Comparison
nas-env vs awesome-AutoML
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.
Markdown twin · nas-env alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | nas-env | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (2282d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- nas-env
- Simple OpenAI Gym environment for Neural Architecture Search (NAS)
- awesome-AutoML
- Curating AutoML research and resources
Stars
- nas-env
- 31
- awesome-AutoML
- 941
Forks
- nas-env
- 3
- awesome-AutoML
- 156
Open issues
- nas-env
- 0
- awesome-AutoML
- 1
Language
- nas-env
- Python
- awesome-AutoML
- -
Adopt for
- nas-env
- nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- nas-env
- -
- awesome-AutoML
- -
Runtime
- nas-env
- -
- awesome-AutoML
- -
License
- nas-env
- MIT
- awesome-AutoML
- GPL-3.0
Last pushed
- nas-env
- May 4, 2020
- awesome-AutoML
- Mar 24, 2026
Categories
- nas-env
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- nas-env
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- nas-env
- 2282d
- awesome-AutoML
- 133d
Open issues (now)
- nas-env
- 0
- awesome-AutoML
- 1
Full report
- nas-env
- Trust report
- awesome-AutoML
- Trust report
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (gomerudo/nas-env) · observed Aug 4, 2026
- GitHub forks (gomerudo/nas-env) · observed Aug 4, 2026
- Last push (gomerudo/nas-env) · observed May 4, 2020
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: nas-env 31 · awesome-AutoML 941 (synced Aug 4, 2026).
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 and awesome-AutoML alternatives (nas-env markdown twin, awesome-AutoML markdown twin), 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 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; awesome-AutoML trust report.