--- title: "auto-sklearn vs automl-gs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-sklearn-vs-minimaxir-automl-gs" tools: ["automl-auto-sklearn", "minimaxir-automl-gs"] --- # auto-sklearn vs automl-gs *GraphCanon updated Aug 4, 2026* ## Verdict Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data. [auto-sklearn](https://automl.github.io/auto-sklearn) reports 8.1k GitHub stars, 1.3k forks, and 209 open issues, last pushed Jun 29, 2026. [automl-gs](https://github.com/minimaxir/automl-gs) has 1.9k stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. Figures are from public GitHub metadata via [auto-sklearn's repository](https://github.com/automl/auto-sklearn) and [automl-gs's repository](https://github.com/minimaxir/automl-gs). | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [automl-gs](/tools/minimaxir-automl-gs.md) | | --- | --- | --- | | Tagline | Automated Machine Learning with scikit-learn | Automatically generate machine-learning models and code with input CSV and target field | | Stars | 8,127 | 1,869 | | Forks | 1,327 | 181 | | Open issues | 209 | 28 | | Language | Python | Python | | Adopt for | auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | automl-gs: Python tool for automated machine-learning model creation from CSV data | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | MIT | | Categories | Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [automl-gs](/tools/minimaxir-automl-gs.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 35d | 2477d | | Open issues (now) | 209 | 28 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-auto-sklearn/trust.md) | [trust report](/tools/minimaxir-automl-gs/trust.md) | ## 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. ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Choose when ### Choose auto-sklearn if… - License: auto-sklearn is BSD-3-Clause, automl-gs is MIT. - Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search. - 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. ### Choose automl-gs if… - License: automl-gs is MIT, auto-sklearn is BSD-3-Clause. - 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 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. ## When NOT to use automl-gs - Complex feature engineering or non-standard data inputs required - Sensitive about licensing of the generated code ## Common questions ### What is the difference between auto-sklearn and automl-gs? auto-sklearn: Automated Machine Learning with scikit-learn. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-sklearn over automl-gs? Choose auto-sklearn over automl-gs when License: auto-sklearn is BSD-3-Clause, automl-gs is MIT; Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search; 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 choose automl-gs over auto-sklearn? Choose automl-gs over auto-sklearn when License: automl-gs is MIT, auto-sklearn is BSD-3-Clause; 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 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. ### When should I avoid automl-gs? Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code ### Is auto-sklearn or automl-gs more popular on GitHub? auto-sklearn has more GitHub stars (8,127 vs 1,869). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-sklearn and automl-gs open source? Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, automl-gs: MIT). ### Where can I find alternatives to auto-sklearn or automl-gs? GraphCanon lists graph-backed alternatives at [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) and [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) ([auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md), [automl-gs markdown twin](/tools/minimaxir-automl-gs/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/automl-auto-sklearn-vs-minimaxir-automl-gs.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-sklearn or automl-gs? auto-sklearn: Steady. automl-gs: Dormant. 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 auto-sklearn and automl-gs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-sklearn trust report](/tools/automl-auto-sklearn/trust); [automl-gs trust report](/tools/minimaxir-automl-gs/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-sklearn`](/api/graphcanon/graph?tool=automl-auto-sklearn) - 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/_