--- title: "judgeval vs evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/judgmentlabs-judgeval-vs-openai-evals" tools: ["judgmentlabs-judgeval", "openai-evals"] --- # judgeval vs evals *GraphCanon updated Aug 7, 2026* ## Verdict Pick judgeval if judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering; pick evals if 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. [judgeval](https://judgmentlabs.ai/) reports 1.0k GitHub stars, 95 forks, and 28 open issues, last pushed Jul 27, 2026. [evals](https://github.com/openai/evals) has 19k stars, 3.0k forks, and 213 open issues, last pushed Apr 14, 2026. Figures are from public GitHub metadata via [judgeval's repository](https://github.com/JudgmentLabs/judgeval) and [evals's repository](https://github.com/openai/evals). | | [judgeval](/tools/judgmentlabs-judgeval.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Tagline | The Continuous-Improvement Stack for Agents | Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks. | | Stars | 1,047 | 19,127 | | Forks | 95 | 3,050 | | Open issues | 28 | 213 | | Language | Python | Python | | Adopt for | Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. | 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 | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [judgeval](/tools/judgmentlabs-judgeval.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 115d | | Open issues (now) | 28 | 213 | | Full report | [trust report](/tools/judgmentlabs-judgeval/trust.md) | [trust report](/tools/openai-evals/trust.md) | ## Shared compatibility - **Python**: [judgeval](/tools/judgmentlabs-judgeval.md) - Python runtime; [evals](/tools/openai-evals.md) - Python runtime ## Decision facts: judgeval - **Adopt for:** Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. ## 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 judgeval if… - License: judgeval is Apache-2.0, evals is Other. - Tags unique to judgeval: agent, agentic-ai, agents, grpo. - Also covers AI Agents. - You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### Choose evals if… - License: evals is Other, judgeval is Apache-2.0. - Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large language models. - * 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 judgeval - If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. - You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ## 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 ## Common questions ### What is the difference between judgeval and evals? judgeval: The Continuous-Improvement Stack for Agents. 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 judgeval over evals? Choose judgeval over evals when License: judgeval is Apache-2.0, evals is Other; Tags unique to judgeval: agent, agentic-ai, agents, grpo; Also covers AI Agents; You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### When should I choose evals over judgeval? Choose evals over judgeval when License: evals is Other, judgeval is Apache-2.0; Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large language models; * 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 judgeval? If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ### 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 ### Is judgeval or evals more popular on GitHub? evals has more GitHub stars (19,127 vs 1,047). Stars measure visibility, not whether either tool fits your constraints. ### Are judgeval and evals open source? Yes - both are open-source projects on GitHub (judgeval: Apache-2.0, evals: Other). ### Where can I find alternatives to judgeval or evals? GraphCanon lists graph-backed alternatives at [judgeval alternatives](/tools/judgmentlabs-judgeval/alternatives) and [evals alternatives](/tools/openai-evals/alternatives) ([judgeval markdown twin](/tools/judgmentlabs-judgeval/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/judgmentlabs-judgeval-vs-openai-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, judgeval or evals? judgeval: Very active. evals: Slowing. 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 judgeval and evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [judgeval trust report](/tools/judgmentlabs-judgeval/trust); [evals trust report](/tools/openai-evals/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=judgmentlabs-judgeval`](/api/graphcanon/graph?tool=judgmentlabs-judgeval) - 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/_