--- title: "autoai vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blobcity-autoai-vs-windmaple-awesome-automl" tools: ["blobcity-autoai", "windmaple-awesome-automl"] --- # autoai vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## 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. [autoai](https://github.com/blobcity/autoai) reports 186 GitHub stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [autoai's repository](https://github.com/blobcity/autoai) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [autoai](/tools/blobcity-autoai.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | Curating AutoML research and resources | | Stars | 186 | 941 | | Forks | 46 | 156 | | Open issues | 9 | 1 | | Language | Python | - | | Adopt for | Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoai](/tools/blobcity-autoai.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 496d | 133d | | Open issues (now) | 9 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/blobcity-autoai/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: autoai - **Adopt for:** Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### 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. ### 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 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 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. ## 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 (941 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](/tools/blobcity-autoai/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([autoai markdown twin](/tools/blobcity-autoai/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/blobcity-autoai-vs-windmaple-awesome-automl.md) 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](/tools/blobcity-autoai/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=blobcity-autoai`](/api/graphcanon/graph?tool=blobcity-autoai) - 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/_