RAG vs Agent Memory: What Each Does and When to Combine Them

RAG retrieves authorized external information, including documents and potentially conversation history, while agent memory adds persistent policies for retaining, updating, scoping, retrieving, and deleting information across interactions. Whether memory is needed depends on application requirements beyond retrieval, such as maintaining user preferences, handling corrections, isolating users’ data, and preserving relevant cross-session context; graphs are optional implementation tools rather than universal prerequisites. Different questions require distinct sources, with policy questions needing authoritative documents, customer-history questions needing scoped interaction records, and live status questions requiring current systems of record. A document-retrieval baseline may be sufficient for simple, stable use cases, but memory lifecycles become valuable when records change, require provenance and scope, or must be expired or deleted. Applications should combine shared knowledge, personal context, and transactional data appropriately, avoid indiscriminately supplying full histories, and evaluate simpler and memory-enhanced architectures using consistent tests for correctness, permissions, deletion, latency, cost, and source support.

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