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 (LogProducer, manifests, EvidenceRun)
pip install darkroom-ai[playwright] # adds ScreenshotProducer
The distribution is named darkroom-ai (the bare darkroom name is squatted on PyPI); the import name is darkroom throughout.
Usage
from darkroom import EvidenceCapture
from darkroom.run import start_run, end_run
# Start a run (typically in a pytest session hook)
run = start_run(project="my-project")
# Per-scenario capture (typically via a pytest fixture)
evidence = EvidenceCapture("login_flow")
evidence.screenshot(page, "login_page")
evidence.screenshot(page, "after_login", full_page=True)
evidence.log("api_response", {"status": 200})
# End the run -- writes manifest.json
manifest_path = end_run()
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
Release files for darkroom-ai 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| darkroom_ai-0.1.0.tar.gz | 58.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| darkroom_ai-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 74.2 kB
Release files / darkroom_ai-0.1.0.tar.gz
| Download URL | darkroom_ai-0.1.0.tar.gz |
|---|---|
| Size | 58.9 kB |
| Tags | Source |
|
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| Tags | Python 3 |
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Yes |
| Uploaded via |
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|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 18, 2026.
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