--- title: "every_eval_ever vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evaleval-every-eval-ever-vs-onejune2018-awesome-llm-eval" tools: ["evaleval-every-eval-ever", "onejune2018-awesome-llm-eval"] --- # every_eval_ever vs Awesome-LLM-Eval *GraphCanon updated Sep 20, 2026* ## Verdict Pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results; pick Awesome-LLM-Eval if awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. [every_eval_ever](https://evalevalai.com/projects/every-eval-ever/) reports 111 GitHub stars, 49 forks, and 27 open issues, last pushed Sep 7, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 656 stars, 90 forks, and 55 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [every_eval_ever's repository](https://github.com/evaleval/every_eval_ever) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | Shared schema and crowdsourced eval database | Curated list for evaluation of large language models | | Stars | 111 | 656 | | Forks | 49 | 90 | | Open issues | 27 | 55 | | Language | Python | - | | Adopt for | Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 300d | | Open issues (now) | 27 | 55 | | Stars delta | +9 (30d) | +2 (30d) | | Open issues delta | +3 (30d) | +11 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/evaleval-every-eval-ever/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## 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. ## Decision facts: Awesome-LLM-Eval - **Pricing:** freemium - The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms. - **Requirements:** The resources listed may vary in their own requirements, including software dependencies and hardware specifications. - **Adopt for:** Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. ## Choose when ### 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. ### Choose Awesome-LLM-Eval if… - Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.. - Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.. - Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation. - When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment. ## 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. ## When NOT to use Awesome-LLM-Eval - You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. - If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool. ## Common questions ### What is the difference between every_eval_ever and Awesome-LLM-Eval? every_eval_ever: Shared schema and crowdsourced eval database. Awesome-LLM-Eval: Curated list for evaluation of large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose every_eval_ever over Awesome-LLM-Eval? Choose every_eval_ever over Awesome-LLM-Eval 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 choose Awesome-LLM-Eval over every_eval_ever? Choose Awesome-LLM-Eval over every_eval_ever when Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.; Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.; Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment. ### 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. ### When should I avoid Awesome-LLM-Eval? You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool. ### Is every_eval_ever or Awesome-LLM-Eval more popular on GitHub? Awesome-LLM-Eval has more GitHub stars (656 vs 111). Stars measure visibility, not whether either tool fits your constraints. ### Are every_eval_ever and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub (every_eval_ever: MIT, Awesome-LLM-Eval: MIT). ### Where can I find alternatives to every_eval_ever or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [every_eval_ever alternatives](/tools/evaleval-every-eval-ever/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([every_eval_ever markdown twin](/tools/evaleval-every-eval-ever/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/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/evaleval-every-eval-ever-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, every_eval_ever or Awesome-LLM-Eval? every_eval_ever: Very active. Awesome-LLM-Eval: 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 every_eval_ever and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [every_eval_ever trust report](/tools/evaleval-every-eval-ever/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evaleval-every-eval-ever`](/api/graphcanon/graph?tool=evaleval-every-eval-ever) - 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/_