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 OpenAgentPlatform · MCP Server · ★ 1.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Dive safe to install? View the security audit →
Dive AI Agent Dive is an open-source MCP Host Desktop Application that seamlessly integrates with any LLMs supporting function calling capabilities. ✨ Features 🎯 🌐 Universal LLM Support: Compatible with ChatGPT, Anthropic, Ollama and OpenAI-compatible models 💻 Cross-Platform: Available for Windows, MacOS, and Linux 🔄 Model Context Protocol: Enabling seamless MCP AI agent integration on both stdio and SSE mode ☁️ OAP Cloud Integration: One
| Stars | 1,825 |
| Forks | 174 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.1628287608773/100 |
| Open Issues | 33 |
| Last Updated | 2026-07-31 |
| Created | 2025-01-24 |
| Platforms | cli, mcp, node |
| Est. Tokens | ~15k |
These tools work well together with Dive for enhanced workflows:
Looking for a Dive alternative? If you're comparing Dive 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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Dive is Dive is an open-source MCP Host Desktop Application that seamlessly integrates with any LLMs supporting function calling capabilities. ✨. It is categorized as a MCP Server with 1.8k GitHub stars.
Dive is primarily written in TypeScript. It covers topics such as ai, ai-agents, llm-interface.
You can find installation instructions and usage details in the Dive GitHub repository at github.com/OpenAgentPlatform/Dive. The project has 1.8k stars and 174 forks, indicating an active community.
Dive is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Dive on Agent Skills Hub include klavis, archestra, npcpy. 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: