Kensaku: search inside 100,000 files in 1 ms, locally, with MCP for agents
A free, MIT-licensed local file launcher for Windows, macOS and Linux that searches inside documents, code and screenshots, and exposes the same index to AI agents over MCP.
Why it matters
Name search over 100,000 files in about 1 ms, fully on-device, plus OCR on screenshots and semantic search with a 7 MB embedding table, and Claude Code can query that same index read-only.
What you could build with it
A researcher with a decade of PDFs and screenshots could point an agent at the local index and ask it to pull every document mentioning a topic, with citations to the exact files, without uploading anything to a cloud.
Does it hold up?
Looks usable today: v1.0.16 shipped 6 Nov with installers for three platforms and a 14 MB footprint; the 1 ms claim is the maker's own Linux measurement, and first indexing of 100k files takes about 3 minutes.
Built with Kensaku
- Kensaku: Open-source, local-first desktop search with semantic retrieval, OCR, full-textalternativeto · AlternativeTo listing: local-first desktop search combining fuzzy filename search, BM25 full-text retrieval, semantic search and OCR, with an MCP server for agents.
Learn more
First spotted on github: source.
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