--- title: "awesome-evals vs awesome-ai-safety" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-giskard-ai-awesome-ai-safety" tools: ["benchflow-ai-awesome-evals", "giskard-ai-awesome-ai-safety"] --- # awesome-evals vs awesome-ai-safety *GraphCanon updated Aug 1, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick awesome-ai-safety if awesome-ai-safety is a curated list of papers and technical articles focused on ensuring AI quality and safety across various machine learning domains including CV and NLP. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [awesome-ai-safety](https://giskard.ai) has 220 stars, 39 forks, and 17 open issues, last pushed Apr 14, 2025. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | A curated list of papers and technical articles on AI Quality & Safety | | Stars | 761 | 220 | | Forks | 71 | 39 | | Open issues | 21 | 17 | | Language | - | - | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | awesome-ai-safety is a curated list of papers and technical articles focused on ensuring AI quality and safety across various machine learning domains including CV and NLP. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | 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) | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 26d | 473d | | Open issues (now) | 21 | 17 | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/giskard-ai-awesome-ai-safety/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Decision facts: awesome-ai-safety - **Pricing:** freemium - The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs. - **Adopt for:** awesome-ai-safety is a curated list of papers and technical articles focused on ensuring AI quality and safety across various machine learning domains including CV and NLP. ## Choose when ### Choose awesome-evals if… - License: awesome-evals is Other, awesome-ai-safety is Apache-2.0. - Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks. - Also covers AI Agents. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose awesome-ai-safety if… - License: awesome-ai-safety is Apache-2.0, awesome-evals is Other. - Pricing: The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs.. - Tags unique to awesome-ai-safety: ai, ai safety, ai-alignment, ai-quality. - When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models. ## 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 awesome-ai-safety - Not suitable if your requirement is a repository with hands-on coding examples rather than research papers and articles. - Avoid this resource if you are searching for datasets or tools that are not in the form of academic literature but practical utilities. - This platform may not provide sufficient guidance on hardware-specific testing, where practical constraints diverge from theoretical models. ## Common questions ### What is the difference between awesome-evals and awesome-ai-safety? awesome-evals: A curated library of resources for building and evaluating AI agents. awesome-ai-safety: A curated list of papers and technical articles on AI Quality & Safety. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over awesome-ai-safety? Choose awesome-evals over awesome-ai-safety when License: awesome-evals is Other, awesome-ai-safety is Apache-2.0; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose awesome-ai-safety over awesome-evals? Choose awesome-ai-safety over awesome-evals when License: awesome-ai-safety is Apache-2.0, awesome-evals is Other; Pricing: The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs.; Tags unique to awesome-ai-safety: ai, ai safety, ai-alignment, ai-quality; When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models. ### 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 awesome-ai-safety? Not suitable if your requirement is a repository with hands-on coding examples rather than research papers and articles. Avoid this resource if you are searching for datasets or tools that are not in the form of academic literature but practical utilities. This platform may not provide sufficient guidance on hardware-specific testing, where practical constraints diverge from theoretical models. ### Is awesome-evals or awesome-ai-safety more popular on GitHub? awesome-evals has more GitHub stars (761 vs 220). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and awesome-ai-safety open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, awesome-ai-safety: Apache-2.0). ### Where can I find alternatives to awesome-evals or awesome-ai-safety? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [awesome-ai-safety alternatives](/tools/giskard-ai-awesome-ai-safety/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/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-giskard-ai-awesome-ai-safety.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-evals or awesome-ai-safety? awesome-evals: Active. awesome-ai-safety: Dormant. 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 awesome-ai-safety? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [awesome-ai-safety trust report](/tools/giskard-ai-awesome-ai-safety/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/_