Metadata-Version: 2.4
Name: redcrown
Version: 0.1.1
Summary: Localized model benchmarking with receipts: run head-to-head evals on your own data, locally, and turn them into shareable proof reports
License: Proprietary
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: pydantic>=2.9
Requires-Dist: pydantic-settings>=2.5
Requires-Dist: httpx>=0.27
Requires-Dist: aioboto3>=15
Requires-Dist: amazon-transcribe>=0.6
Provides-Extra: server
Requires-Dist: fastapi>=0.115; extra == "server"
Requires-Dist: uvicorn[standard]>=0.30; extra == "server"
Requires-Dist: python-multipart>=0.0.30; extra == "server"
Requires-Dist: sse-starlette>=2.1; extra == "server"
Requires-Dist: asyncpg>=0.30; extra == "server"
Requires-Dist: PyJWT>=2.9; extra == "server"
Requires-Dist: cryptography>=43; extra == "server"
Requires-Dist: stripe>=10; extra == "server"

# RedCrown

**Localized model benchmarking with receipts.** Run head-to-head evals across every
model, provider, and config on your own data, locally, then turn the results into
ranked, receipted proof you can hand to a client, a CFO, or a regulator.

> Agents and harnesses run benchmarks for free. RedCrown is the neutral layer that
> turns them into a decision you can defend, and keeps re-proving it as prices and
> models change. **Run anywhere, prove here.**

## Install

```bash
pip install redcrown
```

Python 3.11+. The CLI has a small dependency footprint; server extras
(`pip install "redcrown[server]"`) are only needed if you run the API yourself.

## Quickstart

```bash
# 1. build a dataset (the public PriMock57 clinical-transcription corpus, or bring your own)
redcrown build-dataset primock57 --out exp.json

# 2. run the fan-out locally, on your machine, with your keys
redcrown eval exp.json --report-json out.json

# 3. (optional) sign in once per machine via device-code OAuth
redcrown login

# 4. (optional) push the results to a shareable, no-login proof page
redcrown push out.json --proof-link
```

`redcrown eval` ranks every config on cost, quality, and latency against your own
ground truth and names the cheapest one that clears your quality bar. Example:

```
RANKED  transcription · cheapest config at or above your 0.85 quality bar
  deepgram · nova-3-medical    quality 0.883    $294/mo   winner, 40% cheaper
  aws · transcribe-standard    quality 0.879    $487/mo   incumbent
  openai · whisper-1           quality 0.820    $122/mo   below your bar
```

That run is published as a live, no-login proof page:
<https://app.redcrown.ai/proof/O9iYVdWuaYjaL6mnImeIsD6TB1W_S6h4Frbx04YqAYQ>

## Free by construction

Evals run on your machine with your own provider keys, so RedCrown never sees your
raw data and the run costs you nothing beyond your own inference. Only the results
you choose to `push` become a cloud proof. `--no-receipts` keeps raw outputs local
and uploads aggregates only.

## Already ran an eval elsewhere?

You do not have to run anything through RedCrown to get a proof. Take the results from
an eval you already ran, as a JSON in the RedCrown results format, and push them:

```bash
redcrown push results.json --proof-link
```

You get the same ranked, receipted, shareable report. The fastest path, with no install,
is the web app at <https://app.redcrown.ai/upload>.

## For coding agents (MCP)

Coding agents (Claude, Cursor, Codex) drive the whole loop over the hosted MCP server
at `mcp.redcrown.ai`: scaffold an experiment, run it, review outputs, and mint a proof.
The server is open source: <https://github.com/RedCrown-ai/redcrown-mcp>

## Links

- Site: <https://redcrown.ai>
- App: <https://app.redcrown.ai>
- MCP server (open source): <https://github.com/RedCrown-ai/redcrown-mcp>

## License

Proprietary. (c) Method Data Science LLC.
