An orchestration framework for building stateful, cyclical multi-agent workflows as directed graphs with native HITL support.
LangGraph is an open-source orchestration framework developed by LangChain for building agentic applications as stateful, directed graphs. It addresses the core challenge of reliable multi-step agentic workflows by providing a principled graph-based execution model with first-class support for state management, cyclical reasoning, and Human-in-the-Loop interruptions.
Key architectural concepts:
Nodes are Python functions representing units of work—LLM calls, tool invocations, business logic, or human-facing interactions. Each node receives the current state and returns state updates.
Edges define control flow between nodes. Conditional edges allow the graph to branch based on current state, enabling reactive behavior in response to tool results, LLM decisions, or external events.
State is a typed schema (defined with TypedDict or Pydantic) that all nodes share. This shared state model prevents the context corruption that plagues message-list-based agent frameworks.
Checkpointers persist complete graph state at each step, enabling time-travel debugging, workflow resumption after failures, and clean HITL pause/resume cycles.
LangGraph's killer feature is its elegant HITL model: workflows can be compiled with interrupt_before or interrupt_after parameters that pause execution at any node and wait for human input before continuing—with full state persistence across the pause.
In 2026, LangGraph is the dominant orchestration choice for production agentic systems requiring complex, stateful workflows. LangGraph Platform provides the hosted deployment, monitoring, and scaling infrastructure for production deployments.