by feder-cr · MCP Server · ★ 2.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
dots Every AI agent is a model and a browser.You can swap the model with one flag. The browser is what the website sees. Windows, in PowerShell: Linux: Then open http://127.0.0.1:8765. The conversation on the left, the browser on the right, live. It all comes down to the browser When a web agent fails, the model is rarely why. The page never loaded, a challenge appeared, the login expired, the click did not land. All of that happens in the browser, before the model gets to think. So dots is built around one: A real Firefox engine, patched in C++. The fingerprint is decided inside the engine, not painted over with JavaScript that a page can inspect. One identity per seed. Screen, fonts, GPU, timezone and language agree with each other, and gives back the same person on every run. Nothing for a page to find. No WebDriver flag, no DevTools protocol, no automation globals in the page. A person's hands. The pointer travels to what it clicks and keys are pressed one at a
| Stars | 2,579 |
| Forks | 436 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.7152125152915/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-03 |
| Created | 2026-09-29 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~14k |
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dots is Open-source dots for the web: an AI agent with its own browser, one that does not get blocked.. It is categorized as a MCP Server with 2.6k GitHub stars.
dots is primarily written in Python. It covers topics such as ai-agent, ai-agents, ai-browser.
You can find installation instructions and usage details in the dots GitHub repository at github.com/feder-cr/dots. The project has 2.6k stars and 436 forks, indicating an active community.
dots is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dots on Agent Skills Hub include gptme, browser-agent-py, wcgw. 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.
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