Comparison
auto-sklearn vs archai
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.
Markdown twin · auto-sklearn alternatives · archai alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | archai |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Slowing (252d 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 | 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
- auto-sklearn
- Automated Machine Learning with scikit-learn
- archai
- Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Stars
- auto-sklearn
- 8.1k
- archai
- 485
Forks
- auto-sklearn
- 1.3k
- archai
- 93
Open issues
- auto-sklearn
- 209
- archai
- 4
Language
- auto-sklearn
- Python
- archai
- Python
Adopt for
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
- archai
- 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
- auto-sklearn
- -
- archai
- -
Runtime
- auto-sklearn
- -
- archai
- -
License
- auto-sklearn
- BSD-3-Clause
- archai
- MIT
Last pushed
- auto-sklearn
- Jun 29, 2026
- archai
- Nov 24, 2025
Categories
- auto-sklearn
- Model Training
- archai
- Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- archai
- Slowing (36%)
Days since push
- auto-sklearn
- 35d
- archai
- 252d
Open issues (now)
- auto-sklearn
- 209
- archai
- 4
OSV dependency advisories
- auto-sklearn
- Published findings
- archai
- No lockfile (source not queried)
Full report
- auto-sklearn
- Trust report
- archai
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · archai: Python runtime
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (microsoft/archai) · observed Aug 4, 2026
- GitHub forks (microsoft/archai) · observed Aug 4, 2026
- Last push (microsoft/archai) · observed Nov 24, 2025
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · archai 485 (synced Aug 4, 2026).
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 and archai alternatives (auto-sklearn markdown twin, archai 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, 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; archai trust report.