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#1010th of 12 in Vector databases

leann

Vector index that recomputes embeddings on demand to cut storage

Live demo ↗ (opens in a new tab)Documentation ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)
Stars
13k
License
MIT
Last commit
Oct 2026
Last release
Sep 2026
Language
Python

Missed the latest GitHub check; these facts may be older.

Overview

LEANN is a Python vector database that stores a pruned graph and recomputes embeddings at query time instead of storing them. The README claims 97% less storage, for example 60M chunks in 6GB versus 201GB. It ships HNSW and DiskANN backends, a CLI, RAG examples for files, mail and chat history, and an MCP server for Claude Code.

Who it is for: Developers wanting a low-storage local vector index for personal data RAG

Strengths

  • Graph-based selective recomputation avoids storing embeddings; README claims 97% less storage
  • HNSW and DiskANN backends, selectable with --backend-name
  • Works with Ollama, LM Studio, vLLM, llama.cpp and any OpenAI-compatible endpoint
  • Includes ready RAG loaders for PDFs, Apple Mail, WeChat, iMessage, ChatGPT and Claude history

Weaknesses

  • Recomputing embeddings at query time adds latency compared with stored vectors
  • DiskANN needs a native toolchain (Boost, protobuf, MKL) when built from source
  • GPU acceleration is only a survey item for a future version
  • Linux uv hardlink installs can fail with a nltk pathsec error without a workaround

What it needs

  • GPU optional
  • Needs uv, Ollama or OpenAI-compatible API (optional, for generation)
  • Models: HuggingFace, Ollama, OpenAI-compatible APIs, Anthropic, sentence-transformers

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