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Evals on every pull request.
Evalo runs your LLM eval suite on every PR that touches a prompt, model, or agent — and comments the quality diff right where the review happens. Catch regressions before they ship, not after the support ticket lands.
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github.com/acme/agent · PR #842
evalo-bot
commented · 12s ago
Eval suite: customer-support-v3
⚠ regression87%
pass rate ↓ 6%
240
cases
$0.42
run cost
14 regressions vs main
• refund_followup — refused valid request (5 cases)
• tone_check — formal → casual drift (4 cases)
• hallucination — invented order ID (3 cases)
✓build
2m
✓lint
14s
✓test
48s
⚠evalo / quality
87% pass
✓vercel
preview
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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.
✕
Prompt drift
Small wording changes silently break 20% of cases.
✕
Model swaps
Upgrade to a new model, lose 15% accuracy in production.
✕
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
Three steps. One afternoon to set up.
01
Define your evals in code
Write eval cases as JSONL, or import 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 it from CI yourself.
03
Ship with confidence
Every PR gets a quality check with pass/fail and a diff vs main. Block merges on regression. See traces inline.
Everything your team needs to ship AI quality.
From the first eval to enterprise rollout — without leaving GitHub.
PR checks
Pass/fail comment + check-run on every PR. Block merges on regression.
Trace observability
Production trace ingestion. Drill into multi-step agents with full token + tool-call detail.
Dataset versioning
Eval datasets live next to your code. Promote production traces to test cases in one click.
LLM-as-judge
GPT-4o / Claude as a rubric judge. Calibrate against human labels until correlation > 0.85.
Multi-model
OpenAI, Anthropic, Bedrock, Vertex, Azure. Compare model swaps side-by-side.
Self-host or cloud
SOC 2 Type II in progress. Single-tenant deploys for regulated industries.
5-line install
Wrap your LLM call.
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One SDK, every framework. Works with OpenAI, Anthropic, LangChain, Vercel AI SDK, LlamaIndex, and your custom agent loop.
import { evalo } from "@evalo/sdk";
import OpenAI from "openai";
const openai = evalo.wrap(new OpenAI());
const r = await openai.chat.completions.create({
model: "gpt-4o-mini",
messages: [{ role: "user", content: "summarize: ..." }],
// evalo: { name: "summarize", dataset: "summaries-v1" }
});“Before Evalo we shipped prompt changes and prayed. Now PR review includes the eval delta and we caught three regressions in the first week.”
— Eng lead, Series A fintech (design partner)
“The PR-check model is the obvious right answer. We replaced two homegrown scripts and a Notion doc with one button.”
— Staff engineer, AI infra startup
Stop shipping prompt regressions.
Free forever for 1k traces / month. Set up in an afternoon.