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 log10-io · Agent Tool · ★ 97
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is log10 safe to install? View the security audit →
log10 ⚡ Unified LLM data management to drive accuracy at scale ⚡ Quick Install 🤔 What is this? A one-line Python integration to manage your LLM data. For OpenAI v1, use instead of Access your LLM data at log10.io 🚀 What can this help with? 📝📊 Logging Use Log10 to log both closed and open-source LLM calls, e.g. OpenAI, Anthropic, Google Gemini, Llama, Mistral, etc. It helps you: Compare and identify the best models and prompts (try playground and llmeval) Store feedback for fine-tuning Collect performance metrics such as latency and usage Perform analytics and monitor compliance for LLM powered applications Log10 offers various integration methods, incl
| Stars | 97 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 61.3427304459488/100 |
| Open Issues | 10 |
| Last Updated | 2025-02-11 |
| Created | 2023-03-16 |
| Platforms | cli, python |
| Est. Tokens | ~1125k |
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log10 is Python client library for improving your LLM app accuracy. It is categorized as a Agent Tool with 97 GitHub stars.
log10 is primarily written in Python. It covers topics such as agents, ai, anthropic.
You can find installation instructions and usage details in the log10 GitHub repository at github.com/log10-io/log10. The project has 97 stars and 12 forks, indicating an active community.
log10 is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to log10 on Agent Skills Hub include agent-builder, mcp-server, llm-use. 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: