--- title: "deepeval vs evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-openai-evals" tools: ["confident-ai-deepeval", "openai-evals"] --- # deepeval vs evals *GraphCanon updated Jul 11, 2026* ## Verdict Pick deepeval when license: deepeval is Apache-2.0, evals is Other; pick evals when license: evals is Other, deepeval is Apache-2.0. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.6k forks, and 334 open issues, last pushed Jul 10, 2026. [evals](https://github.com/openai/evals) has 19k stars, 3.0k forks, and 217 open issues, last pushed Apr 14, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [evals's repository](https://github.com/openai/evals). | | [deepeval](/tools/confident-ai-deepeval.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Tagline | The LLM Evaluation Framework | Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks. | | Stars | 16,767 | 18,890 | | Forks | 1,641 | 3,017 | | Open issues | 334 | 217 | | Language | Python | Python | | Adopt for | - | Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 87d | | Open issues (now) | 334 | 217 | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/openai-evals/trust.md) | ## Decision facts: evals - **Adopt for:** Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. ## Choose when ### Choose deepeval if… - License: deepeval is Apache-2.0, evals is Other. - Tags unique to deepeval: evaluation-metrics, llm-evaluation, llm-evaluation-framework, llm-evaluation-metrics. - Also covers LLM Frameworks. ### Choose evals if… - License: evals is Other, deepeval is Apache-2.0. - Tags unique to evals: benchmarking, custom eval creation, large-language-models, llm systems. - * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ## When NOT to use deepeval - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use evals - * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. - * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a繁 ## Common questions ### What is the difference between deepeval and evals? deepeval: The LLM Evaluation Framework. evals: Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over evals? Choose deepeval over evals when License: deepeval is Apache-2.0, evals is Other; Tags unique to deepeval: evaluation-metrics, llm-evaluation, llm-evaluation-framework, llm-evaluation-metrics; Also covers LLM Frameworks. ### When should I choose evals over deepeval? Choose evals over deepeval when License: evals is Other, deepeval is Apache-2.0; Tags unique to evals: benchmarking, custom eval creation, large-language-models, llm systems; * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ### When should I avoid deepeval? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid evals? * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a繁 ### Is deepeval or evals more popular on GitHub? evals has more GitHub stars (18,890 vs 16,767). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and evals open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, evals: Other). ### Where can I find alternatives to deepeval or evals? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [evals alternatives](/tools/openai-evals/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [evals markdown twin](/tools/openai-evals/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/confident-ai-deepeval-vs-openai-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or evals? deepeval: Very active. evals: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for deepeval and evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [evals trust report](/tools/openai-evals/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_