@langchain/langgraph

Low-level orchestration framework for building stateful agents.

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.

npm install @langchain/langgraph @langchain/core

[!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 Python library, check out LangGraph and the Python 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’s 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 (JS) — Build agents that can plan, use subagents, and leverage file systems for complex tasks. A higher-level package built on top of LangGraph.
• 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.

Additional resources

• LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
• LangChain Academy: Learn the basics of LangGraph in our free, structured course.
• Streaming Cookbook: Documentation and examples around LangGraphs's streaming capabilities.
• API Reference: Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
• Built with LangGraph: Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.

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.