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github.com / acme / llm-agent · PR #842 · feat: upgrade to gpt-4o-mini
E
evalo-bot
commented just now
Eval suite: customer-support-v3
⚠ regression detected87%
pass rate
↓ 6%
240
eval cases
+0
$0.42
run cost
↑ $0.04
14 regressions vs main
•refund_followup — refused valid request (5 cases)
•tone_check — formal → casual drift (4 cases)
•hallucination — invented order ID (3 cases)
Checks
✓build
2m 14s
✓lint
14s
✓test
48s
⚠evalo/quality
87% pass
✓vercel
preview ready
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The problem
Your AI feature works in dev. Then it ships, and breaks.
Every team shipping LLM features runs the same loop: tweak a prompt, hope nothing regressed, ship it, find out from a customer. Existing eval tools live in a separate dashboard nobody opens. Evalo lives in the PR — where decisions actually get made.
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Prompt drift
Small wording changes silently break 20% of cases.
✕
Model swaps
Upgrade to a new model, lose 15% accuracy in production.
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Tool regressions
Agent stops calling the right function. You find out from support.
✕
No ground truth
Your team disagrees on what 'good' even means.
How it works
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📝
01
Define your evals
Write eval cases as JSONL or promote production traces. Add LLM-as-judge rubrics, exact match, or custom assertions. Versioned in your repo.
⚡
02
Install the GitHub App
One click. Evalo watches PRs that touch your prompts, agents, or model configs — or trigger from CI yourself.
🚀
03
Ship with confidence
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PR checks
Pass/fail comment + check-run on every PR. Block merges on regression.
◎
Trace observability
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⚙
Dataset versioning
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✦
LLM-as-judge
GPT-4o / Claude as a rubric judge. Calibrate against human labels until correlation > 0.85.
⊕
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import { evalo } from "@evalo/sdk";
import OpenAI from "openai";
// Wrap once — get traces, evals, and PR checks
const openai = evalo.wrap(new OpenAI(), {
project: "customer-support",
dataset: "summaries-v1",
});
const response = await openai.chat.completions.create({
model: "gpt-4o-mini",
messages: [{ role: "user", content: input }],
});What teams say
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Before Evalo we shipped prompt changes and prayed. Now PR review includes the eval delta and we caught three regressions in the first week.
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— Eng lead, Series A fintech
“
The PR-check model is the obvious right answer. We replaced two homegrown scripts and a Notion doc with one button.
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— Staff engineer, AI infra startup
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