End-to-End Agentic AI Automation Lab
Explore real-world projects and advanced implementations of agentic AI systems, multi-agent frameworks, RAG pipelines, and AI workflow automation.
About
This repository serves as a comprehensive, hands-on portfolio of projects demonstrating advanced agentic AI systems. Explore real-world implementations of multi-agent frameworks, Retrieval-Augmented Generation (RAG) pipelines, and AI workflow automation. It offers developers, researchers, and enthusiasts the resources to build, deploy, and manage intelligent AI agents at scale using tools like LangChain, CrewAI, and deployment strategies with Docker and AWS.
Key Features
- Multi-Agent Collaboration & Memory Management
- Model Context Protocol (MCP) Integration
- Adaptive & Agentic RAG Systems
- 1 GitHub stars
- AI Agent Frameworks (LangChain, LangGraph, CrewAI, AutoGen)
- End-to-End Deployment with CI/CD
Use Cases
- Integrating standardized protocols like MCP into AI pipelines
- Building scalable and intelligent multi-agent applications
- Automating and monitoring AI workflows
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