--- title: "dragonfly vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dragonfly-dragonfly-vs-keras-team-autokeras" tools: ["dragonfly-dragonfly", "keras-team-autokeras"] --- # dragonfly vs autokeras *GraphCanon updated Aug 4, 2026* ## Verdict Pick dragonfly if dragonfly is an open-source Python library that specializes in scalable Bayesian optimization; 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+. [dragonfly](https://github.com/dragonfly/dragonfly) reports 894 GitHub stars, 238 forks, and 43 open issues, last pushed Jun 19, 2023. [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 [dragonfly's repository](https://github.com/dragonfly/dragonfly) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [dragonfly](/tools/dragonfly-dragonfly.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | An open source Python library for scalable Bayesian optimisation. | AutoML library for deep learning | | Stars | 894 | 9,328 | | Forks | 238 | 1,393 | | Open issues | 43 | 161 | | Language | Python | Python | | Adopt for | Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization | 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 | MIT | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dragonfly](/tools/dragonfly-dragonfly.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1141d | 251d | | Open issues (now) | 43 | 161 | | Full report | [trust report](/tools/dragonfly-dragonfly/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) | ## Shared compatibility - **Python**: [dragonfly](/tools/dragonfly-dragonfly.md) - Python runtime; [autokeras](/tools/keras-team-autokeras.md) - Python runtime ## Decision facts: dragonfly - **Pricing:** freemium - Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works. - **Requirements:** Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager. - **Adopt for:** Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization ## 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 dragonfly if… - License: dragonfly is MIT, autokeras is Apache-2.0. - Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.. - Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.. - Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation. - When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### Choose autokeras if… - License: autokeras is Apache-2.0, dragonfly is MIT. - Tags unique to autokeras: autodl, automl, deep-learning, keras. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ## When NOT to use dragonfly - If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. - In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ## 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 dragonfly and autokeras? dragonfly: An open source Python library for scalable Bayesian optimisation.. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose dragonfly over autokeras? Choose dragonfly over autokeras when License: dragonfly is MIT, autokeras is Apache-2.0; Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.; Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.; Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation; When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### When should I choose autokeras over dragonfly? Choose autokeras over dragonfly when License: autokeras is Apache-2.0, dragonfly is MIT; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### When should I avoid dragonfly? If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ### 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 dragonfly or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 894). Stars measure visibility, not whether either tool fits your constraints. ### Are dragonfly and autokeras open source? Yes - both are open-source projects on GitHub (dragonfly: MIT, autokeras: Apache-2.0). ### Where can I find alternatives to dragonfly or autokeras? GraphCanon lists graph-backed alternatives at [dragonfly alternatives](/tools/dragonfly-dragonfly/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([dragonfly markdown twin](/tools/dragonfly-dragonfly/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/dragonfly-dragonfly-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, dragonfly or autokeras? dragonfly: Dormant. 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 dragonfly and autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dragonfly trust report](/tools/dragonfly-dragonfly/trust); [autokeras trust report](/tools/keras-team-autokeras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dragonfly-dragonfly`](/api/graphcanon/graph?tool=dragonfly-dragonfly) - 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/_