LangGraph 101: Let’s Build A Deep Research Agent

Building functional LLM agents involves orchestrating complex, stateful workflows, a challenge addressed by the LangGraph library. LangGraph models these workflows using graphs, where nodes represent processing steps and edges define the transitions between them. This tutorial explains these concepts by dissecting a full-stack Deep Research Agent implementation from Google, demonstrating how to define a graph, add nodes for specific tasks, and use conditional edges for dynamic decision-making. The approach allows for creating modular, observable, and robust agents capable of iterative tasks like searching and evaluating information.