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Our take
Redis Iris positions itself as an in-memory platform for AI applications, combining vector storage with agent memory capabilities. Strongest use-case: powering conversational AI with persistent, contextual state.
Best for: Teams building chatbots and multi-turn AI agents requiring persistent context
Try Redis Iris's free tier to see if it fits your workflow.
See how Redis Iris fits your stackBenefits
In-memory architecture enables low-latency retrieval for real-time AI interactions
Vector database native support simplifies semantic search and embedding storage
Agent memory features address state persistence across multi-turn conversations
Purpose-built for developers needing integrated memory infrastructure rather than custom assembly
About
Redis Iris is a fast memory layer for chatbots and AI agents, providing tools needed to build AI applications. It's designed for developers looking to create and manage interactivity within their applications efficiently.
Vector database
Agent Memory
Context Retriever
Pricing
Contact Redis Iris for pricing details
Contact sales for pricing details
Ecosystem
MCP servers, AI skills, and integrations that work with Redis Iris
Use Redis Iris with AI agents via these MCP servers
Mcp Server Redis
Interact with Redis Server and AWS Memory DB for caching.
MCP Memory with Redis Graph
MCP Server for long term memory graph
MCP SwitchBoard
A GUI MCP Switchboard for managing multiple MCP servers
FAQs
Common questions about Redis Iris and its capabilities
Redis Iris offers custom pricing based on your team size and requirements. Contact their sales team for a personalized quote. Many AI Tools tools in this range offer free trials.
Our team can help you integrate Redis Iris with your existing tools and build custom automation workflows.
Pulse delivers engineering-specific AI insights every week. Free.
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