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 ypollak2 · MCP Server · ★ 81
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is llm-router safe to install? View the security audit →
llm-router Make Claude Code, Codex, and Gemini CLI use the cheapest model that can still do the job well. Save 35-80% on routine prompts, protect premium quota, and fall back automatically when providers fail. <img src="https://img.shields.io/github/actions/workflow/status/ypollak2/llm-router/ci.yml?style=flat-square&label=tests
| Stars | 81 |
| Forks | 11 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.0791873833109/100 |
| Open Issues | 8 |
| Last Updated | 2026-09-18 |
| Created | 2026-03-28 |
| Platforms | claude-code, cli, codex, gemini, mcp, python |
| Est. Tokens | ~18k |
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llm-router is Universal LLM router for AI coding tools. Works with Claude Code, Cursor, Codex, Gemini CLI, Copilot and more. Free-first fallback chain cuts costs 35-80%.. It is categorized as a MCP Server with 81 GitHub stars.
llm-router is primarily written in Python. It covers topics such as ai-routing, anthropic, claude.
You can find installation instructions and usage details in the llm-router GitHub repository at github.com/ypollak2/llm-router. The project has 81 stars and 11 forks, indicating an active community.
llm-router is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-router on Agent Skills Hub include ccproxy, NadirClaw, SimplerLLM. 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: