# Semantic memory search for AI agents

DevFeed: [Semantic memory search for AI agents](<https://devfeed.tech/articles/semantic-memory-search-for-ai-agents-4850.md>)

Original publisher: [Read original article](<https://redis.io/blog/semantic-memory-search-ai-agents/>)

Author: Jeff Mills

Published: 2026-08-04T00:00:00Z

Content type: article

Language: en

Sources: [Redis Blog](<https://devfeed.tech/sources/redis-blog.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Redis](<https://devfeed.tech/topics/redis.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [data](<https://devfeed.tech/tags/data.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [redis](<https://devfeed.tech/tags/redis.md>), [tech-de](<https://devfeed.tech/tags/tech-de.md>)

## AI overview

Semantic memory search gives AI agents durable recall by storing facts outside the language model and retrieving them by meaning. The article explains how vector embeddings and similarity search supply relevant context, with Redis Iris combining memory, live data, and retrieval for fast agent context.

## Source excerpt

Your AI agent handles a long onboarding conversation. The next day, it asks the same user for their name. That's not a bug. A language model keeps no memory of earlier calls, so without an external memory layer, each request starts fresh and the agent...