Understanding the Map
Identify the basic parts of an AI flow.
Part 1/3 — Advanced Theory & Mechanics
The shift from linear Directed Acyclic Graphs (DAGs) to cyclic agentic architectures marks a paradigm shift in the orchestration of Large Language Models (LLMs). LangGraph, as an extension of the LangChain ecosystem, addresses the limitations of standard chain-of-thought prompting by introducing a stateful, multi-actor framework built upon the principles of Pregel-style graph processing. Unlike traditional pipelines that follow a rigid Input -> Prompt -> LLM -> Output trajectory, LangGraph allows for iterative loops, conditional branching, and persistence of state across multiple turns. This architecture is essential for building agents that can self-correct, utilize external tools recursively, and maintain a consistent context (State) while navigating complex, non-linear task trees.