--- title: "awesome-evals vs every_eval_ever" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-evaleval-every-eval-ever" tools: ["benchflow-ai-awesome-evals", "evaleval-every-eval-ever"] --- # awesome-evals vs every_eval_ever *GraphCanon updated Sep 20, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 900 GitHub stars, 104 forks, and 34 open issues, last pushed Sep 15, 2026. [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 [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [every_eval_ever's repository](https://github.com/evaleval/every_eval_ever). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | Shared schema and crowdsourced eval database | | Stars | 900 | 111 | | Forks | 104 | 49 | | Open issues | 34 | 27 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | | --- | --- | --- | | Days since push | 4d | 1d | | Open issues (now) | 34 | 27 | | Stars delta | +139 (30d) | +9 (30d) | | Open issues delta | +13 (30d) | +3 (30d) | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/evaleval-every-eval-ever/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## 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 awesome-evals if… - License: awesome-evals is Other, every_eval_ever is MIT. - Tags unique to awesome-evals: ai-agents, awesome-list, benchmarks, rl-environments. - Also covers AI Agents. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose every_eval_ever if… - License: every_eval_ever is MIT, awesome-evals is Other. - 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: 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 awesome-evals - Require real-time interactive support or direct tool integrations not covered by a static resource list - Seeking proprietary tools from specific vendors rather than open resources and community content ## 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 awesome-evals and every_eval_ever? awesome-evals: A curated library of resources for building and evaluating AI agents. every_eval_ever: Shared schema and crowdsourced eval database. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over every_eval_ever? Choose awesome-evals over every_eval_ever when License: awesome-evals is Other, every_eval_ever is MIT; Tags unique to awesome-evals: ai-agents, awesome-list, benchmarks, rl-environments; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose every_eval_ever over awesome-evals? Choose every_eval_ever over awesome-evals when License: every_eval_ever is MIT, awesome-evals is Other; 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: 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 awesome-evals? Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content ### 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 awesome-evals or every_eval_ever more popular on GitHub? awesome-evals has more GitHub stars (900 vs 111). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and every_eval_ever open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, every_eval_ever: MIT). ### Where can I find alternatives to awesome-evals or every_eval_ever? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [every_eval_ever alternatives](/tools/evaleval-every-eval-ever/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-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/benchflow-ai-awesome-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, awesome-evals or every_eval_ever? awesome-evals: Very active. 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 awesome-evals and every_eval_ever? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [every_eval_ever trust report](/tools/evaleval-every-eval-ever/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-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/_