LangGraph
A library for building stateful, multi-actor applications with LLMs, built on top of LangChain. LangGraph models agent workflows as graphs where nodes are functions (LLM calls, tools, logic) and edges are state transitions. It enables complex patterns like cycles, branching, and human-in-the-loop that are hard to express in linear chains.
Implements
Concepts this tool claims to implement:
- Agent Orchestration primary
StateGraph class for defining agent workflows as graphs. Built-in support for supervisor patterns, tool nodes, and conditional routing.
- Multi-Agent System primary
Native support for multi-agent patterns including supervisor-worker, hierarchical teams, and agent handoffs.
- Agent State primary
Explicit TypedDict state schemas that flow through the graph. State channels, reducers, and checkpointing for persistence.
- Human-in-the-Loop primary
interrupt_before and interrupt_after for pausing execution. Human approval nodes and state modification patterns.
- Workflow secondary
Can express both agent-like (dynamic routing) and workflow-like (deterministic paths) patterns in the same graph.
Integration Surfaces
Details
- Vendor
- LangChain Inc.
- License
- MIT
- Runs On
- local, cloud
- Used By
- human, agent, system
Links
Notes
LangGraph emerged as the recommended approach for complex agents in the LangChain ecosystem, addressing limitations of the linear AgentExecutor. Its graph-based model makes state transitions explicit and debuggable.