AI agents in production need to survive failures, keep state and often pause for a human to decide. LangGraph is a low-level framework built exactly for that: it models the agent as a durable state graph.
What is LangGraph?
With primitives like nodes, edges, a typed state schema, checkpoints (persistence) and interrupts, LangGraph enables agents that survive failures and resume where they left off, with inspection and editing of state mid-run. It is adopted by companies like Klarna, Uber, LinkedIn and Replit.
Key features
- Durable execution: the agent survives failures and resumes from a checkpoint
- Typed state and persistence via checkpointers
- Native human-in-the-loop: inspect and change state mid-flow
- Python and JavaScript/TypeScript versions
- Production adoption by Klarna, Uber, LinkedIn, Replit
How Reche uses it
Human-in-the-loop and durability are not luxuries, they are what separates a production agent from a demo. Reche uses these principles in RecheOS and implements reliable agents for clients, with the control real cases demand.