--- title: "code-eval vs awesome-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-benchflow-ai-awesome-evals" tools: ["abacaj-code-eval", "benchflow-ai-awesome-evals"] --- # code-eval vs awesome-evals *GraphCanon updated Oct 6, 2026* ## Verdict Pick code-eval if code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability; pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance. [code-eval](https://github.com/abacaj/code-eval) reports 432 GitHub stars, 36 forks, and 5 open issues, last pushed Sep 12, 2023. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) has 901 stars, 104 forks, and 34 open issues, last pushed Sep 15, 2026. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals). | | [code-eval](/tools/abacaj-code-eval.md) | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | | --- | --- | --- | | Tagline | Run evaluation on LLMs using human-eval benchmark. | A curated library of resources for building and evaluating AI agents | | Stars | 432 | 901 | | Forks | 36 | 104 | | Open issues | 5 | 34 | | Language | Python | - | | Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [code-eval](/tools/abacaj-code-eval.md) | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1120d | 4d | | Open issues (now) | 5 | 34 | | Stars delta | +1 (30d) | +140 (30d) | | Open issues delta | 0 (30d) | +13 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | ## Decision facts: code-eval - **Adopt for:** code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Choose when ### Choose code-eval if… - License: code-eval is MIT, awesome-evals is Other. - Tags unique to code-eval: humaneval, wizardcoder. - When you need clear comparisons of pass rates for different LLMs using standardized tests ### Choose awesome-evals if… - License: awesome-evals is Other, code-eval 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 NOT to use code-eval - If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences - For real-time or dynamic evaluations as this repo offers pre-computed static results only ## 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 ## Common questions ### What is the difference between code-eval and awesome-evals? code-eval: Run evaluation on LLMs using human-eval benchmark.. awesome-evals: A curated library of resources for building and evaluating AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose code-eval over awesome-evals? Choose code-eval over awesome-evals when License: code-eval is MIT, awesome-evals is Other; Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests. ### When should I choose awesome-evals over code-eval? Choose awesome-evals over code-eval when License: awesome-evals is Other, code-eval 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 avoid code-eval? If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences For real-time or dynamic evaluations as this repo offers pre-computed static results only ### 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 ### Is code-eval or awesome-evals more popular on GitHub? awesome-evals has more GitHub stars (901 vs 432). Stars measure visibility, not whether either tool fits your constraints. ### Are code-eval and awesome-evals open source? Yes - both are open-source projects on GitHub (code-eval: MIT, awesome-evals: Other). ### Where can I find alternatives to code-eval or awesome-evals? GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/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/abacaj-code-eval-vs-benchflow-ai-awesome-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, code-eval or awesome-evals? code-eval: Dormant. awesome-evals: 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 code-eval and awesome-evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=abacaj-code-eval`](/api/graphcanon/graph?tool=abacaj-code-eval) - 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/_