Comparison
dragonfly vs awesome-AutoML
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.
Markdown twin · dragonfly alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | dragonfly | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (1141d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- dragonfly
- An open source Python library for scalable Bayesian optimisation.
- awesome-AutoML
- Curating AutoML research and resources
Stars
- dragonfly
- 894
- awesome-AutoML
- 941
Forks
- dragonfly
- 238
- awesome-AutoML
- 156
Open issues
- dragonfly
- 43
- awesome-AutoML
- 1
Language
- dragonfly
- Python
- awesome-AutoML
- -
Adopt for
- dragonfly
- Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- dragonfly
- -
- awesome-AutoML
- -
Runtime
- dragonfly
- -
- awesome-AutoML
- -
License
- dragonfly
- MIT
- awesome-AutoML
- GPL-3.0
Last pushed
- dragonfly
- Jun 19, 2023
- awesome-AutoML
- Mar 24, 2026
Categories
- dragonfly
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- dragonfly
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- dragonfly
- 1141d
- awesome-AutoML
- 133d
Open issues (now)
- dragonfly
- 43
- awesome-AutoML
- 1
Owner type
- dragonfly
- Organization
- awesome-AutoML
- User
OSV dependency advisories
- dragonfly
- No published findings from this source as of 2026-07-11
- awesome-AutoML
- No lockfile (source not queried)
Full report
- dragonfly
- Trust report
- awesome-AutoML
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dragonfly/dragonfly) · observed Aug 4, 2026
- GitHub forks (dragonfly/dragonfly) · observed Aug 4, 2026
- Last push (dragonfly/dragonfly) · observed Jun 19, 2023
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: dragonfly 894 · awesome-AutoML 941 (synced Aug 4, 2026).
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
pippackage 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 and awesome-AutoML alternatives (dragonfly markdown twin, awesome-AutoML markdown twin), 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 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; awesome-AutoML trust report.