RAG & Knowledge
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Graph RAG conversational knowledge base built on TiDB Serverless vector storage
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View guide →The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
LEANN is an open-source rag & knowledge project by StarTrail-org (StarTrail-org/LEANN on GitHub). It has 13,012 GitHub stars and 1,179 forks, is released under the MIT license, and was last updated on GitHub on 29 September 2026.
Pick a source: documents, email, browser history, chat exports, a codebase, or live data from platforms such as Slack via MCP servers.
Install uv if needed, clone the repo for the examples, and install the leann package from PyPI into a fresh virtual environment.
Use the builder API or the leann build CLI to index your data - the graph index stays dramatically smaller than a conventional embedding store.
Query with LeannSearcher, chat with LeannChat using a local or API model, or plug LEANN into Claude Code as its semantic-search MCP service.
Graph-based selective recomputation with high-degree-preserving pruning avoids storing every embedding, while the project reports no accuracy loss versus heavyweight solutions.
Ready-made support for files (PDF, TXT, Markdown), Apple Mail, browser history, WeChat and iMessage, ChatGPT and Claude conversations, codebases, and live data via MCP servers.
Your data never leaves your laptop: no cloud, no OpenAI requirement - local Hugging Face or Ollama models cover embedding and generation.
Build, search and chat in a few lines with LeannBuilder, LeannSearcher and LeannChat, plus a full CLI for scripting.
Acts as a drop-in semantic search MCP service for Claude Code; the project's ContextBench comparison reported 2.1x the initial relevant-code recall of BM25 on 30 SWE-Bench Pro tasks.
The tiny index makes it cheap to copy your entire personal knowledge base between devices or share it with others.
Clone LEANN (the README clones the original yichuan-w repository into a folder named leann) to access all examples and apps.
git clone https://github.com/StarTrail-org/LEANN.git
cd LEANNCreate a uv virtual environment and install the leann package into it.
uv venv
uv pip install leannFollow the README quick start: build an index with LeannBuilder, then query it with LeannSearcher or chat with LeannChat.
python demo.py # adapt the README quick-start snippet - see README for the exact codeLEANN is maintained by StarTrail-org and contributors; the underlying research paper (arXiv 2506.08276) received a best paper award at MLSys 2026, per the project.
Yes. It is MIT-licensed and runs on your own machine with zero cloud costs.
No: embedding and generation can run fully locally (Hugging Face models, Ollama, LM Studio and others). API providers such as OpenAI are optional and use your own key.
Those store full embedding vectors for every chunk; LEANN recomputes embeddings on demand from a pruned graph, trading some compute for roughly 97% less storage at personal scale.
Install uv, clone the repo, uv pip install leann, and run the quick-start snippet to build and search your first index.
Graph RAG conversational knowledge base built on TiDB Serverless vector storage
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