trovex

trovex — one canonical doc for your coding agents, ~60% fewer tokens.

Your coding agents (Claude Code, Cursor, Windsurf, Zed, any MCP client) reread the repo every session to work out which .md is current, then answer from a guess. You pay for that on every session, every agent, every teammate.

trovex indexes your repo's markdown and exposes one MCP tool. Your agent asks a question; trovex returns the single current doc that answers it as a path:line pointer with a freshness marker (canonical / stale / duplicate), and serves just the section that answers instead of the whole file. Agents also write what they learn back through one shared point, so every agent and teammate reads the same source of truth instead of re-deriving it.

About 60% fewer tokens on doc lookups, same context quality. Runs locally: vectors in SQLite, embeddings via ONNX, no cloud or API keys.

What you're installing

A 30-second trust check, since the decision happens on the README, not the directory listing:

• First-party, open source. Built and run in production by tsukumo, the team behind it. AGPL-3.0-or-later.
• Local-first, nothing leaves your machine. Vectors in SQLite, embeddings via ONNX. No cloud, no API keys, no network call to answer a query.
• Confined writes, no shell. Six MCP tools: three read-only (trovex, trovex_read, trovex_search) and three that mutate only trovex's own doc store (trovex_write, trovex_tag, trovex_delete). No shell execution, no writes to your source files.
• The ~60% is reproducible. Measured on real repos; trovex search prints the savings on yours (method in the benchmark writeup).

Quick start

trovex is in public beta, on PyPI. No clone needed — uv tool install puts trovex on your PATH:

uv tool install trovex   # one-time, no clone

trovex index /path/to/your/repo        # index your markdown (~1 min)
trovex search "how do we roll back a deploy?"   # ask — prints the tokens it saved
trovex serve                           # wire into your agent: MCP at /mcp, dashboard at /savings

Don't have uv? It's a one-line install: curl -LsSf https://astral.sh/uv/install.sh | sh (or brew install uv).

The search step is the fast way to see the point: it returns the one canonical doc and prints how many tokens that saved versus reading the top few candidates. Once trovex is wired into your agent over MCP, the same numbers accumulate on the savings dashboard at http://localhost:8765/savings.

Prefer not to install anything yet? uvx trovex search "..." runs a single command in a throwaway environment — no install.

Wire it into your agent

trovex is an MCP server. Point your client at http://localhost:8765/mcp after trovex serve. Per-client setup (Claude Code, Cursor, Windsurf, Cline, Zed, Roo) is at trovex.dev/for. For Claude Code:

claude mcp add --transport http trovex http://localhost:8765/mcp

For Cursor, one click (after trovex serve): Add trovex to Cursor

MCP tools

• trovex(q) — route a question to the right on-disk .md and get back path:line pointers with freshness markers, not a pile of files to rank.
• trovex_write(content, kind?, doc_id?, tags?) / trovex_read(query | doc_id, section?) — docs owned inside trovex. An agent stores a record (an incident, a decision, "what actually worked") once; every other agent and a second dev read it back as content (optionally just one section) instead of re-deriving it. See REFONTE.md.
• trovex_search(query, k?, tags?) — passage-level retrieval across the store with tag filters, for when you want the top matching chunks rather than one canonical doc.
• trovex_tag(...) / trovex_delete(...) — tag or soft-delete a stored doc; delete is a recoverable archive, not a hard wipe. Both touch only the trovex store, never your files.

Humans read trovex-owned docs at /doc/{id} in the rendered reader. To make agents route .md writes through trovex_write instead of the disk, install the PreToolUse hook deploy/hooks/trovex-md-guard.sh and carve out exceptions in .trovexignore.

How it compares

• vs CLAUDE.md / AGENTS.md: one static file that goes stale and can't route a question to the right doc, vs many docs kept canonical and served per query. More.
• vs repomix / files-to-prompt: pack the whole repo into the window vs retrieve the one answer. More.
• vs a vector DB / plain RAG: a ranked pile of candidate chunks with no freshness signal vs one current doc with an explicit marker. More.

The reasoning behind the ~60% number is written up in the token cost of agents rereading docs.

Stack

• Python 3.11 + uv
• FastAPI (MCP HTTP + server-rendered HTML UI)
• fastembed (local embeddings, ONNX under the hood)
• sqlite-vec (vector search in SQLite)
• Jinja2 + HTMX (UI, no build step)

Public beta

trovex is in public beta. Install it, run it on your repo, and if it saves you tokens a GitHub star helps other devs find it. Issues and PRs welcome.

Questions, or comparing notes with other people running agents? Join the community Slack: tsukumo.ch/slack.

License

trovex is licensed under the GNU AGPL-3.0-or-later (see LICENSE). You can self-host and modify it freely; if you run a modified version as a network service, AGPL requires you to share your changes.

Working with a team?

trovex is free to run yourself. If your team is rolling out coding agents at scale and wants hands-on help doing it well, or to embed and host a modified trovex privately without the AGPL's copyleft obligations, that's what the consulting is for. Reach out: tsukumo, the team behind trovex.