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 THUDM · Agent Tool · ★ 274
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is VisualAgentBench safe to install? View the security audit →
VisualAgentBench (VAB) 🌐 Website 🗂️ VAB Training (ModelScope) VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents VisualAgentBench (VAB) is the first benchmark designed to systematically evaluate and develop large multi models (LMMs) as visual foundation agents, which comprises 5 distinct environments across 3 types of representative visual agent tasks (Embodied, GUI, and Visual Design) https://github.com/user-attachments/assets/4a1a5980-48f9-4a70-a900-e5f58ded69b4 VAB-OmniGibson (Embodied) VAB-Minecraft (Embodied) VAB-Mobile (GUI) VAB-WebArena-Lite (GUI, based on WebArena and VisualWebArena) VAB-CSS (Visual Design) Compared to its predecessor AgentBench, VAB highlights visual inputs and the enabling of Foundation Agent capability development with training open LLMs/LMMs on trajectories. Table of Contents Quick Start Dataset Summary Leaderboard Quick Start Acknowledgement [Cita
| Stars | 274 |
| Forks | 13 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 68.7540784955115/100 |
| Open Issues | 17 |
| Last Updated | 2025-04-24 |
| Created | 2024-08-08 |
| Platforms | python |
| Est. Tokens | ~378k |
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VisualAgentBench is Towards Large Multimodal Models as Visual Foundation Agents. It is categorized as a Agent Tool with 274 GitHub stars.
VisualAgentBench is primarily written in Python. It covers topics such as gpt, llm-agent, multimodal-large-language-models.
You can find installation instructions and usage details in the VisualAgentBench GitHub repository at github.com/THUDM/VisualAgentBench. The project has 274 stars and 13 forks, indicating an active community.
VisualAgentBench is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to VisualAgentBench on Agent Skills Hub include tribe, claude-delegator, awesome-openclaw. 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: