--- title: "awesome-list-of-awesomes vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nachimak28-awesome-list-of-awesomes-vs-onejune2018-awesome-llm-eval" tools: ["nachimak28-awesome-list-of-awesomes", "onejune2018-awesome-llm-eval"] --- # awesome-list-of-awesomes vs Awesome-LLM-Eval *GraphCanon updated Aug 1, 2026* ## Verdict Pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV; 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. [awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes) reports 345 GitHub stars, 48 forks, and 1 open issues, last pushed Nov 13, 2023. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 654 stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [awesome-list-of-awesomes's repository](https://github.com/Nachimak28/awesome-list-of-awesomes) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research | Curated list for evaluation of large language models | | Stars | 345 | 654 | | Forks | 48 | 82 | | Open issues | 1 | 44 | | Language | - | - | | Adopt for | A directory of curated 'awesome lists' on AI topics like ML, DL, CV. | 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 | Computer Vision, Evaluation & Observability, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 991d | 246d | | Open issues (now) | 1 | 44 | | Full report | [trust report](/tools/nachimak28-awesome-list-of-awesomes/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## Decision facts: awesome-list-of-awesomes - **Adopt for:** A directory of curated 'awesome lists' on AI topics like ML, DL, CV. ## 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 awesome-list-of-awesomes if… - Tags unique to awesome-list-of-awesomes: computer-vision, data-science, deep-learning, machine-learning. - Also covers Computer Vision, Model Training. - When you need diverse resources covering specific areas in data science and machine learning ### 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 awesome-list-of-awesomes - If you require the latest updates, as not all linked lists are actively maintained - For deeply curated content on new or niche topics not covered ## 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 awesome-list-of-awesomes and Awesome-LLM-Eval? awesome-list-of-awesomes: A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research. 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 awesome-list-of-awesomes over Awesome-LLM-Eval? Choose awesome-list-of-awesomes over Awesome-LLM-Eval when Tags unique to awesome-list-of-awesomes: computer-vision, data-science, deep-learning, machine-learning; Also covers Computer Vision, Model Training; When you need diverse resources covering specific areas in data science and machine learning. ### When should I choose Awesome-LLM-Eval over awesome-list-of-awesomes? Choose Awesome-LLM-Eval over awesome-list-of-awesomes 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 awesome-list-of-awesomes? If you require the latest updates, as not all linked lists are actively maintained For deeply curated content on new or niche topics not covered ### 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 awesome-list-of-awesomes or Awesome-LLM-Eval more popular on GitHub? Awesome-LLM-Eval has more GitHub stars (654 vs 345). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-list-of-awesomes and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub (awesome-list-of-awesomes: MIT, Awesome-LLM-Eval: MIT). ### Where can I find alternatives to awesome-list-of-awesomes or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [awesome-list-of-awesomes alternatives](/tools/nachimak28-awesome-list-of-awesomes/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([awesome-list-of-awesomes markdown twin](/tools/nachimak28-awesome-list-of-awesomes/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/nachimak28-awesome-list-of-awesomes-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, awesome-list-of-awesomes or Awesome-LLM-Eval? awesome-list-of-awesomes: Dormant. 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 awesome-list-of-awesomes and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-list-of-awesomes trust report](/tools/nachimak28-awesome-list-of-awesomes/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nachimak28-awesome-list-of-awesomes`](/api/graphcanon/graph?tool=nachimak28-awesome-list-of-awesomes) - 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/_