--- title: "athina-evals vs every_eval_ever" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-evaleval-every-eval-ever" tools: ["athina-ai-athina-evals", "evaleval-every-eval-ever"] --- # athina-evals vs every_eval_ever *GraphCanon updated Sep 20, 2026* ## Verdict Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 4 open issues, last pushed Jun 6, 2025. [every_eval_ever](https://evalevalai.com/projects/every-eval-ever/) has 111 stars, 49 forks, and 27 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [every_eval_ever's repository](https://github.com/evaleval/every_eval_ever). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Shared schema and crowdsourced eval database | | Stars | 301 | 111 | | Forks | 22 | 49 | | Open issues | 4 | 27 | | Language | Python | Python | | Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 470d | 1d | | Open issues (now) | 4 | 27 | | Stars delta | 0 (30d) | +9 (30d) | | Open issues delta | +1 (30d) | +3 (30d) | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/evaleval-every-eval-ever/trust.md) | ## Decision facts: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## Decision facts: every_eval_ever - **Pricing:** freemium - Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database. - **Requirements:** Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip. - **Adopt for:** Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. ## Choose when ### Choose athina-evals if… - Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - More GitHub stars (301 vs 111) - visibility, not fit. ### Choose every_eval_ever if… - Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.. - Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.. - Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra. - Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema. ## When NOT to use athina-evals - If open-source alternatives with transparent customization options are preferred over athina-evals' approach - In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ## When NOT to use every_eval_ever - Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset. - If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable. ## Common questions ### What is the difference between athina-evals and every_eval_ever? athina-evals: Python SDK for evaluating LLM generated responses. every_eval_ever: Shared schema and crowdsourced eval database. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over every_eval_ever? Choose athina-evals over every_eval_ever when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More GitHub stars (301 vs 111) - visibility, not fit. ### When should I choose every_eval_ever over athina-evals? Choose every_eval_ever over athina-evals when Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.; Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.; Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra; Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema. ### When should I avoid athina-evals? If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ### When should I avoid every_eval_ever? Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset. If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable. ### Is athina-evals or every_eval_ever more popular on GitHub? athina-evals has more GitHub stars (301 vs 111). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and every_eval_ever open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or every_eval_ever? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [every_eval_ever alternatives](/tools/evaleval-every-eval-ever/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [every_eval_ever markdown twin](/tools/evaleval-every-eval-ever/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/athina-ai-athina-evals-vs-evaleval-every-eval-ever.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or every_eval_ever? athina-evals: Dormant. every_eval_ever: Very active. 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 athina-evals and every_eval_ever? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [every_eval_ever trust report](/tools/evaleval-every-eval-ever/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=athina-ai-athina-evals`](/api/graphcanon/graph?tool=athina-ai-athina-evals) - 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/_