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 dsd2077 · Agent Tool · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CyberVerse safe to install? View the security audit →
CyberVerse CyberVerse is an open-source real-time audio/video agent platform. It uses WebRTC, persona memory, tools, RAG, and optional digital-human video capabilities to help you build AI agents centered on voice interaction. English · 简体中文 · 日本語 · 한국어 One Photo. A Living Digital Human. Ever dreamed of having your own J.A.R.V.I.S. — an AI that truly sees you, hears you, and talks back in real time? Want to see someone you've lost again, hear their voice, watch them smile at you? Or maybe there's a character you've always wished you could bring to life? Just one photo. CyberVerse makes them al
| Stars | 1,092 |
| Forks | 148 |
| Language | Python |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 70.531148512969/100 |
| Open Issues | 5 |
| Last Updated | 2026-06-03 |
| Created | 2026-04-18 |
| Platforms | browser, python |
| Est. Tokens | ~1190k |
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CyberVerse is Self hosted, real-time digital human agent platform. Build voice-first AI agents with WebRTC, persona memory, tools, RAG, and optional digital-human video.. It is categorized as a Agent Tool with 1.1k GitHub stars.
CyberVerse is primarily written in Python. It covers topics such as ai-agents, ai-companion, digital-human.
You can find installation instructions and usage details in the CyberVerse GitHub repository at github.com/dsd2077/CyberVerse. The project has 1.1k stars and 148 forks, indicating an active community.
CyberVerse is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to CyberVerse on Agent Skills Hub include CyberVerse, powermem, bolna. 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: