LangGraph
LangGraph is LangChain’s open-source orchestration framework for building resilient, long-running, stateful AI agents with durable execution, memory, human-in-the-loop control, and graph-based workflows.
Overview
LangGraph helps developers build stateful, long-running AI agents that can persist through failures, resume from checkpoints, coordinate tools and subgraphs, and support human oversight during complex execution flows.
Core Features & Capabilities
Ideal for AI engineers, agent developers, LangChain users, backend engineers, platform teams, LLM app builders, enterprise AI teams, automation engineers, AI infrastructure teams, research teams, workflow orchestration teams, and developers building reliable tool-using agents, multi-agent systems, and production LLM applications.
- Build stateful AI agents that persist execution state, resume from checkpoints, and survive failures
- Design graph-based agent workflows with nodes, edges, branching, subgraphs, streaming, and tool calls
- Add human-in-the-loop review by inspecting and modifying agent state during execution
- Use short-term and long-term memory to support persistent reasoning across sessions
- Debug, trace, evaluate, and deploy agent workflows with LangSmith and the broader LangChain ecosystem
Trending Use Cases
Why Developers Choose LangGraph
Visit the LangGraph GitHub repository or official LangChain documentation to review concepts, quickstarts, guides, and API references. Developers can install the Python package with pip install -U langgraph, build a graph using the Graph API or Functional API, add state, tools, memory, checkpointing, and human-in-the-loop controls, then debug and observe workflows with LangSmith. JavaScript and TypeScript teams can use LangGraph.js for equivalent agent orchestration workflows.
“LangGraph gives developers low-level control over stateful agent orchestration, making long-running AI workflows more reliable, inspectable, and production-ready.”
Getting Started with LangGraph
By combining graph-based orchestration, durable execution, checkpointing, memory, human-in-the-loop control, debugging, observability, and LangChain ecosystem integrations, LangGraph gives developers a reliable foundation for building production-grade AI agent systems.
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LangGraph
LangChain is the creator of LangGraph, an open-source low-level orchestration framework for building stateful agents, durable execution workflows, memory-enabled agents, human-in-the-loop systems, multi-agent workflows, debugging, and production AI agent deployment.
LangGraph is a low-level orchestration framework and runtime from LangChain for building, managing, and deploying reliable AI agents. It is designed for long-running, stateful, controllable agent workflows where developers need durable execution, checkpointing, memory, human oversight, branching, subgraphs, streaming, debugging, observability, and production deployment. LangGraph can be used standalone or with the broader LangChain ecosystem, including LangChain components, LangSmith tracing and evaluation, Deep Agents, LangGraph.js, and LangSmith deployment infrastructure. It is especially useful for developers building multi-step agents, multi-agent systems, workflow graphs, tool-using agents, enterprise agent platforms, and production LLM applications that need state persistence and reliability.
github.com/langchain-ai/langgraph
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