failure-gallery

Validate and build a searchable gallery of reproducible synthetic agent and robotics failure records.

failure-gallery is for evaluation educators, review-tool maintainers, and robotics or agent teams that need inspectable fixtures for failure-review workflows. Its differentiator is evidence-first synthetic case design: every record names the failure, domain, expected review label, expected finding, related tool, exact reproduction command, source JSON path, and synthetic-data boundary.

Inspectable Output

  • validate: checks case fields, requires at least 12 records, and requires both agent and robotics domains.
  • render: writes one requested HTML file from the case records.
  • build: deterministically writes both site/index.html and docs/index.html by default.
  • check: fails when either generated deploy target differs from the canonical renderer output.

The case JSON under cases/ is the review evidence. src/failure_gallery/render.py and packaged local CSS/JavaScript assets are the canonical generator inputs; generated HTML should not be edited by hand.

Runtime Boundary

Validation and rendering read local JSON, CSS, and JavaScript files and make no network requests. The generated page is standalone, although its navigation links point to public AuraOne and GitHub pages when a browser follows them. No customer, private-lab, or production incident data is bundled.

Install

Install the current package from PyPI:

python -m pip install "failure-gallery==0.2.1"

Quickstart

failure-gallery validate cases/
failure-gallery build cases/
failure-gallery check cases/

Release Status

Registry status verified July 13, 2026:

  • PyPI: failure-gallery==0.2.1
  • GitHub release: v0.2.1
  • The wheel includes the failure-gallery CLI and canonical local CSS and JavaScript assets used by render, build, and check.

The project is alpha software. No incident-volume, customer, benchmark, or adoption claim is made.

Limits

The records are synthetic tutorials with expected review outcomes. They are not real incidents, benchmark results, model comparisons, or proof that a related tool will detect every production failure.

Next Action

Run validate, build, and check, then choose one record and execute its reproduce_command; investigate any mismatch with the documented expected_finding.

Project links

Data verified by PyPI on Jul 13, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for auraoneai from gravatar.com auraoneai

Credits

Author: AuraOne

GitHub Statistics

Data verified by PyPI on Jul 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.10

Tags

agent-evaluation robotics-evaluation failure-analysis review-fixtures evals synthetic-data

Classifiers

Report project as malware

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