orchestration
LangGraph
LangGraph is a library for building stateful, graph-based agent workflows with explicit control flow and durable execution patterns.
- Python
- JavaScript
- orchestration
Website: langchain-ai.github.io/langgraph/
GitHub: github.com/langchain-ai/langgraph
Languages: Python, JavaScript
License: MIT
Best for
- Production agent control flows with explicit state
- Human-in-the-loop nodes and durable runs
- Teams already using LangChain ecosystem components
Pros and cons
Pros
- First-class state graphs and edges
- Strong fit for auditability and complex branching
- Active ecosystem and integrations
Cons / trade-offs
- Steeper learning curve than role-based frameworks
- Easy to over-engineer simple loops
Overview
LangGraph models agents as graphs of nodes with shared state. That makes control flow explicit: you define how the system moves between planning, tool calls, waiting for humans, and completion.
Fit signals
Choose LangGraph when you care about:
- Deterministic edges mixed with model decisions
- Checkpointing / resume
- Complex branching that would be messy in free-form multi-agent chat
Notes for production
- Keep state schemas typed and versioned
- Log node transitions as first-class telemetry
- Pair with offline evals for each critical path
Comparison: LangGraph vs CrewAI vs AutoGen.
Related reading
LangChain
LangChain is a broad framework for building LLM applications with chains, tools, retrieval, and integrations used alongside agent orchestrators.
LangGraph vs CrewAI vs AutoGen
Vendor-neutral comparison of LangGraph, CrewAI, and AutoGen for building multi-step and multi-agent systems.
How to Choose an Agent Framework
A practical framework selection guide for agentic AI: control model, language, multi-agent needs, ecosystem, and production constraints.