This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, CrewAI, RAG, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.
Overview
What is End-to-End Agentic AI Automation Lab?
The End-to-End Agentic AI Automation Lab is a comprehensive repository that showcases hands-on projects and code examples focused on multi-agent systems, AI workflow automation, and deployment workflows using tools like LangChain, LangGraph, and Docker.
How to use the End-to-End Agentic AI Automation Lab?
To get started, clone the repository using the command: git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git. Each module contains a README for guidance, along with implementation scripts and configuration files.
Key features of the End-to-End Agentic AI Automation Lab?
- AI Agent Frameworks (LangChain, LangGraph, CrewAI)
- Multi-Agent Collaboration & Memory Management
- Workflow automation with n8n
- End-to-End Deployment with CI/CD using GitHub Actions
- Monitoring and debugging tools integration
- Real-world use cases including chatbots and financial agents
Use cases of the End-to-End Agentic AI Automation Lab?
- Building scalable multi-agent applications.
- Automating AI workflows for various applications.
- Integrating standardized protocols for AI systems.
FAQ from End-to-End Agentic AI Automation Lab?
- What technologies are used in this project?
The project utilizes Python, Docker, AWS, and various AI frameworks like LangChain and CrewAI.
- Is this project open for contributions?
Yes! Contributions and suggestions are welcome as part of the open-source community.
- What is the licensing for this project?
The project is licensed under the MIT License.