LangGraph
LangGraph is LangChain's open-source agent orchestration framework for building stateful, multi-actor AI agents with durable execution, checkpointing, human-in-the-loop, and streaming. Used in production by Klarna, LinkedIn, Uber, Cloudflare, Nvidia, and Harvey.
Use Cases
Build multi-agent workflows with state graphs in Python or JS
Add human-in-the-loop approvals to autonomous agents
Persist conversation history across sessions with built-in memory
Stream tokens and reasoning steps in real time to the UI
Time-travel debug agent runs from any checkpoint
Deploy production agents via LangSmith
Pros
MIT-licensed open source, free for self-hosted use
Checkpointing every state transition enables time-travel debugging
Streaming primitives built in for token-by-token UX
Used in production by Klarna, LinkedIn, Uber, and Cloudflare
LangSmith observability available as managed platform
Cons
Low-level primitives steeper than CrewAI or AutoGen abstractions
Production deployment via LangSmith adds per-seat cost
Python-first with TypeScript support trailing slightly
Requires understanding of state machines to design well
Open source moves fast, breaking changes occasionally land
Platforms
API
Self-Hosted
MCP
Compliance & Certifications
SOC 2
AICPA
LangGraph models agents as state graphs instead of chat loops, which is why you can pause, inspect, and resume one without losing context. Checkpointing every state transition makes human-in-the-loop approvals and mid-execution failure recovery routine, not retrofitted features.
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