LangGraph is a low-level orchestration framework for building long-running, stateful AI agents and workflows. Rather than providing a fixed agent template, it gives developers primitives for representing agent logic as graphs with explicit state, transitions, persistence, and control over how execution proceeds.

Its production-oriented features include durable execution that can resume after interruptions, human-in-the-loop controls for inspecting or modifying state, short- and long-term memory, streaming, subgraphs, and patterns for complex branching workflows. LangGraph can be used independently, but it also integrates closely with the broader LangChain ecosystem and LangSmith for tracing, evaluation, debugging, and deployment.

LangGraph is best suited to developers building agents whose behavior cannot be expressed as a single prompt-and-response loop. It is particularly useful for multi-step research, workflow automation, approval processes, tool-using agents, and other systems where state must persist across actions or sessions and developers need precise control over orchestration rather than a high-level black-box agent abstraction.