LEANN
StarTrail-org
RAG on everything - graph-based vector index claiming 97% storage savings for private on-device search.
What is LEANN?
LEANN is a Python vector database that recomputes embeddings selectively from a graph instead of storing them all, claiming 97% storage savings versus FAISS while keeping competitive recall (project's own claim). It indexes PDFs, emails, browser history, chat logs and code (AST-aware), integrates via MCP, and is backed by a peer-reviewed MLsys2026 paper.
Pros & Cons
Pros
- Peer-reviewed MLsys2026 paper independently validates the storage approach
- Multi-contributor team with substantive commits (CUDA, GPU, Apple Silicon)
- MCP-native with Claude Code and AST-aware code chunking
Cons
- Recent commits are fixes and CI only, no new features lately
- v0.x signals API instability; storage savings cost recomputation latency
- Requires embedding-model setup - not plug-and-play for non-ML developers
License
MIT (OSI-open)
When it is interesting
Private on-device RAG over personal data (emails, chat logs, code) without the storage cost of traditional vector DBs.
When it is too early
Latency-sensitive production retrieval at scale where recomputation overhead is unacceptable.
Commercial alternative & related
- Commercial counterpart: Pinecone / Weaviate
This repo featured in the 2026-07 edition of the Open-Source AI Radar.
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