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 Oaklight · Agent Tool · ★ 83
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🔒 Is llm-rosetta safe to install? View the security audit →
LLM-Rosetta English Version | 中文版 LLM-Rosetta — A Python library for converting between different LLM provider API formats using a hub-and-spoke architecture with a central IR (Intermediate Representation). Full Documentation Full documentation is available at: English: https://llm-rosetta.readthedocs.io/en/latest/ 中文: https://llm-rosetta.readthedocs.io/zh-cn/latest/ The Problem When building applications that work with multiple LLM providers, you face an N² conversion problem — every provider pair requires its own conversion logic. LLM-Rosetta solves t
| Stars | 83 |
| Forks | 13 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.7017281776891/100 |
| Open Issues | 36 |
| Last Updated | 2026-09-23 |
| Created | 2025-12-09 |
| Platforms | gemini, python |
| Est. Tokens | ~14k |
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llm-rosetta is Production-ready LLM API translation layer — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. . It is categorized as a Agent Tool with 83 GitHub stars.
llm-rosetta is primarily written in Python. It covers topics such as anthropic, anthropic-api, api-adapter.
You can find installation instructions and usage details in the llm-rosetta GitHub repository at github.com/Oaklight/llm-rosetta. The project has 83 stars and 13 forks, indicating an active community.
llm-rosetta is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-rosetta on Agent Skills Hub include WindsurfPoolAPI, UniClaudeProxy, opencode-llm-proxy. 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: