LangGraph is a low-level orchestration framework written in Python for building, managing, and deploying long-running, stateful agents. The repository serves as the foundation for agent development within the LangChain ecosystem, trusted by companies including Klarna, Replit, and Elastic. It provides infrastructure that enables developers to construct resilient agents capable of persisting through failures and maintaining state across extended execution periods.
The framework addresses several critical requirements for production agent systems. Durable execution allows agents to automatically resume from exactly where they left off after failures, ensuring continuity in long-running workflows. Human-in-the-loop capabilities enable seamless incorporation of human oversight by allowing inspection and modification of agent state at any point during execution. The comprehensive memory system supports both short-term working memory for ongoing reasoning and long-term persistent memory across sessions, creating truly stateful agents. Integration with LangSmith provides deep visibility into complex agent behavior through visualization tools that trace execution paths, capture state transitions, and deliver detailed runtime metrics. Production-ready deployment infrastructure is designed specifically to handle the unique challenges of stateful, long-running workflows at scale.
LangGraph integrates within a broader ecosystem of LangChain products. While it can be used standalone, it pairs with Deep Agents for building agents capable of planning and leveraging subagents, with LangChain for composable components and integrations, and with LangSmith for evaluation and observability. The framework is inspired by Pregel and Apache Beam, with its public interface drawing from NetworkX conventions.
The repository demonstrates active maintenance and community engagement.
The framework is classified across multiple domains including graph-based language models, graph-based reasoning, AI frameworks, semantic analysis, information extraction, graph algorithms, knowledge representation, knowledge graphs, information retrieval, and natural language processing. Comprehensive documentation is available through docs.langchain.com, with API reference documentation at reference.langchain.com/python/langgraph. The project includes guides for streaming, memory and persistence, design patterns like branching and subgraphs, and a free structured course through LangChain Academy. The codebase is open source under the MIT license.
