No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by crmne · Agent Tool · ★ 4.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ruby_llm safe to install? View the security audit →
One beautiful Ruby API for GPT, Claude, Gemini, and more. Battle tested at — Your AI coworker [!NOTE] Using RubyLLM? Share your story! Takes 5 minutes. Build chatbots, AI agents, RAG applications. Works with OpenAI, xAI, Anthropic, Google, AWS, local models, and any OpenAI-compatible API. ##
| Stars | 4,426 |
| Forks | 509 |
| Language | Ruby |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.8284756469904/100 |
| Open Issues | 9 |
| Last Updated | 2026-10-03 |
| Created | 2025-01-30 |
| Platforms | gemini, ruby |
| Est. Tokens | ~14k |
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ruby_llm is The Ruby-native AI framework. Chats, agents, tools, images, audio, and video through one consistent API, in plain Ruby or Rails.. It is categorized as a Agent Tool with 4.4k GitHub stars.
ruby_llm is primarily written in Ruby. It covers topics such as agent-framework, agentic-ai, agents.
You can find installation instructions and usage details in the ruby_llm GitHub repository at github.com/crmne/ruby_llm. The project has 4.4k stars and 509 forks, indicating an active community.
ruby_llm is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ruby_llm on Agent Skills Hub include bytebot, open-multi-agent, ax. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: