Comparison
flower vs awesome-AutoML
Verdict
Pick flower if a customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · flower alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | flower | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (0d 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 lockfile (source not queried) 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
- flower
- A Friendly Federated AI Framework
- awesome-AutoML
- Curating AutoML research and resources
Stars
- flower
- 7.1k
- awesome-AutoML
- 941
Forks
- flower
- 1.2k
- awesome-AutoML
- 156
Open issues
- flower
- 368
- awesome-AutoML
- 1
Language
- flower
- Python
- awesome-AutoML
- -
Adopt for
- flower
- A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- flower
- -
- awesome-AutoML
- -
Runtime
- flower
- -
- awesome-AutoML
- -
License
- flower
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- flower
- Aug 4, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- flower
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- flower
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- flower
- 0d
- awesome-AutoML
- 133d
Open issues (now)
- flower
- 368
- awesome-AutoML
- 1
Owner type
- flower
- Organization
- awesome-AutoML
- User
Full report
- flower
- Trust report
- awesome-AutoML
- Trust report
Choose flower if…
- License: flower is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to flower: ai-frameworks, federated-learning, python, pytorch.
- When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning
When NOT to use flower
- Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support
- Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, flower is Apache-2.0.
- 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 (flwrlabs/flower) · observed Aug 4, 2026
- GitHub forks (flwrlabs/flower) · observed Aug 4, 2026
- Last push (flwrlabs/flower) · observed Aug 4, 2026
- License file (Apache-2.0) · 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: flower 7.1k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between flower and awesome-AutoML?
- flower: A Friendly Federated AI Framework. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose flower over awesome-AutoML?
- Choose flower over awesome-AutoML when License: flower is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to flower: ai-frameworks, federated-learning, python, pytorch; When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning.
- When should I choose awesome-AutoML over flower?
- Choose awesome-AutoML over flower when License: awesome-AutoML is GPL-3.0, flower is Apache-2.0; 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 flower?
- Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer
- 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 flower or awesome-AutoML more popular on GitHub?
- flower has more GitHub stars (7,067 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are flower and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (flower: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to flower or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at flower alternatives and awesome-AutoML alternatives (flower 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, flower or awesome-AutoML?
- flower: Very active. 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 flower and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: flower trust report; awesome-AutoML trust report.