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 adham90 · Agent Tool · ★ 136
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ruby_llm-agents safe to install? View the security audit →
RubyLLM::Agents Production-ready Rails engine for building, managing, and monitoring LLM-powered AI agents Build intelligent AI agents in Ruby with a clean DSL, automatic execution tracking, cost analytics, budget controls, and a beautiful real-time dashboard. Supports OpenAI GPT-4, Anthropic Claude, Google Gemini, and more through RubyLLM. Why RubyLLM::Agents? Rails-Native - Seamlessly integrates with your Rails app: models, jobs, caching, and Hotwire Production-Ready - Built-in retries, model fallbacks, circuit breaker
| Stars | 136 |
| Forks | 8 |
| Language | Ruby |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 76.7698024514287/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-02 |
| Created | 2025-11-25 |
| Platforms | ruby |
| Est. Tokens | ~17k |
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ruby_llm-agents is A Rails agent framework for RubyLLM — define AI agents with prompts, schemas, caching, logging, cost tracking, and a built-in dashboard for monitoring LLM usage in production.. It is categorized as a Agent Tool with 136 GitHub stars.
ruby_llm-agents is primarily written in Ruby. It covers topics such as dashboard, gem, llm.
You can find installation instructions and usage details in the ruby_llm-agents GitHub repository at github.com/adham90/ruby_llm-agents. The project has 136 stars and 8 forks, indicating an active community.
ruby_llm-agents is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ruby_llm-agents on Agent Skills Hub include action_mcp, vespper, MCP-PostgreSQL-Ops. 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: