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 MODSetter · Agent Tool · ★ 16.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SurfSense safe to install? View the security audit →
English 简体中文 SurfSense: The Open-Source NotebookLM Alternative for Open Web Research SurfSense is the open-source NotebookLM alternative for AI agents, an open web research platform with live data connectors. Your agents research the live web with structured data from Reddit, YouTube, Instagram, TikTok, Amazon, Walmart, Google Maps, Google Search, Indeed, and any page on the open web, through one REST API or MCP server. Scheduled and event-triggered agents turn what they find into briefs and alerts, and a built-in know
| Stars | 16,313 |
| Forks | 1,565 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 64.0903446824398/100 |
| Open Issues | 112 |
| Last Updated | 2026-10-03 |
| Created | 2024-07-30 |
| Platforms | browser, python |
| Est. Tokens | ~17k |
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SurfSense is Air gapped, privacy focused open source NotebookLM alternative. Join our Discord: https://discord.gg/ejRNvftDp9. It is categorized as a Agent Tool with 16.3k GitHub stars.
SurfSense is primarily written in Python. It covers topics such as agent, agents, ai.
You can find installation instructions and usage details in the SurfSense GitHub repository at github.com/MODSetter/SurfSense. The project has 16.3k stars and 1565 forks, indicating an active community.
The top alternatives to SurfSense on Agent Skills Hub include headroom, mindsdb, voltagent. 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: