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.
接入配置
选择客户端，复制配置，再按项目文档补齐环境变量。
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"
]
}
}
}