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, low-latency solution for real-time data management. It addresses the need for extremely fast data access (often sub-millisecond) by keeping data primarily in memory, serving as a versatile tool for caching, session management, and real-time analytics.

How it works

Redis is an in-memory data structure server that uses efficient data structures and a simple text-based protocol. It supports a wide array of native data types—including strings, JSON, hashes, lists, sets, and sorted sets—and can be extended via a modules API to add new commands and functionality. It also includes a query engine for full-text and vector search.

Who it’s for

It is designed for developers building real-time, data-driven applications that require high-speed read/write operations, such as those needing distributed session stores, message brokers, or vector stores for generative AI.

Highlights

  • Multi-purpose Data Store: Functions as a cache, NoSQL database, document store, and message broker.
  • GenAI Integration: Supports vector similarity search, semantic caching, and RAG (Retrieval Augmented Generation) for LLM memory.
  • Rich Data Types: Includes probabilistic structures (Bloom filters, Cuckoo filters) and specialized types like Time Series and Geospatial indexes.
  • Extensible Architecture: Allows developers to implement new functionality through a modules API.

Related

  • Project
    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.

  • Project
    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
    redis/agent-memory-server

    A structured memory layer for AI agents that provides intelligent short-term session memory and persistent long-term context using a two-tier storage model.

  • Project
    dragonflydb/dragonfly

    A highly efficient, multi-threaded in-memory data store that is fully compatible with Redis and Memcached APIs, designed to scale vertically on modern cloud hardware.

  • Project
    Restream/reindexer

    An embeddable, in-memory document database written in C++ and Go that provides a high-performance alternative to Elasticsearch with support for vector and full-text search.