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End-to-End-Agentic-Ai-Automation-Lab
End-to-End-Agentic-Ai-Automation-Lab logo

End-to-End-Agentic-Ai-Automation-Lab

Active·★ 103·MIT·Updated 2026-10-22

This repository provides a comprehensive, hands-on project portfolio for end-to-end agentic AI automation, showcasing advanced implementations of multi-agent frameworks, RAG pipelines, and AI workflow automation. It is designed for developers interested in building, deploying, and managing intelligent AI agents at scale.

#Agentic AI#Multi-Agent Systems#RAG#LLM Orchestration#AI Automation#Cloud Deployment#CI/CD#Monitoring
$ Install
$ git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git
↗ Visit site★ GitHub
01

Features

01AI Agent Frameworks (LangChain, LangGraph, CrewAI, Agno, AutoGen)
02Multi-Agent Collaboration & Memory Management
03Adaptive & Agentic RAG Systems
04End-to-End Deployment with CI/CD
05Monitoring, Debugging & Human Feedback Integration
02

Compatibility

Python
Runtime
Verified via docs
Docker
Containerization
Verified via docs
AWS
Cloud Platform
Verified via docs
GitHub Actions
CI/CD
Verified via docs
03

Quick start

1
$ git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git
04

Use cases

↳Chatbots
↳Financial Agents
↳Automation
05

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On this page
01Features02Compatibility03Quick start04Use cases05Alternatives
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.

103MIT
Last commit1d ago
StatusActive
CategoryRAG / Knowledge Base
Links
Documentation↗Discussion↗Issues↗Releases↗
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