph_node(state):
+            return {"other_sub_counter": 3}
+
+        sub_graph_2 = (
+            StateGraph(OtherSubgraphState)
+            .add_node(other_subgraph_node)
+            .add_edge(START, "other_subgraph_node")
+            .compile()
+        )
+
+        class ParentState(TypedDict):
+            parent_counter: int
+
+        async def parent_node(state):
+            result = await sub_graph_1.ainvoke({"sub_counter": state["parent_counter"]})
+            other_result = await sub_graph_2.ainvoke(
+                {"other_sub_counter": result["sub_counter"]}
+            )
+            return {"parent_counter": other_result["other_sub_counter"]}
+
+        parent_graph = (
+            StateGraph(ParentState)
+            .add_node(parent_node)
+            .add_edge(START, "parent_node")
+            .compile(checkpointer=checkpointer)
+        )
+
+        config = {"configurable": {"thread_id": "1"}}
+        assert await parent_graph.ainvoke({"parent_counter": 0}, config) == {
+            "parent_counter": 5
+        }
+        assert await parent_graph.ainvoke({"parent_counter": 0}, config) == {
+            "parent_counter": 7
+        }
+        config = {"configurable": {"thread_id": "2"}}
+        assert [
+            c
+            async for c in parent_graph.astream(
+                {"parent_counter": 0}, config, subgraphs=True, stream_mode="updates"
+            )
+        ] == [
+            (("parent_node",), {"subgraph_node": {"sub_counter": 2}}),
+            (
+                (AnyStr("parent_node:"), "1"),
+                {"other_subgraph_node": {"other_sub_counter": 3}},
+            ),
+            ((), {"parent_node": {"parent_counter": 5}}),
+        ]
+        assert [
+            c
+            async for c in parent_graph.astream(
+                {"parent_counter": 0}, config, subgraphs=True, stream_mode="updates"
+            )
+        ] == [
+            (("parent_node",), {"subgraph_node": {"sub_counter": 2}}),
+            (
+                (AnyStr("parent_node:"), "1"),
+                {"other_subgraph_node": {"other_sub_counter": 3}},
+            ),
+            ((), {"parent_node": {"parent_counter": 7}}),
+        ]
+
+
+@NEEDS_CONTEXTVARS
+async def test_async_entrypoint_without_checkpointer() -> None:
+    """Test no checkpointer."""
+    states = []
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Test without previous
+    @entrypoint()
+    async def foo(inputs: Any) -> Any:
+        states.append(inputs)
+        return inputs
+
+    assert (await foo.ainvoke({"a": "1"}, config)) == {"a": "1"}
+
+    @entrypoint()
+    async def foo(inputs: Any, *, previous: Any) -> Any:
+        states.append(previous)
+        return {"previous": previous, "current": inputs}
+
+    assert (await foo.ainvoke({"a": "1"}, config)) == {
+        "current": {"a": "1"},
+        "previous": None,
+    }
+    assert (await foo.ainvoke({"a": "1"}, config)) == {
+        "current": {"a": "1"},
+        "previous": None,
+    }
+
+
+@NEEDS_CONTEXTVARS
+async def test_entrypoint_from_async_generator() -> None:
+    """@entrypoint does not support sync generators."""
+    # Test invoke
+    previous_return_values = []
+
+    # In this version reducers do not work
+    @entrypoint(checkpointer=MemorySaver())
+    async def foo(inputs, previous=None) -> Any:
+        previous_return_values.append(previous)
+        yield "a"
+        yield "b"
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    assert list(await foo.ainvoke({"a": "1"}, config)) == ["a", "b"]
+    assert previous_return_values == [None]
+
+
+@NEEDS_CONTEXTVARS
+async def test_named_tasks_functional() -> None:
+    class Foo:
+        async def foo(self, value: str) -> dict:
+            return value + "foo"
+
+    f = Foo()
+
+    # class method task
+    foo = task(f.foo, name="custom_foo")
+
+    # regular function task
+    @task(name="custom_bar")
+    async def bar(value: str) -> dict:
+        return value + "|bar"
+
+    async def baz(update: str, value: str) -> dict:
+        return value + f"|{update}"
+
+    # partial function task (unnamed)
+    baz_task = task(functools.partial(baz, "baz"))
+    # partial function task (named_)
+    custom_baz_task = task(functools.partial(baz, "custom_baz"), name="custom_baz")
+
+    class Qux:
+        def __call__(self, value: str) -> dict:
+            return value + "|qux"
+
+    qux_task = task(Qux(), name="qux")
+
+    @entrypoint()
+    async def workflow(inputs: dict) -> dict:
+        foo_result = await foo(inputs)
+        bar_result = await bar(foo_result)
+        baz_result = await baz_task(bar_result)
+        custom_baz_result = await custom_baz_task(baz_result)
+        qux_result = await qux_task(custom_baz_result)
+        return qux_result
+
+    assert [c async for c in workflow.astream("", stream_mode="updates")] == [
+        {"custom_foo": "foo"},
+        {"custom_bar": "foo|bar"},
+        {"baz": "foo|bar|baz"},
+        {"custom_baz": "foo|bar|baz|custom_baz"},
+        {"qux": "foo|bar|baz|custom_baz|qux"},
+        {"workflow": "foo|bar|baz|custom_baz|qux"},
+    ]

libs/scheduler-kafka/langgraph/scheduler/kafka/orchestrator.py

@@ -13,10 +13,8 @@ from typing_extensions import Self
 
 import langgraph.scheduler.kafka.serde as serde
 from langgraph.constants import (
-    CONF,
     CONFIG_KEY_DEDUPE_TASKS,
     CONFIG_KEY_ENSURE_LATEST,
-    CONFIG_KEY_SCRATCHPAD,
     INTERRUPT,
     SCHEDULED,
 )
@@ -178,8 +176,6 @@ class AsyncKafkaOrchestrator(AbstractAsyncContextManager):
                             CONFIG_KEY_ENSURE_LATEST: True,
                         },
                     )
-                    if CONFIG_KEY_SCRATCHPAD in config[CONF]:
-                        config[CONF][CONFIG_KEY_SCRATCHPAD]["subgraph_counter"] = 0
                     # send messages to executor
                     futures = await asyncio.gather(
                         *(
@@ -366,8 +362,6 @@ class KafkaOrchestrator(AbstractContextManager):
                             CONFIG_KEY_ENSURE_LATEST: True,
                         },
                     )
-                    if CONFIG_KEY_SCRATCHPAD in config[CONF]:
-                        config[CONF][CONFIG_KEY_SCRATCHPAD]["subgraph_counter"] = 0
                     # send messages to executor
                     futures = [
                         self.producer.send(