Comparison
autogluon vs auto-sklearn
Verdict
Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
Markdown twin · autogluon alternatives · auto-sklearn alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | autogluon | auto-sklearn |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Steady (35d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- autogluon
- Fast and Accurate ML in 3 Lines of Code
- auto-sklearn
- Automated Machine Learning with scikit-learn
Stars
- autogluon
- 11k
- auto-sklearn
- 8.1k
Forks
- autogluon
- 1.2k
- auto-sklearn
- 1.3k
Open issues
- autogluon
- 388
- auto-sklearn
- 209
Language
- autogluon
- Python
- auto-sklearn
- Python
Adopt for
- autogluon
- AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP.
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
Persona
- autogluon
- -
- auto-sklearn
- -
Runtime
- autogluon
- -
- auto-sklearn
- -
License
- autogluon
- Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors.
- auto-sklearn
- BSD-3-Clause
Last pushed
- autogluon
- Aug 3, 2026
- auto-sklearn
- Jun 29, 2026
Categories
- autogluon
- Developer Tools, Model Training
- auto-sklearn
- Model Training
Trust and health
Maintenance
- autogluon
- Very active (96%)
- auto-sklearn
- Steady (60%)
Days since push
- autogluon
- 0d
- auto-sklearn
- 35d
Open issues (now)
- autogluon
- 388
- auto-sklearn
- 209
OSV dependency advisories
- autogluon
- No lockfile (source not queried)
- auto-sklearn
- Published findings
Full report
- autogluon
- Trust report
- auto-sklearn
- Trust report
Shared compatibility
- Python · autogluon: Python runtime · auto-sklearn: Python runtime
Choose autogluon if…
- License: autogluon is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Tags unique to autogluon: computer-vision, data-science, deep-learning, ensemble-learning.
- Also covers Developer Tools.
- When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
When NOT to use autogluon
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation.
- For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, autogluon is Apache-2.0.
- Tags unique to auto-sklearn: bayesian-optimization, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning.
- auto-sklearn ships Docker support for self-hosted deployment.
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
When NOT to use auto-sklearn
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers.
- In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (autogluon/autogluon) · observed Aug 4, 2026
- GitHub forks (autogluon/autogluon) · observed Aug 4, 2026
- Last push (autogluon/autogluon) · observed Aug 3, 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 (automl/auto-sklearn) · observed Aug 4, 2026
- GitHub forks (automl/auto-sklearn) · observed Aug 4, 2026
- Last push (automl/auto-sklearn) · observed Jun 29, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autogluon 11k · auto-sklearn 8.1k (synced Aug 4, 2026).
Common questions
- What is the difference between autogluon and auto-sklearn?
- autogluon: Fast and Accurate ML in 3 Lines of Code. auto-sklearn: Automated Machine Learning with scikit-learn. See the comparison table for live GitHub stats and shared categories.
- When should I choose autogluon over auto-sklearn?
- Choose autogluon over auto-sklearn when License: autogluon is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to autogluon: computer-vision, data-science, deep-learning, ensemble-learning; Also covers Developer Tools; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
- When should I choose auto-sklearn over autogluon?
- Choose auto-sklearn over autogluon when License: auto-sklearn is BSD-3-Clause, autogluon is Apache-2.0; Tags unique to auto-sklearn: bayesian-optimization, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning; auto-sklearn ships Docker support for self-hosted deployment; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- When should I avoid autogluon?
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
- When should I avoid auto-sklearn?
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
- Is autogluon or auto-sklearn more popular on GitHub?
- autogluon has more GitHub stars (10,576 vs 8,127). Stars measure visibility, not whether either tool fits your constraints.
- Are autogluon and auto-sklearn open source?
- Yes - both are open-source projects on GitHub (autogluon: Apache-2.0, auto-sklearn: BSD-3-Clause).
- Where can I find alternatives to autogluon or auto-sklearn?
- GraphCanon lists graph-backed alternatives at autogluon alternatives and auto-sklearn alternatives (autogluon markdown twin, auto-sklearn 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, autogluon or auto-sklearn?
- autogluon: Very active. auto-sklearn: Steady. 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 autogluon and auto-sklearn?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogluon trust report; auto-sklearn trust report.