LangGraph

What is LangGraph?

LangGraph, from the LangChain team, models an AI agent's logic as a graph of nodes and edges with explicit shared state, giving developers precise control over loops, branches, and retries — useful for agents that need more reliability than a simple chat loop.

Key features

• Graph-based control flow with explicit state at every step
• Built-in support for cycles, retries, and human-in-the-loop steps
• Streaming and persistence for long-running agent sessions
• Tight integration with the broader LangChain ecosystem
• LangGraph Platform for deploying and monitoring agents in production

How to get started

1. Install the LangGraph Python or JavaScript package
2. Define nodes representing steps in your agent's logic
3. Connect nodes with edges to define control flow and branching
4. Run locally, or deploy via LangGraph Platform for production use

Use cases

• Production agents that need reliable, auditable control flow
• Workflows requiring retries, loops, or human approval steps
• Long-running agent sessions with persisted state
• Teams already using LangChain wanting tighter orchestration

LangGraph vs. alternatives

FAQ

Is LangGraph free to use?

LangGraph's plans: Free, open-source; paid LangGraph Platform for deployment. Pricing and free-tier limits change over time, so check the official site above for the latest details.

What is LangGraph used for?

Stateful, graph-based orchestration for AI agents.

Who is LangGraph best for?

LangGraph is a good fit for production agents that need reliable, auditable control flow, or workflows requiring retries, loops, or human approval steps.

What are the alternatives to LangGraph?

Commonly compared alternatives include CrewAI and Flowise, see the comparison above for how they differ.