GraphCanon updated 2w · GitHub synced 2w
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
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (2282d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install nas-envSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
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 · Aug 4, 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:
cd ${GIT_STORAGE}/nas-env
git checkout develop
pip install -e .
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 package in PiPy.
For agents
This page has a .md twin and JSON over the API.