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 Jovancoding · MCP Server · ★ 76
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Network-AI safe to install? View the security audit →
Network-AI TypeScript/Node.js multi-agent orchestrator — shared state, guardrails, budgets, and cross-framework coordination [](https://clawhub
| Stars | 76 |
| Forks | 12 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.1379921641738/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-21 |
| Created | 2026-02-04 |
| Platforms | mcp, node |
| Est. Tokens | ~22k |
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Network-AI is Traffic light for AI Agents and TypeScript/Node multi-agent orchestrator with shared state, guardrails, and adapters for 32 AI frameworks. It is categorized as a MCP Server with 76 GitHub stars.
Network-AI is primarily written in TypeScript. It covers topics such as agent-framework, agent-orchestration, ai-agents.
You can find installation instructions and usage details in the Network-AI GitHub repository at github.com/Jovancoding/Network-AI. The project has 76 stars and 12 forks, indicating an active community.
Network-AI is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Network-AI on Agent Skills Hub include openlegion, DashClaw, oreilly-ai-agents. 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: