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

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.

No Package No Dependents
Maintenance 13 / 25
Adoption 8 / 25
Maturity 15 / 25
Community 20 / 25

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Forks

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Language

Jupyter Notebook

License

MIT

Last pushed

Mar 09, 2026

Commits (30d)

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