--- title: "auto-sklearn vs archai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-sklearn-vs-microsoft-archai" tools: ["automl-auto-sklearn", "microsoft-archai"] --- # auto-sklearn vs archai *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 archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. [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. [archai](https://microsoft.github.io/archai) has 485 stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [auto-sklearn's repository](https://github.com/automl/auto-sklearn) and [archai's repository](https://github.com/microsoft/archai). | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Tagline | Automated Machine Learning with scikit-learn | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | | Stars | 8,127 | 485 | | Forks | 1,327 | 93 | | Open issues | 209 | 4 | | 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. | Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 35d | 252d | | Open issues (now) | 209 | 4 | | Full report | [trust report](/tools/automl-auto-sklearn/trust.md) | [trust report](/tools/microsoft-archai/trust.md) | ## Shared compatibility - **Python**: [auto-sklearn](/tools/automl-auto-sklearn.md) - Python runtime; [archai](/tools/microsoft-archai.md) - Python runtime ## 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: archai - **Adopt for:** Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. ## Choose when ### Choose auto-sklearn if… - License: auto-sklearn is BSD-3-Clause, archai is MIT. - Tags unique to auto-sklearn: bayesian-optimization, hyperparameter-search, hyperparameter-tuning, meta-learning. - 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 archai if… - License: archai is MIT, auto-sklearn is BSD-3-Clause. - Tags unique to archai: darts, deep-learning, model-compression, nas. - Need rapid iteration in NAS projects while ensuring reproducibility ## 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 archai - Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions - Development occurs outside Python 3.8+, limiting the application of Archai tools ## Common questions ### What is the difference between auto-sklearn and archai? auto-sklearn: Automated Machine Learning with scikit-learn. archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-sklearn over archai? Choose auto-sklearn over archai when License: auto-sklearn is BSD-3-Clause, archai is MIT; Tags unique to auto-sklearn: bayesian-optimization, hyperparameter-search, hyperparameter-tuning, meta-learning; 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 archai over auto-sklearn? Choose archai over auto-sklearn when License: archai is MIT, auto-sklearn is BSD-3-Clause; Tags unique to archai: darts, deep-learning, model-compression, nas; Need rapid iteration in NAS projects while ensuring reproducibility. ### 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 archai? Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools ### Is auto-sklearn or archai more popular on GitHub? auto-sklearn has more GitHub stars (8,127 vs 485). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-sklearn and archai open source? Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, archai: MIT). ### Where can I find alternatives to auto-sklearn or archai? GraphCanon lists graph-backed alternatives at [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) and [archai alternatives](/tools/microsoft-archai/alternatives) ([auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md), [archai markdown twin](/tools/microsoft-archai/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-microsoft-archai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-sklearn or archai? auto-sklearn: Steady. archai: 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 auto-sklearn and archai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-sklearn trust report](/tools/automl-auto-sklearn/trust); [archai trust report](/tools/microsoft-archai/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/_