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Redis and AI Agent Memory with Andrew Brookins
A key challenge with designing AI agents is that large language models are stateless and have limited context windows. This requires careful engineering to maintain continuity and reliability across sequential LLM interactions. To perform well, agents need fast systems for storing and retrieving short-term conversations, summaries, and long-term facts. Redis is an open‑source, in‑memory data store widely used for high‑performance caching, analytics, and message brokering. Recent advances have extended Redis’ capabilities to vector search and semantic caching, which has made it an…
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