The Zen7 Payment Agent is the first practical implementation of a DePA decentralized payment agent. It adopts a multi - agent collaborative architecture, supports the A2A and MCP protocols, and provides multi - chain and multi - currency payment solutions for AI agents and Dapp applications, enabling automated cryptocurrency payments and LLM - driven intent recognition.

The Pierre Fitness API is a multi-protocol fitness data API that supports securely obtaining fitness data from providers such as Strava and Fitbit. It provides intelligent analysis for AI applications through MCP, A2A, and REST APIs, including enterprise-level API management, real-time analysis, and intelligent analysis of multiple sports types.

The A2A MCP Server is a service that bridges the Model Context Protocol (MCP) and the Agent - to - Agent Protocol (A2A), enabling MCP - compatible AI assistants (such as Claude) to seamlessly interact with A2A agents.

The AI agent project integrates the Google ADK development kit, supports the A2A protocol for communication between agents, and extends functions through the MCP protocol, providing Dockerized deployment and Phoenix monitoring.

Grasp is an open-source and self-hostable humanized browser automation tool that supports the MCP and A2A protocols and can seamlessly integrate with various AI applications. Through Docker containerization deployment, it provides a browser operation experience controlled by natural language.

A2AMCP is a real-time collaboration protocol between AI agents based on Redis, which solves the code conflict problem during parallel development of multiple AI agents, provides functions such as file locking, interface sharing, and task coordination, and supports Docker deployment and integration with mainstream AI frameworks.

A tutorial project for learning MCP and A2A protocols using Python, including multiple MCP service examples and development tutorials.

The official website of A2A-MCP showcases the technical vision, core advantages, and application scenarios of the multi-agent collaboration protocol, and is built using a modern front-end technology stack.

This project demonstrates how future DevOps engineers can use the MCP and A2A protocols to simplify workflows, improve efficiency, and automate complex tasks by installing Nginx in three ways: manual simulation, MCP, and A2A.

A production-level task management system based on the MCP protocol and Kafka, supporting task management, real-time notifications, and event stream processing, providing an interaction interface for AI agents.