--- title: "autogluon vs auto-sklearn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autogluon-autogluon-vs-automl-auto-sklearn" tools: ["autogluon-autogluon", "automl-auto-sklearn"] --- # autogluon vs auto-sklearn *GraphCanon updated Aug 4, 2026* ## 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. [autogluon](https://auto.gluon.ai/) reports 11k GitHub stars, 1.2k forks, and 388 open issues, last pushed Aug 3, 2026. [auto-sklearn](https://automl.github.io/auto-sklearn) has 8.1k stars, 1.3k forks, and 209 open issues, last pushed Jun 29, 2026. Figures are from public GitHub metadata via [autogluon's repository](https://github.com/autogluon/autogluon) and [auto-sklearn's repository](https://github.com/automl/auto-sklearn). | | [autogluon](/tools/autogluon-autogluon.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Tagline | Fast and Accurate ML in 3 Lines of Code | Automated Machine Learning with scikit-learn | | Stars | 10,576 | 8,127 | | Forks | 1,171 | 1,327 | | Open issues | 388 | 209 | | Language | Python | Python | | Adopt for | 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 is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. | BSD-3-Clause | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autogluon](/tools/autogluon-autogluon.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 35d | | Open issues (now) | 388 | 209 | | Full report | [trust report](/tools/autogluon-autogluon/trust.md) | [trust report](/tools/automl-auto-sklearn/trust.md) | ## Shared compatibility - **Python**: [autogluon](/tools/autogluon-autogluon.md) - Python runtime; [auto-sklearn](/tools/automl-auto-sklearn.md) - Python runtime ## Decision facts: autogluon - **Adopt for:** 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. - **License detail:** Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. ## Decision facts: auto-sklearn - **Adopt for:** auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/autogluon-autogluon/alternatives) and [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) ([autogluon markdown twin](/tools/autogluon-autogluon/alternatives.md), [auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md)), 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](/compare/autogluon-autogluon-vs-automl-auto-sklearn.md) 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](/tools/autogluon-autogluon/trust); [auto-sklearn trust report](/tools/automl-auto-sklearn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autogluon-autogluon`](/api/graphcanon/graph?tool=autogluon-autogluon) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_