LangGraph is an open source library from the LangChain team for building stateful, multi agent applications with language models. It models workflows as graphs, where nodes represent steps or agents and edges define how control and state move between them. This structure lets developers create loops, branches, and persistent state that are harder to express with simple linear chains. LangGraph supports human in the loop steps, memory, and controllable agent behavior, and it pairs with a managed platform for deploying and monitoring these workflows in production environments.
The library is aimed at developers and engineering teams building complex, reliable agent systems rather than quick prototypes. It fits people creating multi step assistants, tool using agents, and workflows that require precise control over how state evolves and when humans intervene. Because it is code first and Python based, it appeals to teams already comfortable with the LangChain ecosystem who need more structure and durability. Its graph model is especially useful for applications where predictability, resumability, and clear control flow matter for production use.
In practice, developers define a graph of nodes and edges, manage shared state, and let the library route execution between agents and tools. Workflows can pause for human input, persist progress, and resume later. The accompanying platform adds deployment, observability, and debugging support for running these graphs at scale.
