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EvalCore is a single-binary test runner for LLM apps and agents. Define cases and scorers in YAML, run local targets on every PR, and replay model or judge calls offline for $0. Use REST or shell targets, baselines, trials, model comparisons, and OTel traces.
Hi Product Hunt, I’m @abhishek_manyam. @kuladeep_mantri and I built EvalCore because an AI app can change after a prompt edit, model swap, or dependency update, and the failure often appears after release.
EvalCore makes the eval suite a normal repository artifact: one YAML config, JSONL cases, and one binary. Local shell targets run on each PR. Model and judge calls can be recorded in a SQLite cassette and replayed offline. A suite exits 0 or 1, so CI can block regressions without depending on live model calls.
EvalCore supports deterministic scorers, LLM-as-judge, custom scorers over JSON stdin/stdout, repeated trials, model comparisons, baselines, HTML reports, and OTel/OpenInference agent traces. Targets use HTTP or shell, so your app and custom scoring code can use any language.
The project is Apache-2.0. There is no account, hosted service, or telemetry.
We want feedback from engineers who already run evals in CI: where does your current setup become flaky, expensive, or hard to review? The quickstart takes about five minutes and uses no network. If you try it, tell us what target or scorer is missing.
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About EvalCore on Product Hunt
“Snapshot-test AI behavior in CI”
EvalCore was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #30 on the daily leaderboard. EvalCore is a single-binary test runner for LLM apps and agents. Define cases and scorers in YAML, run local targets on every PR, and replay model or judge calls offline for $0. Use REST or shell targets, baselines, trials, model comparisons, and OTel traces.
EvalCore was featured in Open Source (68.6k followers), Developer Tools (516.2k followers), Artificial Intelligence (474.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 223.9k products, making this a competitive space to launch in.
Who hunted EvalCore?
EvalCore was hunted by abhishek manyam. 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.
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abhishek manyam
Hi Product Hunt, I’m @abhishek_manyam. @kuladeep_mantri and I built EvalCore because an AI app can change after a prompt edit, model swap, or dependency update, and the failure often appears after release.
EvalCore makes the eval suite a normal repository artifact: one YAML config, JSONL cases, and one binary. Local shell targets run on each PR. Model and judge calls can be recorded in a SQLite cassette and replayed offline. A suite exits 0 or 1, so CI can block regressions without depending on live model calls.
EvalCore supports deterministic scorers, LLM-as-judge, custom scorers over JSON stdin/stdout, repeated trials, model comparisons, baselines, HTML reports, and OTel/OpenInference agent traces. Targets use HTTP or shell, so your app and custom scoring code can use any language.
The project is Apache-2.0. There is no account, hosted service, or telemetry.
We want feedback from engineers who already run evals in CI: where does your current setup become flaky, expensive, or hard to review? The quickstart takes about five minutes and uses no network. If you try it, tell us what target or scorer is missing.
GitHub: https://github.com/eval-core/eva...
Quickstart: https://evalcore.cc/getting-star...