LangGraph

LangGraph is LangChain's low-level orchestration framework for building agents and multi-agent systems. It is designed for developers who want fine-grained control over how their agents behave — the ability to model workflows as explicit graphs of nodes and edges, manage state explicitly, branch on conditions, and loop until a goal is met. This deliberate low-level design is what makes LangGraph suited to long-running, stateful agents that need to be reliable rather than best-effort. The framework treats an agent as a graph: nodes are steps (an LLM call, a tool, custom logic), edges define control flow, and a shared state object carries information between them. Because state is first-class, agents can persist across calls, pause and resume, and support human-in-the-loop checkpoints where a person reviews or approves before the agent continues. This is the machinery needed for production agents that run for many steps, hit errors, and must recover gracefully. LangGraph also powers LangGraph Platform (and Studio) for deployment, monitoring, and tracing of these graphs in production. LangGraph has become a foundational layer for serious agent engineering and is trusted by companies shaping the future of agents — including Klarna, Replit, and Elastic. While higher-level frameworks like CrewAI or LangChain's own agents offer more convenience and opinionation, LangGraph is the choice when teams need predictability, controllability, and the ability to express complex, conditional, or cyclic agent logic. It is aimed at developers and engineering teams building production-grade agents — especially long-running, multi-step, or multi-agent systems — who are willing to trade some convenience for control over how their agents actually run.