Comparison
autoai vs awesome-AutoML
Verdict
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; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · autoai alternatives · awesome-AutoML alternatives
GraphCanon updated Oct 4, 2026
15views this month
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Trust & integrity
| Signal | autoai | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (554d since push) As of Sep 30, 2026 · github_public_v1 | Slowing (194d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- autoai
- Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation
- awesome-AutoML
- Curating AutoML research and resources
Stars
- autoai
- 186
- awesome-AutoML
- 944
Forks
- autoai
- 46
- awesome-AutoML
- 157
Open issues
- autoai
- 9
- awesome-AutoML
- 1
Language
- autoai
- Python
- awesome-AutoML
- -
Adopt for
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- autoai
- -
- awesome-AutoML
- -
Runtime
- autoai
- -
- awesome-AutoML
- -
License
- autoai
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- autoai
- Mar 25, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- autoai
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- autoai
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- autoai
- 554d
- awesome-AutoML
- 194d
Open issues (now)
- autoai
- 9
- awesome-AutoML
- 1
Stars delta
- autoai
- 0 (30d)
- awesome-AutoML
- +3 (30d)
Owner type
- autoai
- Organization
- awesome-AutoML
- User
OSV dependency advisories
- autoai
- Published findings
- awesome-AutoML
- No lockfile (source not queried)
Full report
- autoai
- Trust report
- awesome-AutoML
- Trust report
Choose autoai if…
- License: autoai is Apache-2.0, awesome-AutoML is GPL-3.0.
- 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.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, autoai is Apache-2.0.
- Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (blobcity/autoai) · observed Sep 30, 2026
- GitHub forks (blobcity/autoai) · observed Sep 30, 2026
- Last push (blobcity/autoai) · observed Mar 25, 2025
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Oct 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Oct 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autoai 186 · awesome-AutoML 944 (synced Sep 30, 2026).
Common questions
- What is the difference between autoai and awesome-AutoML?
- autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose autoai over awesome-AutoML?
- Choose autoai over awesome-AutoML when License: autoai is Apache-2.0, awesome-AutoML is GPL-3.0; 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 choose awesome-AutoML over autoai?
- Choose awesome-AutoML over autoai when License: awesome-AutoML is GPL-3.0, autoai is Apache-2.0; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- 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.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is autoai or awesome-AutoML more popular on GitHub?
- awesome-AutoML has more GitHub stars (944 vs 186). Stars measure visibility, not whether either tool fits your constraints.
- Are autoai and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (autoai: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to autoai or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at autoai alternatives and awesome-AutoML alternatives (autoai markdown twin, awesome-AutoML 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, autoai or awesome-AutoML?
- autoai: Dormant. awesome-AutoML: 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 autoai and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autoai trust report; awesome-AutoML trust report.