description LangGraph Overview
LangGraph is an extension of the LangChain ecosystem designed specifically for building stateful, multi-actor applications. Unlike linear chains, it allows for cycles, making it ideal for complex agents that need to loop back and correct their own mistakes. It provides fine-grained control over the execution flow, persistence for long-running tasks, and native support for human-in-the-loop interactions, which are critical for enterprise-grade AI safety.
help LangGraph FAQ
What problem does LangGraph solve that plain LangChain chains do not?
LangGraph is designed for stateful agent workflows where steps can loop, branch, pause, and resume. Plain linear chains are simpler, but LangGraph's nodes, edges, and shared state fit agents that need retries, tool calls, review steps, or multi-agent coordination.
What is a StateGraph in LangGraph?
StateGraph is the core pattern where each node reads and updates a shared state object. A developer defines the state schema, adds nodes for actions like LLM calls or tools, and connects them with normal or conditional edges.
Why do LangGraph examples talk so much about checkpoints?
Checkpoints let a workflow persist state between steps, which is important for long-running agents and human-in-the-loop review. They allow a graph to pause, recover, inspect prior state, or continue after an interruption.
How does LangGraph compare with CrewAI or AutoGen for agent apps?
CrewAI and AutoGen often present higher-level multi-agent patterns, while LangGraph gives developers lower-level control over graph state and transitions. Teams already using LangChain usually pick LangGraph when they want explicit workflow control rather than a mostly prebuilt agent abstraction.
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