prebuilt: release 0.1.3 (#3830)

This PR fixes the type of `foo` in the `State` class in Persistence
documentation. The type was previously defined as `int`, but the code
uses it as a `str`.

Updated the type of `foo` in the documentation to `str` to match its
actual usage in the code.

No changes to the functionality or codebase, only a documentation fix.

.github/workflows/_lint.yml

@@ -54,7 +54,7 @@ jobs:
         if: steps.changed-files.outputs.all
         shell: bash
         working-directory: ${{ inputs.working-directory }}
-        run: poetry lock --check
+        run: poetry check --lock
 
       - name: Install dependencies
         if: steps.changed-files.outputs.all

.github/workflows/_test.yml

@@ -39,6 +39,12 @@ jobs:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
 
+      - name: Check Lock
+        shell: bash
+        working-directory: ${{ inputs.working-directory }}
+        run: |
+          poetry check --lock
+
       - name: Install dependencies
         shell: bash
         working-directory: ${{ inputs.working-directory }}

.github/workflows/deploy_docs.yml

@@ -127,6 +127,7 @@ jobs:
               --check-links-ignore "https://openai\.com/.*" \
               --check-links-ignore "https://www\.uber\.com/.*" \
               --check-links-ignore "https://pepy\.tech/.*" \
+              --check-links-ignore "docs/docs/static/wordmark_*" \
               --check-links $(find site -name "index.html" | grep -v 'storm/index.html')
               
           else
@@ -147,6 +148,7 @@ jobs:
                 --check-links-ignore "https://twitter.com/.*" \
                 --check-links-ignore "https://github\.com/.*" \
                 --check-links-ignore "/.*\.(ipynb|html)$" \
+                --check-links-ignore "docs/docs/static/wordmark_*" \
                 --check-links ${CHANGED_FILES} \
                 || ([ $? = 5 ] && exit 0 || exit $?)
             else

README.md

@@ -1,339 +1,87 @@
-# 🦜🕸️LangGraph
+<picture class="github-only">
+  <source media="(prefers-color-scheme: light)" srcset="docs/docs/static/wordmark_dark.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
+  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
+</picture>
 
-![Version](https://img.shields.io/pypi/v/langgraph)
+<div>
+<br>
+</div>
+
+[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
-⚡ Building language agents as graphs ⚡
-
 > [!NOTE]
 > Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
 
-## Overview
+LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
 
-[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.
-
-### Why use LangGraph?
-
-LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. 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:
-
-- **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.
-
-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](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
-- **Background runs**: Runs agents asynchronously in the background
-- **Support for long running agents**: Infrastructure that can handle long running processes
-- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
-- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
-
-## Installation
-
-```shell
+```bash
 pip install -U langgraph
 ```
 
-## Example
-
-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
-```
-
-```shell
-export ANTHROPIC_API_KEY=sk-...
-```
-
-Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
-
-```shell
-export LANGSMITH_TRACING=true
-export LANGSMITH_API_KEY=lsv2_sk_...
-```
-
-The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
-
-<details open>
-  <summary>High-level implementation</summary>
+To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
 
 ```python
+# This code depends on pip install langchain[anthropic]
 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}}
+agent = create_react_agent("anthropic:claude-3-7-sonnet-latest", tools=[search])
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
 )
-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)
+## Why use LangGraph?
 
-```python
-final_state = app.invoke(
-    {"messages": [{"role": "user", "content": "what about ny"}]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
 
-```
-"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>
+- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
+- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
+- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
 
-> [!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.
+LangGraph is trusted in production and powering agents for companies like:
 
-<details>
-<summary>Low-level implementation</summary>
+- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
+- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
+- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
+- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
+- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
 
-```python
-from typing import Literal
+## LangGraph’s ecosystem
 
-from langchain_anthropic import ChatAnthropic
-from langchain_core.tools import tool
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.graph import END, START, StateGraph, MessagesState
-from langgraph.prebuilt import ToolNode
+While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
+- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 
-# 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."
+## Pairing with LangGraph Platform
 
+While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
 
-tools = [search]
+LangGraph Platform can help engineering teams:
 
-tool_node = ToolNode(tools)
+- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
+- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
+- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
 
-model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
+## Additional resources
 
-# Define the function that determines whether to continue or not
-def should_continue(state: MessagesState) -> Literal["tools", END]:
-    messages = state['messages']
-    last_message = messages[-1]
-    # If the LLM makes a tool call, then we route to the "tools" node
-    if last_message.tool_calls:
-        return "tools"
-    # Otherwise, we stop (reply to the user)
-    return END
+- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
+- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
+- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
+- [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
 
+## Acknowledgements
 
-# Define the function that calls the model
-def call_model(state: MessagesState):
-    messages = state['messages']
-    response = model.invoke(messages)
-    # We return a list, because this will get added to the existing list
-    return {"messages": [response]}
-
-
-# Define a new graph
-workflow = StateGraph(MessagesState)
-
-# Define the two nodes we will cycle between
-workflow.add_node("agent", call_model)
-workflow.add_node("tools", tool_node)
-
-# Set the entrypoint as `agent`
-# This means that this node is the first one called
-workflow.add_edge(START, "agent")
-
-# We now add a conditional edge
-workflow.add_conditional_edges(
-    # First, we define the start node. We use `agent`.
-    # This means these are the edges taken after the `agent` node is called.
-    "agent",
-    # Next, we pass in the function that will determine which node is called next.
-    should_continue,
-)
-
-# We now add a normal edge from `tools` to `agent`.
-# This means that after `tools` is called, `agent` node is called next.
-workflow.add_edge("tools", 'agent')
-
-# Initialize memory to persist state between graph runs
-checkpointer = MemorySaver()
-
-# Finally, we compile it!
-# This compiles it into a LangChain Runnable,
-# meaning you can use it as you would any other runnable.
-# Note that we're (optionally) passing the memory when compiling the graph
-app = workflow.compile(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
-```
-
-<b>Step-by-step Breakdown</b>:
-
-<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/how_to/custom_tools/">here</a>.
-  </li>
-</ul>
-</details>
-
-<details>
-<summary>Initialize graph with state.</summary>
-
-<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>
-
-<details>
-<summary>Define graph nodes.</summary>
-
-There are two main nodes we need:
-
-<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>
-
-<details>
-<summary>Define entry point and graph edges.</summary>
-
-First, we need to set the entry point for graph execution - <code>agent</code> node.
-
-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.
-
-<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>
-
-<details>
-<summary>Compile the graph.</summary>
-
-<ul>
-  <li>
-    When we compile the graph, we turn it into a LangChain 
-    <a href="https://python.langchain.com/docs/concepts/runnables/">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>
-
-<details>
-<summary>Execute the graph.</summary>
-
-<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
-
-* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
-* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
-* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
-* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
-* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
-
-## Resources
-
-* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
-
-## Contributing
-
-For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
+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.

docs/_scripts/third_party_page/packages.yml

@@ -30,3 +30,6 @@ packages:
   - name: "langgraph-bigtool"
     repo: "langchain-ai/langgraph-bigtool"
     description: "Build LangGraph agents with large numbers of tools."
+  - name: "langgraph-reflection"
+    repo: "langchain-ai/langgraph-reflection"
+    description: "LangGraph agent that runs a reflection step."

docs/docs/cloud/how-tos/generative_ui_react.md

@@ -0,0 +1,305 @@
+# How to implement Generative User Interfaces with LangGraph
+
+!!! info "Prerequisites"
+
+    - [LangGraph Platform](../../concepts/langgraph_platform.md)
+    - [LangGraph Server](../../concepts/langgraph_server.md)
+    - [`useStream()` React Hook](./use_stream_react.md)
+
+Generative user interfaces (Generative UI) allows agents to go beyond text and generate rich user interfaces. This enables creating more interactive and context-aware applications where the UI adapts based on the conversation flow and AI responses.
+
+![Generative UI Sample](./img/generative_ui_sample.jpg)
+
+LangGraph Platform supports colocating your React components with your graph code. This allows you to focus on building specific UI components for your graph while easily plugging into existing chat interfaces such as [Agent Chat](https://agentchat.vercel.app) and loading the code only when actually needed.
+
+!!! warning "LangGraph.js only"
+
+    Currently only LangGraph.js supports Generative UI. Support for Python is coming soon.
+
+## Tutorial
+
+### 1. Define and configure UI components
+
+First, create your first UI component. For each component you need to provide an unique identifier that will be used to reference the component in your graph code.
+
+```tsx title="src/agent/ui.tsx"
+const WeatherComponent = (props: { city: string }) => {
+  return <div>Weather for {props.city}</div>;
+};
+
+export default {
+  weather: WeatherComponent,
+};
+```
+
+Next, define your UI components in your `langgraph.json` configuration:
+
+```json
+{
+  "node_version": "20",
+  "graphs": {
+    "agent": "./src/agent/index.ts:graph"
+  },
+  "ui": {
+    "agent": "./src/agent/ui.tsx"
+  }
+}
+```
+
+The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
+
+LangGraph Platform will automatically bundle your UI components code and styles and serve them as external assets that can be loaded by the `LoadExternalComponent` component. Some dependencies such as `react` and `react-dom` will be automatically excluded from the bundle.
+
+CSS and Tailwind 4.x is also supported out of the box, so you can freely use Tailwind classes as well as `shadcn/ui` in your UI components.
+
+=== "`src/agent/ui.tsx`"
+
+    ```tsx
+    import "./styles.css";
+
+    const WeatherComponent = (props: { city: string }) => {
+      return <div className="bg-red-500">Weather for {props.city}</div>;
+    };
+
+    export default {
+      weather: WeatherComponent,
+    };
+    ```
+
+=== "`src/agent/styles.css`"
+
+    ```css
+    @import "tailwindcss";
+    ```
+
+### 2. Send the UI components in your graph
+
+Use the `typedUi` utility to emit UI elements from your agent nodes:
+
+```typescript title="src/agent/index.ts"
+import {
+  typedUi,
+  uiMessageReducer,
+} from "@langchain/langgraph-sdk/react-ui/server";
+
+import { ChatOpenAI } from "@langchain/openai";
+import { v4 as uuidv4 } from "uuid";
+import { z } from "zod";
+
+import type ComponentMap from "./ui.js";
+
+import {
+  Annotation,
+  MessagesAnnotation,
+  StateGraph,
+  type LangGraphRunnableConfig,
+} from "@langchain/langgraph";
+
+const AgentState = Annotation.Root({
+  ...MessagesAnnotation.spec,
+  ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
+});
+
+export const graph = new StateGraph(AgentState)
+  .addNode("weather", async (state, config) => {
+    // Provide the type of the component map to ensure
+    // type safety of `ui.push()` calls.
+    const ui = typedUi<typeof ComponentMap>(config);
+
+    const weather = await new ChatOpenAI({ model: "gpt-4o-mini" })
+      .withStructuredOutput(z.object({ city: z.string() }))
+      .withConfig({ tags: ["langsmith:nostream"] })
+      .invoke(state.messages);
+
+    const response = {
+      id: uuidv4(),
+      type: "ai",
+      content: `Here's the weather for ${weather.city}`,
+    };
+
+    // Emit UI elements with associated AI message
+    ui.push({ name: "weather", props: weather }, { message: response });
+
+    return { messages: [response], ui: ui.items };
+  })
+  .addEdge("__start__", "weather")
+  .compile();
+```
+
+### 3. Handle UI elements in your React application
+
+On the client side, you can use `useStream()` and `LoadExternalComponent` to display the UI elements.
+
+```tsx title="src/app/page.tsx"
+"use client";
+
+import { useStream } from "@langchain/langgraph-sdk/react";
+import { LoadExternalComponent } from "@langchain/langgraph-sdk/react-ui";
+
+export default function Page() {
+  const { thread, values } = useStream({
+    apiUrl: "http://localhost:2024",
+    assistantId: "agent",
+  });
+
+  return (
+    <div>
+      {thread.messages.map((message) => (
+        <div key={message.id}>
+          {message.content}
+          {values.ui
+            ?.filter((ui) => ui.metadata?.message_id === message.id)
+            .map((ui) => (
+              <LoadExternalComponent key={ui.id} stream={thread} message={ui} />
+            ))}
+        </div>
+      ))}
+    </div>
+  );
+}
+```
+
+Behind the scenes, `LoadExternalComponent` will fetch the JS and CSS for the UI components from LangGraph Platform and render them in a shadow DOM, thus ensuring style isolation from the rest of your application.
+
+## How-to guides
+
+### Show loading UI when components are loading
+
+You can provide a fallback UI to be rendered when the components are loading.
+
+```tsx
+<LoadExternalComponent
+  stream={thread}
+  message={ui}
+  fallback={<div>Loading...</div>}
+/>
+```
+
+### Provide custom components on the client side
+
+If you already have the components loaded in your client application, you can provide a map of such components to be rendered directly without fetching the UI code from LangGraph Platform.
+
+```tsx
+const clientComponents = {
+  weather: WeatherComponent,
+};
+
+<LoadExternalComponent
+  stream={thread}
+  message={ui}
+  components={clientComponents}
+/>;
+```
+
+### Customise the namespace of UI components.
+
+By default `LoadExternalComponent` will use the `assistantId` from `useStream()` hook to fetch the code for UI components. You can customise this by providing a `namespace` prop to the `LoadExternalComponent` component.
+
+=== "`src/app/page.tsx`"
+
+    ```tsx
+    <LoadExternalComponent
+      stream={thread}
+      message={ui}
+      namespace="custom-namespace"
+    />
+    ```
+
+=== "`langgraph.json`"
+
+    ```json
+    {
+      "ui": {
+        "custom-namespace": "./src/agent/ui.tsx"
+      }
+    }
+    ```
+
+### Access and interact with the thread state from the UI component
+
+You can access the thread state from the UI component by using the `useStreamContext` hook.
+
+```tsx
+import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";
+
+const WeatherComponent = (props: { city: string }) => {
+  const { thread, submit } = useStreamContext();
+  return (
+    <>
+      <div>Weather for {props.city}</div>
+
+      <button
+        onClick={() => {
+          const newMessage = {
+            type: "human",
+            content: `What's the weather in ${props.city}?`,
+          };
+
+          submit({ messages: [newMessage] });
+        }}
+      >
+        Retry
+      </button>
+    </>
+  );
+};
+```
+
+### Pass additional context to the client components
+
+You can pass additional context to the client components by providing a `meta` prop to the `LoadExternalComponent` component.
+
+```tsx
+<LoadExternalComponent stream={thread} message={ui} meta={{ userId: "123" }} />
+```
+
+Then, you can access the `meta` prop in the UI component by using the `useStreamContext` hook.
+
+```tsx
+const WeatherComponent = (props: { city: string }) => {
+  const { meta } = useStreamContext();
+  return (
+    <div>
+      Weather for {props.city} (user: {meta?.userId})
+    </div>
+  );
+};
+```
+
+### Streaming UI updates before the node execution is finished
+
+You can stream UI updates before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook.
+
+```tsx
+import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";
+
+const { thread, submit } = useStream({
+  apiUrl: "http://localhost:2024",
+  assistantId: "agent",
+  onCustomEvent: (event, options) => {
+    options.mutate((prev) => {
+      const ui = uiMessageReducer(prev.ui ?? [], event);
+      return { ...prev, ui };
+    });
+  },
+});
+```
+
+### Remove UI messages from state
+
+Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `ui.delete` with the ID of the UI message.
+
+```tsx
+// pushed message
+const message = ui.push({ name: "weather", props: { city: "London" } });
+
+// remove said message
+ui.delete(message.id);
+
+// return new state to persist changes
+return { ui: ui.items };
+```
+
+## Learn more
+
+- [JS/TS SDK Reference](../reference/sdk/js_ts_sdk_ref.md)

docs/docs/concepts/persistence.md

@@ -32,7 +32,7 @@ from typing_extensions import TypedDict
 from operator import add
 
 class State(TypedDict):
-    foo: int
+    foo: str
     bar: Annotated[list[str], add]
 
 def node_a(state: State):

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

@@ -220,7 +220,7 @@
    "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
    "metadata": {},
    "source": [
-    "Notice that when we pass the same the same thread ID, the chat history is preserved"
+    "Notice that when we pass the same thread ID, the chat history is preserved."
    ]
   },
   {

docs/docs/how-tos/index.md

@@ -198,7 +198,6 @@ Learn how to set up your app for deployment to LangGraph Platform:
 - [How to test locally](../cloud/deployment/test_locally.md)
 - [How to rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
 - [How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks](autogen-langgraph-platform.ipynb)
-- [How to integrate LangGraph into your React application](../cloud/how-tos/use_stream_react.md)
 
 ### Deployment
 
@@ -257,6 +256,13 @@ Streaming the results of your LLM application is vital for ensuring a good user
 - [How to stream in debug mode](../cloud/how-tos/stream_debug.md)
 - [How to stream multiple modes](../cloud/how-tos/stream_multiple.md)
 
+### Frontend and Generative UI
+
+With LangGraph Platform you can integrate LangGraph agents into your React applications and colocate UI components with your agent code. 
+
+- [How to integrate LangGraph into your React application](../cloud/how-tos/use_stream_react.md)
+- [How to implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)
+
 ### Human-in-the-loop
 
 When designing complex graphs, relying entirely on the LLM for decision-making can be risky, particularly when it involves tools that interact with files, APIs, or databases. These interactions may lead to unintended data access or modifications, depending on the use case. To mitigate these risks, LangGraph allows you to integrate human-in-the-loop behavior, ensuring your LLM applications operate as intended without undesirable outcomes.

docs/docs/index.md

@@ -3,4 +3,26 @@ hide_comments: true
 title: Home
 ---
 
+<script>
+  // This script only runs in MkDocs, not on GitHub
+  var hideGitHubVersion = function() {
+    document.querySelectorAll('.github-only').forEach(el => el.style.display = 'none');
+  };
+
+  // Handle both initial load and subsequent navigation
+  document.addEventListener('DOMContentLoaded', hideGitHubVersion);
+  document$.subscribe(hideGitHubVersion);
+</script>
+
+<p class="mkdocs-only">
+  <img class="logo-light" src="static/wordmark_dark.svg" alt="LangGraph Logo" width="80%">
+  <img class="logo-dark" src="static/wordmark_light.svg" alt="LangGraph Logo" width="80%">
+</p>
+
+<style>
+h1 {
+  display: none;
+}
+</style>
+
 {!../README.md!}

docs/docs/llms.txt

@@ -0,0 +1,191 @@
+# LangGraph
+
+## Quickstart
+
+These guides are designed to help you get started with LangGraph.
+
+- [LangGraph Quickstart](https://langchain-ai.github.io/langgraph/tutorials/introduction/): 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.
+- [Common Workflows](https://langchain-ai.github.io/langgraph/tutorials/workflows/): Overview of the most common workflows using LLMs implemented with LangGraph.
+- [LangGraph Server Quickstart](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
+- [Deploy with LangGraph Cloud Quickstart](https://langchain-ai.github.io/langgraph/cloud/quick_start/): Deploy a LangGraph app using LangGraph Cloud.
+
+## Concepts
+
+These guides provide explanations of the key concepts behind the LangGraph framework.
+
+- [Why LangGraph?](https://langchain-ai.github.io/langgraph/concepts/high_level/): Motivation for LangGraph, a library for building agentic applications with LLMs.
+- [LangGraph Glossary](https://langchain-ai.github.io/langgraph/concepts/low_level/): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
+- [Common Agentic Patterns](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
+- [Multi-Agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
+- [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/breakpoints/): Breakpoints allow pausing the execution of a graph at specific points. Breakpoints allow stepping through graph execution for debugging purposes.
+- [Human-in-the-Loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Explains different ways of integrating human feedback into a LangGraph application.
+- [Time Travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/): Time travel allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues.
+- [Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
+- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.
+- [Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
+- [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/): `@entrypoint` and `@task` decorators that allow you to add LangGraph functionality to an existing codebase.
+- [Durable Execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): LangGraph's built-in [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store. 
+- [Pregel](https://langchain-ai.github.io/langgraph/concepts/pregel/): Pregel is LangGraph's runtime, which is responsible for managing the execution of LangGraph applications.
+- [FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): Frequently asked questions about LangGraph.
+
+## How-tos
+
+Here you’ll find answers to “How do I...?” types of questions. 
+
+These guides are **goal-oriented** and concrete. 
+
+They're meant to help you complete a specific task.
+
+### Graph API Basics
+
+- [How to update graph state from nodes](https://langchain-ai.github.io/langgraph/how-tos/state-reducers/)
+- [How to create a sequence of steps](https://langchain-ai.github.io/langgraph/how-tos/sequence/)
+- [How to create branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/branching/)
+- [How to create and control loops with recursion limits](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/)
+- [How to visualize your graph](https://langchain-ai.github.io/langgraph/how-tos/visualization/)
+
+### Fine-grained Control
+
+These guides demonstrate LangGraph features that grant fine-grained control over the execution of your graph.
+
+- [How to create map-reduce branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/)
+- [How to update state and jump to nodes in graphs and subgraphs](https://langchain-ai.github.io/langgraph/how-tos/command/)
+- [How to add runtime configuration to your graph](https://langchain-ai.github.io/langgraph/how-tos/configuration/)
+- [How to add node retries](https://langchain-ai.github.io/langgraph/how-tos/node-retries/)
+- [How to return state before hitting recursion limit](https://langchain-ai.github.io/langgraph/how-tos/return-when-recursion-limit-hits/)
+
+### Persistence
+ 
+Persistence 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](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
+- [How to add thread-level persistence to a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-persistence/)
+- [How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence/)
+- [How to use Postgres checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/)
+- [How to use MongoDB checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/)
+- [How to create a custom checkpointer using Redis](https://langchain-ai.github.io/langgraph/how-tos/persistence_redis/)
+
+See the below guides for how-to add persistence to your workflow using the [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/):
+
+- [How to add thread-level persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/persistence-functional/)
+- [How to add cross-thread persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence-functional/)
+
+### Memory
+
+LangGraph makes it easy to manage conversation memory in your graph. These how-to guides show how to implement different strategies for that.
+
+- [How to manage conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/)
+- [How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/)
+- [How to add summary conversation memory](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/)
+- [How to add long-term memory (cross-thread)](https://langchain-ai.github.io/langgraph/how-tos/memory/cross-thread-persistence/)
+- [How to use semantic search for long-term memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/)
+
+### Human-in-the-loop
+
+Human-in-the-loop functionality allows you to involve humans in the decision-making process of your graph.
+
+These how-to guides show how to implement human-in-the-loop workflows in your graph.
+
+- [How to wait for user input](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
+- [How to review tool calls](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/review-tool-calls/): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
+- [How to add static breakpoints](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): Use for debugging purposes. For human-in-the-loop workflows, we recommend the [`interrupt` function](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.interrupt) instead.
+- [How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**.
+
+See the below guides for how-to implement human-in-the-loop workflows with the Functional API.
+
+- [How to wait for user input (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/wait-user-input-functional/)
+- [How to review tool calls (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/review-tool-calls-functional/)
+
+### Time Travel
+
+[Time travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
+
+- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/time-travel/)
+
+### Streaming
+
+[Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
+
+- [How to stream](https://langchain-ai.github.io/langgraph/how-tos/streaming/)
+- [How to stream LLM tokens](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens/)
+- [How to stream LLM tokens from specific nodes](https://langchain-ai.github.io/langgraph/how-tos/streaming-specific-nodes/)
+- [How to stream data from within a tool](https://langchain-ai.github.io/langgraph/how-tos/streaming-events-from-within-tools/)
+- [How to stream from subgraphs](https://langchain-ai.github.io/langgraph/how-tos/streaming-subgraphs/)
+- [How to disable streaming for models that don't support it](https://langchain-ai.github.io/langgraph/how-tos/disable-streaming/)
+
+### Tool calling
+
+[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.
+
+It 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:
+
+- [How to call tools using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
+- [How to handle tool calling errors](https://langchain-ai.github.io/langgraph/how-tos/tool-calling-errors/)
+- [How to pass runtime values to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)
+- [How to pass config to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-config-to-tools/)
+- [How to update graph state from tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/)
+- [How to handle large numbers of tools](https://langchain-ai.github.io/langgraph/how-tos/many-tools/)
+
+### Subgraphs
+
+Subgraphs allow you to reuse an existing graph from another graph. 
+
+These how-to guides show how to use subgraphs:
+
+- [How to use subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraph/)
+- [How to view and update state in subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraphs-manage-state/)
+- [How to transform inputs and outputs of a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/)
+
+### Multi-agent
+
+Multi-agent systems are useful to break down complex LLM applications into multiple agents, each responsible for a different part of the application. 
+
+These how-to guides show how to implement multi-agent systems in LangGraph:
+
+- [How to implement handoffs between agents](https://langchain-ai.github.io/langgraph/how-tos/agent-handoffs/)
+- [How to build a multi-agent network](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/)
+- [How to add multi-turn conversation in a multi-agent application](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo/)
+
+### State Management
+
+- [How to use Pydantic model as graph state](https://langchain-ai.github.io/langgraph/how-tos/state-model/)
+- [How to define input/output schema for your graph](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/)
+- [How to pass private state between nodes inside the graph](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/)
+
+### Other
+
+- [How to run graph asynchronously](https://langchain-ai.github.io/langgraph/how-tos/async/)
+- [How to force tool-calling agent to structure output](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/)
+- [How to pass custom LangSmith run ID for graph runs](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/)
+- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration/)
+
+## Use cases 
+
+Explore practical implementations tailored for specific scenarios:
+
+### Chatbots
+
+- [Customer Support](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/): Build a multi-functional support bot for flights, hotels, and car rentals.
+- [Prompt Generation from User Requirements](https://langchain-ai.github.io/langgraph/tutorials/chatbots/information-gather-prompting/): Build an information gathering chatbot.
+- [Code Assistant](https://langchain-ai.github.io/langgraph/tutorials/code_assistant/langgraph_code_assistant/): Build a code analysis and generation assistant.
+
+### RAG
+
+- [Agentic RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
+- [Adaptive RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
+    - For a version that uses a local LLM: [Adaptive RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/)
+- [Corrective RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag/): Uses an LLM to grade the quality of the retrieved information from the given source, and if the quality is low, it will try to retrieve the information from another source. Implementation of: https://arxiv.org/pdf/2401.15884.pdf 
+    - For a version that uses a local LLM: [Corrective RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag_local/)
+- [Self-RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/): Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. Implementation of https://arxiv.org/abs/2310.11511.
+    - For a version that uses a local LLM: [Self-RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag_local/) 
+- [SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/): Build a SQL agent that can answer questions about a SQL database.
+
+### Multi-Agent Systems
+
+- [Network](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/): Enable two or more agents to collaborate on a task
+- [Supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/): Use an LLM to orchestrate and delegate to individual agents
+- [Hierarchical Teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/): Orchestrate nested teams of agents to solve problems

docs/mkdocs.yml

@@ -85,7 +85,7 @@ plugins:
 
 nav:
   - Home:
-    - Introduction: index.md
+    - index.md
     - Get started:
       - Learn the basics: tutorials/introduction.ipynb
       - Deployment:
@@ -231,6 +231,7 @@ nav:
                 - cloud/how-tos/stream_debug.md
                 - cloud/how-tos/stream_multiple.md
                 - cloud/how-tos/use_stream_react.md
+                - cloud/how-tos/generative_ui_react.md
             - Human-in-the-loop:
                 - Human-in-the-loop: how-tos#human-in-the-loop_1
                 - cloud/how-tos/human_in_the_loop_breakpoint.md

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

@@ -39,6 +39,7 @@ from langgraph.store.base import (
     Result,
     SearchItem,
     SearchOp,
+    TTLConfig,
     ensure_embeddings,
     get_text_at_path,
     tokenize_path,
@@ -622,6 +623,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
             Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]
         ] = None,
         index: Optional[PostgresIndexConfig] = None,
+        ttl: Optional[TTLConfig] = None,
     ) -> None:
         super().__init__()
         self._deserializer = deserializer
@@ -634,6 +636,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
             self.embeddings, self.index_config = _ensure_index_config(self.index_config)
         else:
             self.embeddings = None
+        self.ttl_config = ttl
 
     @classmethod
     @contextmanager

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

@@ -11,9 +11,19 @@ Core types:
 
 from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Any, Iterable, Literal, NamedTuple, Optional, TypedDict, Union, cast
+from typing import (
+    Any,
+    Iterable,
+    Literal,
+    NamedTuple,
+    Optional,
+    TypedDict,
+    Union,
+    cast,
+)
 
 from langchain_core.embeddings import Embeddings
+from typing_extensions import override
 
 from langgraph.store.base.embed import (
     AEmbeddingsFunc,
@@ -24,6 +34,20 @@ from langgraph.store.base.embed import (
 )
 
 
+class NotProvided:
+    """Sentinel singleton."""
+
+    def __bool__(self) -> Literal[False]:
+        return False
+
+    @override
+    def __repr__(self) -> str:
+        return "NOT_GIVEN"
+
+
+NOT_PROVIDED = NotProvided()
+
+
 class Item:
     """Represents a stored item with metadata.
 
@@ -496,6 +520,25 @@ class InvalidNamespaceError(ValueError):
     """Provided namespace is invalid."""
 
 
+class TTLConfig(TypedDict, total=False):
+    """Configuration for TTL (time-to-live) behavior in the store."""
+
+    refresh_on_read: bool
+    """Default behavior for refreshing TTLs on read operations (GET and SEARCH).
+    
+    If True, TTLs will be refreshed on read operations (get/search) by default.
+    This can be overridden per-operation by explicitly setting refresh_ttl.
+    Defaults to True if not configured.
+    """
+    default_ttl: Optional[float]
+    """Default TTL (time-to-live) in minutes for new items.
+    
+    If provided, new items will expire after this many minutes after their last access.
+    The expiration timer refreshes on both read and write operations.
+    Defaults to None (no expiration).
+    """
+
+
 class IndexConfig(TypedDict, total=False):
     """Configuration for indexing documents for semantic search in the store.
 
@@ -640,7 +683,8 @@ class BaseStore(ABC):
         Subclasses must explicitly set `supports_ttl = True` to enable this feature.
     """
 
-    supports_ttl = False
+    supports_ttl: bool = False
+    ttl_config: Optional[TTLConfig] = None
 
     __slots__ = ("__weakref__",)
 
@@ -669,7 +713,11 @@ class BaseStore(ABC):
         """
 
     def get(
-        self, namespace: tuple[str, ...], key: str, *, refresh_ttl: bool = True
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        *,
+        refresh_ttl: Optional[bool] = None,
     ) -> Optional[Item]:
         """Retrieve a single item.
 
@@ -677,12 +725,15 @@ class BaseStore(ABC):
             namespace: Hierarchical path for the item.
             key: Unique identifier within the namespace.
             refresh_ttl: Whether to refresh TTLs for the returned item.
+                If None (default), uses the store's default refresh_ttl setting.
                 If no TTL is specified, this argument is ignored.
 
         Returns:
             The retrieved item or None if not found.
         """
-        return self.batch([GetOp(namespace, str(key), refresh_ttl)])[0]
+        return self.batch(
+            [GetOp(namespace, str(key), _ensure_refresh(self.ttl_config, refresh_ttl))]
+        )[0]
 
     def search(
         self,
@@ -693,7 +744,7 @@ class BaseStore(ABC):
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: bool = True,
+        refresh_ttl: Optional[bool] = None,
     ) -> list[SearchItem]:
         """Search for items within a namespace prefix.
 
@@ -743,7 +794,16 @@ class BaseStore(ABC):
             and requires proper embedding configuration.
         """
         return self.batch(
-            [SearchOp(namespace_prefix, filter, limit, offset, query, refresh_ttl)]
+            [
+                SearchOp(
+                    namespace_prefix,
+                    filter,
+                    limit,
+                    offset,
+                    query,
+                    _ensure_refresh(self.ttl_config, refresh_ttl),
+                )
+            ]
         )[0]
 
     def put(
@@ -753,7 +813,7 @@ class BaseStore(ABC):
         value: dict[str, Any],
         index: Optional[Union[Literal[False], list[str]]] = None,
         *,
-        ttl: Optional[float] = None,
+        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
     ) -> None:
         """Store or update an item in the store.
 
@@ -806,12 +866,22 @@ class BaseStore(ABC):
             ```
         """
         _validate_namespace(namespace)
-        if ttl is not None and not self.supports_ttl:
+        if ttl not in (NOT_PROVIDED, None) and not self.supports_ttl:
             raise NotImplementedError(
                 f"TTL is not supported by {self.__class__.__name__}. "
                 f"Use a store implementation that supports TTL or set ttl=None."
             )
-        self.batch([PutOp(namespace, str(key), value, index=index, ttl=ttl)])
+        self.batch(
+            [
+                PutOp(
+                    namespace,
+                    str(key),
+                    value,
+                    index=index,
+                    ttl=_ensure_ttl(self.ttl_config, ttl),
+                )
+            ]
+        )
 
     def delete(self, namespace: tuple[str, ...], key: str) -> None:
         """Delete an item.
@@ -876,7 +946,11 @@ class BaseStore(ABC):
         return self.batch([op])[0]
 
     async def aget(
-        self, namespace: tuple[str, ...], key: str, *, refresh_ttl: bool = True
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        *,
+        refresh_ttl: Optional[bool] = None,
     ) -> Optional[Item]:
         """Asynchronously retrieve a single item.
 
@@ -887,7 +961,17 @@ class BaseStore(ABC):
         Returns:
             The retrieved item or None if not found.
         """
-        return (await self.abatch([GetOp(namespace, str(key), refresh_ttl)]))[0]
+        return (
+            await self.abatch(
+                [
+                    GetOp(
+                        namespace,
+                        str(key),
+                        _ensure_refresh(self.ttl_config, refresh_ttl),
+                    )
+                ]
+            )
+        )[0]
 
     async def asearch(
         self,
@@ -898,7 +982,7 @@ class BaseStore(ABC):
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: bool = True,
+        refresh_ttl: Optional[bool] = None,
     ) -> list[SearchItem]:
         """Asynchronously search for items within a namespace prefix.
 
@@ -909,8 +993,8 @@ class BaseStore(ABC):
             limit: Maximum number of items to return.
             offset: Number of items to skip before returning results.
             refresh_ttl: Whether to refresh TTLs for the returned items.
-                Defaults to True. If no TTL is specified, this argument
-                is ignored.
+                If None (default), uses the store's TTLConfig.refresh_default setting.
+                If TTLConfig is not provided or no TTL is specified, this argument is ignored.
 
         Returns:
             List of items matching the search criteria.
@@ -950,7 +1034,16 @@ class BaseStore(ABC):
         """
         return (
             await self.abatch(
-                [SearchOp(namespace_prefix, filter, limit, offset, query, refresh_ttl)]
+                [
+                    SearchOp(
+                        namespace_prefix,
+                        filter,
+                        limit,
+                        offset,
+                        query,
+                        _ensure_refresh(self.ttl_config, refresh_ttl),
+                    )
+                ]
             )
         )[0]
 
@@ -961,7 +1054,7 @@ class BaseStore(ABC):
         value: dict[str, Any],
         index: Optional[Union[Literal[False], list[str]]] = None,
         *,
-        ttl: Optional[float] = None,
+        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
     ) -> None:
         """Asynchronously store or update an item in the store.
 
@@ -1022,12 +1115,22 @@ class BaseStore(ABC):
             ```
         """
         _validate_namespace(namespace)
-        if ttl is not None and not self.supports_ttl:
+        if ttl not in (NOT_PROVIDED, None) and not self.supports_ttl:
             raise NotImplementedError(
                 f"TTL is not supported by {self.__class__.__name__}. "
                 f"Use a store implementation that supports TTL or set ttl=None."
             )
-        await self.abatch([PutOp(namespace, str(key), value, index=index, ttl=ttl)])
+        await self.abatch(
+            [
+                PutOp(
+                    namespace,
+                    str(key),
+                    value,
+                    index=index,
+                    ttl=_ensure_ttl(self.ttl_config, ttl),
+                )
+            ]
+        )
 
     async def adelete(self, namespace: tuple[str, ...], key: str) -> None:
         """Asynchronously delete an item.
@@ -1116,6 +1219,27 @@ def _validate_namespace(namespace: tuple[str, ...]) -> None:
         )
 
 
+def _ensure_refresh(
+    ttl_config: Optional[TTLConfig], refresh_ttl: Optional[bool] = None
+) -> bool:
+    if refresh_ttl is not None:
+        return refresh_ttl
+    if ttl_config is not None:
+        return ttl_config.get("refresh_on_read", True)
+    return True
+
+
+def _ensure_ttl(
+    ttl_config: Optional[TTLConfig],
+    ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
+) -> Optional[float]:
+    if ttl is NOT_PROVIDED:
+        if ttl_config:
+            return ttl_config.get("default_ttl")
+        return None
+    return ttl
+
+
 __all__ = [
     "BaseStore",
     "Item",

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

@@ -5,17 +5,21 @@ from collections.abc import Iterable
 from typing import Any, Callable, Literal, Optional, TypeVar, Union
 
 from langgraph.store.base import (
+    NOT_PROVIDED,
     BaseStore,
     GetOp,
     Item,
     ListNamespacesOp,
     MatchCondition,
     NamespacePath,
+    NotProvided,
     Op,
     PutOp,
     Result,
     SearchItem,
     SearchOp,
+    _ensure_refresh,
+    _ensure_ttl,
     _validate_namespace,
 )
 
@@ -65,11 +69,24 @@ class AsyncBatchedBaseStore(BaseStore):
             pass
 
     async def aget(
-        self, namespace: tuple[str, ...], key: str, *, refresh_ttl: bool = True
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        *,
+        refresh_ttl: Optional[bool] = None,
     ) -> Optional[Item]:
         assert not self._task.done()
         fut = self._loop.create_future()
-        self._aqueue.put_nowait((fut, GetOp(namespace, key, refresh_ttl=refresh_ttl)))
+        self._aqueue.put_nowait(
+            (
+                fut,
+                GetOp(
+                    namespace,
+                    key,
+                    refresh_ttl=_ensure_refresh(self.ttl_config, refresh_ttl),
+                ),
+            )
+        )
         return await fut
 
     async def asearch(
@@ -81,7 +98,7 @@ class AsyncBatchedBaseStore(BaseStore):
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: bool = True,
+        refresh_ttl: Optional[bool] = None,
     ) -> list[SearchItem]:
         assert not self._task.done()
         fut = self._loop.create_future()
@@ -94,7 +111,7 @@ class AsyncBatchedBaseStore(BaseStore):
                     limit,
                     offset,
                     query,
-                    refresh_ttl=refresh_ttl,
+                    refresh_ttl=_ensure_refresh(self.ttl_config, refresh_ttl),
                 ),
             )
         )
@@ -107,12 +124,19 @@ class AsyncBatchedBaseStore(BaseStore):
         value: dict[str, Any],
         index: Optional[Union[Literal[False], list[str]]] = None,
         *,
-        ttl: Optional[float] = None,
+        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
     ) -> None:
         assert not self._task.done()
         _validate_namespace(namespace)
         fut = self._loop.create_future()
-        self._aqueue.put_nowait((fut, PutOp(namespace, key, value, index, ttl=ttl)))
+        self._aqueue.put_nowait(
+            (
+                fut,
+                PutOp(
+                    namespace, key, value, index, ttl=_ensure_ttl(self.ttl_config, ttl)
+                ),
+            )
+        )
         return await fut
 
     async def adelete(
@@ -157,7 +181,11 @@ class AsyncBatchedBaseStore(BaseStore):
 
     @_check_loop
     def get(
-        self, namespace: tuple[str, ...], key: str, *, refresh_ttl: bool = True
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        *,
+        refresh_ttl: Optional[bool] = None,
     ) -> Optional[Item]:
         return asyncio.run_coroutine_threadsafe(
             self.aget(namespace, key=key, refresh_ttl=refresh_ttl), self._loop
@@ -173,7 +201,7 @@ class AsyncBatchedBaseStore(BaseStore):
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: bool = True,
+        refresh_ttl: Optional[bool] = None,
     ) -> list[SearchItem]:
         return asyncio.run_coroutine_threadsafe(
             self.asearch(
@@ -195,11 +223,18 @@ class AsyncBatchedBaseStore(BaseStore):
         value: dict[str, Any],
         index: Optional[Union[Literal[False], list[str]]] = None,
         *,
-        ttl: Optional[float] = None,
+        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
     ) -> None:
         _validate_namespace(namespace)
         asyncio.run_coroutine_threadsafe(
-            self.aput(namespace, key=key, value=value, index=index, ttl=ttl), self._loop
+            self.aput(
+                namespace,
+                key=key,
+                value=value,
+                index=index,
+                ttl=_ensure_ttl(self.ttl_config, ttl),
+            ),
+            self._loop,
         ).result()
 
     @_check_loop

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.18"
+version = "2.0.19"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/cli/langgraph_cli/config.py

@@ -11,6 +11,24 @@ MIN_NODE_VERSION = "20"
 MIN_PYTHON_VERSION = "3.11"
 
 
+class TTLConfig(TypedDict, total=False):
+    """Configuration for TTL (time-to-live) behavior in the store."""
+
+    refresh_on_read: bool
+    """Default behavior for refreshing TTLs on read operations (GET and SEARCH).
+    
+    If True, TTLs will be refreshed on read operations (get/search) by default.
+    This can be overridden per-operation by explicitly setting refresh_ttl.
+    Defaults to True if not configured.
+    """
+    default_ttl: Optional[float]
+    """Optional. Default TTL (time-to-live) in minutes for new items.
+    
+    If provided, all new items will have this TTL unless explicitly overridden.
+    If omitted, items will have no TTL by default.
+    """
+
+
 class IndexConfig(TypedDict, total=False):
     """Configuration for indexing documents for semantic search in the store.
 
@@ -79,6 +97,13 @@ class StoreConfig(TypedDict, total=False):
     If omitted, no vector index is initialized.
     """
 
+    ttl: Optional[TTLConfig]
+    """Optional. Defines the TTL (time-to-live) behavior configuration.
+    
+    If provided, the store will apply TTL settings according to the configuration.
+    If omitted, no TTL behavior is configured.
+    """
+
 
 class SecurityConfig(TypedDict, total=False):
     """Configuration for OpenAPI security definitions and requirements.

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.75"
+version = "0.1.76"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

libs/cli/schemas/schema.json

@@ -397,6 +397,17 @@
             }
           ],
           "description": "Optional. Defines the vector-based semantic search configuration.\n\n- Generate embeddings according to `index.embed`\n- Enforce the embedding dimension given by `index.dims`\n- Embed only specified JSON fields (if any) from `index.fields`\n\nIf omitted, no vector index is initialized.\n"
+        },
+        "ttl": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/TTLConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines the TTL (time-to-live) behavior configuration.\n\nIf provided, the store will apply TTL settings according to the configuration.\nIf omitted, no TTL behavior is configured.\n"
         }
       },
       "required": []
@@ -430,6 +441,27 @@
         }
       },
       "required": []
+    },
+    "TTLConfig": {
+      "title": "TTLConfig",
+      "description": "Configuration for TTL (time-to-live) behavior in the store.",
+      "type": "object",
+      "properties": {
+        "default_ttl": {
+          "anyOf": [
+            {
+              "type": "number"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "refresh_on_read": {
+          "type": "boolean"
+        }
+      },
+      "required": []
     }
   },
   "title": "LangGraph CLI Configuration",

libs/cli/schemas/schema.v0.json

@@ -397,6 +397,17 @@
             }
           ],
           "description": "Optional. Defines the vector-based semantic search configuration.\n\n- Generate embeddings according to `index.embed`\n- Enforce the embedding dimension given by `index.dims`\n- Embed only specified JSON fields (if any) from `index.fields`\n\nIf omitted, no vector index is initialized.\n"
+        },
+        "ttl": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/TTLConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines the TTL (time-to-live) behavior configuration.\n\nIf provided, the store will apply TTL settings according to the configuration.\nIf omitted, no TTL behavior is configured.\n"
         }
       },
       "required": []
@@ -430,6 +441,27 @@
         }
       },
       "required": []
+    },
+    "TTLConfig": {
+      "title": "TTLConfig",
+      "description": "Configuration for TTL (time-to-live) behavior in the store.",
+      "type": "object",
+      "properties": {
+        "default_ttl": {
+          "anyOf": [
+            {
+              "type": "number"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "refresh_on_read": {
+          "type": "boolean"
+        }
+      },
+      "required": []
     }
   },
   "title": "LangGraph CLI Configuration",

libs/langgraph/README.md

@@ -1,339 +1,87 @@
-# 🦜🕸️LangGraph
+<picture class="github-only">
+  <source media="(prefers-color-scheme: light)" srcset="docs/docs/static/wordmark_dark.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
+  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
+</picture>
 
-![Version](https://img.shields.io/pypi/v/langgraph)
+<div>
+<br>
+</div>
+
+[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
-⚡ Building language agents as graphs ⚡
-
 > [!NOTE]
 > Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
 
-## Overview
+LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
 
-[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.
-
-### Why use LangGraph?
-
-LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. 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:
-
-- **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.
-
-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](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
-- **Background runs**: Runs agents asynchronously in the background
-- **Support for long running agents**: Infrastructure that can handle long running processes
-- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
-- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
-
-## Installation
-
-```shell
+```bash
 pip install -U langgraph
 ```
 
-## Example
-
-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
-```
-
-```shell
-export ANTHROPIC_API_KEY=sk-...
-```
-
-Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
-
-```shell
-export LANGSMITH_TRACING=true
-export LANGSMITH_API_KEY=lsv2_sk_...
-```
-
-The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
-
-<details open>
-  <summary>High-level implementation</summary>
+To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
 
 ```python
+# This code depends on pip install langchain[anthropic]
 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}}
+agent = create_react_agent("anthropic:claude-3-7-sonnet-latest", tools=[search])
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
 )
-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)
+## Why use LangGraph?
 
-```python
-final_state = app.invoke(
-    {"messages": [{"role": "user", "content": "what about ny"}]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
 
-```
-"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>
+- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
+- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
+- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
 
-> [!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.
+LangGraph is trusted in production and powering agents for companies like:
 
-<details>
-<summary>Low-level implementation</summary>
+- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
+- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
+- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
+- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
+- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
 
-```python
-from typing import Literal
+## LangGraph’s ecosystem
 
-from langchain_anthropic import ChatAnthropic
-from langchain_core.tools import tool
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.graph import END, START, StateGraph, MessagesState
-from langgraph.prebuilt import ToolNode
+While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
+- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 
-# 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."
+## Pairing with LangGraph Platform
 
+While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
 
-tools = [search]
+LangGraph Platform can help engineering teams:
 
-tool_node = ToolNode(tools)
+- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
+- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
+- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
 
-model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
+## Additional resources
 
-# Define the function that determines whether to continue or not
-def should_continue(state: MessagesState) -> Literal["tools", END]:
-    messages = state['messages']
-    last_message = messages[-1]
-    # If the LLM makes a tool call, then we route to the "tools" node
-    if last_message.tool_calls:
-        return "tools"
-    # Otherwise, we stop (reply to the user)
-    return END
+- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
+- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
+- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
+- [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
 
+## Acknowledgements
 
-# Define the function that calls the model
-def call_model(state: MessagesState):
-    messages = state['messages']
-    response = model.invoke(messages)
-    # We return a list, because this will get added to the existing list
-    return {"messages": [response]}
-
-
-# Define a new graph
-workflow = StateGraph(MessagesState)
-
-# Define the two nodes we will cycle between
-workflow.add_node("agent", call_model)
-workflow.add_node("tools", tool_node)
-
-# Set the entrypoint as `agent`
-# This means that this node is the first one called
-workflow.add_edge(START, "agent")
-
-# We now add a conditional edge
-workflow.add_conditional_edges(
-    # First, we define the start node. We use `agent`.
-    # This means these are the edges taken after the `agent` node is called.
-    "agent",
-    # Next, we pass in the function that will determine which node is called next.
-    should_continue,
-)
-
-# We now add a normal edge from `tools` to `agent`.
-# This means that after `tools` is called, `agent` node is called next.
-workflow.add_edge("tools", 'agent')
-
-# Initialize memory to persist state between graph runs
-checkpointer = MemorySaver()
-
-# Finally, we compile it!
-# This compiles it into a LangChain Runnable,
-# meaning you can use it as you would any other runnable.
-# Note that we're (optionally) passing the memory when compiling the graph
-app = workflow.compile(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
-```
-
-<b>Step-by-step Breakdown</b>:
-
-<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/how_to/custom_tools/">here</a>.
-  </li>
-</ul>
-</details>
-
-<details>
-<summary>Initialize graph with state.</summary>
-
-<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>
-
-<details>
-<summary>Define graph nodes.</summary>
-
-There are two main nodes we need:
-
-<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>
-
-<details>
-<summary>Define entry point and graph edges.</summary>
-
-First, we need to set the entry point for graph execution - <code>agent</code> node.
-
-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.
-
-<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>
-
-<details>
-<summary>Compile the graph.</summary>
-
-<ul>
-  <li>
-    When we compile the graph, we turn it into a LangChain 
-    <a href="https://python.langchain.com/docs/concepts/runnables/">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>
-
-<details>
-<summary>Execute the graph.</summary>
-
-<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
-
-* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
-* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
-* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
-* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
-* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
-
-## Resources
-
-* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
-
-## Contributing
-
-For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
+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.

libs/langgraph/langgraph/graph/graph.py

@@ -472,7 +472,7 @@ class CompiledGraph(Pregel):
                 )
                 subgraph.trim_first_node()
                 subgraph.trim_last_node()
-                if len(subgraph.nodes) > 1:
+                if len(subgraph.nodes) >= 1:
                     e, s = graph.extend(subgraph, prefix=key)
                     if e is None:
                         raise ValueError(

libs/langgraph/langgraph/graph/state.py

@@ -626,6 +626,11 @@ class StateGraph(Graph):
         compiled = CompiledStateGraph(
             builder=self,
             config_type=self.config_schema,
+            input_model=self.input
+            if len(self.channels) > 1
+            and isclass(self.input)
+            and issubclass(self.input, (BaseModel, BaseModelV1))
+            else None,
             nodes={},
             channels={
                 **self.channels,
@@ -755,23 +760,22 @@ class CompiledStateGraph(CompiledGraph):
                         updates.extend(_get_updates(i) or ())
                 return updates
             elif get_type_hints(type(input)):
-                # if input is a Pydantic model, only update values
-                # for the keys that have been explicitly set by the users
-                # (this is needed to avoid sending updates for fields with None defaults)
-                output_keys_ = output_keys
                 # Pydantic v2
-                if hasattr(input, "model_fields_set"):
-                    output_keys_ = [
-                        k for k in output_keys if k in input.model_fields_set
-                    ]
+                if hasattr(input, "model_fields"):
+                    defaults = {k: v.default for k, v in input.model_fields.items()}
                 # Pydantic v1
-                elif hasattr(input, "__fields_set__"):
-                    output_keys_ = [k for k in output_keys if k in input.__fields_set__]
+                elif hasattr(input, "__fields__"):
+                    defaults = {k: v.default for k, v in input.__fields__.items()}
+                else:
+                    defaults = {}
 
+                # if input is a Pydantic model, only update values
+                # that are different from the default values
                 return [
-                    (k, getattr(input, k))
-                    for k in output_keys_
-                    if getattr(input, k, MISSING) is not MISSING
+                    (k, value)
+                    for k in output_keys
+                    if (value := getattr(input, k, MISSING)) is not MISSING
+                    and value != defaults.get(k)
                 ]
             else:
                 msg = create_error_message(
@@ -812,11 +816,7 @@ class CompiledStateGraph(CompiledGraph):
                 # read state keys and managed values
                 channels=(list(input_values) if is_single_input else input_values),
                 # coerce state dict to schema class (eg. pydantic model)
-                mapper=(
-                    None
-                    if is_single_input or issubclass(input_schema, dict)
-                    else partial(_coerce_state, input_schema)
-                ),
+                mapper=_pick_mapper(list(input_values), input_schema),
                 writers=[
                     # publish to this channel and state keys
                     ChannelWrite(
@@ -931,14 +931,34 @@ def _get_state_reader(
         select=select[0] if select == ["__root__"] else select,
         fresh=True,
         # coerce state dict to schema class (eg. pydantic model)
-        mapper=(
-            None
-            if state_keys == ["__root__"] or issubclass(schema, dict)
-            else partial(_coerce_state, schema)
-        ),
+        mapper=_pick_mapper(state_keys, schema),
     )
 
 
+def _pick_mapper(
+    state_keys: Sequence[str], schema: Type[Any]
+) -> Optional[Callable[[Any], Any]]:
+    if state_keys == ["__root__"]:
+        return None
+    if issubclass(schema, dict):
+        return None
+    if issubclass(schema, BaseModel):
+        return partial(_coerce_state_pydantic, schema)
+    if issubclass(schema, BaseModelV1):
+        return partial(_coerce_state_pydantic_v1, schema)
+    return partial(_coerce_state, schema)
+
+
+def _coerce_state_pydantic(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
+    return schema.model_construct(**input)
+
+
+def _coerce_state_pydantic_v1(
+    schema: Type[Any], input: dict[str, Any]
+) -> dict[str, Any]:
+    return schema.construct(**input)
+
+
 def _coerce_state(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
     return schema(**input)
 

libs/langgraph/langgraph/pregel/__init__.py

@@ -496,6 +496,8 @@ class Pregel(PregelProtocol):
 
     config_type: Optional[Type[Any]] = None
 
+    input_model: Optional[Type[BaseModel]] = None
+
     config: Optional[RunnableConfig] = None
 
     name: str = "LangGraph"
@@ -519,6 +521,7 @@ class Pregel(PregelProtocol):
         store: Optional[BaseStore] = None,
         retry_policy: Optional[RetryPolicy] = None,
         config_type: Optional[Type[Any]] = None,
+        input_model: Optional[Type[BaseModel]] = None,
         config: Optional[RunnableConfig] = None,
         name: str = "LangGraph",
     ) -> None:
@@ -537,6 +540,7 @@ class Pregel(PregelProtocol):
         self.store = store
         self.retry_policy = retry_policy
         self.config_type = config_type
+        self.input_model = input_model
         self.config = config
         self.name = name
         if auto_validate:
@@ -650,6 +654,8 @@ class Pregel(PregelProtocol):
     def get_input_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> Type[BaseModel]:
+        if self.input_model is not None:
+            return self.input_model
         config = merge_configs(self.config, config)
         if isinstance(self.input_channels, str):
             return super().get_input_schema(config)
@@ -1967,6 +1973,7 @@ class Pregel(PregelProtocol):
                 )
             with SyncPregelLoop(
                 input,
+                input_model=self.input_model,
                 stream=StreamProtocol(stream.put, stream_modes),
                 config=config,
                 store=store,
@@ -2257,6 +2264,7 @@ class Pregel(PregelProtocol):
                 )
             async with AsyncPregelLoop(
                 input,
+                input_model=self.input_model,
                 stream=StreamProtocol(stream.put_nowait, stream_modes),
                 config=config,
                 store=store,

libs/langgraph/langgraph/pregel/loop.py

@@ -23,6 +23,7 @@ from typing import (
 
 from langchain_core.callbacks import AsyncParentRunManager, ParentRunManager
 from langchain_core.runnables import RunnableConfig
+from pydantic import BaseModel
 from typing_extensions import ParamSpec, Self
 
 from langgraph.channels.base import BaseChannel
@@ -125,6 +126,7 @@ P = ParamSpec("P")
 
 INPUT_DONE = object()
 INPUT_RESUMING = object()
+INPUT_SHOULD_VALIDATE = object()
 SPECIAL_CHANNELS = (ERROR, INTERRUPT, SCHEDULED)
 
 
@@ -139,6 +141,7 @@ def DuplexStream(*streams: StreamProtocol) -> StreamProtocol:
 
 class PregelLoop(LoopProtocol):
     input: Optional[Any]
+    input_model: Optional[Type[BaseModel]]
     checkpointer: Optional[BaseCheckpointSaver]
     nodes: Mapping[str, PregelNode]
     specs: Mapping[str, Union[BaseChannel, ManagedValueSpec]]
@@ -202,6 +205,7 @@ class PregelLoop(LoopProtocol):
         interrupt_after: Union[All, Sequence[str]] = EMPTY_SEQ,
         interrupt_before: Union[All, Sequence[str]] = EMPTY_SEQ,
         manager: Union[None, AsyncParentRunManager, ParentRunManager] = None,
+        input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
     ) -> None:
         super().__init__(
@@ -212,6 +216,7 @@ class PregelLoop(LoopProtocol):
             store=store,
         )
         self.input = input
+        self.input_model = input_model
         self.checkpointer = checkpointer
         self.nodes = nodes
         self.specs = specs
@@ -395,7 +400,7 @@ class PregelLoop(LoopProtocol):
         if self.status != "pending":
             raise RuntimeError("Cannot tick when status is no longer 'pending'")
 
-        if self.input not in (INPUT_DONE, INPUT_RESUMING):
+        if self.input not in (INPUT_DONE, INPUT_RESUMING, INPUT_SHOULD_VALIDATE):
             self._first(input_keys=input_keys)
         elif self.to_interrupt:
             # if we need to interrupt, do so
@@ -425,6 +430,13 @@ class PregelLoop(LoopProtocol):
             # apply writes to managed values
             for key, values in mv_writes.items():
                 self._update_mv(key, values)
+            # validate input if requested
+            if self.input is INPUT_SHOULD_VALIDATE:
+                self.input = INPUT_DONE
+                # validate
+                cast(Type[BaseModel], self.input_model)(
+                    **read_channels(self.channels, self.stream_keys)
+                )
             # produce values output
             self._emit(
                 "values", map_output_values, self.output_keys, writes, self.channels
@@ -622,6 +634,8 @@ class PregelLoop(LoopProtocol):
             self._emit(
                 "values", map_output_values, self.output_keys, True, self.channels
             )
+            # set flag
+            self.input = INPUT_RESUMING
         # map inputs to channel updates
         elif input_writes := deque(map_input(input_keys, self.input)):
             # TODO shouldn't these writes be passed to put_writes too?
@@ -662,10 +676,19 @@ class PregelLoop(LoopProtocol):
             assert not mv_writes, "Can't write to SharedValues in graph input"
             # save input checkpoint
             self._put_checkpoint({"source": "input", "writes": dict(input_writes)})
+            # set flag
+            if (
+                self.input_model is not None
+                and not isinstance(self.input, self.input_model)
+                and not isinstance(self.stream_keys, str)
+            ):
+                self.input = INPUT_SHOULD_VALIDATE
+            else:
+                self.input = INPUT_DONE
         elif CONFIG_KEY_RESUMING not in configurable:
             raise EmptyInputError(f"Received no input for {input_keys}")
-        # done with input
-        self.input = INPUT_RESUMING if is_resuming else INPUT_DONE
+        else:
+            self.input = INPUT_DONE
         # update config
         if not self.is_nested:
             self.config = patch_configurable(
@@ -840,10 +863,12 @@ class SyncPregelLoop(PregelLoop, ContextManager):
         interrupt_before: Union[All, Sequence[str]] = EMPTY_SEQ,
         output_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
         stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
+        input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
     ) -> None:
         super().__init__(
             input,
+            input_model=input_model,
             stream=stream,
             config=config,
             checkpointer=checkpointer,
@@ -979,10 +1004,12 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
         manager: Union[None, AsyncParentRunManager, ParentRunManager] = None,
         output_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
         stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
+        input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
     ) -> None:
         super().__init__(
             input,
+            input_model=input_model,
             stream=stream,
             config=config,
             checkpointer=checkpointer,

libs/langgraph/langgraph/utils/config.py

@@ -1,4 +1,5 @@
 from collections import ChainMap
+from os import getenv
 from typing import Any, Optional, Sequence, cast
 
 from langchain_core.callbacks import (
@@ -11,7 +12,6 @@ from langchain_core.runnables import RunnableConfig
 from langchain_core.runnables.config import (
     CONFIG_KEYS,
     COPIABLE_KEYS,
-    DEFAULT_RECURSION_LIMIT,
     var_child_runnable_config,
 )
 
@@ -26,6 +26,8 @@ from langgraph.constants import (
     NS_SEP,
 )
 
+DEFAULT_RECURSION_LIMIT = int(getenv("LANGGRAPH_DEFAULT_RECURSION_LIMIT", "25"))
+
 
 def recast_checkpoint_ns(ns: str) -> str:
     """Remove task IDs from checkpoint namespace.

libs/langgraph/poetry.lock

@@ -1324,19 +1324,19 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.30"
+version = "0.3.44"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main", "dev"]
 files = [
-    {file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
-    {file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
+    {file = "langchain_core-0.3.44-py3-none-any.whl", hash = "sha256:d989ce8bd62f1d07765acd575e6ec1254aec0cf7775aaea39fe4af8102377459"},
+    {file = "langchain_core-0.3.44.tar.gz", hash = "sha256:7c0a01e78360f007cbca448178fe7e032404068e6431dbe8ce905f84febbdfa5"},
 ]
 
 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.125,<0.3"
+langsmith = ">=0.1.125,<0.4"
 packaging = ">=23.2,<25"
 pydantic = [
     {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@@ -1348,7 +1348,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.16"
+version = "2.0.18"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1366,7 +1366,7 @@ url = "../checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.15"
+version = "2.0.16"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1386,7 +1386,7 @@ url = "../checkpoint-postgres"
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.5"
+version = "2.0.6"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0"
@@ -1404,7 +1404,7 @@ url = "../checkpoint-sqlite"
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.1.1"
+version = "0.1.2"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1422,7 +1422,7 @@ url = "../prebuilt"
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.53"
+version = "0.1.55"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.3.6"
+version = "0.3.8"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"

libs/langgraph/tests/__snapshots__/test_large_cases.ambr

@@ -3116,8 +3116,6 @@
   	__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__;
@@ -3126,6 +3124,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -3141,8 +3141,6 @@
   	__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__;
@@ -3151,6 +3149,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -3166,8 +3166,6 @@
   	__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__;
@@ -3176,6 +3174,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -3191,8 +3191,6 @@
   	__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__;
@@ -3201,6 +3199,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -3216,8 +3216,6 @@
   	__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__;
@@ -3226,6 +3224,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -3241,8 +3241,6 @@
   	__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__;
@@ -3251,6 +3249,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2

libs/langgraph/tests/__snapshots__/test_large_cases_async.ambr

@@ -6,8 +6,6 @@
   	__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__;
@@ -16,6 +14,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -31,8 +31,6 @@
   	__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__;
@@ -41,6 +39,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -56,8 +56,6 @@
   	__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__;
@@ -66,6 +64,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -81,8 +81,6 @@
   	__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__;
@@ -91,6 +89,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -106,8 +106,6 @@
   	__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__;
@@ -116,6 +114,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2
@@ -131,8 +131,6 @@
   	__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__;
@@ -141,6 +139,8 @@
   	router_node -.-> weather_graph_model_node;
   	router_node -.-> __end__;
   	subgraph weather_graph
+  	weather_graph_model_node(model_node)
+  	weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
   	weather_graph_model_node --> weather_graph_weather_node;
   	end
   	classDef default fill:#f2f0ff,line-height:1.2

libs/langgraph/tests/__snapshots__/test_pregel.ambr

@@ -1722,13 +1722,13 @@
   	__start__([<p>__start__</p>]):::first
   	uno(uno)
   	dos(dos)
-  	subgraph_one(one)
-  	subgraph_two(two)
-  	subgraph_three(three)
   	__start__ --> uno;
   	uno -.-> dos;
   	uno -.-> subgraph_one;
   	subgraph subgraph
+  	subgraph_one(one)
+  	subgraph_two(two)
+  	subgraph_three(three)
   	subgraph_one -.-> subgraph_two;
   	subgraph_one -.-> subgraph_three;
   	end
@@ -1752,12 +1752,14 @@
   %%{init: {'flowchart': {'curve': 'linear'}}}%%
   graph TD;
   	__start__([<p>__start__</p>]):::first
-  	inner(inner)
   	side(side)
   	__end__([<p>__end__</p>]):::last
-  	__start__ --> inner;
-  	inner --> side;
+  	__start__ --> inner_up;
+  	inner_up --> side;
   	side --> __end__;
+  	subgraph inner
+  	inner_up(up)
+  	end
   	classDef default fill:#f2f0ff,line-height:1.2
   	classDef first fill-opacity:0
   	classDef last fill:#bfb6fc
@@ -1895,10 +1897,6 @@
   graph TD;
   	__start__([<p>__start__</p>]):::first
   	tool_one(tool_one)
-  	tool_two___start__(<p>__start__</p>)
-  	tool_two_tool_two_slow(tool_two_slow)
-  	tool_two_tool_two_fast(tool_two_fast)
-  	tool_two___end__(<p>__end__</p>)
   	tool_three(tool_three)
   	__end__([<p>__end__</p>]):::last
   	__start__ -.-> tool_one;
@@ -1908,6 +1906,10 @@
   	__start__ -.-> tool_three;
   	tool_three --> __end__;
   	subgraph tool_two
+  	tool_two___start__(<p>__start__</p>)
+  	tool_two_tool_two_slow(tool_two_slow)
+  	tool_two_tool_two_fast(tool_two_fast)
+  	tool_two___end__(<p>__end__</p>)
   	tool_two___start__ -.-> tool_two_tool_two_slow;
   	tool_two_tool_two_slow --> tool_two___end__;
   	tool_two___start__ -.-> tool_two_tool_two_fast;
@@ -1962,24 +1964,24 @@
   graph TD;
   	__start__([<p>__start__</p>]):::first
   	gp_one(gp_one)
-  	gp_two___start__(<p>__start__</p>)
-  	gp_two_p_one(p_one)
-  	gp_two_p_two___start__(<p>__start__</p>)
-  	gp_two_p_two_c_one(c_one)
-  	gp_two_p_two_c_two(c_two)
-  	gp_two_p_two___end__(<p>__end__</p>)
-  	gp_two___end__(<p>__end__</p>)
   	__end__([<p>__end__</p>]):::last
   	__start__ --> gp_one;
   	gp_two___end__ --> gp_one;
   	gp_one -. &nbsp;0&nbsp; .-> gp_two___start__;
   	gp_one -. &nbsp;1&nbsp; .-> __end__;
   	subgraph gp_two
+  	gp_two___start__(<p>__start__</p>)
+  	gp_two_p_one(p_one)
+  	gp_two___end__(<p>__end__</p>)
   	gp_two___start__ --> gp_two_p_one;
   	gp_two_p_two___end__ --> gp_two_p_one;
   	gp_two_p_one -. &nbsp;0&nbsp; .-> gp_two_p_two___start__;
   	gp_two_p_one -. &nbsp;1&nbsp; .-> gp_two___end__;
   	subgraph p_two
+  	gp_two_p_two___start__(<p>__start__</p>)
+  	gp_two_p_two_c_one(c_one)
+  	gp_two_p_two_c_two(c_two)
+  	gp_two_p_two___end__(<p>__end__</p>)
   	gp_two_p_two___start__ --> gp_two_p_two_c_one;
   	gp_two_p_two_c_two --> gp_two_p_two_c_one;
   	gp_two_p_two_c_one -. &nbsp;0&nbsp; .-> gp_two_p_two_c_two;
@@ -1998,16 +2000,16 @@
   graph TD;
   	__start__([<p>__start__</p>]):::first
   	p_one(p_one)
-  	p_two___start__(<p>__start__</p>)
-  	p_two_c_one(c_one)
-  	p_two_c_two(c_two)
-  	p_two___end__(<p>__end__</p>)
   	__end__([<p>__end__</p>]):::last
   	__start__ --> p_one;
   	p_two___end__ --> p_one;
   	p_one -. &nbsp;0&nbsp; .-> p_two___start__;
   	p_one -. &nbsp;1&nbsp; .-> __end__;
   	subgraph p_two
+  	p_two___start__(<p>__start__</p>)
+  	p_two_c_one(c_one)
+  	p_two_c_two(c_two)
+  	p_two___end__(<p>__end__</p>)
   	p_two___start__ --> p_two_c_one;
   	p_two_c_two --> p_two_c_one;
   	p_two_c_one -. &nbsp;0&nbsp; .-> p_two_c_two;

libs/langgraph/tests/test_pregel.py

@@ -6658,6 +6658,40 @@ def test_pydantic_none_state_update() -> None:
     assert graph.invoke({"foo": ""}) == {"foo": None}
 
 
+def test_pydantic_state_mutation() -> None:
+    from pydantic import BaseModel, Field
+
+    class Inner(BaseModel):
+        a: int = 0
+
+    class State(BaseModel):
+        inner: Inner = Inner()
+        outer: int = 0
+
+    def my_node(state: State) -> State:
+        state.inner.a = 5
+        state.outer = 10
+        return state
+
+    graph = StateGraph(State).add_node(my_node).add_edge(START, "my_node").compile()
+
+    assert graph.invoke({"outer": 1}) == {"outer": 10, "inner": Inner(a=5)}
+
+    # test w/ default_factory
+    class State(BaseModel):
+        inner: Inner = Field(default_factory=Inner)
+        outer: int = 0
+
+    def my_node(state: State) -> State:
+        state.inner.a = 5
+        state.outer = 10
+        return state
+
+    graph = StateGraph(State).add_node(my_node).add_edge(START, "my_node").compile()
+
+    assert graph.invoke({"outer": 1}) == {"outer": 10, "inner": Inner(a=5)}
+
+
 def test_get_stream_writer() -> None:
     class State(TypedDict):
         foo: str

libs/langgraph/tests/test_pregel_async.py

@@ -7697,3 +7697,56 @@ async def test_interrupt_subgraph_reenter_checkpointer_true(
         }
         # confirm that we preserve the state values from the previous invocation
         assert bar_values == [None, "barbaz", "quxbaz"]
+
+
+@NEEDS_CONTEXTVARS
+async def test_handles_multiple_interrupts_from_tasks() -> None:
+    @task
+    async def add_participant(name: str) -> str:
+        feedback = interrupt(f"Hey do you want to add {name}?")
+
+        if feedback is False:
+            return f"The user changed their mind and doesn't want to add {name}!"
+
+        if feedback is True:
+            return f"Added {name}!"
+
+        raise ValueError("Invalid feedback")
+
+    @entrypoint(checkpointer=MemorySaver())
+    async def program(_state: Any) -> list[str]:
+        first = await add_participant("James")
+        second = await add_participant("Will")
+        return [first, second]
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    result = await program.ainvoke("this is ignored", config=config)
+    assert result is None
+
+    state = await program.aget_state(config=config)
+    assert len(state.tasks[0].interrupts) == 1
+    task_interrupt = state.tasks[0].interrupts[0]
+    assert task_interrupt.resumable is True
+    assert len(task_interrupt.ns) == 2
+    assert task_interrupt.ns[0].startswith("program:")
+    assert task_interrupt.ns[1].startswith("add_participant:")
+    assert task_interrupt.value == "Hey do you want to add James?"
+
+    result = await program.ainvoke(Command(resume=True), config=config)
+    assert result is None
+
+    state = await program.aget_state(config=config)
+    assert len(state.tasks[0].interrupts) == 1
+    task_interrupt = state.tasks[0].interrupts[0]
+    assert task_interrupt.resumable is True
+    assert len(task_interrupt.ns) == 2
+    assert task_interrupt.ns[0].startswith("program:")
+    assert task_interrupt.ns[1].startswith("add_participant:")
+    assert task_interrupt.value == "Hey do you want to add Will?"
+
+    result = await program.ainvoke(Command(resume=True), config=config)
+    assert result is not None
+    assert len(result) == 2
+    assert result[0] == "Added James!"
+    assert result[1] == "Added Will!"

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -257,7 +257,7 @@ def _validate_chat_history(
 @_convert_modifier_to_prompt
 def create_react_agent(
     model: Union[str, LanguageModelLike],
-    tools: Union[Sequence[BaseTool], ToolNode],
+    tools: Union[Sequence[Union[BaseTool, Callable]], ToolNode],
     *,
     prompt: Optional[Prompt] = None,
     response_format: Optional[
@@ -859,4 +859,7 @@ __all__ = [
     "create_react_agent",
     "create_tool_calling_executor",
     "AgentState",
+    "AgentStatePydantic",
+    "AgentStateWithStructuredResponse",
+    "AgentStateWithStructuredResponsePydantic",
 ]

libs/prebuilt/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-prebuilt"
-version = "0.1.2"
+version = "0.1.3"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 license = "MIT"

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.52",
+  "version": "0.0.54",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

libs/sdk-js/src/react-ui/client.tsx

@@ -115,6 +115,9 @@ interface LoadExternalComponentProps
   /** Stream of the assistant */
   stream: ReturnType<typeof useStream>;
 
+  /** Namespace of UI components. Defaults to assistant ID. */
+  namespace?: string;
+
   /** UI message to be rendered */
   message: UIMessage;
 
@@ -133,6 +136,7 @@ interface LoadExternalComponentProps
 
 export function LoadExternalComponent({
   stream,
+  namespace,
   message,
   meta,
   fallback,
@@ -152,10 +156,11 @@ export function LoadExternalComponent({
   const clientComponent = components?.[message.name];
   const hasClientComponent = clientComponent != null;
 
+  const uiNamespace = namespace ?? stream.assistantId;
   const uiClient = stream.client["~ui"];
   React.useEffect(() => {
     if (hasClientComponent) return;
-    uiClient.getComponent(stream.assistantId, message.name).then((html) => {
+    uiClient.getComponent(uiNamespace, message.name).then((html) => {
       const dom = ref.current;
       if (!dom) return;
       const root = dom.shadowRoot ?? dom.attachShadow({ mode: "open" });
@@ -166,16 +171,10 @@ export function LoadExternalComponent({
         );
       root.appendChild(fragment);
     });
-  }, [
-    uiClient,
-    stream.assistantId,
-    message.name,
-    shadowRootId,
-    hasClientComponent,
-  ]);
+  }, [uiClient, uiNamespace, message.name, shadowRootId, hasClientComponent]);
 
   if (hasClientComponent) {
-    return React.createElement(clientComponent, message.content);
+    return React.createElement(clientComponent, message.props);
   }
 
   return (
@@ -185,7 +184,7 @@ export function LoadExternalComponent({
       <UseStreamContext.Provider value={{ stream, meta }}>
         {state?.target != null
           ? ReactDOM.createPortal(
-              React.createElement(state.comp, message.content),
+              React.createElement(state.comp, message.props),
               state.target,
             )
           : fallback}

libs/sdk-js/src/react-ui/server/server.ts

@@ -30,8 +30,8 @@ export const typedUi = <Decl extends Record<string, ElementType>>(config: {
     message: {
       id?: string;
       name: K;
-      content: PropMap[K];
-      additional_kwargs?: Record<string, unknown>;
+      props: PropMap[K];
+      metadata?: Record<string, unknown>;
     },
     options?: { message?: MessageLike },
   ): UIMessage => {
@@ -39,10 +39,10 @@ export const typedUi = <Decl extends Record<string, ElementType>>(config: {
       type: "ui" as const,
       id: message?.id ?? uuidv4(),
       name: message?.name,
-      content: message?.content,
-      additional_kwargs: {
+      props: message?.props,
+      metadata: {
         ...metadata,
-        ...message?.additional_kwargs,
+        ...message?.metadata,
         ...(options?.message ? { message_id: options.message.id } : null),
       },
     };

libs/sdk-js/src/react-ui/types.ts

@@ -3,8 +3,8 @@ export interface UIMessage {
 
   id: string;
   name: string;
-  content: Record<string, unknown>;
-  additional_kwargs: {
+  props: Record<string, unknown>;
+  metadata: {
     run_id: string;
     message_id?: string;
     [key: string]: unknown;

libs/sdk-py/langgraph_sdk/client.py

@@ -2163,7 +2163,7 @@ class StoreClient:
             "index": index,
             "ttl": ttl,
         }
-        await self.http.put("/store/items", json=payload)
+        await self.http.put("/store/items", json=_provided_vals(payload))
 
     async def get_item(
         self,
@@ -4307,7 +4307,7 @@ class SyncStoreClient:
             "index": index,
             "ttl": ttl,
         }
-        self.http.put("/store/items", json=payload)
+        self.http.put("/store/items", json=_provided_vals(payload))
 
     def get_item(
         self,

libs/sdk-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-sdk"
-version = "0.1.55"
+version = "0.1.56"
 description = "SDK for interacting with LangGraph API"
 authors = []
 license = "MIT"