--- title: "flower vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/flwrlabs-flower-vs-windmaple-awesome-automl" tools: ["flwrlabs-flower", "windmaple-awesome-automl"] --- # flower vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick flower if a customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [flower](https://flower.ai) reports 7.1k GitHub stars, 1.2k forks, and 368 open issues, last pushed Aug 4, 2026. [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 [flower's repository](https://github.com/flwrlabs/flower) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [flower](/tools/flwrlabs-flower.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | A Friendly Federated AI Framework | Curating AutoML research and resources | | Stars | 7,067 | 941 | | Forks | 1,214 | 156 | | Open issues | 368 | 1 | | Language | Python | - | | Adopt for | A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python. | 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._ | | [flower](/tools/flwrlabs-flower.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 133d | | Open issues (now) | 368 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/flwrlabs-flower/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: flower - **Adopt for:** A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose flower if… - License: flower is Apache-2.0, awesome-AutoML is GPL-3.0. - Tags unique to flower: ai-frameworks, federated-learning, python, pytorch. - When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, flower is Apache-2.0. - Tags unique to awesome-AutoML: 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 flower - Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support - Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer ## 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 flower and awesome-AutoML? flower: A Friendly Federated AI Framework. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose flower over awesome-AutoML? Choose flower over awesome-AutoML when License: flower is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to flower: ai-frameworks, federated-learning, python, pytorch; When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning. ### When should I choose awesome-AutoML over flower? Choose awesome-AutoML over flower when License: awesome-AutoML is GPL-3.0, flower is Apache-2.0; Tags unique to awesome-AutoML: 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 flower? Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer ### 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 flower or awesome-AutoML more popular on GitHub? flower has more GitHub stars (7,067 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are flower and awesome-AutoML open source? Yes - both are open-source projects on GitHub (flower: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to flower or awesome-AutoML? GraphCanon lists graph-backed alternatives at [flower alternatives](/tools/flwrlabs-flower/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([flower markdown twin](/tools/flwrlabs-flower/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/flwrlabs-flower-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, flower or awesome-AutoML? flower: Very active. 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 flower and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [flower trust report](/tools/flwrlabs-flower/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=flwrlabs-flower`](/api/graphcanon/graph?tool=flwrlabs-flower) - 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/_