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.
Setup
Choose your client, copy the config, then fill in any required environment variables.
claude mcp add end-to-end-agentic-ai-automation-lab -- python -m venv venv~/.codex/config.toml
[mcp_servers.end-to-end-agentic-ai-automation-lab]
command = "python"
args = ["-m", "venv", "venv"]
# codex mcp add end-to-end-agentic-ai-automation-lab -- python -m venv venv~/.cursor/mcp.json
{
"mcpServers": {
"end-to-end-agentic-ai-automation-lab": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}.vscode/mcp.json
{
"servers": {
"end-to-end-agentic-ai-automation-lab": {
"type": "stdio",
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}~/.gemini/settings.json
{
"mcpServers": {
"end-to-end-agentic-ai-automation-lab": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}claude_desktop_config.json
{
"mcpServers": {
"end-to-end-agentic-ai-automation-lab": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}.trae/mcp.json
{
"mcpServers": {
"end-to-end-agentic-ai-automation-lab": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}