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 nex-agi · Agent Tool · ★ 204
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is NexAU safe to install? View the security audit →
中文   ｜   English NexAU Framework A general-purpose agent framework for building intelligent agents with tool capabilities. This framework provides a modular tool system, a flexible agent architecture, and seamless integration with various LLM providers. ➡️ For the full documentation, please see the directory. Windows support: NexAU supports Windows 10 and Windows 11 with the default PowerShell backend. Git Bash is optional for explicit bash-compatible mode or bash-only commands. See Windows Support. Installation From GitHub Release (Recommended) Using pip: Using uv: Install Latest from Main Branch Using pip: Using uv: From Source bash Clo
| Stars | 204 |
| Forks | 34 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 68.537577796822/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-10 |
| Created | 2025-11-12 |
| Platforms | python |
| Est. Tokens | ~15k |
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NexAU is NexAU (AU for Agent Universe), a general-purpose agent framework for building intelligent agents with tool capabilities.. It is categorized as a Agent Tool with 204 GitHub stars.
NexAU is primarily written in Python.
You can find installation instructions and usage details in the NexAU GitHub repository at github.com/nex-agi/NexAU. The project has 204 stars and 34 forks, indicating an active community.
NexAU is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to NexAU on Agent Skills Hub include awesome-android-agent-skills, claude-code-skill-factory, agent-skills. 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: