Comparison
automl-gs vs awesome-AutoML
Verdict
Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · automl-gs alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | automl-gs | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (2477d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- automl-gs
- Automatically generate machine-learning models and code with input CSV and target field
- awesome-AutoML
- Curating AutoML research and resources
Stars
- automl-gs
- 1.9k
- awesome-AutoML
- 941
Forks
- automl-gs
- 181
- awesome-AutoML
- 156
Open issues
- automl-gs
- 28
- awesome-AutoML
- 1
Language
- automl-gs
- Python
- awesome-AutoML
- -
Adopt for
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- automl-gs
- -
- awesome-AutoML
- -
Runtime
- automl-gs
- -
- awesome-AutoML
- -
License
- automl-gs
- MIT
- awesome-AutoML
- GPL-3.0
Last pushed
- automl-gs
- Oct 22, 2019
- awesome-AutoML
- Mar 24, 2026
Categories
- automl-gs
- Data & Retrieval, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- automl-gs
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- automl-gs
- 2477d
- awesome-AutoML
- 133d
Open issues (now)
- automl-gs
- 28
- awesome-AutoML
- 1
OSV dependency advisories
- automl-gs
- Published findings
- awesome-AutoML
- No lockfile (source not queried)
Full report
- automl-gs
- Trust report
- awesome-AutoML
- Trust report
Choose automl-gs if…
- License: automl-gs is MIT, awesome-AutoML is GPL-3.0.
- Tags unique to automl-gs: keras, machine-learning, python, tensorflow.
- Also covers Data & Retrieval.
- Need to rapidly prototype models with limited ML expertise
When NOT to use automl-gs
- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, automl-gs is MIT.
- Tags unique to awesome-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 (minimaxir/automl-gs) · observed Aug 4, 2026
- GitHub forks (minimaxir/automl-gs) · observed Aug 4, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- 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: automl-gs 1.9k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between automl-gs and awesome-AutoML?
- automl-gs: Automatically generate machine-learning models and code with input CSV and target field. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose automl-gs over awesome-AutoML?
- Choose automl-gs over awesome-AutoML when License: automl-gs is MIT, awesome-AutoML is GPL-3.0; Tags unique to automl-gs: keras, machine-learning, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.
- When should I choose awesome-AutoML over automl-gs?
- Choose awesome-AutoML over automl-gs when License: awesome-AutoML is GPL-3.0, automl-gs is MIT; Tags unique to awesome-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 automl-gs?
- Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
- 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 automl-gs or awesome-AutoML more popular on GitHub?
- automl-gs has more GitHub stars (1,869 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are automl-gs and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (automl-gs: MIT, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to automl-gs or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at automl-gs alternatives and awesome-AutoML alternatives (automl-gs 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, automl-gs or awesome-AutoML?
- automl-gs: 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 automl-gs and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: automl-gs trust report; awesome-AutoML trust report.