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 agent-labs-dev · LLM Plugin · ★ 113
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fastbrowse safe to install? View the security audit →
A browser agent that picks instead of generating. Jev chooses each action, an LLM plans and reads, and code owns verification, safety and secrets. Why Most browser agents generate each action from a screenshot. fastbrowse indexes the page into candidates and has Jev, a choice model, pick one, so it cannot click something that was never on the page. Every claim in an answer cites a verbatim quote from the page. Against Browser Use The same 14 answer tasks (lookups, sign-ins, checkout, Google Flights), three passes each, on the same cloud browser with the same limits (30 steps, $0.25, 300s): Every one of Browser Use's 28 failures ran past the $
| Stars | 113 |
| Forks | 14 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 64.9101623464305/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-03 |
| Created | 2026-09-17 |
| Platforms | browser, python |
| Est. Tokens | ~16k |
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fastbrowse is A fast browser agent: Jev picks each action from what is on the page, an LLM reads and plans, and every claim in an answer cites a quote from the page.. It is categorized as a LLM Plugin with 113 GitHub stars.
fastbrowse is primarily written in Python. It covers topics such as agents, browser-agent, llm.
You can find installation instructions and usage details in the fastbrowse GitHub repository at github.com/agent-labs-dev/fastbrowse. The project has 113 stars and 14 forks, indicating an active community.
fastbrowse is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fastbrowse on Agent Skills Hub include concierge, web-agent-protocol, atlas-mcp-server. 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: