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 idosal · Agent Tool · ★ 471
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AgentLLM safe to install? View the security audit →
AgentLLM AgentLLM is the first proof of concept to utilize an open-source large language model (LLM) to develop autonomous agents that operate solely on the browser. Its main goal is to demonstrate that embedded LLMs have the ability to handle the complex goal-oriented tasks of autonomous agents with acceptable performance. Learn more on Medium and feel free to check out the demo. The implementation of the embedded LLM builds on the fantastic research of WebLLM, which takes advantage of Chromium's bleeding edge introduction of <a href="https:/
| Stars | 471 |
| Forks | 90 |
| Language | JavaScript |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 66.5689366397922/100 |
| Open Issues | 11 |
| Last Updated | 2023-07-31 |
| Created | 2023-04-26 |
| Platforms | browser, node |
| Est. Tokens | ~701k |
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AgentLLM is AgentLLM is a PoC for browser-native autonomous agents. It is categorized as a Agent Tool with 471 GitHub stars.
AgentLLM is primarily written in JavaScript. It covers topics such as agents, autogpt, babyagi.
You can find installation instructions and usage details in the AgentLLM GitHub repository at github.com/idosal/AgentLLM. The project has 471 stars and 90 forks, indicating an active community.
AgentLLM is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to AgentLLM on Agent Skills Hub include sre, DemoGPT, vllora. 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: