--- title: "awesome-evals vs zeno" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-zeno-ml-zeno" tools: ["benchflow-ai-awesome-evals", "zeno-ml-zeno"] --- # awesome-evals vs zeno *GraphCanon updated Aug 3, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick zeno if zeno combines Python API with an interactive UI for evaluating ML models across various tasks. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [zeno](https://zenoml.com) has 214 stars, 11 forks, and 45 open issues, last pushed Oct 5, 2023. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [zeno's repository](https://github.com/zeno-ml/zeno). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [zeno](/tools/zeno-ml-zeno.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | AI Data Management & Evaluation Platform | | Stars | 761 | 214 | | Forks | 71 | 11 | | Open issues | 21 | 45 | | Language | - | Svelte | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | Zeno combines Python API with an interactive UI for evaluating ML models across various tasks. | | 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) | [zeno](/tools/zeno-ml-zeno.md) | | --- | --- | --- | | Maintenance | Active (82%) | Archived (8%) | | Days since push | 26d | 1032d | | Archived on GitHub | No | Yes | | Open issues (now) | 21 | 45 | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/zeno-ml-zeno/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Decision facts: zeno - **Adopt for:** Zeno combines Python API with an interactive UI for evaluating ML models across various tasks. ## Choose when ### Choose awesome-evals if… - License: awesome-evals is Other, zeno is MIT. - 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 zeno if… - License: zeno is MIT, awesome-evals is Other. - Tags unique to zeno: ai, data-science, evaluation-framework, machine-learning. - You need to analyze model performance interactively via a user interface ## 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 zeno - Your project requires real-time monitoring capabilities not provided by Zeno's evaluation framework - If you are looking for a specialized tool tailored only to specific data types, like just images or text without the modular versatility of Zeno ## Common questions ### What is the difference between awesome-evals and zeno? awesome-evals: A curated library of resources for building and evaluating AI agents. zeno: AI Data Management & Evaluation Platform. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over zeno? Choose awesome-evals over zeno when License: awesome-evals is Other, zeno is MIT; 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 zeno over awesome-evals? Choose zeno over awesome-evals when License: zeno is MIT, awesome-evals is Other; Tags unique to zeno: ai, data-science, evaluation-framework, machine-learning; You need to analyze model performance interactively via a user interface. ### 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 zeno? Your project requires real-time monitoring capabilities not provided by Zeno's evaluation framework If you are looking for a specialized tool tailored only to specific data types, like just images or text without the modular versatility of Zeno ### Is awesome-evals or zeno more popular on GitHub? awesome-evals has more GitHub stars (761 vs 214). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and zeno open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, zeno: MIT). ### Where can I find alternatives to awesome-evals or zeno? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [zeno alternatives](/tools/zeno-ml-zeno/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [zeno markdown twin](/tools/zeno-ml-zeno/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-zeno-ml-zeno.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-evals or zeno? awesome-evals: Active. zeno: Archived. 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 zeno? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [zeno trust report](/tools/zeno-ml-zeno/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/_