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.
Implements standardized Model Context Protocol (MCP) for interoperable tool and data integration across agent frameworks, with dedicated monitoring via LangSmith, Opik, and ClearML for production debugging and human feedback loops. Features adaptive RAG pipelines and multi-agent memory management patterns, alongside LangFlow for no-code agent composition. Includes CI/CD automation through GitHub Actions and cloud-native deployment templates for AWS and BentoML.
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50
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26
Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 09, 2026
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