LangGraph
LangChain's library for building stateful agent workflows as graphs — nodes, edges, checkpoints, and human-in-the-loop interrupts — one of the common substrates for production agent orchestration.
Production AI is a system: context, tools, permissions, traces, evals, and feedback loops around the model.
What it is
LangGraph models an agent workflow as an explicit graph: nodes do the work, edges route between them, and a checkpointer persists state so a workflow can pause, resume, and survive restarts. It supports interrupts for human approval, sub-graphs for delegation, and streaming of intermediate state.
Why it matters
An agent loop that lives in a while-loop doesn't survive production: no restartability, no visibility, no place to attach an approval gate. Making the control flow an explicit, checkpointed graph is one credible answer — the supervisor and sub-agent patterns most teams reach for are documented LangGraph patterns.
How we use it
LangGraph is one of the substrates we extend when a client already runs it, alongside the OpenAI Agents SDK and durable-execution engines like Temporal. Our own default is Pydantic AI with a deliberately simple stage-and-gate executor; we add the same runtime contract — versioning, evals, traces, gates — on either.
Related resources
The most widely adopted open-source framework for building LLM applications — chains, agents, retrieval, and integrations across hundreds of providers — and the ecosystem around LangGraph and LangSmith.
The scaffolding around an AI agent — prompt construction, tool dispatch, retry logic, trace emission, state management — that turns a model into a workflow participant.
The engine that runs an AI agent workflow as a durable, observable, restartable process instead of a one-shot script — what separates an agent demo from an agent deployment.
A workflow where one or more AI agents make decisions about what to do next — choosing tools, gathering context, escalating to humans — rather than executing a fixed script.