End-to-End Agentic AI 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.
What is End-to-End Agentic AI Lab?
End-to-End Agentic AI Lab is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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 age...
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.
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- This repository contains hands-on projects, code examples, a
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx end-to-end-agentic-ai-automation-labConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use End-to-End Agentic AI Lab
End-to-End Agentic AI Automation Lab is an open-source educational repository containing hands-on Jupyter notebook projects, code examples, and deployment workflows that teach developers how to build production-grade AI agent systems. It covers multi-agent frameworks (LangGraph, AutoGen, CrewAI), retrieval-augmented generation (RAG) pipelines with hybrid search and LlamaParse, MCP server integrations, workflow automation with n8n, fine-tuning with Unsloth/LoRA, and scalable deployment via Docker, AWS, and BentoML. Rather than a single deployable MCP server, it is a structured learning lab where each folder demonstrates a specific agentic pattern with runnable code.
Prerequisites
- Python 3.10 or later with pip and virtualenv
- Jupyter Notebook or JupyterLab for running .ipynb files
- An OpenAI API key (OPENAI_API_KEY) and/or Anthropic API key (ANTHROPIC_API_KEY) for LLM-backed examples
- Tavily API key (TAVILY_API_KEY) for examples using web search in RAG pipelines
- Docker (optional) for containerized deployment examples
Clone the repository
Clone the lab repository to your local machine and navigate into it.
git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git
cd End-to-End-Agentic-Ai-Automation-LabNavigate to the module you want to study
Each module lives in its own subfolder (e.g., LangGraph/, AutoGen/, RAG/, MCP/). Change into the relevant directory before installing dependencies.
ls # view available modules
cd LangGraph # example: exploring LangGraphInstall dependencies for the chosen module
Each module has its own requirements.txt. Install dependencies into a virtual environment to avoid conflicts between modules.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtSet environment variables
Create a .env file in the module directory and populate it with the API keys required for that module's examples.
# .env
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
TAVILY_API_KEY=your_tavily_api_keyLaunch JupyterLab and open a notebook
Start JupyterLab and open the first notebook in the module to follow the guided exercises.
jupyter labEnd-to-End Agentic AI Lab Examples
Client configuration
This is a learning lab, not a standalone MCP server. For MCP-related notebooks in the lab that run a local MCP server, use this pattern in Claude Desktop.
{
"mcpServers": {
"lab-mcp-example": {
"command": "python",
"args": ["mcp/server.py"],
"env": {
"OPENAI_API_KEY": "your_openai_api_key",
"ANTHROPIC_API_KEY": "your_anthropic_api_key"
}
}
}
}Prompts to try
Concepts and prompts to explore with Claude while working through the lab notebooks.
- "Explain how LangGraph StateGraphs differ from simple LangChain chains"
- "Help me debug this AutoGen RoundRobin agent that is looping indefinitely"
- "Show me how to add BM25 hybrid search to this RAG pipeline"
- "What Docker configuration would I need to deploy this CrewAI agent to AWS ECS?"Troubleshooting End-to-End Agentic AI Lab
Import errors when running a notebook (e.g., ModuleNotFoundError: langchain)
Each module has its own requirements.txt. Make sure you installed dependencies from the correct subfolder and that your Jupyter kernel is using the same virtual environment where you ran pip install.
API calls fail with authentication errors
Verify that your .env file is in the same directory as the notebook and that python-dotenv is installed. Add from dotenv import load_dotenv; load_dotenv() at the top of the notebook if it is missing.
Docker-based deployment examples fail to build
Ensure Docker Desktop is running and you are logged in. Each deployment folder contains a Dockerfile and a docker-compose.yml — run docker compose up --build from that folder and check for missing environment variables in the compose file.
Frequently Asked Questions about End-to-End Agentic AI Lab
What is End-to-End Agentic AI Lab?
End-to-End Agentic AI Lab is a Model Context Protocol (MCP) server that 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. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install End-to-End Agentic AI Lab?
Follow the installation instructions on the End-to-End Agentic AI Lab GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with End-to-End Agentic AI Lab?
End-to-End Agentic AI Lab works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is End-to-End Agentic AI Lab free to use?
Yes, End-to-End Agentic AI Lab is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
End-to-End Agentic AI Lab Alternatives — Similar Coding Agents Servers
Looking for alternatives to End-to-End Agentic AI Lab? Here are other popular coding agents servers you can use with Claude, Cursor, and VS Code.
Dify
★ 142.2kProduction-ready platform for agentic workflow development.
Ruflo
★ 54.0k🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integrat
Goose
★ 45.7kan open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM
Antigravity Awesome Skills
★ 38.3kInstallable GitHub library of 1,400+ agentic skills for Claude Code, Cursor, Codex CLI, Gemini CLI, Antigravity, and more. Includes installer CLI, bundles, workflows, and official/community skill collections.
AgentScope
★ 25.5kBuild and run agents you can see, understand and trust.
Serena
★ 24.5kA coding agent toolkit that provides IDE-like semantic code retrieval and editing tools, enabling LLMs to efficiently navigate and modify codebases using symbol-level operations instead of basic file reading and string replacements.
Browse More Coding Agents MCP Servers
Explore all coding agents servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.
Set Up End-to-End Agentic AI Lab in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
Ready to use End-to-End Agentic AI Lab?
Browse our complete directory of 33,000+ MCP servers, read setup guides for your editor, and start building with the Model Context Protocol.