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
CLI
Installed as darkroom (alias: darkrm):
darkroom show evidence/runs/<run>/manifest.json # summarize a run
darkroom verify evidence/runs/<run>/manifest.json # structural checks
darkroom verify runs/*/manifest.json --contract evidence-contract.toml
verify exits nonzero when a run fails its evidence contract -- a
declared set of per-scenario capture requirements -- making "this build
produced its proof" a CI gate. A contract is TOML:
[[scenario]]
name = "client_approves_proof"
[[scenario.requires]]
kind = "screenshot"
steps = ["proof_awaiting_approval", "proof_approved"]
[[scenario.requires]]
kind = "http_transcript"
Requirements may declare trials = N for nondeterministic scenarios
checked across a series of runs (pass several manifests to verify).
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
Release files for darkroom-ai 0.6.0
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-0.6.0.tar.gz | 103.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| darkroom_ai-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 151.4 kB
Release files / darkroom_ai-0.6.0.tar.gz
| Download URL | darkroom_ai-0.6.0.tar.gz |
|---|---|
| Size | 103.3 kB |
| Tags | Source |
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| 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.
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