garage
A toolkit for reproducible reinforcement learning research
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is garage?
Garage is a comprehensive toolkit designed to facilitate reproducible reinforcement learning research. It provides researchers and developers with the tools necessary to conduct experiments in a consistent and reliable manner, ensuring that results are both accurate and repeatable.
Key differentiator
“Garage stands out by focusing on reproducibility and modularity in reinforcement learning research, making it an ideal choice for researchers who need to ensure the reliability of their experiments.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary support for Python means limited out-of-the-box functionality for other languages
GitHub activity indicates low participation beyond core contributors
Fit analysis
Who is it for?
✓ Best for
Research teams looking to conduct reproducible experiments in reinforcement learning
Academic institutions that need a robust framework for teaching and research in RL
Developers who want to integrate various RL algorithms into their projects
✕ Not a fit for
Projects requiring real-time decision-making capabilities, as garage is more suited for offline experimentation
Teams looking for a cloud-based service with managed infrastructure
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with garage
Step-by-step setup guide with code examples and common gotchas.