Python サンプル
| 名前 | 内容説明 |
|---|---|
| NGINX / Flask / MongoDB | A sample Python/Flask application with Nginx proxy and a Mongo database. |
| NGINX / Flask / MySQL | A sample Python/Flask application with an Nginx proxy and a MySQL database. |
| NGINX / WSGI / Flask | A sample Nginx reverse proxy with a Flask backend using WSGI. |
| Python / Flask / Redis | A sample Python/Flask and a Redis database. |
| Flask | A sample Flask application. |
| Django | A sample Django application. |
| FastAPI | A sample FastAPI application. |
| example-voting-app | A sample Docker Compose app. |
| Compose and Django | This quick-start guide demonstrates how to use Docker Compose to set up and run a simple Django/PostgreSQL app. |
| AI/ML with Docker | Get started with AI and ML using Docker, Neo4j, LangChain, and Ollama |
| Agent-to-Agent | This app is a modular AI agent runtime built on Google's Agent Development Kit (ADK) and the A2A (Agent-to-Agent) protocol. It wraps a large language model (LLM)-based agent in an HTTP API and uses structured execution flows with streaming responses, memory, and tools. It is designed to make agents callable as network services and composable with other agents. |
| ADK Multi-Agent Fact Checker | This project demonstrates a collaborative multi-agent system built with the Agent Development Kit (ADK), where a top-level Auditor agent coordinates the workflow to verify facts. The Critic agent gathers evidence via live internet searches using DuckDuckGo through the Model Context Protocol (MCP), while the Reviser agent analyzes and refines the conclusion using internal reasoning alone. The system showcases how agents with distinct roles and tools can collaborate under orchestration. |
| DevDuck agents | A multi-agent system for Go programming assistance built with Google Agent Development Kit (ADK). This project features a coordinating agent (DevDuck) that manages two specialized sub-agents (Bob and Cerebras) for different programming tasks. |
| Agno | This app is a multi-agent orchestration system powered by LLMs (like Qwen and OpenAI) and connected to tools via a Model Control Protocol (MCP) gateway. Its purpose is to retrieve, summarize, and document GitHub issues—automatically creating Notion pages from the summaries. It also supports file content summarization from GitHub. |
| CrewAI | This project showcases an autonomous, multi-agent virtual marketing team built with CrewAI. It automates the creation of a high-quality, end-to-end marketing strategy — from research to copywriting — using task delegation, web search, and creative synthesis. |
| SQL Agent with LangGraph | This project demonstrates a zero-config AI agent that uses LangGraph to answer natural language questions by querying a SQL database — all orchestrated with Docker Compose. |
さらなるサンプルを探しますか？
以下の GitHub リポジトリから、さらなる Docker サンプルを確認してください。
Awesome Compose: 30 以上の Docker Compose サンプルを集約したリポジトリです。 これらのサンプルからは、Compose ファイルを使ってさまざまなサービスを組み合わせる方法を学ぶことができます。
Docker サンプル: 30 以上のリポジトリからなり、デモアプリケーション、チュートリアル、ラボなどのコンテナー化サンプルを提供しています。