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 emirsahin1 · Agent Tool · ★ 275
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is llm-axe safe to install? View the security audit →
llm-axe Goal llm-axe is meant to be a flexible toolkit that provides simple abstractions for commonly used functions related to LLMs. It's not meant to intrude in your development workflow as other larger frameworks often do. It has functions for automatic schema generation, pre-made agents with self-tracking chat history and fully customizable agents. Have feedback/questions? Join the Discord Read the Development Documentation Installation Example Snippets Streaming Support: python llm = OllamaChat(model="llama3.1") ag = Agent(llm, customsystemprompt="", stream=True) res = ag.ask("Explain who you are in 20 paragraphs")
| Stars | 275 |
| Forks | 39 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 72.9084734827803/100 |
| Last Updated | 2025-01-05 |
| Created | 2024-04-22 |
| Platforms | python |
| Est. Tokens | ~17k |
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llm-axe is A simple, intuitive toolkit for quickly implementing LLM powered applications.. It is categorized as a Agent Tool with 275 GitHub stars.
llm-axe is primarily written in Python. It covers topics such as function-calling, llama3, llm.
You can find installation instructions and usage details in the llm-axe GitHub repository at github.com/emirsahin1/llm-axe. The project has 275 stars and 39 forks, indicating an active community.
llm-axe is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-axe on Agent Skills Hub include empower-functions, Promptimizer, MakerAi. 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.
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