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 oxylabs · Agent Tool · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is browser-agent-py safe to install? View the security audit →
Browser Agent Browser Agent is an AI browser automation tool from Oxylabs AI Studio. It simulates real user browsing by executing multi-step actions like clicking links, filling forms, scrolling, capturing screenshots, and then extracting structured data – all controlled through natural language prompts. Unlike traditional automation frameworks (e.g., Puppeteer or Selenium), Browser Agent requires no static scraping rules or manual scripting. Users can describe tasks in plain English or provide a sequence of steps, and the AI will carry them out just like a human would. Key features Full control through browser AI – execute clicks, inputs, navigation, and scrolling. Multi-step task execution – define browsing flows in natural language. Multiple outputs – get results in JSON, Markdo
| Stars | 1,565 |
| Forks | 2 |
| Category | Agent Tool |
| Quality Score | 70.9806410415285/100 |
| Last Updated | 2026-04-02 |
| Created | 2025-10-13 |
| Platforms | browser |
| Est. Tokens | ~65k |
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browser-agent-py is AI Browser Agent is an advanced Browser AI tool developed by Oxylabs AI Studio that automates real user browsing tasks using natural language instructions.. It is categorized as a Agent Tool with 1.6k GitHub stars.
You can find installation instructions and usage details in the browser-agent-py GitHub repository at github.com/oxylabs/browser-agent-py. The project has 1.6k stars and 2 forks, indicating an active community.
The top alternatives to browser-agent-py on Agent Skills Hub include webbrain, oya-browser, bux. 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: