May 08, 2026
While most AI frameworks focus on linear "chains," real-world problem-solving is often iterative. LangGraph is a library that allows you to build agents with "cycles," where the AI can loop back and refine its work.
LangGraph treats your agent workflow as a graph where each node is a function and edges define the flow. Unlike linear pipelines, an edge can point back to a previous node. This allows for "self-correction" loops where an agent can review its own output, find errors, and try again until the result meets a specific quality threshold.
One of LangGraph's biggest advantages is native state management. It can "checkpoint" the state of your agent at every node. This means if a long-running task is interrupted, it can resume exactly where it left off. It also enables "human-in-the-loop" workflows where an agent pauses for human approval before continuing.