LangGraph is an orchestration framework from the LangChain ecosystem for building agents and multi-step systems modeled as a directed graph with shared state. Each node in the graph is a function or an agent, and the edges define the execution path, including branches, cycles and subgraphs.
What it is for
- Stateful agents that must survive failures, restarts and new deployments
- Multi-step workflows with conditional decisions, loops and parallelism
- Human-in-the-loop approval before sensitive actions
- Per-conversation and per-thread memory with persisted history
- Multi-agent systems with coordinators, subgraphs and specialized roles
How it works
- The developer defines the (typed) state and the nodes that transform it
- A checkpointer saves a snapshot of the state at every step (super-step) and lets execution resume from the exact point
- The
interrupt()API pauses the graph for human review and resumes it withCommand(resume=...) - Token and graph-event streaming for user interfaces
- Available in Python and JavaScript/TypeScript, with support for multiple model providers through LangChain
License and maintenance
- Open-source core under the MIT license, maintained by LangChain, Inc.
- Stable 1.0 release since October 2025, under active development
- LangSmith and LangGraph Platform are optional commercial services for observability and deployment
Strengths
- Explicit control flow with an inspectable, testable graph
- Durable execution: interruptions do not lose completed work
- Broad ecosystem of model, tool and database integrations
Limitations
- The graph model requires more code and explicit design than prompt-only approaches
- Durable production execution requires configuring a persistence backend (for example, PostgreSQL)