Langgraph is a framework designed for building stateful, multi-agent workflows using graph-based abstractions. It models workflow logic as directed graphs, where nodes represent computational steps and edges define the flow of control and data between them. This graph structure allows developers to represent complex agent interactions and decision-making processes in a structured, visual manner.
Core Architecture
The framework operates by decomposing workflows into discrete nodes connected by directed edges. Each node encapsulates a computational unit—such as an agent operation, data transformation, or decision point—while edges specify how data and control flow between these units. This architecture enables explicit representation of parallel paths, conditional branching, and feedback loops within multi-agent systems.
Statefulness and Persistence
A key feature of Langgraph is its support for stateful execution. Workflows maintain state across execution steps, allowing agents to access and modify shared information as they process. This statefulness is essential for building coherent multi-agent systems where agents need to coordinate, share context, or build upon previous decisions.
Use Cases
Langgraph is particularly suited for applications requiring orchestration of multiple AI agents working toward common goals, complex decision workflows with conditional logic, and systems that need to maintain execution state across distributed or long-running processes. The graph-based approach provides transparency in workflow structure and facilitates debugging and modification of agent interactions.