Building an Adaptive RAG Agent with LangGraph: Dynamic Routing and Stateful Memory

Building an Adaptive RAG Agent with LangGraph: Dynamic Routing and Stateful Memory Building a basic "Retrieve and Generate" (RAG) pipeline takes about ten lines of code these days. But what happens when a user asks a simple greeting? Your system wastes compute querying a vector database. What happens on turn five of a conversation when the user says, "Wait, explain that second point again?" A naive RAG system suffers from amnesia and fails entirely. To build a production-grade AI assistant, you need more than a linear chain. You need a stateful, decision-making agent. Here is how I engineered an Adaptive RAG Assistant using LangGraph to handle dynamic search routing and stateful memory injection, completely eliminating context amnesia. 1. The Core Problem: Linear Chains vs. State Machines Standard LangChain workflows are Directed Acyclic Graphs (DAGs). Data flows from A -> B -> C. But real human conversation is cyclical. We loop back, we clarify, and we change topics. I migrated the ar