darkroom

Evidence capture and manifest management for autonomous software delivery.

darkroom is the evidence subsystem of the Judge-Builder framework. It provides typed records for evidence items, a producer protocol for capturing diverse evidence kinds, and manifest serialization for the handoff between Builder and Judge.

The name references the dark factory pattern -- lights-off autonomous production -- and the clean room pattern -- independent implementation from specification. A darkroom is a controlled, light-sealed environment where evidence is developed and evaluated without contamination from the implementation side.

Install

pip install darkroom-ai            # core: manifests, run lifecycle, pytest plugin,
                                   # log/command/HTTP/file-snapshot/diff/video producers
pip install darkroom-ai[playwright] # adds the screenshot producers

The distribution is named darkroom-ai (the bare darkroom name is squatted on PyPI); the import name is darkroom throughout.

Usage

With pytest (recommended)

Installing the package registers a pytest plugin -- no conftest wiring needed. Tests request the evidence fixture; the scenario name derives from the test name:

def test_login_flow(page, evidence):
    evidence.screenshot(page, "login_page")
    evidence.screenshot(page, "after_login", full_page=True)
    evidence.log("api_response", {"status": 200})

Run in evidence mode to get a manifest-backed run (otherwise captures fall back to flat directories and the session hooks stay out of the way):

EVIDENCE_MODE=1 EVIDENCE_DIR=./evidence pytest

The plugin starts the run at session start, records a full-page screenshot for any failing test that used a page fixture, and writes manifest.json at session end. Set the manifest's project name via ini:

[pytest]
darkroom_project = my-project

Direct API

from darkroom import EvidenceCapture
from darkroom.run import start_run, end_run

run = start_run(project="my-project")

evidence = EvidenceCapture("login_flow")
evidence.screenshot(page, "login_page")
evidence.log("api_response", {"status": 200})

manifest_path = end_run()  # writes manifest.json

Manifest Format (v2)

{
  "schema_version": "2.0",
  "run_id": "2026-03-17T13-43-29",
  "project": "my-project",
  "timestamp": "2026-03-17T13:44:02.049178",
  "scenarios": [
    {
      "scenario": "login_flow",
      "items": [
        {
          "kind": "screenshot",
          "mime": "image/png",
          "path": "login_flow/01-login_page.png",
          "scenario": "login_flow",
          "step": "login_page",
          "captured_at": "2026-03-17T13:43:48.707534",
          "metadata": {}
        }
      ]
    }
  ]
}

All path values are relative to the manifest file's parent directory. Absolute paths (starting with /) are also accepted. v1 manifests are loaded transparently by load_manifest.

Documentation

• ROADMAP.md -- milestones from foundation through v1
• docs/vision.md -- design fiction: building a web app in the dark
• docs/rubric-lifecycle.md -- how a rubric is made, hardened, and revised
• docs/generalization-plan.md -- how darkroom absorbs the Judge-Builder framework

Development

make venv       # create virtualenv and install deps
make test-unit  # run unit tests
make test       # run all tests with coverage
make help       # see all targets