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 OpenBMB · MCP Server · ★ 5.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is UltraRAG safe to install? View the security audit →
Less Code, Lower Barrier, Faster Deployment 简体中文 &n
| Stars | 5,703 |
| Forks | 448 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 62.9049840593519/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-21 |
| Created | 2025-01-16 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
Looking for a UltraRAG alternative? If you're comparing UltraRAG 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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UltraRAG is A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines. It is categorized as a MCP Server with 5.7k GitHub stars.
UltraRAG is primarily written in Python. It covers topics such as deepseek, demo, easy.
You can find installation instructions and usage details in the UltraRAG GitHub repository at github.com/OpenBMB/UltraRAG. The project has 5.7k stars and 448 forks, indicating an active community.
UltraRAG is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to UltraRAG on Agent Skills Hub include ai-guide, repomix, casibase. 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: