LangGraph Explained: How AI Agents Share Work Using Nodes, Edges, and State

LangGraph is an AI orchestration framework designed to manage complex, multi-agent workflows that branch, retry, and loop until conditions are met. Unlike linear pipelines, it structures agent collaboration around three core components: nodes representing individual agents or tasks, edges defining the flow between them, and a shared state object that every agent reads from and writes to. An e-commerce order processing workflow illustrates this clearly, where inventory, fraud, and shipping agents each act independently yet remain coordinated through a single central data file. In code, developers define a typed state schema, write modular Python functions as nodes, and compile the graph into an executable state machine using LangGraph's API. The framework's key advantages include full observability, auditability, failure recovery via time-travel states, and the ability to mix deterministic logic with AI reasoning in enterprise systems.