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 AgentEvalHQ · Agent Tool · ★ 154
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AgentEval safe to install? View the security audit →
# AgentEval The .NET Evaluation Toolkit for AI Agents AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality m
| Stars | 154 |
| Forks | 16 |
| Language | C# |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.7678485646016/100 |
| Open Issues | 4 |
| Last Updated | 2026-10-03 |
| Created | 2026-01-02 |
| Platforms | dotnet |
| Est. Tokens | ~25k |
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AgentEval is AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (M. It is categorized as a Agent Tool with 154 GitHub stars.
AgentEval is primarily written in C#. It covers topics such as agent, agentic, evals.
You can find installation instructions and usage details in the AgentEval GitHub repository at github.com/AgentEvalHQ/AgentEval. The project has 154 stars and 16 forks, indicating an active community.
AgentEval is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AgentEval on Agent Skills Hub include SimpleLLMFunc, n8n-claw, deep-seek. 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: