AI Potluck
Model components / Evaluation code

DeepEval

Confident AI

Pytest-style evaluation framework for LLM applications, with more than forty built-in metrics including G-Eval, faithfulness, hallucination, task completion and RAG-specific scores. Metrics run locally and drop into an existing test suite, so a merge can be gated on a metric threshold.

A commercial cloud platform is sold beside the framework for comparing runs and sharing reports; the metrics themselves do not require it. Verified 2026-08-13 via the confident-ai/deepeval repository, its license endpoint and its README.

Openness

5 high confidence
5.0
license
Apache-2.0(OSI, OSS framework)
source
public(GitHub)
commercial-tier
Confident AI managed eval/observability cloud on top
core-gated
ungated(Confident AI is a separate hosted platform scored as its own product, the library evaluates locally without an account, no enterprise directory in the repo)

Apache-2.0 library whose metrics and test runs execute locally with no account. Confident AI is a separate hosted platform, scored on the map as its own product; selling it alongside does not gate the library, and no component is withheld from the published source.

  • https://github.com/confident-ai/deepeval recorded 2026-06-04

    Apache-2.0 license; Confident AI commercial cloud platform

  • https://raw.githubusercontent.com/confident-ai/deepeval/main/README.md recorded 2026-08-11

    README states the metrics 'run locally on your machine', and directs readers to 'Sign up for Confident AI, the enterprise AI evals and observability platform' only to 'compare iterations, share evaluation reports, and monitor your AI in production'. The account section says using the platform 'will allow you to generate sharable testing reports on the cloud. It is free, takes no additional code to setup' - optional, and not a gate on any metric.

  • https://api.github.com/repos/confident-ai/deepeval/contents recorded 2026-08-11

    Repository root listing: deepeval, docs, examples, tests, typescript, scripts, skills, assets and packaging files. No ee/, enterprise/, or cloud/ directory under a second license. The repo's declared license is Apache-2.0.

  • https://api.github.com/repos/confident-ai/deepeval/license recorded 2026-08-13

    GitHub's license endpoint reports spdx_id Apache-2.0 for confident-ai/deepeval and returns the Apache-2.0 text as the LICENSE body.

  • https://raw.githubusercontent.com/confident-ai/deepeval/main/README.md recorded 2026-08-13

    README states the metrics 'run locally on your machine' and frames the Confident AI account as optional reporting that 'is free, takes no additional code to setup'. No metric is described as paid, licensed or enterprise-only.

Adoption

4 high confidence
4.0

deepeval draws 6,218,151 PyPI downloads in the trailing 30 days, which sits firmly in the 1M-10M band, alongside 17.6k GitHub stars and 1.3k projects that list it as a dependency. The download volume is what the level rests on.

Capability

4 high confidence
4.0

Strong, broad coverage of application, RAG and agentic evaluation with LLM-as-judge metrics - top-tier for product evaluation, and just below the academic-benchmark standards such as lm-evaluation-harness on benchmark-standardization breadth.

Verified 2026-08-13