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 open-webui · MCP Server · ★ 154.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-webui safe to install? View the security audit →
Open WebUI 👋 Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution. Passionate about open-source AI? Join our team → [!TIP] Looking for an [Enterprise Plan](https://docs.openwebui.co
| Stars | 154,137 |
| Forks | 22,523 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 68.5248309976565/100 |
| Open Issues | 281 |
| Last Updated | 2026-10-07 |
| Created | 2023-10-06 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~17k |
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Looking for a open-webui alternative? If you're comparing open-webui with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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CowAgent是基于大模型的超级AI助理，能主动思考和任务规划、访问操作系统和外部资源、创造和执行Skills、拥有长期记忆并不断成长，比OpenClaw更轻量和便捷。同时支持微信、飞书、钉钉、企微、QQ、公众号、网页
AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature,
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open-webui is User-friendly AI Interface (Supports Ollama, OpenAI API, ...). It is categorized as a MCP Server with 154.1k GitHub stars.
open-webui is primarily written in Python. It covers topics such as ai, llm, llm-ui.
You can find installation instructions and usage details in the open-webui GitHub repository at github.com/open-webui/open-webui. The project has 154.1k stars and 22523 forks, indicating an active community.
The top alternatives to open-webui on Agent Skills Hub include ragflow, headroom, nanobot. 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: