LangGraph is an open-source framework(built by LangChain team) for building AI agents and workflows using a graph structure instead of simple linear steps. Most of the enterprise applications use LangGraph which is stateful . LangGraph is mainly used to build : AI Agents (independent assistants) Multi-step workflows (research - analyze - summarize) Systems with memory/state (chatbots that remember context) Multi-agent systems (agents that collaborating) It's especially helpful when workflows are : non-linear (loops, retries, decisions) long-running stateful (need memory across steps) Core components of LangGraph : 1) State Analogy : In a factory, the state is the product on the assembly line State is the shared memory/data structure that flows through a graph Nodes don't pass custom variables around; instead they read and update fields in the state. Defined using TypedDict, dataclass, or Pydantic 2) Node Analogy : A node is like a machine station (ex : cutting, painting, packi...