ClaudeMap

LangGraph

Graph-based orchestration framework for stateful, multi-agent LLM workflows.

Plugins & Agentsagent-frameworkgraphstatefulorchestration

LangGraph models agent workflows as directed graphs with explicit state, cycles, and human-in-the-loop checkpoints. Pairs with LangChain but standalone — works with any LLM SDK including the Anthropic SDK.

What it does

  • Low-level orchestration for stateful, multi-step agents — graphs of nodes with explicit state channels
  • First-class checkpointing: persist state, resume, rewind, and time-travel through agent runs
  • Human-in-the-loop interrupts, streaming, and durable execution built into the runtime
  • LangGraph Platform for managed deployment; LangSmith for tracing
  • Python and JavaScript parity (pip install langgraph / npm install @langchain/langgraph)

When to use it

When you outgrow prompt-and-loop agents: workflows that need resumable long runs, auditable state transitions, or human approval gates. Deliberately more verbose than CrewAI — you trade boilerplate for control.

Getting started

# Python
pip install -U langgraph

# JavaScript / TypeScript
npm install @langchain/langgraph

Things to know

'Draw your own graph' means a real design phase — budget for it. Claude runs through the langchain-anthropic package (init_chat_model), which you install and configure separately. The LangSmith/Platform pull can be more ecosystem than a solo project needs.

Repository

langchain-ai/langgraph

Charted

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