--- title: "automl-gs vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minimaxir-automl-gs-vs-windmaple-awesome-automl" tools: ["minimaxir-automl-gs", "windmaple-awesome-automl"] --- # automl-gs vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [automl-gs](https://github.com/minimaxir/automl-gs) reports 1.9k GitHub stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. [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 [automl-gs's repository](https://github.com/minimaxir/automl-gs) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [automl-gs](/tools/minimaxir-automl-gs.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Automatically generate machine-learning models and code with input CSV and target field | Curating AutoML research and resources | | Stars | 1,869 | 941 | | Forks | 181 | 156 | | Open issues | 28 | 1 | | Language | Python | - | | Adopt for | automl-gs: Python tool for automated machine-learning model creation from CSV data | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | Data & Retrieval, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [automl-gs](/tools/minimaxir-automl-gs.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2477d | 133d | | Open issues (now) | 28 | 1 | | Full report | [trust report](/tools/minimaxir-automl-gs/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose automl-gs if… - License: automl-gs is MIT, awesome-AutoML is GPL-3.0. - Tags unique to automl-gs: keras, machine-learning, python, tensorflow. - Also covers Data & Retrieval. - Need to rapidly prototype models with limited ML expertise ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, automl-gs is MIT. - 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 automl-gs - Complex feature engineering or non-standard data inputs required - Sensitive about licensing of the generated code ## 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 automl-gs and awesome-AutoML? automl-gs: Automatically generate machine-learning models and code with input CSV and target field. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose automl-gs over awesome-AutoML? Choose automl-gs over awesome-AutoML when License: automl-gs is MIT, awesome-AutoML is GPL-3.0; Tags unique to automl-gs: keras, machine-learning, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise. ### When should I choose awesome-AutoML over automl-gs? Choose awesome-AutoML over automl-gs when License: awesome-AutoML is GPL-3.0, automl-gs is MIT; 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 automl-gs? Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code ### 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 automl-gs or awesome-AutoML more popular on GitHub? automl-gs has more GitHub stars (1,869 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are automl-gs and awesome-AutoML open source? Yes - both are open-source projects on GitHub (automl-gs: MIT, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to automl-gs or awesome-AutoML? GraphCanon lists graph-backed alternatives at [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([automl-gs markdown twin](/tools/minimaxir-automl-gs/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/minimaxir-automl-gs-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, automl-gs or awesome-AutoML? automl-gs: 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 automl-gs and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [automl-gs trust report](/tools/minimaxir-automl-gs/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=minimaxir-automl-gs`](/api/graphcanon/graph?tool=minimaxir-automl-gs) - 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/_