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 MDalamin5 · MCP Server · ★ 99
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is End-to-End-Agentic-Ai-Automation-Lab safe to install? View the security audit →
End-to-End Agentic AI Automation Lab Welcome to the official repository for End-to-End Agentic AI Automation Lab, a comprehensive and hands-on project portfolio developed as part of the Agentic AI and GenAI v2.0 course. This repository showcases real-world projects and advanced implementations of agentic AI systems, multi-agent frameworks, RAG pipelines, and AI workflow automation. It is designed for developers, researchers, and enthusiasts interested in building, deploying, and managing intelligent AI agents at scale. 📄 About the Course This work is based on the curriculum from Agentic AI v2.0, which provides in-depth knowledge and practical experience with: LangChain, LangGraph, LangFlow CrewAI, AutoGen, Agno Retrieval-Augmented Generation (RAG), Adaptive RAG Workflow automation with n8n Monitoring tools: LangSmith, Opik, ClearML Deployment tools: GitHub Actions, Docker, AWS, BentoML Model Context Protocol (MCP) for standardized tool and data integration 📈 Features Covered ✅ AI Agent Frameworks (LangChain, LangGraph, CrewAI, Agno, AutoGen) ✅ Multi-Agent Collaboration & Memory Management ✅ LangFlow UI-based App Building ✅ Adaptive & Agentic RAG Systems ✅ Model Context Protocol...
| Stars | 99 |
| Forks | 40 |
| Language | Jupyter Notebook |
| Category | MCP Server |
| License | MIT |
| Quality Score | 61.1904101363242/100 |
| Last Updated | 2026-09-22 |
| Created | 2025-05-18 |
| Platforms | aws, docker, mcp |
| Est. Tokens | ~16k |
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End-to-End-Agentic-Ai-Automation-Lab is This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable age. It is categorized as a MCP Server with 99 GitHub stars.
End-to-End-Agentic-Ai-Automation-Lab is primarily written in Jupyter Notebook. It covers topics such as agentic-ai, agentic-rag, ambient-ai.
You can find installation instructions and usage details in the End-to-End-Agentic-Ai-Automation-Lab GitHub repository at github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab. The project has 99 stars and 40 forks, indicating an active community.
End-to-End-Agentic-Ai-Automation-Lab is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to End-to-End-Agentic-Ai-Automation-Lab on Agent Skills Hub include wavefront, oreilly-ai-agents, idun-agent-platform. 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: