LangGraph

Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.

pip install -U langgraph

[!TIP] If you’re looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.

For an equivalent JS/TS library, check out LangGraph.js and the JS docs.

Why use LangGraph?

LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:

• Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
• Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
• Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent 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.

[!TIP] For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

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:

• Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
• LangChain – Provides integrations and composable components to streamline LLM application development.
• 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 Deployment – 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 LangSmith Studio.

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Documentation

• docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
• reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
• LangGraph Quickstart – Get started building with LangGraph
• Chat LangChain – Chat with the LangChain documentation and get answers to your questions

Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.

Additional resources

• Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
• LangChain Academy – Learn the basics of LangGraph in our free, structured course.
• Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
• Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
• Code of Conduct – Our community guidelines and standards for participation.

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