Hey all,

I kept watching AI coding assistants (Claude, Cursor, Copilot…) answer questions about my codebase the slow, expensive way: fire off a bunch of greps, read three whole files, and still guess wrong about who calls what. Every step is a round-trip that costs tokens and time, and half the time the model just pattern-matches the raw text and hallucinates a function that doesn't exist.

The idea

grep searches text. It has no idea what a function or a caller is — it just matches letters. zest instead parses your code into a real syntax tree (the same tech editors use for highlighting) plus your git history, and answers structural questions directly:

• Who calls this function? → one command, names the caller — not 40 lines of grep output the AI has to squint at.
• Is this code reached? Is it tested? What breaks if I change it? → computed, not guessed.
• Does this symbol even exist? → a real yes/no with an exit code the AI can trust. grep's substring match happily matches a typo and misleads it.

No AI inside zest. No database to build. No index to keep fresh. The point: the AI asks one question and gets one trustworthy answer, instead of a grep-read-grep loop

Where it loses

For raw text search, grep/ripgrep are blazing byte scanners (~10ms) while zest parses syntax trees (~100ms). But 100ms is irrelevant next to a model step measured in seconds — round-trips and context are what you're billed for, not local scan time. For literal text search, still use grep. zest is for "how does this code fit together."

Scope

Currently supports C, C++, Rust, JavaScript, TypeScript/TSX, and PHP. One zest install drops a brief into your repo that tells any agent (Claude, Cursor, Copilot, Gemini) to reach for it first.

Repo + benchmark: https://github.com/Rajaonarison-Andry-Misandratra-Fiderana/zest

Would love feedback, especially on how it performs on codebases.