--- title: "dragonfly vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dragonfly-dragonfly-vs-windmaple-awesome-automl" tools: ["dragonfly-dragonfly", "windmaple-awesome-automl"] --- # dragonfly vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick dragonfly if dragonfly is an open-source Python library that specializes in scalable Bayesian optimization; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [dragonfly](https://github.com/dragonfly/dragonfly) reports 894 GitHub stars, 238 forks, and 43 open issues, last pushed Jun 19, 2023. [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 [dragonfly's repository](https://github.com/dragonfly/dragonfly) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [dragonfly](/tools/dragonfly-dragonfly.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | An open source Python library for scalable Bayesian optimisation. | Curating AutoML research and resources | | Stars | 894 | 941 | | Forks | 238 | 156 | | Open issues | 43 | 1 | | Language | Python | - | | Adopt for | Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dragonfly](/tools/dragonfly-dragonfly.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1141d | 133d | | Open issues (now) | 43 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/dragonfly-dragonfly/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: dragonfly - **Pricing:** freemium - Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works. - **Requirements:** Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager. - **Adopt for:** Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose dragonfly if… - License: dragonfly is MIT, awesome-AutoML is GPL-3.0. - Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.. - Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.. - Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation. - When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, dragonfly is MIT. - 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 dragonfly - If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. - In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ## 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 dragonfly and awesome-AutoML? dragonfly: An open source Python library for scalable Bayesian optimisation.. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose dragonfly over awesome-AutoML? Choose dragonfly over awesome-AutoML when License: dragonfly is MIT, awesome-AutoML is GPL-3.0; Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.; Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.; Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation; When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### When should I choose awesome-AutoML over dragonfly? Choose awesome-AutoML over dragonfly when License: awesome-AutoML is GPL-3.0, dragonfly is MIT; 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 dragonfly? If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ### 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 dragonfly or awesome-AutoML more popular on GitHub? awesome-AutoML has more GitHub stars (941 vs 894). Stars measure visibility, not whether either tool fits your constraints. ### Are dragonfly and awesome-AutoML open source? Yes - both are open-source projects on GitHub (dragonfly: MIT, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to dragonfly or awesome-AutoML? GraphCanon lists graph-backed alternatives at [dragonfly alternatives](/tools/dragonfly-dragonfly/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([dragonfly markdown twin](/tools/dragonfly-dragonfly/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/dragonfly-dragonfly-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, dragonfly or awesome-AutoML? dragonfly: 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 dragonfly and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dragonfly trust report](/tools/dragonfly-dragonfly/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dragonfly-dragonfly`](/api/graphcanon/graph?tool=dragonfly-dragonfly) - 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/_