langgraphjs
ActiveDescription
Framework to build resilient language agents as graphs.
Key Features
- Durable execution - Agents persist through failures and resume from exactly where they left off
- Human-in-the-loop - Seamlessly incorporate human oversight by inspecting and modifying agent state at any point
- Comprehensive memory - Both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- LangSmith debugging - Deep visibility with execution path tracing, state transition capture, and runtime metrics
- Production-ready deployment - Scalable infrastructure designed for stateful, long-running workflows
- Graph-based orchestration - Low-level directed graph agent orchestration with customizable architectures
Use Cases
💡 Build complex AI agent workflows requiring long-running execution and fault recovery
💡 Introduce human oversight at critical decision points for reliable and safe agent behavior
💡 Define multi-step agent logic using graph structures with loops, conditional branching, and parallel execution
💡 Integrate with LangChain ecosystem and use LangSmith for deep debugging and observability
Categories
Quick Start
Install npm install @langchain/langgraph @langchain/core, define your agent graph with createGraph(), compile and invoke. Use @observe() for tracing. Check out LangChain Academy free courses for structured learning and the streaming cookbook for advanced patterns.