Comparison
autokeras vs awesome-AutoML
Verdict
Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · autokeras alternatives · awesome-AutoML alternatives
GraphCanon updated Oct 4, 2026
12views this month
vs
Trust & integrity
| Signal | autokeras | awesome-AutoML |
|---|---|---|
| Maintenance | Slowing (309d since push) As of Sep 30, 2026 · github_public_v1 | Slowing (194d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- autokeras
- AutoML library for deep learning
- awesome-AutoML
- Curating AutoML research and resources
Stars
- autokeras
- 9.3k
- awesome-AutoML
- 944
Forks
- autokeras
- 1.4k
- awesome-AutoML
- 157
Open issues
- autokeras
- 161
- awesome-AutoML
- 1
Language
- autokeras
- Python
- awesome-AutoML
- -
Adopt for
- autokeras
- AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- autokeras
- -
- awesome-AutoML
- -
Runtime
- autokeras
- -
- awesome-AutoML
- -
License
- autokeras
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- autokeras
- Nov 25, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- autokeras
- Developer Tools, Model Training
- awesome-AutoML
- Model Training
Trust and health
Days since push
- autokeras
- 309d
- awesome-AutoML
- 194d
Open issues (now)
- autokeras
- 161
- awesome-AutoML
- 1
Stars delta
- autokeras
- +7 (30d)
- awesome-AutoML
- +3 (30d)
Owner type
- autokeras
- Organization
- awesome-AutoML
- User
Full report
- autokeras
- Trust report
- awesome-AutoML
- Trust report
Choose autokeras if…
- License: autokeras is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to autokeras: autodl, deep-learning, keras, machine-learning.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
When NOT to use autokeras
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, autokeras is Apache-2.0.
- Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning.
- 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 (keras-team/autokeras) · observed Sep 30, 2026
- GitHub forks (keras-team/autokeras) · observed Sep 30, 2026
- Last push (keras-team/autokeras) · observed Nov 25, 2025
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Oct 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Oct 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autokeras 9.3k · awesome-AutoML 944 (synced Sep 30, 2026).
Common questions
- What is the difference between autokeras and awesome-AutoML?
- autokeras: AutoML library for deep learning. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose autokeras over awesome-AutoML?
- Choose autokeras over awesome-AutoML when License: autokeras is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to autokeras: autodl, deep-learning, keras, machine-learning; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- When should I choose awesome-AutoML over autokeras?
- Choose awesome-AutoML over autokeras when License: awesome-AutoML is GPL-3.0, autokeras is Apache-2.0; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid autokeras?
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
- 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 autokeras or awesome-AutoML more popular on GitHub?
- autokeras has more GitHub stars (9,335 vs 944). Stars measure visibility, not whether either tool fits your constraints.
- Are autokeras and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to autokeras or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at autokeras alternatives and awesome-AutoML alternatives (autokeras 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, autokeras or awesome-AutoML?
- autokeras: Slowing. 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 autokeras and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autokeras trust report; awesome-AutoML trust report.