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The Redis Labs Blog offers insights, tutorials, and best practices for using Redis, an open-source in-memory data store, and other Redis-related technologies. Developers can learn about Redis data structures, caching strategies, and real-time data processing using Redis Streams. Additionally, the blog covers topics such as Redis clustering, high availability setups, and integrating Redis with other databases and frameworks, providing resources for building scalable and performant applications.

Redis

Redis is launching Redis Iris, a context and memory engine for AI agents composed of five tools: Context Retriever, Agent Memory, Redis Data Integration, LangCache, and Redis Search. Context Retriever lets developers define a semantic model for business data and auto-generates MCP tools for agents to use instead of direct database queries. Agent Memory handles short- and long-term state across sessions. The suite aims to solve the core production AI problem of fragmented, stale, or slow context by providing navigable, real-time, and compounding context in a single Redis-based runtime. Context Retriever and Agent Memory are now available in preview.

Context is all you need: Introducing Redis Iris, our new agent context and memory solution