redis/redis

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

What it solves

Redis provides a high-performance, in-memory data store that solves the problem of high latency in data-driven applications. It serves as a fast cache, a versatile data structure server, and a query engine for documents and vectors, allowing developers to access frequently used data without the overhead of querying a primary database.

How it works

Redis keeps data primarily in memory and utilizes efficient data structures to achieve sub-millisecond read and write latency. It uses a simple text-based protocol and is extensible via a modules API. It supports a wide array of data types—including strings, JSON, hashes, lists, sets, and sorted sets—and includes specialized processing engines for full-text search, geospatial queries, and vector similarity search.

Who it’s for

It is designed for developers building real-time applications that require extreme speed, such as those needing distributed session stores, message brokers, real-time analytics, or vector stores for Generative AI applications.

Highlights

  • Multi-purpose Data Store: Functions as a cache, NoSQL database, and message broker (pub/sub, streams).
  • GenAI Integration: Supports vector sets and vector search for RAG, semantic caching, and LLM memory.
  • Rich Data Types: Offers native support for everything from basic strings and JSON to probabilistic structures like Bloom filters and HyperLogLog.
  • Extensible Architecture: Allows developers to add new functionality through a modules API.

Related

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    RediSearch/RediSearch

    A Redis module that adds full-text search, secondary indexing, and vector similarity search to Redis, enabling high-performance querying of database records.

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    redis/redis-vl-python

    An AI-native Python client for Redis that provides tools for vector search, hybrid search, and production-ready extensions for LLM memory and semantic caching.

  • Project
    BetterDB-inc/monitor

    A monitoring and persistence layer for Valkey and Redis that preserves historical logs and activity for deep debugging and AI vector search observability.

  • Project
    dragonflydb/dragonfly

    An ultra-fast, multi-threaded in-memory data store that is fully compatible with Redis and Memcached APIs but scales vertically to deliver significantly higher throughput and lower resource usage.

  • Project
    redis/agent-memory-server

    A managed memory layer for AI agents that provides persistent storage for facts and preferences across sessions using a two-tier session and long-term memory model.