code context · by infino

Let your coding agent
search, not crawl.

Local code search for AI coding agents: a CLI and an MCP server over a ranked index that lives in plain files inside your repo. Hybrid keyword and semantic search, plus SQL relevance-ranked aggregation, so your agent answers questions about the codebase without reading it file by file. No accounts, no keys, no server.

up to 22×
fewer tokens on whole-repo questions

One query, break this repo down by language, is about 6K tokens instead of about 140K reading files. Measured on real agent runs, with a harness in the repo so you can reproduce it on your own.

Install
# global CLI
npm install -g @infino-ai/code-context
cx install && cx index
# or zero-install into Claude Code
claude mcp add code-context -- npx -y @infino-ai/code-context mcp

Keyword search is live within seconds of indexing; semantic and hybrid ranking backfill in the background. Works in Claude Code, Cursor, and any MCP client.

Three tools, one index

search

Exact terms and meaning in one ranked pass. Hits carry the code with path:line ranges, so answers come straight from results.

sql

Read-only SQL over the index. Search composes with GROUP BY, so "which files have the most code about X" is one query.

reindex

Incremental sync: only changed files re-chunk and re-embed. The server also auto-syncs in the background as queries arrive.

Why it is different

Hybrid, not just vectors

BM25 and vector rankings fuse in one pass, so a query works whether or not you know the exact identifier.

SQL over your code

Rank and aggregate across the whole repo in one query, the kind of question file tools cannot express at any budget.

Local and private

Embedding is a small local model, downloaded once. No accounts, no API keys, no server; code never leaves the machine.

The index is just files

Everything lives in .infino/ inside your repo: plain files you can copy, cache in CI, or gitignore. A live index, not a snapshot.