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 BrowserOperator · MCP Server · ★ 508
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is browser-operator-core safe to install? View the security audit →
Open-source, privacy-focused AI browser that transforms how you work on the web. An intelligent platform for research, analysis, and automation—running locally in your browser. Download • Docs • Community 🚀 Getting Started ⬇️ Download for macOS | ⬇️ Download for Windows System Requirements: macOS 10.15+ or Windows 10 (64-bit)+ • 8GB RAM (16GB recommended) • 2GB free disk space AI Provider Setup
| Stars | 508 |
| Forks | 83 |
| Language | TypeScript |
| Category | MCP Server |
| License | BSD-3-Clause |
| Quality Score | 56.5383068090268/100 |
| Open Issues | 15 |
| Last Updated | 2026-03-19 |
| Created | 2025-05-10 |
| Platforms | browser, cli, mcp, node |
| Est. Tokens | ~72609k |
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browser-operator-core is Browser Operator - The AI browser with built in Multi-Agent platform! Open source alternative to ChatGPT Atlas, Perplexity Comet, Dia and Microsoft CoPilot Edge Browser. It is categorized as a MCP Server with 508 GitHub stars.
browser-operator-core is primarily written in TypeScript. It covers topics such as agent-browser, agent-workflow, agentic-ai.
You can find installation instructions and usage details in the browser-operator-core GitHub repository at github.com/BrowserOperator/browser-operator-core. The project has 508 stars and 83 forks, indicating an active community.
browser-operator-core is released under the BSD-3-Clause license, making it free to use and modify according to the license terms.
The top alternatives to browser-operator-core on Agent Skills Hub include tools, samples, argo. 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: