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LEANN

Compact local vector index for private RAG over personal data

  • Agent frameworks and tools
  • AI models and runtimes
View on GitHub
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What it is

LEANN is a vector database and RAG toolkit that recomputes embeddings on demand from a pruned graph instead of storing them, cutting index size by around 97% with little accuracy loss. It can index files, email, browser history, chat logs from apps like WeChat and iMessage, and code, then answer questions using local models through Ollama or other backends. It also ships an MCP server that gives coding agents such as Claude Code semantic code search. Developers and privacy-minded users run it on a laptop to search large personal datasets without sending anything to the cloud.

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Facts

Stars
13,039 GitHub stars.
13,039

Forks
1,346
Commits
646
Contributors
57
Language
Python
License
MIT
Last commit
Oct 16, 2026
On GitHub since
June 2025
First release
July 2025
Platforms
macOS, Windows, Linux, CLI, library
Self-hostable
No
Alternative to
Pinecone
Tags
  • mcp-server
  • semantic-search
  • embeddings
  • local-ai
  • rag
  • vector-database

Last synced from GitHub on Oct 20, 2026

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What people are saying

Updated Oct 21, 2026
  • Bluesky“RT @YichuanM: LEANN just won the Best Paper Award at #MLSys26 🥹 still processing this. paper: repo: huge thanks to all the amazing collaborators, advisors…”May 18, 2026 · 2 likes · 0 replies (opens Bluesky in a new tab)
  • Bluesky“LEANN is an interesting approach for vector storage. This algorithm trades CPU and GPU cycles for more disk space and RAM. More eyes are needed to explore…”May 19, 2026 · 0 likes · 0 replies (opens Bluesky in a new tab)
  • Bluesky“LEANN: A Low-Storage Vector Index Introduces a storage-efficient ANN search index that reduces storage to under 5% of raw data while maintaining 90% top-3…”Jun 11, 2025 · 0 likes · 0 replies (opens Bluesky in a new tab)