--- title: "nas-env vs archai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gomerudo-nas-env-vs-microsoft-archai" tools: ["gomerudo-nas-env", "microsoft-archai"] --- # nas-env vs archai *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 archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. [nas-env](https://github.com/gomerudo/nas-env) reports 31 GitHub stars, 3 forks, and 0 open issues, last pushed May 4, 2020. [archai](https://microsoft.github.io/archai) has 485 stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [nas-env's repository](https://github.com/gomerudo/nas-env) and [archai's repository](https://github.com/microsoft/archai). | | [nas-env](/tools/gomerudo-nas-env.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Tagline | Simple OpenAI Gym environment for Neural Architecture Search (NAS) | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | | Stars | 31 | 485 | | Forks | 3 | 93 | | Open issues | 0 | 4 | | Language | Python | Python | | Adopt for | nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. | Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nas-env](/tools/gomerudo-nas-env.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2282d | 252d | | Open issues (now) | 0 | 4 | | Owner type | User | Organization | | Full report | [trust report](/tools/gomerudo-nas-env/trust.md) | [trust report](/tools/microsoft-archai/trust.md) | ## Shared compatibility - **Python**: [nas-env](/tools/gomerudo-nas-env.md) - Python runtime; [archai](/tools/microsoft-archai.md) - Python runtime ## Decision facts: nas-env - **Adopt for:** nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. ## Decision facts: archai - **Adopt for:** Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. ## Choose when ### Choose nas-env if… - 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 - Leaner open-issue backlog (0). ### Choose archai if… - Tags unique to archai: automated-machine-learning, automl, darts, deep-learning. - Need rapid iteration in NAS projects while ensuring reproducibility - More GitHub stars (485 vs 31) - visibility, not fit. ## 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 archai - Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions - Development occurs outside Python 3.8+, limiting the application of Archai tools ## Common questions ### What is the difference between nas-env and archai? nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose nas-env over archai? Choose nas-env over archai when 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; Leaner open-issue backlog (0). ### When should I choose archai over nas-env? Choose archai over nas-env when Tags unique to archai: automated-machine-learning, automl, darts, deep-learning; Need rapid iteration in NAS projects while ensuring reproducibility; More GitHub stars (485 vs 31) - visibility, not fit. ### 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 archai? Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools ### Is nas-env or archai more popular on GitHub? archai has more GitHub stars (485 vs 31). Stars measure visibility, not whether either tool fits your constraints. ### Are nas-env and archai open source? Yes - both are open-source projects on GitHub (nas-env: MIT, archai: MIT). ### Where can I find alternatives to nas-env or archai? GraphCanon lists graph-backed alternatives at [nas-env alternatives](/tools/gomerudo-nas-env/alternatives) and [archai alternatives](/tools/microsoft-archai/alternatives) ([nas-env markdown twin](/tools/gomerudo-nas-env/alternatives.md), [archai markdown twin](/tools/microsoft-archai/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-microsoft-archai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, nas-env or archai? nas-env: Dormant. archai: 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 archai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [nas-env trust report](/tools/gomerudo-nas-env/trust); [archai trust report](/tools/microsoft-archai/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/_