--- title: "moby vs Good-GYM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/moby-moby-vs-yo-wassup-good-gym" tools: ["moby-moby", "yo-wassup-good-gym"] --- # moby vs Good-GYM *GraphCanon updated Jul 11, 2026* ## Verdict Pick moby when moby is primarily Go; Good-GYM is Python; pick Good-GYM when good-GYM is primarily Python; moby is Go. [moby](https://mobyproject.org/) reports 72k GitHub stars, 19k forks, and 3.8k open issues, last pushed Jul 10, 2026. [Good-GYM](https://github.com/yo-WASSUP/Good-GYM) has 372 stars, 61 forks, and 1 open issues, last pushed Jul 2, 2026. Figures are from public GitHub metadata via [moby's repository](https://github.com/moby/moby) and [Good-GYM's repository](https://github.com/yo-WASSUP/Good-GYM). | | [moby](/tools/moby-moby.md) | [Good-GYM](/tools/yo-wassup-good-gym.md) | | --- | --- | --- | | Tagline | The Moby Project - a collaborative project for the container ecosystem to assemble container-based systems | AI-powered fitness assistant for real-time pose estimation, exercise counting, and workout feedback. | | Stars | 71,899 | 372 | | Forks | 19,126 | 61 | | Open issues | 3,821 | 1 | | Language | Go | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Developer Tools, Inference & Serving, LLM Frameworks | Computer Vision, Developer Tools, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [moby](/tools/moby-moby.md) | [Good-GYM](/tools/yo-wassup-good-gym.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 9d | | Open issues (now) | 3.8k | 1 | | Owner type | Organization | User | | Security scan | No criticals | 79 low (79 low) | | Full report | [trust report](/tools/moby-moby/trust.md) | [trust report](/tools/yo-wassup-good-gym/trust.md) | ## Choose when ### Choose moby if… - moby is primarily Go; Good-GYM is Python. - License: moby is Apache-2.0, Good-GYM is MIT. - Tags unique to moby: containers, docker, go, golang. - Also covers LLM Frameworks. - moby ships Docker support for self-hosted deployment. ### Choose Good-GYM if… - Good-GYM is primarily Python; moby is Go. - License: Good-GYM is MIT, moby is Apache-2.0. - Tags unique to Good-GYM: ai, computer-vision, exercise, fitness. - Also covers Computer Vision. ## When NOT to use moby - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use Good-GYM - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between moby and Good-GYM? moby: The Moby Project - a collaborative project for the container ecosystem to assemble container-based systems. Good-GYM: AI-powered fitness assistant for real-time pose estimation, exercise counting, and workout feedback.. See the comparison table for live GitHub stats and shared categories. ### When should I choose moby over Good-GYM? Choose moby over Good-GYM when moby is primarily Go; Good-GYM is Python; License: moby is Apache-2.0, Good-GYM is MIT; Tags unique to moby: containers, docker, go, golang; Also covers LLM Frameworks; moby ships Docker support for self-hosted deployment. ### When should I choose Good-GYM over moby? Choose Good-GYM over moby when Good-GYM is primarily Python; moby is Go; License: Good-GYM is MIT, moby is Apache-2.0; Tags unique to Good-GYM: ai, computer-vision, exercise, fitness; Also covers Computer Vision. ### When should I avoid moby? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid Good-GYM? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is moby or Good-GYM more popular on GitHub? moby has more GitHub stars (71,899 vs 372). Stars measure visibility, not whether either tool fits your constraints. ### Are moby and Good-GYM open source? Yes - both are open-source projects on GitHub (moby: Apache-2.0, Good-GYM: MIT). ### Where can I find alternatives to moby or Good-GYM? GraphCanon lists graph-backed alternatives at [moby alternatives](/tools/moby-moby/alternatives) and [Good-GYM alternatives](/tools/yo-wassup-good-gym/alternatives) ([moby markdown twin](/tools/moby-moby/alternatives.md), [Good-GYM markdown twin](/tools/yo-wassup-good-gym/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/moby-moby-vs-yo-wassup-good-gym.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, moby or Good-GYM? moby: Very active. Good-GYM: 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 moby and Good-GYM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [moby trust report](/tools/moby-moby/trust); [Good-GYM trust report](/tools/yo-wassup-good-gym/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=moby-moby`](/api/graphcanon/graph?tool=moby-moby) - 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/_