--- title: "featuretools vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-featuretools-vs-keras-team-autokeras" tools: ["alteryx-featuretools", "keras-team-autokeras"] --- # featuretools vs autokeras *GraphCanon updated Aug 4, 2026* ## Verdict Pick featuretools if a Python library dedicated to automating feature engineering processes designed to craft features from complex datasets that are interpretable and potentially boost the accuracy of machine learning models; 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+. [featuretools](https://www.featuretools.com) reports 7.7k GitHub stars, 915 forks, and 168 open issues, last pushed Jul 27, 2026. [autokeras](http://autokeras.com/) has 9.3k stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. Figures are from public GitHub metadata via [featuretools's repository](https://github.com/alteryx/featuretools) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [featuretools](/tools/alteryx-featuretools.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | An open source python library for automated feature engineering | AutoML library for deep learning | | Stars | 7,665 | 9,328 | | Forks | 915 | 1,393 | | Open issues | 168 | 161 | | Language | Python | Python | | Adopt for | A Python library dedicated to automating feature engineering processes designed to craft features from complex datasets that are interpretable and potentially boost the accuracy of machine learning models. | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [featuretools](/tools/alteryx-featuretools.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 251d | | Open issues (now) | 168 | 161 | | Full report | [trust report](/tools/alteryx-featuretools/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) | ## Shared compatibility - **Python**: [featuretools](/tools/alteryx-featuretools.md) - Python runtime; [autokeras](/tools/keras-team-autokeras.md) - Python runtime ## Decision facts: featuretools - **Adopt for:** A Python library dedicated to automating feature engineering processes designed to craft features from complex datasets that are interpretable and potentially boost the accuracy of machine learning models. ## 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+. ## Choose when ### Choose featuretools if… - License: featuretools is BSD-3-Clause, autokeras is Apache-2.0. - Tags unique to featuretools: automated-feature-engineering, feature-engineering. - When the goal is to create high-quality features in a semi-automated manner using prior knowledge of relationships within data, enhancing interpretability of machine learning models ### Choose autokeras if… - License: autokeras is Apache-2.0, featuretools is BSD-3-Clause. - 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 NOT to use featuretools - If a project requires extremely lightweight solutions that avoid dependencies and overheads associated with complex library packages - In settings where the underlying data lacks clear relational structure, as Featuretools excels when data relationships are well-defined and can be exploited ## 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. ## Common questions ### What is the difference between featuretools and autokeras? featuretools: An open source python library for automated feature engineering. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose featuretools over autokeras? Choose featuretools over autokeras when License: featuretools is BSD-3-Clause, autokeras is Apache-2.0; Tags unique to featuretools: automated-feature-engineering, feature-engineering; When the goal is to create high-quality features in a semi-automated manner using prior knowledge of relationships within data, enhancing interpretability of machine learning models. ### When should I choose autokeras over featuretools? Choose autokeras over featuretools when License: autokeras is Apache-2.0, featuretools is BSD-3-Clause; 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 avoid featuretools? If a project requires extremely lightweight solutions that avoid dependencies and overheads associated with complex library packages In settings where the underlying data lacks clear relational structure, as Featuretools excels when data relationships are well-defined and can be exploited ### 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. ### Is featuretools or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 7,665). Stars measure visibility, not whether either tool fits your constraints. ### Are featuretools and autokeras open source? Yes - both are open-source projects on GitHub (featuretools: BSD-3-Clause, autokeras: Apache-2.0). ### Where can I find alternatives to featuretools or autokeras? GraphCanon lists graph-backed alternatives at [featuretools alternatives](/tools/alteryx-featuretools/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([featuretools markdown twin](/tools/alteryx-featuretools/alternatives.md), [autokeras markdown twin](/tools/keras-team-autokeras/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/alteryx-featuretools-vs-keras-team-autokeras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, featuretools or autokeras? featuretools: Very active. autokeras: 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 featuretools and autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [featuretools trust report](/tools/alteryx-featuretools/trust); [autokeras trust report](/tools/keras-team-autokeras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-featuretools`](/api/graphcanon/graph?tool=alteryx-featuretools) - 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/_