--- title: "Spearmint vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hips-spearmint-vs-windmaple-awesome-automl" tools: ["hips-spearmint", "windmaple-awesome-automl"] --- # Spearmint vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick Spearmint if a specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [Spearmint](https://github.com/HIPS/Spearmint) reports 1.6k GitHub stars, 327 forks, and 77 open issues, last pushed Dec 27, 2019. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [Spearmint's repository](https://github.com/HIPS/Spearmint) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [Spearmint](/tools/hips-spearmint.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Bayesian optimization codebase | Curating AutoML research and resources | | Stars | 1,573 | 941 | | Forks | 327 | 156 | | Open issues | 77 | 1 | | Language | Python | - | | Adopt for | A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Other | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Spearmint](/tools/hips-spearmint.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2411d | 133d | | Open issues (now) | 77 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/hips-spearmint/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: Spearmint - **Adopt for:** A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose Spearmint if… - License: Spearmint is Other, awesome-AutoML is GPL-3.0. - Tags unique to Spearmint: automated-experimentation, bayesian-optimization, hyperparameter-tuning. - - When you require automated experimentation with parameters that can be iteratively adjusted ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, Spearmint is Other. - Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## When NOT to use Spearmint - - If your project requires a permissive license as Spearmint operates under an Academic and Non-Commercial Research Use License - - If you need real-time or continuous parameter tuning outside of batch experimentation contexts as Spearmint is suited for controlled experiment setups ## When NOT to use awesome-AutoML - If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. - When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ## Common questions ### What is the difference between Spearmint and awesome-AutoML? Spearmint: Bayesian optimization codebase. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose Spearmint over awesome-AutoML? Choose Spearmint over awesome-AutoML when License: Spearmint is Other, awesome-AutoML is GPL-3.0; Tags unique to Spearmint: automated-experimentation, bayesian-optimization, hyperparameter-tuning; - When you require automated experimentation with parameters that can be iteratively adjusted. ### When should I choose awesome-AutoML over Spearmint? Choose awesome-AutoML over Spearmint when License: awesome-AutoML is GPL-3.0, Spearmint is Other; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### When should I avoid Spearmint? - If your project requires a permissive license as Spearmint operates under an Academic and Non-Commercial Research Use License - If you need real-time or continuous parameter tuning outside of batch experimentation contexts as Spearmint is suited for controlled experiment setups ### When should I avoid awesome-AutoML? If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ### Is Spearmint or awesome-AutoML more popular on GitHub? Spearmint has more GitHub stars (1,573 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are Spearmint and awesome-AutoML open source? Yes - both are open-source projects on GitHub (Spearmint: Other, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to Spearmint or awesome-AutoML? GraphCanon lists graph-backed alternatives at [Spearmint alternatives](/tools/hips-spearmint/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([Spearmint markdown twin](/tools/hips-spearmint/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/hips-spearmint-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Spearmint or awesome-AutoML? Spearmint: Dormant. awesome-AutoML: 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 Spearmint and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Spearmint trust report](/tools/hips-spearmint/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hips-spearmint`](/api/graphcanon/graph?tool=hips-spearmint) - 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/_