RAG vs Memory for AI Agents: What’s the Difference

AI agents face a fundamental challenge: handling knowledge and context over time since large language models are stateless. Two solutions have emerged: RAG (Retrieval-Augmented Generation) retrieves external knowledge from databases at query time, while Memory allows agents to store and recall experiences across interactions. RAG provides up-to-date factual information but lacks personalization, whereas Memory enables continuity and learning but may lack current external knowledge. The most effective approach combines both patterns, creating agents that can access external information and remember past interactions.