Semantic memory search for AI agents

Semantic memory search enables AI agents to retain information across interactions by storing durable facts externally and retrieving them by meaning through vector embeddings, rather than relying on a language model’s limited, session-bound context. It addresses the cost, context-window limits, and declining recall associated with repeatedly supplying complete conversation histories, while hybrid approaches combining semantic, keyword, and metadata search can improve retrieval for both paraphrased concepts and exact identifiers. The discussion distinguishes semantic memory for facts from episodic memory for prior experiences and procedural memory for behavioral instructions, and argues that retrieval must be both low-latency and current because agents may consult memory repeatedly and outdated details can produce incorrect responses. It cites research and product examples suggesting persistent memory can improve personalization, reduce negative feedback, and enable semantic caching to lower inference costs. Redis positions its Iris platform, including Redis Search, LangCache, and Agent Memory, as an integrated system for vector retrieval, session state, long-term memory, hybrid search, and real-time data freshness.

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