Retrieval-Augmented Generation (RAG) has evolved far beyond simple vector search. Today, developers face a choice between several advanced architectures, each with distinct strengths. Self-RAG introduces self-reflection and token-level critique to improve answer quality. CRAG (Corrective RAG) adds a retrieval evaluator that triggers web searches when local retrieval is insufficient. GraphRAG leverages knowledge graphs to capture relationships and support multi-hop reasoning. Agentic RAG goes further by embedding RAG within an agent loop, enabling dynamic tool use and iterative refinement. This comparison helps developers understand the trade-offs in complexity, latency, and accuracy. For instance, GraphRAG excels at global questions, while Agentic RAG suits complex workflows requiring multiple steps. Choosing the right architecture depends on your data structure, query patterns, and performance requirements. This guide provides a clear framework for making that decision.
A practical guide to understanding the differences between Self-RAG, CRAG, GraphRAG, and Agentic RAG, helping developers select the best architecture for their use case.