Multi-Agent RAG Building Intelligent, Collaborative Retrieval Systems with LangChain

Retrieval-Augmented Generation (RAG) has fundamentally changed how AI systems access and reason over external knowledge. Instead of relying purely on what a model learned during training, RAG lets the model pull in fresh, relevant documents at query time, grounding its answers in real data. But as real-world use cases grow more complex, the single-agent RAG model starts to show cracks. What happens when your knowledge lives in three different places? Documentation, support tickets, and live web data each require different retrieval strategies. A single retriever trying to do it all will either miss important context or drown the LLM in irrelevant noise. Multi-Agent RAG is the answer. Instead of one agent doing everything, you build a team: specialised agents that each own a knowledge source, a smart router that decides who to call, and a synthesis agent that assembles the final answer. This post walks you through exactly how to build it with LangChain. Use Case Imagine you are building