What is AI evaluation testing?

AI evaluation testing measures model behavior against defined criteria and turns reviewed production failures into regression coverage.

How does EvalGate generate regression tests?

EvalGate captures production traces, detects failures and behavioral drift, then creates candidate eval cases that can be reviewed and promoted into regression coverage.

What programming languages does EvalGate support?

EvalGate provides TypeScript and Python SDKs, with integrations for popular frameworks like LangChain, CrewAI, and AutoGen.

How does EvalGate ensure CI/CD integration?

EvalGate integrates with existing CI/CD pipelines through GitHub Actions and provides regression gates that fail builds when AI behavior degrades.

EvalGate

CI for AI behavior

Stop the same AI failure from shipping twice

EvalGate captures real AI failures, promotes reviewed cases into reusable eval coverage, and blocks regressions before release.

trace→eval→gate

Start local with no account. Add the platform when your AI reaches production scale.

Use the local gate first, then add traces, LLM judges, review workflows, cost controls, and governance as your team needs them.

How teams ship with EvalGate

One wedge: trace what breaks in the real world, promote it into eval coverage, then enforce it in CI.

Step 1
Start with one local gate
Install the SDK, snapshot your current behavior, and block regressions in CI before you adopt the full platform.
Step 2
Capture failures from real AI behavior
Trace production and staging behavior with structured context so reviewed evals reflect what users actually hit.
Step 3
Promote coverage into release gates
Turn failures into suites, run them on every change, and give reviewers evidence before a merge.

Built for the trace → eval → gate loop

Three reasons teams standardize on EvalGate for AI quality — not a broad platform catalog.

Local gate first

Prove the workflow in a repo before you ask a team to adopt another dashboard.

Trace to eval to gate in one product

One operating loop connects production failures, reviewed eval coverage, and CI enforcement.

Governance when you need it

Add judges, reviews, cost controls, benchmarks, annotations, and audit trails as the rollout grows.

See It in Action

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At-a-glance stats, recent runs, and quick actions

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Try AI Evaluation in 30 Seconds

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💬

Beginner30s

Chatbot Accuracy

See how well a customer service chatbot handles common questionsPreview: quality score, pass/fail split, and top failure notes

🔍

Intermediate45s

RAG Hallucination

Detect when AI makes up information not in source documentsPreview: hallucination flags with expected vs actual output

💻

Advanced1m

Code Generation

Evaluate if generated code actually works and follows best practicesPreview: failed test cases, score breakdown, and recommendations

🧪

Custominstant

Test Your Own

Paste your AI's input and output, pick assertions, see results instantlyPreview: instant assertion checks using your own output

Stop the same AI failure from shipping twice

EvalGate captures real AI failures, promotes reviewed cases into reusable eval coverage, and blocks regressions before release.

trace→eval→gate

Start local with no account. Add the platform when your AI reaches production scale.

Use the local gate first, then add traces, LLM judges, review workflows, cost controls, and governance as your team needs them.

How teams ship with EvalGate

One wedge: trace what breaks in the real world, promote it into eval coverage, then enforce it in CI.

Step 1

Start with one local gate

Install the SDK, snapshot your current behavior, and block regressions in CI before you adopt the full platform.

Step 2

Capture failures from real AI behavior

Trace production and staging behavior with structured context so reviewed evals reflect what users actually hit.

Step 3

Promote coverage into release gates

Turn failures into suites, run them on every change, and give reviewers evidence before a merge.