Local code search for AI coding agents: a CLI and MCP server with hybrid keyword + semantic search and SQL relevance-ranked aggregation over an index in plain files. No accounts, no keys, no server.
code-context is the retrieval layer under your coding agent: one local index over the whole repo (keyword, semantic, hybrid, and SQL), reached through an MCP server and a CLI, with the index living in plain files inside your repo. Your agent answers questions about the codebase without reading it file by file.
The rule of thumb: the more a question spans the repo, the more this saves, because the answer comes from a ranked index instead of pulling source into context one file at a time.
Up to 22× fewer tokens. One query, "break this repo down by language," is ~6K tokens with code-context versus ~140K reading files. The harness is in the repo, so you can reproduce it on your own codebase.
Try it live (early preview): ask questions about any public GitHub repo at lantern.infino.ai, a demo agent that runs on code-context.
- 🔎 Find code by words or meaning. One ranked pass fuses exact keyword matching with semantic similarity, and every hit carries the code with path:line citations. - 📊 Ask questions grep can't answer. Search works as a SQL table function, so "which files have the most code about X" is one query: ranked by relevance, tallied by GROUP BY. - ⚡ Searching in seconds, fresh forever. The keyword index commits before the embedding model even finishes downloading, vectors backfill in the background, and edits re-sync incrementally: only changed files re-chunk and re-embed. - 🔒 Nothing leaves your machine. No accounts, no API keys, no database server, no telemetry. Embedding is a small local model, downloaded once; after that everything works offline.
From the project README.
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