--- title: "nas-env vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gomerudo-nas-env-vs-windmaple-awesome-automl" tools: ["gomerudo-nas-env", "windmaple-awesome-automl"] --- # nas-env vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick nas-env if nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [nas-env](https://github.com/gomerudo/nas-env) reports 31 GitHub stars, 3 forks, and 0 open issues, last pushed May 4, 2020. [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 [nas-env's repository](https://github.com/gomerudo/nas-env) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [nas-env](/tools/gomerudo-nas-env.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Simple OpenAI Gym environment for Neural Architecture Search (NAS) | Curating AutoML research and resources | | Stars | 31 | 941 | | Forks | 3 | 156 | | Open issues | 0 | 1 | | Language | Python | - | | Adopt for | nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nas-env](/tools/gomerudo-nas-env.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2282d | 133d | | Open issues (now) | 0 | 1 | | Full report | [trust report](/tools/gomerudo-nas-env/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: nas-env - **Adopt for:** nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose nas-env if… - License: nas-env is MIT, awesome-AutoML is GPL-3.0. - Tags unique to nas-env: openai-gym, python, reinforcement-learning. - When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, nas-env is MIT. - Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## When NOT to use nas-env - If you require a fully documented package as documentation for nas-env remains under development - During production phases when stability is crucial because nas-env is still undergoing architectural changes ## 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 nas-env and awesome-AutoML? nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose nas-env over awesome-AutoML? Choose nas-env over awesome-AutoML when License: nas-env is MIT, awesome-AutoML is GPL-3.0; Tags unique to nas-env: openai-gym, python, reinforcement-learning; When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym. ### When should I choose awesome-AutoML over nas-env? Choose awesome-AutoML over nas-env when License: awesome-AutoML is GPL-3.0, nas-env is MIT; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### When should I avoid nas-env? If you require a fully documented package as documentation for nas-env remains under development During production phases when stability is crucial because nas-env is still undergoing architectural changes ### 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 nas-env or awesome-AutoML more popular on GitHub? awesome-AutoML has more GitHub stars (941 vs 31). Stars measure visibility, not whether either tool fits your constraints. ### Are nas-env and awesome-AutoML open source? Yes - both are open-source projects on GitHub (nas-env: MIT, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to nas-env or awesome-AutoML? GraphCanon lists graph-backed alternatives at [nas-env alternatives](/tools/gomerudo-nas-env/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([nas-env markdown twin](/tools/gomerudo-nas-env/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/gomerudo-nas-env-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, nas-env or awesome-AutoML? nas-env: 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 nas-env and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [nas-env trust report](/tools/gomerudo-nas-env/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gomerudo-nas-env`](/api/graphcanon/graph?tool=gomerudo-nas-env) - 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/_