--- title: "autokeras vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/keras-team-autokeras-vs-windmaple-awesome-automl" tools: ["keras-team-autokeras", "windmaple-awesome-automl"] --- # autokeras vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [autokeras](http://autokeras.com/) reports 9.3k GitHub stars, 1.4k forks, and 161 open issues, last pushed Nov 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 [autokeras's repository](https://github.com/keras-team/autokeras) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [autokeras](/tools/keras-team-autokeras.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | AutoML library for deep learning | Curating AutoML research and resources | | Stars | 9,328 | 941 | | Forks | 1,393 | 156 | | Open issues | 161 | 1 | | Language | Python | - | | Adopt for | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autokeras](/tools/keras-team-autokeras.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Days since push | 251d | 133d | | Open issues (now) | 161 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/keras-team-autokeras/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose autokeras if… - License: autokeras is Apache-2.0, awesome-AutoML is GPL-3.0. - Tags unique to autokeras: autodl, deep-learning, keras, machine-learning. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, autokeras is Apache-2.0. - Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## When NOT to use autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## 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 autokeras and awesome-AutoML? autokeras: AutoML library for deep learning. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose autokeras over awesome-AutoML? Choose autokeras over awesome-AutoML when License: autokeras is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to autokeras: autodl, deep-learning, keras, machine-learning; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### When should I choose awesome-AutoML over autokeras? Choose awesome-AutoML over autokeras when License: awesome-AutoML is GPL-3.0, autokeras is Apache-2.0; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### When should I avoid autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### 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 autokeras or awesome-AutoML more popular on GitHub? autokeras has more GitHub stars (9,328 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are autokeras and awesome-AutoML open source? Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to autokeras or awesome-AutoML? GraphCanon lists graph-backed alternatives at [autokeras alternatives](/tools/keras-team-autokeras/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([autokeras markdown twin](/tools/keras-team-autokeras/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/keras-team-autokeras-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, autokeras or awesome-AutoML? autokeras: Slowing. 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 autokeras and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autokeras trust report](/tools/keras-team-autokeras/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=keras-team-autokeras`](/api/graphcanon/graph?tool=keras-team-autokeras) - 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/_