Building a LangGraph Agent from Scratch

AI agents enhance LLMs and Retrieval-Augmented Generation (RAG) by introducing state, decision-making, and memory. LangGraph is a framework used to build these agents by constructing graphs with nodes representing state and edges defining control flow. This guide provides a detailed Python tutorial for building a soccer-themed agent capable of answering questions about players. The process involves defining a state class, creating tools for data retrieval, and implementing a planner node that allows the LLM to dynamically choose which tools to execute based on the user's query.