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 age