The index for AI agents
Your agent re-learns your codebase every session — grep, read, guess, repeat. RepoQL hands it a living index instead: every file, symbol, and relationship, understood in seconds for a fraction of the tokens.
Where your tokens go
Every file your agent opens to get its bearings evicts a piece of the task you actually gave it. Thirty tool calls of searching is thirty answers' worth of context, gone.
RepoQL parses your repository ahead of time and keeps the index warm as you work. Your agent asks; the index answers with exactly the slice that matters.
Sessions start already knowing the shape of your code. Answers arrive in one call instead of thirty. The window stays free for the work itself.
One index, four senses
Every file is pre-parsed into three levels — a one-line headline, its structure, its content. Agents spend tokens only on what matters.
Hybrid lexical + semantic search over everything. The landscape, ranked by meaning — before committing to read anything.
A single method body. A line range. A glob across every file. Progressive detail that honors a token budget exactly.
SQL over the whole graph — code, git history, parsed data files. What calls what; what depends on what.
A synthesized answer with citations, drawn from up to 50k tokens of source the agent never had to hold.
Progressive disclosure
Every file is pre-computed at three levels — a one-line headline, its structure, its full content. The token budget decides which one you get; the address never changes.
An agent scans a thousand headlines to know what exists, narrows to twenty structures to see the shape, and reads three bodies to understand — never opening a file it didn't need.
Try the tabs. Same file, three bets.
What the agent sees
Everything is addressable. Files, symbols, line ranges, globs — one URI scheme across your repo, imported repos, and the docs themselves.
Ask for every Service member's signature across the codebase and the index answers from what it already knows — nothing is opened, nothing is wasted.
The risk is asymmetric. A bad query costs 1,500 tokens. A good one saves 50,000.
Why it's different
Everyone else builds AI. RepoQL is deterministic software with an agent as its user — and agent-first shows in the details.
Ask for 3,000 tokens and get exactly 3,000 — the richest view of the code that fits. Nothing overflows into your context uninvited.
A miss never dead-ends. The response says what was tried, what exists nearby, and the call that will work — agents recover on their own.
Every answer carries what the index knew when it answered — fresh, stale, or still indexing. Honest, or absent.
Works where your agent works
One long-lived host per machine. Agents connect and disconnect freely; the index is always warm.
22 format families
Code, data, documents — parsed to symbols and structure, not just text. Even the PDFs.