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

⭐ 8095 · Python · Last activity on GitHub: 2026-01-02

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

Why it matters for engineering teams

LEANN addresses the challenge of running retrieval-augmented generation (RAG) applications efficiently and privately on personal devices. It offers significant storage savings of up to 97%, making it practical for teams needing fast, local vector search without relying on cloud services. This open source tool for engineering teams is particularly suited for machine learning and AI engineers focused on privacy-preserving solutions and offline-first architectures. Its maturity and active community support make it a production ready solution for embedding RAG capabilities in real-world products. However, LEANN may not be the right choice for projects requiring large-scale distributed systems or extensive cloud integration, as it prioritises local storage and privacy over scalability in multi-node environments.

When to use this project

LEANN is a strong choice when teams need a self hosted option for private, efficient vector search and RAG workflows on personal or edge devices. Teams should consider alternatives if their use case demands high scalability, cloud-native deployment, or integration with managed vector databases.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from LEANN, using it to build privacy-focused applications that require fast retrieval of relevant information from local datasets. It often appears in products where data privacy and offline access are critical, such as personal assistants, research tools, and enterprise knowledge bases. These teams rely on LEANN as a production ready solution to implement retrieval-augmented generation without compromising on data control.

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: 2026-01-02. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.