What is AI evaluation testing?

AI evaluation testing measures model behavior against defined criteria and automatically creates regression tests from production failures.

How does EvalGate generate regression tests?

EvalGate captures production traces, detects failures and behavioral drifts, then automatically generates test cases from those failures to prevent regressions.

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

Trace real failures. Turn them into eval cases. Gate regressions in CI.

From production traces to CI you can trust

Collect traces from real AI behavior, convert failures into reusable eval coverage, and block regressions before release — without a separate observability stack.

trace→eval→gate

No infra. No lock-in. Remove anytime.

LLMs drift silently — a small prompt change can tank quality before anyone notices. EvalGate closes the loop: real failures become test cases and merge gates so the same issue does not ship twice.

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
Collect traces from real AI behavior
Capture production and staging behavior with structured context so you debug what users actually hit.
Step 2
Turn failures into reusable eval coverage
Promote failing patterns into test cases and suites you can run on every change.
Step 3
Block regressions before release
Run the same assertions in CI so bad behavior never merges unnoticed.

Built for the trace → eval → gate loop

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

One loop across TypeScript and Python

Same assertion model in app code, tracing, and CI — pick the SDK that fits your stack.

Trace → eval → gate in one product

Stop duct-taping observability, spreadsheets, and flaky scripts. One workflow from signal to merge gate.

Drop in without rewrites

Use your existing runners and repos; remove EvalGate anytime without migrating data you do not own.

See It in Action

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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

From production traces to CI you can trust

Collect traces from real AI behavior, convert failures into reusable eval coverage, and block regressions before release — without a separate observability stack.

trace→eval→gate

No infra. No lock-in. Remove anytime.

LLMs drift silently — a small prompt change can tank quality before anyone notices. EvalGate closes the loop: real failures become test cases and merge gates so the same issue does not ship twice.

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

Collect traces from real AI behavior

Capture production and staging behavior with structured context so you debug what users actually hit.

Step 2

Turn failures into reusable eval coverage

Promote failing patterns into test cases and suites you can run on every change.

Step 3

Block regressions before release

Run the same assertions in CI so bad behavior never merges unnoticed.