LEANN open source analysis

RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

Project overview

⭐ 4379 · Python · Last activity on GitHub: 2025-11-14

GitHub: https://github.com/yichuan-w/LEANN

Why it matters for engineering teams

LEANN addresses the challenge of running retrieval-augmented generation (RAG) applications locally while maintaining privacy and efficiency. It offers significant storage savings of up to 97%, enabling fast and accurate vector search without relying on cloud services. This makes it particularly suited for machine learning and AI engineering teams seeking a production ready solution that supports offline-first workflows and local storage. The project demonstrates a solid level of maturity and reliability for production use, backed by a strong community and consistent updates. However, LEANN may not be the right choice for teams requiring large-scale distributed deployments or those who prefer fully managed cloud services due to its focus on self hosted options and device-level privacy.

When to use this project

LEANN is a strong choice when teams need a self hosted option for privacy-sensitive RAG applications or want to reduce cloud costs with local vector search. Teams should consider alternatives if they require extensive cloud integration or need to scale beyond single-device deployments.

Team fit and typical use cases

Machine learning and AI engineers benefit most from LEANN as an open source tool for engineering teams focused on privacy and efficiency in natural language processing tasks. It is typically used to build applications that require fast, local retrieval of information, such as knowledge bases or personal assistants. LEANN often appears in products where data privacy and offline capabilities are critical, providing a practical, production ready solution for embedding and vector search.

Best suited for

Topics and ecosystem

ai faiss gpt-oss langchain llama-index llm localstorage offline-first ollama privacy python rag retrieval-augmented-generation vector-database vector-search vectors

Activity and freshness

Latest commit on GitHub: 2025-11-14. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.