Mastering Agentic RAG flows with LangGraph. Building intelligent retrieval systems across multiple data sources

A simple RAG implementation might work for isolated use cases, but real-world scenarios are more demanding. Users ask about data stored in PDFs, internal databases, and web services, often combining multiple questions in a single query. A well-architected RAG flow handles multiple intents, accesses data across formats, and minimizes LLM hallucinations by integrating a ReAct agent model that retrieves, reasons, and acts.

Building such a system requires several key steps: detecting user intent to trigger the right operation, splitting complex queries into separate tasks, retrieving data from multiple sources like vector stores and SQL databases, reranking results for relevance, and generating answers with proper citations. When questions require structured data, the system calls dedicated agents to run SQL queries or API calls, then integrates the results into a coherent response.

What you will learn when reading the full blog post

In the full version of this blog post, we walk through the complete architecture of an agentic RAG flow using LangGraph. You will learn how to implement intent detection, handle multi-source retrieval, process and rerank context, and integrate SQL and API agents. We also cover practical tooling including LangGraph for visual flow definition and LangGraph Studio for debugging. Whether you are building enterprise search systems or complex AI assistants, the extended version provides a detailed technical guide to creating robust retrieval systems that deliver accurate, well-sourced answers.