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LangGraph v1.0Welcome to the new LangGraph documentation! If you encounter any issues or have feedback, please open an issue so we can improve. Archived v0 documentation can be found here.See the release notes and migration guide for a complete list of changes and instructions on how to upgrade your code.
Trusted by companies shaping the future of agents— including Klarna, Replit, Elastic, and more— LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. LangGraph is very low-level, and focused entirely on agent orchestration. Before using LangGraph, we recommend you familiarize yourself with some of the components used to build agents, starting with models and tools. We will commonly use LangChain components throughout the documentation to integrate models and tools, but you don’t need to use LangChain to use LangGraph. If you are just getting start with agents or want a higher level abstraction, we recommend you use LangChain’s agents that provide pre-built architectures for common LLM and tool calling loops. LangGraph is focused on the underlying capabilities important for agent orchestration: durable execution, streaming, human-in-the-loop, and more.

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Install

npm install @langchain/langgraph @langchain/core
Then, create a simple hello world example: 
import { MessagesAnnotation, StateGraph, START, END } from "@langchain/langgraph";

const mockLlm = (state: typeof MessagesAnnotation.State) => {
  return { messages: [{ role: "ai", content: "hello world" }] };
};

const graph = new StateGraph(MessagesAnnotation)
  .addNode("mock_llm", mockLlm)
  .addEdge(START, "mock_llm")
  .addEdge("mock_llm", END)
  .compile();

await graph.invoke({ messages: [{ role: "user", content: "hi!" }] });

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Core benefits

LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
  • Durable execution: Build agents that persist through failures and can run for extended periods, resuming from where they left off.
  • Human-in-the-loop: Incorporate human oversight by inspecting and modifying agent state at any point.
  • Comprehensive memory: Create stateful agents with both short-term working memory for ongoing reasoning and long-term memory across sessions.
  • Debugging with LangSmith: Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
  • Production-ready deployment: Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.

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LangGraph ecosystem

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 — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
  • LangSmith — 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 Studio.
  • LangChain - Provides integrations and composable components to streamline LLM application development. Contains agent abstractions built on top of LangGraph.

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Acknowledgements

LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
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