MDalamin5/End-to-End-Agentic-Ai-Automation-Lab

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 agent deployment using Docker, AWS, and BentoML.

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Established

This project helps AI developers and researchers build and deploy intelligent AI systems that can automate complex tasks. It provides practical examples and code for creating multi-agent systems and integrating them with workflow automation tools. Developers can use these resources to build scalable AI applications, from initial concept to cloud deployment.

Use this if you are an AI developer or researcher looking for hands-on experience and code examples to build, deploy, and manage intelligent AI agents and multi-agent systems at scale.

Not ideal if you are looking for a ready-to-use AI application and do not have programming or AI development experience.

AI development multi-agent systems AI workflow automation LLM orchestration cloud deployment
No Package No Dependents
Maintenance 10 / 25
Adoption 8 / 25
Maturity 15 / 25
Community 20 / 25

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Stars

50

Forks

26

Language

Jupyter Notebook

License

MIT

Last pushed

Mar 09, 2026

Commits (30d)

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