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.
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
- ✅ 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 (MCP) Integration
- ✅ End-to-End Deployment with CI/CD
- ✅ Monitoring, Debugging & Human Feedback Integration
- ✅ Cloud-Native Deployment using AWS, Docker
- ✅ Real-World Agentic AI Use Cases (Chatbots, Financial Agents, Automation)
By exploring this repository, you will:
- Understand the architecture of agentic AI systems
- Gain experience with LLM orchestration tools
- Build scalable and intelligent multi-agent applications
- Learn how to automate and monitor AI workflows
- Integrate standardized protocols like MCP into real-world AI pipelines
To clone the repository:
git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.gitEach folder will contain:
README.mdwith module overviewnotebooks/orscripts/for implementationsconfigs/for deployment & environment setup
- Languages: Python
- Frameworks: LangChain, LangGraph, CrewAI, AutoGen
- Orchestration: n8n, LangFlow
- Deployment: GitHub Actions, Docker, AWS EC2/S3/ECR, BentoML
- Monitoring: LangSmith, Opik, ClearML
- Databases: FAISS, ChromaDB, vector stores
- Protocols & Standards: Model Context Protocol (MCP)
This project is licensed under the MIT License.
This repository reflects a complete and evolving body of work in agentic AI systems and automation. Contributions, suggestions, and forks are welcome as part of the open-source learning community.
For questions or collaborations, feel free to reach out via GitHub Issues.