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LEANN Roadmap

LEANN aims to be a personal knowledge layer — not just a storage-efficient vector database, but a unified, always-up-to-date knowledge base that runs entirely on your own machine. It connects your code, images, and personal data (documents, emails, browser history, chats) into a single multimodal search interface.

Contributions and feedback are welcome. Join our Slack to discuss.


Completed

  • HNSW backend integration
  • DiskANN backend with MIPS/L2/Cosine support
  • Real-time embedding pipeline
  • Memory-efficient graph pruning
  • IVF backend with incremental add/remove (#231, #89, #141)
  • Merkle tree file-change detection — leann watch (#41)

P0 — Core (Q1 2026)

LEANN MCP — The Best Code Retrieval MCP

The primary near-term goal: make LEANN the go-to MCP server for code-aware AI assistants. This means dynamic updates (your index stays current as you edit code), rich code context (AST-aware chunking that understands functions, classes, and modules — not just raw text), and a dead-simple interface (one command to build, automatic incremental updates, zero configuration for common setups).

  • IVF backend — incremental add/remove without full rebuild (#231, #89, #141)
  • Merkle tree file-change detection — leann watch for automatic re-indexing on file changes (#41)
  • Cold start optimization — faster first-build experience for new users (#166, #177)
  • Live index updates — push index changes as files are saved, not just on rebuild
  • Smarter code context — cross-file symbol resolution, call graph awareness, import tracking

Search Quality

  • Hybrid search — combine dense vector retrieval with sparse keyword matching (BM25) for better recall on exact identifiers and variable names (#233, #90)

Documentation

  • ReadTheDocs — hosted documentation site (#234)
  • Benchmarks — recall@k, latency, and storage comparisons across backends

P1 (Q2 2026)

Multimodal

  • Video retrieval (#160)
  • CLIP support — image-text cross-modal search (#94)
  • OCR — extract text from images/scanned documents (#158)

Platform & Distribution

  • Windows support (#14)
  • Web UI (#229)

Applications & Integrations

  • Agent + Deep research (#104)
  • Local Cursor — local model + local retrieval for code assistance (#47)
  • LlamaIndex integration (#217)
  • Obsidian support (#96)

Contributing

If you're interested in working on any of the items above, please reach out to @yichuan-w or @andylizf. See CONTRIBUTING.md for the full contributor workflow.