Build stateful, multi-actor AI agents with graph-based workflows. From LangChain, the most popular LLM framework.
LangGraph is worth checking the docs before setup with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
Repository setup guidance
gh repo view langchain-ai/langgraph --webOpen the official repository or website.
Check the README for package manager, auth, and platform requirements.
Try it in a small test task inside your agent workflow.
LangGraph is a framework for building stateful agent workflows as directed graphs. Each node is an action, each edge is a condition. Build complex agent loops, human-in-the-loop workflows, and multi-agent systems that maintain state across interactions.
LangGraph is a framework for building stateful agent workflows as directed graphs. Each node is an action, each edge is a condition. Build complex agent loops, human-in-the-loop workflows, and multi-agent systems that maintain state across interactions.
Strong trust signals; still review the README and permissions before production use.
Last commit was about 57 days ago.
9000 GitHub stars indicate community interest.
110 open issues signal maintenance load.
MIT license detected.
Stateful agent workflows
Multi-step reasoning systems
Agent orchestration
Python-based, full control over execution
Graph topology determines agent safety boundaries
9,000
Stars
1,400
Forks
110
Issues
MIT
License
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Build stateful, multi-actor AI agents with graph-based workflows. From LangChain, the most popular LLM framework.
AI Engineers, LangChain Users
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Python-based, full control over execution Graph topology determines agent safety boundaries
Similar or complementary options to evaluate include Ollama, anthropic-skills, Claude Code.
May 31, 2026
2 security/trust notes recorded.
Setup difficulty is 3/5.