RediSearch open source analysis
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.
Project overview
⭐ 6050 · C · Last activity on GitHub: 2026-01-06
Why it matters for engineering teams
RediSearch addresses the challenge of adding fast and flexible search capabilities directly within Redis, a popular in-memory data store. It enables software engineers to implement secondary indexing, full-text search, and vector similarity search without relying on external search engines. This makes it a practical choice for machine learning and AI engineering teams who need a production ready solution that integrates tightly with their existing Redis infrastructure. The project is mature and widely used, with a strong community and proven reliability in production environments. However, it may not be the best choice for teams requiring complex search features beyond its scope or those preferring fully managed cloud search services over self hosted options.
When to use this project
RediSearch is a strong choice when you need a fast, self hosted option for full-text and vector search tightly coupled with Redis data. Teams should consider alternatives if they require advanced natural language processing or large-scale distributed search across multiple data sources.
Team fit and typical use cases
Machine learning and AI engineering teams benefit most from RediSearch as an open source tool for engineering teams looking to add search and indexing capabilities to their applications. It is commonly used in products involving real-time analytics, recommendation systems, and geospatial queries, where integrating search directly into Redis improves performance and simplifies architecture.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.