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 intelligentnode · MCP Server · ★ 55
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Intelli safe to install? View the security audit →
Intelli A framework for creating chatbots and AI agent workflows. It enables seamless integration with multiple AI models, including OpenAI, LLaMA, deepseek, Stable Diffusion, and Mistral, through a unified access layer. Intelli also supports Model Context Protocol (MCP) for standardized interaction with AI models. Install For detailed usage instructions, refer to the documentation. Code Examples Create Chatbot Switch between multiple chatbot providers without changing your code. python from intelli.function.chatbot import Chatbot, ChatProvider from intelli.model.input.chatbotinput import ChatModelInput def callchatbot(provider, model=None, apikey=None, options=None): # prepare common input input
| Stars | 55 |
| Forks | 14 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65.3196204964991/100 |
| Open Issues | 10 |
| Last Updated | 2026-09-17 |
| Created | 2024-01-31 |
| Platforms | claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~15k |
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Intelli is Build multi-model chatbots and agents from intent.. It is categorized as a MCP Server with 55 GitHub stars.
Intelli is primarily written in Python. It covers topics such as ai-agents, autogpt, automation.
You can find installation instructions and usage details in the Intelli GitHub repository at github.com/intelligentnode/Intelli. The project has 55 stars and 14 forks, indicating an active community.
Intelli is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Intelli on Agent Skills Hub include c4-genai-suite, cai, toolmate. 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: