GOVERNATLAS / PRODUCT DOSSIER

DeepEval

DeepEval's maintainers document Python-based evaluation of LLM applications, including pytest integration and configurable evaluation metrics.

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01 / CONTEXT & SCOPE

Understand the proposition.
Define the boundary.

The decision to make

Evaluate how a Python evaluation workflow fits into your release process. A test runner can report a pass while the selected metric misses the behavior your business cares about. Begin with the failure you need to prevent and define the item-level evidence a reviewer must inspect.

What this record does not establish

The maintainer README supports Python evaluation and pytest integration. It does not establish that a metric is deterministic, calibrated for your use case or suitable as an unattended release gate.

1 current source · 1 published assertion
Source coverage is a research boundary, not a quality score.

02 / YOUR EVALUATION

Turn interest into a useful trial.

Open the evaluation playbook ↗

A test worth running

Create a small fixed test set with an obvious failure, an acceptable answer and an ambiguous case. Run it with a pinned metric configuration, inspect any judge-based result and repeat after a prompt change. Include the evaluation run in a sandbox CI workflow with a deliberately failing case.

GovernAtlas editorial proposal. We have not performed this test.

Evidence to bring back

Record library version, judge model, prompt or rubric and threshold alongside each evaluation run.
Show the failing item and its explanation in CI output rather than relying solely on an aggregate pass status.

03 / DOCUMENTED EVIDENCE

Read the claim. Inspect the basis.

Each statement retains its classification and preserved source. A vendor assertion is not an independent test result.

vendor asserted

DeepEval's maintainers document Python-based evaluation of LLM applications, including pytest integration and configurable evaluation metrics. Scope: vendor documentation only; edition and availability boundaries require confirmation.

Source, dates & preserved provenance

DeepEval official repository README ↗

Authority: vendor owned
Observed 2026-10-15 · Reviewed 2026-10-15 · current until 2026-11-14

Artifact SHA-256: 64b1000d1a1f148a1073162eabc7766bb529c0de7b6a410c7100cb47aeb691d3

The hash identifies the preserved capture. The live source may have changed.

Source register

DeepEval official repository README ↗vendor owned · captured 2026-10-15

Brand asset provenance

Organization identity asset from its official website; inclusion does not imply endorsement.

Official website ↗ · Original asset ↗

04 / BEFORE YOU COMMIT

Better questions.
A clearer decision.

Resolve these with the provider and your own evaluation team.

Which evaluators call an external model, and what data leaves the execution environment?

What deployment, retention and access-control evidence supports the intended use?

What underlying assurance documents are available, and which claims remain unverified?

Regulatory applicability requires a separate, current determination. Inclusion is not certification.

KEEP THE RECORD USEFUL

A dossier should stand up to scrutiny.

Have a source that changes this picture? Submit it for editorial review.

Raise an evidence issue ↗