GraphCanon updated Oct 2, 2026 · GitHub synced Oct 2, 2026
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Decision brief
nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license.
Good fit when
- When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym
- For experimental settings where customization of architecture search elements like performance estimation and search space is anticipated
Avoid when
- 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
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (2341d since push)
- As of Oct 2, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 2, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install nas-envSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
nas-env provides a foundational OpenAI Gym-compatible framework for implementing and testing NAS algorithms in reinforcement learning settings.
Capability facts
- Languages
- python
Source: github.language · Oct 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Installation The recommended way to install the package is in editable mode: Future plans Architectural changes to the source code to ease plugging in the different NAS elements, i.e. the performance estimation strategy and the search space. Generate documentation Publish the pac...
For agents
This page has a .md twin and JSON over the API.