Features
- Graph-based workflow definition with nodes and edges
- Built-in state management and persistence
- Human-in-the-loop support with breakpoints
- Streaming support for real-time output
Pros
- Fine-grained control over agent execution flow
- State persistence enables long-running workflows
- Human-in-the-loop patterns built in
Cons
- Steeper learning curve than simpler agent frameworks
- Graph model adds complexity for straightforward chains
- Tightly coupled to LangChain ecosystem
Overview
LangGraph is a framework for building stateful, multi-actor AI applications, created by the LangChain team. It models agent workflows as directed graphs where nodes represent computation steps (LLM calls, tool executions, data processing) and edges define the control flow between them.
Unlike simple chain-based approaches, LangGraph supports cycles, conditional branching, and persistent state, making it possible to build complex agent architectures including multi-agent systems, iterative refinement loops, and human-in-the-loop workflows.
State persistence is a core feature: LangGraph can checkpoint agent state at any point, enabling long-running workflows that survive restarts, support time-travel debugging, and allow human review before critical actions.
When to Use
Choose LangGraph when building complex agent workflows that require cycles, conditional logic, state persistence, or multi-agent coordination. For simple chain-based LLM applications, LangChain or the Vercel AI SDK may be more appropriate.
Getting Started
npm install @langchain/langgraph @langchain/anthropicimport { StateGraph } from '@langchain/langgraph'
const graph = new StateGraph({ channels: { messages: [] } })
.addNode('agent', agentNode)
.addNode('tools', toolNode)
.addEdge('__start__', 'agent')
.addConditionalEdges('agent', shouldContinue)
const app = graph.compile()