Mantic.sh – A structural code search engine for AI agents
Author here! Some context: I published this 48 hours ago and it was auto-listed on MCPMarket (the MCP tools directory). Got 700+ organic downloads with zero marketing—developers were actively searching for exactly this solution. The "Git Accelerator" optimization story: Initially used a file walker that took 6.6s on Chromium. Profiling showed 90% was filesystem I/O. The fix: git ls-files returns 480k paths in ~200ms. Added smart heuristics for untracked files (only scan dirs <50k files), bringing total to 0.46s. Why this matters: Agents can't wait 10 seconds for search. Sub-500ms makes…
In plain words
Mantic.sh is a structural code search engine designed for AI agents to quickly find specific code patterns and functions across large codebases. It uses Git optimizations to return results in under 500 milliseconds, making it practical for agents that need instant feedback during code exploration. The tool works with Cursor, VS Code, and command-line interfaces, and is best suited for precise queries where code structure matters rather than fuzzy keyword matching.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Author here! Some context: I published this 48 hours ago and it was auto-listed on MCPMarket (the MCP tools directory). Got 700+ organic downloads with zero marketing—developers were actively searching for exactly this solution. The "Git Accelerator" optimization story: Initially used a file walker that took 6.6s on Chromium. Profiling showed 90% was filesystem I/O. The fix: git ls-files returns 480k paths in ~200ms. Added smart heuristics for untracked files (only scan dirs <50k files), bringing total to 0.46s. Why this matters: Agents can't wait 10 seconds for search. Sub-500ms makes it feel instant, changing how they explore codebases. Installation: Cursor: npx mantic.sh@latest VS Code: npx mantic.sh@latest CLI: npm i -g mantic.sh Limitations: Mantic is optimized for precise queries ("find stripe webhook") where structure matters. For fuzzy exploratory search, traditional embeddings may still be better. Curious if HN has ideas for hybrid approaches. Happy to answer questions!
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