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 7xuanlu · MCP Server · ★ 77
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wenlan safe to install? View the security audit →
Useful work with AI shouldn't disappear when a conversation ends. Wenlan builds the right pages and keeps them current as sources change, asking only when judgment is needed. English Español Get started · What is this? · Capabilities · Daily workflow · Evaluation · Learn more
| Stars | 77 |
| Forks | 7 |
| Language | Rust |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 60.8019152061144/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-21 |
| Created | 2026-04-19 |
| Platforms | claude-code, codex, mcp, rust |
| Est. Tokens | ~29k |
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wenlan is Wenlan is a knowledge base for the AI-native age. Your AI agents capture what they learn, Wenlan keeps it current and distills it into source-cited wiki pages you can trust. It is categorized as a MCP Server with 77 GitHub stars.
wenlan is primarily written in Rust. It covers topics such as ai-memory, claude, claude-code.
You can find installation instructions and usage details in the wenlan GitHub repository at github.com/7xuanlu/wenlan. The project has 77 stars and 7 forks, indicating an active community.
wenlan is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to wenlan on Agent Skills Hub include omega-memory, brain-mcp, LLM-Wiki. 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: