Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language models

by JosefAlbers · Agent Tool · ★ 79

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install Roy

git clone https://github.com/JosefAlbers/Roy

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars79
Forks4
LanguageJupyter Notebook
CategoryAgent Tool
Quality Score62/100
Last Updated2023-10-25
Created2023-10-03
Est. Tokens~25k

About Roy

Roy Roy is a lightweight alternative to for developing advanced multi-agent systems using language models. It aims to simplify and democratize the development of emergent collective intelligence. Features Model Agnostic: Use any LLM, no external APIs required. Defaults to a 4-bit quantized wizard-coder-python model for efficiency.

Roy alternative? Top 6 similar tools

Looking for a Roy alternative? If you're comparing Roy with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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