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 Nayjest · LLM Plugin · ★ 150
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is lm-proxy safe to install? View the security audit →
LM-Proxy Lightweight, OpenAI-compatible HTTP proxy server / gatewayunifying access to multiple Large Language Model providers and local inference through a single, standardized API endpoint. Built with Python, FastAPI and [MicroCore](https://github.com/Nayjest/ai-
| Stars | 150 |
| Forks | 14 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 73.9707600986561/100 |
| Open Issues | 8 |
| Last Updated | 2026-08-26 |
| Created | 2025-05-24 |
| Platforms | python |
| Est. Tokens | ~19k |
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lm-proxy is OpenAI-compatible HTTP LLM proxy / gateway for multi-provider inference (Google, Anthropic, OpenAI, PyTorch). Lightweight, extensible Python/FastAPI—use as library or standalone service.. It is categorized as a LLM Plugin with 150 GitHub stars.
lm-proxy is primarily written in Python. It covers topics such as ai, anthropic, api-proxy.
You can find installation instructions and usage details in the lm-proxy GitHub repository at github.com/Nayjest/lm-proxy. The project has 150 stars and 14 forks, indicating an active community.
lm-proxy is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to lm-proxy on Agent Skills Hub include anthropic-max-router, NadirClaw, ccproxy. 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: