"Redis" matching MCP servers:

The official Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis.

Provides access to 21+ AI search parsers including Perplexity, ChatGPT, Claude, Gemini, Copilot, Grok, DeepSeek, and traditional search engines through the Redis API, enabling multi-source AI-powered search and information retrieval.

The Redis Cloud API MCP Server provides an MCP Server for Redis Cloud's API, allowing you to manage your Redis Cloud resources using natural language.

This read-only MCP Server allows you to connect to Redis data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.

Overcomes Claude Code's 200k token context limit by implementing persistent long-term memory using Redis and AI-powered state summarization. Enables seamless checkpoint and resume of project context across sessions.

Persistent memory for AI agents through the Model Context Protocol. MemStack provides tools to store, retrieve, summarize, prune, export, and manage memories using local files, SQLite, Postgres, Redis, or in-memory storage.

An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-agent communication using a Redis-based Pub/Sub system.

Provides MCP agents with persistent memory operations (store, recall, forget, consolidate, list) backed by Redis, Postgres, and pgvector, including semantic search and background consolidation.

Intelligent multi-model orchestrator with dynamic routing that optimizes AI costs by prioritizing free models and escalating to paid tiers only when needed, with stateless architecture using Redis and PostgreSQL.

A production-ready MCP server for persistent AI memory across LLMs like Claude and ChatGPT. Provides automatic conversation backup, multi-user support, and multi-storage (PostgreSQL, Redis, Qdrant).

Enables AI clients to explore MCP concepts, retrieve quickstart guides, and ask questions with Redis-cached answers, supporting deployment on Cloudflare Workers and Docker.

Provides universal database operations for AI assistants through MCP, supporting 40+ databases including PostgreSQL, MySQL, MongoDB, Redis, and SQLite with built-in introspection tools for schema exploration.

Enables coding agents to write and recall long-term memories through a local, MCP-ready pipeline that extracts six memory categories on write and dispatches them by category view on read. Supports external OpenAI-compatible LLM endpoints and local Neo4j/Qdrant/Redis storage.

A FastAPI-based MCP server that enables LLM agents to interact with Ollama models through standardized MCP tools, with optional MySQL and Redis integration for data persistence and caching.

A production-grade, LLM-agnostic MCP server that integrates AI models with tools for managing subscriptions, organizations, and payments via the Updation API. It features Redis-backed conversation memory, structured logging, and role-based access control for secure, scalable orchestration.

A serverless, multi-tenant implementation of MCP servers that runs on Vercel with fluid compute mode, allowing multiple users to connect to the same endpoint while maintaining session state through Redis.

Enables semantic search and retrieval of MCP (Model Context Protocol) documentation using Redis-backed embeddings, allowing users to query and access documentation content through natural language.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.