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LEANN
LEANN is a local vector index for semantic search and retrieval-augmented generation over personal files and other data sources.
Instead of keeping every embedding, LEANN can recompute selected embeddings during search and prune its graph to reduce index storage. The project provides a Python API, command-line tools, and MCP integrations for building, searching, and chatting with indexes. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A local index for search and RAG
LEANN turns documents and other supported sources into an index that can be searched directly or used to supply context to a language model. Its examples cover PDFs and text files, code, browser and chat histories, Apple Mail, iMessage, and live sources reached through MCP servers.
Why it stands out
The index trades storage for recomputation
LEANN avoids storing every full embedding by recomputing selected ones along the search path. In the project's published comparisons, its indexes used 91% to 97% less storage than the traditional-vector-database reference shown in the README; those are project benchmark results, not independent measurements.
Availability
Python, CLI, and MCP routes
The project is available through PyPI and GitHub. You can work through its Python API, use the CLI to build and query named indexes, or add its MCP server to Claude Code for local semantic code search.
Why it matters
What makes it useful
Personal search can produce a surprisingly large vector index once it covers years of files, messages, or browsing history. LEANN offers another tradeoff: keep a smaller index and spend more computation rebuilding selected embeddings when a search runs. That can make a large personal collection more practical to keep on one machine, but the best balance will depend on the dataset, hardware, and search settings.
What to know
Where it fits
LEANN fits builders who want semantic search or personal RAG without adopting a hosted vector database. The CLI is the shortest route for indexing a directory and searching it, while the Python API supports custom applications. Its MCP path is useful when Claude Code needs semantic retrieval across a codebase rather than keyword search alone.
Notable points
What stands out
The storage figures on this page come from LEANN's own README benchmarks. The project reports 324 MB instead of 3.8 GB for 2.1 million DPR chunks and 6 GB instead of 201 GB for 60 million Wiki chunks, with 91% to 97% savings across its table. Reproduce those tests or benchmark your own collection before planning capacity around the figures.
Before using
What to review
Installation is not one-click on every system. LEANN uses uv and Python; source builds add native dependencies. DiskANN requires macOS 13.3 or later on Mac, while Windows setup calls for Visual Studio 2022 Build Tools, vcpkg, and several C++ libraries.
A local index does not guarantee an entirely local workflow. Ollama, LM Studio, vLLM, llama.cpp, and other local endpoints can keep generation and embedding nearby, while OpenAI-compatible cloud providers receive the data sent to their APIs under their own terms.
Some data connectors have their own access requirements. Apple Mail and iMessage examples are macOS-specific, and live Slack or Twitter retrieval needs a separate MCP server plus platform credentials and permissions.
Low storage is a project benchmark, not a promise for every collection. Measure index size, build time, search speed, accuracy, memory use, and embedding-model behavior with the data and hardware that matter to you.
Review what each reader imports before indexing personal mail, messages, browser history, chats, or team data. Protect local files, exports, API keys, and MCP permissions with appropriate access controls. Keep secure backups of the files and indexes you need.
Reader fit
Who may find it relevant
People building local semantic search across documents, notes, code, messages, or browsing history.
Developers comparing a compact recomputed index with FAISS, a hosted vector database, or a conventional local vector store.
Claude Code users who want a local semantic-search MCP server for a codebase.
Less relevant for readers seeking a finished consumer chat app or a setup with no command line, models, providers, or indexing choices.
Editorial note
Why LifeHubber lists it
LEANN makes a useful retrieval tradeoff visible: a vector index can use much less disk space when it recomputes selected embeddings during search. Readers can compare that approach with conventional local indexes and hosted vector databases while keeping speed, accuracy, setup effort, provider choice, and data access in view.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare what the retrieval index saves and what it recomputes.
LEANN reduces local vector-index storage by recomputing selected embeddings during search. Continue with a vectorless document index, an incremental indexing pipeline, or the wider retrieval stack.
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