LangGraph
Also known as: lang graph, langchain langgraph
LangGraph grew out of LangChain as a way to handle agent workflows that need to loop, branch, or recover from errors rather than just chain steps in a straight line. In a graph-based model, each action your agent can take is a node, and you define explicit edges connecting them. That structure gives you fine-grained control over what happens when an agent hits a dead end, needs to retry, or should hand off to a different specialist agent.
The explicit state management is what separates LangGraph from simpler agent setups. You define the state your agent carries between steps, which makes it auditable and debuggable. You can checkpoint progress, replay from a known good state, or inject human review at specific nodes. Production engineering teams find this audit-trail-friendly structure much easier to maintain than less structured approaches.
LangGraph 1.0 shipped in October 2025, marking its first stable release with a no-breaking-changes commitment. By early 2026 it had surpassed CrewAI in GitHub stars, driven by enterprise adoption. Most teams use LangGraph for complex multi-agent orchestration and n8n for rapid deployment of AI-enhanced business automation, treating the two as complementary.