--- title: "nas-env vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gomerudo-nas-env-vs-sindresorhus-awesome" tools: ["gomerudo-nas-env", "sindresorhus-awesome"] --- # nas-env vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick nas-env when license: nas-env is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, nas-env is MIT. [nas-env](https://github.com/gomerudo/nas-env) reports 31 GitHub stars, 3 forks, and 0 open issues, last pushed May 4, 2020. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [nas-env's repository](https://github.com/gomerudo/nas-env) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [nas-env](/tools/gomerudo-nas-env.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | A simple OpenAI Gym environment for Neural Architecture Search (NAS) | 😎 Curated list of awesome topics including hardware resources | | Stars | 31 | 484,026 | | Forks | 3 | 35,799 | | Open issues | 0 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nas-env](/tools/gomerudo-nas-env.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 2259d | 11d | | Open issues (now) | 0 | 92 | | Full report | [trust report](/tools/gomerudo-nas-env/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose nas-env if… - License: nas-env is MIT, awesome is CC0-1.0. - Tags unique to nas-env: neural-architecture-search, reinforcement-learning, python, openai-gym. - Leaner open-issue backlog (0). ### Choose awesome if… - License: awesome is CC0-1.0, nas-env is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 31) - visibility, not fit. ## When NOT to use nas-env - Last GitHub push was 2259 days ago (dormant maintenance, May 4, 2020). Validate activity before betting a new project on nas-env. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between nas-env and awesome? nas-env: A simple OpenAI Gym environment for Neural Architecture Search (NAS). awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose nas-env over awesome? Choose nas-env over awesome when License: nas-env is MIT, awesome is CC0-1.0; Tags unique to nas-env: neural-architecture-search, reinforcement-learning, python, openai-gym; Leaner open-issue backlog (0). ### When should I choose awesome over nas-env? Choose awesome over nas-env when License: awesome is CC0-1.0, nas-env is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 31) - visibility, not fit. ### When should I avoid nas-env? Last GitHub push was 2259 days ago (dormant maintenance, May 4, 2020). Validate activity before betting a new project on nas-env. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is nas-env or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 31). Stars measure visibility, not whether either tool fits your constraints. ### Are nas-env and awesome open source? Yes - both are open-source projects on GitHub (nas-env: MIT, awesome: CC0-1.0). ### Where can I find alternatives to nas-env or awesome? GraphCanon lists graph-backed alternatives at [nas-env alternatives](/tools/gomerudo-nas-env/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([nas-env markdown twin](/tools/gomerudo-nas-env/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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-sindresorhus-awesome.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? nas-env: Dormant. awesome: Active. 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? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [nas-env trust report](/tools/gomerudo-nas-env/trust); [awesome trust report](/tools/sindresorhus-awesome/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/_