AI AGENTS
HuggingFace Challenger Eval Gate -> GitHub Swap PR
Watches a HuggingFace model family for new releases, benchmarks each challenger against the incumbent on your fixed eval set.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires the watcher
- ActionList new HuggingFace releases + read model cardsHugging Face
- LogicDrop seen, license-incompatible, or out-of-band models
- ActionRun fixed eval on challenger and incumbentShell
- LogicChallenger wins by margin?
- OutputOpen GitHub swap PR with scorecardGitHub
What it does
Keeps your production open model honest. When a newer model lands in a HuggingFace collection or org you track, an agent pulls the model card, runs your frozen eval harness against both the challenger and the current incumbent, and decides whether the swap is justified. A win opens a GitHub PR that bumps the model id in config; a loss is logged and dropped.
When to use it
Use it when you ship an open model in production and want to adopt better releases fast without trusting vendor leaderboard claims. The fixed eval and margin threshold keep churn low and protect against regressions hiding behind a higher headline score.
How it works
- 1A schedule wakes the agent on a daily cadence.
- 2It queries HuggingFace for new model versions in the watched org and reads each card's metadata and license.
- 3A filter drops anything already evaluated, license-incompatible, or below a parameter/size band.
- 4The agent runs the fixed eval suite on the challenger and the incumbent, scoring accuracy, latency, and cost.
- 5A branch decides: win by the margin, or stop.
- 6On a win it opens a GitHub PR editing the model config plus a scorecard, ready for human review and merge.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect ShellRun sandboxed commands inside the workspace.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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