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EvalTrim
Prove which AI-agent evals are worth keeping.
EvalTrim 0.6.0 turns AI-agent eval maintenance into an evidence-driven workflow. Instead of only running evals, it identifies redundant tests, finds unique behavioral witnesses, simulates removal before recommending maintenance, tracks regressions and evaluation debt, and protects critical coverage. It runs locally and never silently deletes tests.
Hey Product Hunt 👋
I built EvalTrim around a problem that became more obvious as my AI-agent eval suites grew:
Writing another regression test is easy. Knowing which tests are still worth keeping is much harder.
Over time, an eval suite can accumulate near-duplicates, stale cases, flaky tests, conflicting expectations, and many tests covering the same behavior.
EvalTrim tries to solve the maintenance side of evaluation.
The part I'm most interested in is the counterfactual removal workflow:
Instead of saying “these two tests look similar,” EvalTrim asks what would actually be lost if one were removed.
It tracks unique behavioral witnesses, critical coverage, requirements, historical failures, oracle conflicts, and other evidence before making a recommendation.
The current version is 0.6.0 beta and runs locally by default.
Some included constructed benchmarks, with no LLM and embeddings disabled, currently measure 1.00 precision, 1.00 recall, 1.00 retirement safety, and 1.00 critical coverage across the three included suites.
I’d love feedback from people building AI agents:
What would you need to see before trusting a tool to recommend merging or retiring an eval?
And where does eval-suite maintenance hurt most in your current workflow?
GitHub: https://github.com/lowjieseng181...
About EvalTrim on Product Hunt
“Prove which AI-agent evals are worth keeping.”
EvalTrim was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #36 on the daily leaderboard. EvalTrim 0.6.0 turns AI-agent eval maintenance into an evidence-driven workflow. Instead of only running evals, it identifies redundant tests, finds unique behavioral witnesses, simulates removal before recommending maintenance, tracks regressions and evaluation debt, and protects critical coverage. It runs locally and never silently deletes tests.
On the analytics side, EvalTrim competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how EvalTrim performed against the three products that launched closest to it on the same day.
Who hunted EvalTrim?
EvalTrim was hunted by Malaysia Linguistics Lab. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of EvalTrim including community comment highlights and product details, visit the product overview.
Malaysia Linguistics Lab