Comparison
auto-sklearn vs autoai
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 autoai if python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
Markdown twin · auto-sklearn alternatives · autoai alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | autoai |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Dormant (496d 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 | 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
- auto-sklearn
- Automated Machine Learning with scikit-learn
- autoai
- Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation
Stars
- auto-sklearn
- 8.1k
- autoai
- 186
Forks
- auto-sklearn
- 1.3k
- autoai
- 46
Open issues
- auto-sklearn
- 209
- autoai
- 9
Language
- auto-sklearn
- Python
- autoai
- 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.
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
Persona
- auto-sklearn
- -
- autoai
- -
Runtime
- auto-sklearn
- -
- autoai
- -
License
- auto-sklearn
- BSD-3-Clause
- autoai
- Apache-2.0
Last pushed
- auto-sklearn
- Jun 29, 2026
- autoai
- Mar 25, 2025
Categories
- auto-sklearn
- Model Training
- autoai
- Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- autoai
- Dormant (18%)
Days since push
- auto-sklearn
- 35d
- autoai
- 496d
Open issues (now)
- auto-sklearn
- 209
- autoai
- 9
Full report
- auto-sklearn
- Trust report
- autoai
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · autoai: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, autoai is Apache-2.0.
- 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 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 autoai if…
- License: autoai is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Tags unique to autoai: ai, autoai, codegen, deep-learning.
- Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
When NOT to use autoai
- Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing.
- Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format.
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 (blobcity/autoai) · observed Aug 4, 2026
- GitHub forks (blobcity/autoai) · observed Aug 4, 2026
- Last push (blobcity/autoai) · observed Mar 25, 2025
- 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 on cards: auto-sklearn 8.1k · autoai 186 (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and autoai?
- auto-sklearn: Automated Machine Learning with scikit-learn. autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over autoai?
- Choose auto-sklearn over autoai when License: auto-sklearn is BSD-3-Clause, autoai is Apache-2.0; 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 autoai over auto-sklearn?
- Choose autoai over auto-sklearn when License: autoai is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to autoai: ai, autoai, codegen, deep-learning; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
- 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 autoai?
- Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format.
- Is auto-sklearn or autoai more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 186). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and autoai open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, autoai: Apache-2.0).
- Where can I find alternatives to auto-sklearn or autoai?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and autoai alternatives (auto-sklearn markdown twin, autoai 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 autoai?
- auto-sklearn: Steady. autoai: 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 autoai?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; autoai trust report.