siftr

Find the right code without reading the whole codebase. siftr gives AI coding agents (and you) four read-only tools that answer "where is it?" and "which part matters?" in about a second, for about a cent — so agents spend their tokens on the work, not on searching.

$ siftr "where are users charged credits when a block runs"
0.83  backend/backend/executor/billing.py:114-193
      def charge_usage(
0.81  backend/backend/copilot/tools/helpers.py:439-518
      # still settles billing via asyncio.shield — external side effects

Benchmark results

Measured on SWE-bench Lite — 300 real GitHub issues from 12 Python projects, where we know which file and lines the real fix changed. Numbers are from a frozen test split of 202 issues that siftr was never tuned on.

• Speed: search takes ~2s on a 4,000-file repo (p90 2.9s); read and pick take under half a second.

• Cost: 1–2¢ per search on a large repo; read and pick cost well under a cent.

• Where it doesn't win: if you already know the exact words, use grep — it's instant and free. filter lost to grep on the one labelled log dataset available, so it's marked experimental.

Full method, per-project results, and the ideas that didn't work: BENCHMARKS.md.

Related MCP server: mcp-code-indexer

Install

# macOS / Linux
curl -LsSf https://raw.githubusercontent.com/Bentlybro/siftr/main/install.sh | sh
# Windows (PowerShell)
irm https://raw.githubusercontent.com/Bentlybro/siftr/main/install.ps1 | iex

Nothing needs to be installed first — the script brings its own Python via uv. Then, once:

siftr setup             # paste an OpenRouter API key (openrouter.ai/settings/keys)
siftr agents install    # add siftr to your coding agents

Use it with AI agents

siftr agents install finds your coding agents and adds siftr as an MCP server — Claude Code, Codex, Cursor, Claude Desktop, Gemini CLI, Windsurf, Kiro, opencode, pi and omp. It backs up each config file first, leaves your other servers alone, and is safe to re-run.

The agent gets four tools:

Manual setup for any other client, and a snippet that tells your agent when to use these: docs/agents.md.

Use it yourself

siftr "how does the frontend decide whether to show onboarding"     # search this folder
siftr search "code that retries failed HTTP calls" ~/src/project    # search another folder
siftr read src/billing.py "where is the refund amount calculated"   # just the relevant parts
git ls-files 'tests/*.py' | siftr pick "fix the login redirect bug" -n 10
pytest -x 2>&1 | siftr filter "why does test_login fail"           # experimental

Every command takes --json; search takes --stats for time and cost, and -h lists the rest.

How it works

siftr runs on TypeSafe Jev, a decision model: instead of writing text, it answers yes/no questions with a probability. Every siftr tool asks the same kind of question — "is this relevant?" — about many pieces of text at once:

1. Hundreds of questions per request. Asking about 300 files takes 0.50s; asking about one takes 0.36s.

2. All requests at once. A 4,000-file repo becomes a few dozen parallel requests — 0.9s, versus ~23 minutes checking one file at a time.

3. No waiting on stragglers. A request that hasn't answered after 2s gets a duplicate; whichever finishes first wins.

4. Two passes for search. First rank every file from its name and definitions, then zoom into the best 30 files to point at exact lines.

Each tool, step by step, with diagrams: docs/how-it-works.md.

Safe for agents by design

siftr only suggests; it never deletes or rewrites anything in the agent's context:

• read lists every section it left out, and filter keeps the full output on disk.

• A wrong answer costs one extra read, never lost information.

• Every tool tells the agent to fall back to grep or a full read if nothing fits.

Privacy and cost

Each call sends file paths, definition names and relevant snippets to OpenRouter, which routes them to TypeSafe. Don't use it on code you can't send to a third-party API. Pricing is $0.042 per million input tokens — a large-repo search costs 1–2¢.

Development

python -m unittest discover -s tests -t .     # offline tests, no key needed
python bench/swebench.py run all              # search benchmark (needs a key)
python bench/swebench_tools.py read test      # read / pick benchmarks

MIT licensed.