awesome-a2a — MCP Server by pab1it0

by pab1it0 · MCP Server · ★ 169

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About awesome-a2a

Awesome A2A A curated list of awesome Agent2Agent (A2A) protocol servers. This repository collects and organizes A2A-compliant server implementations to help developers build interoperable AI agent systems. What is A2A? Clients Tutorials Community Legend Server Implementations Frameworks Utilities Contributing License What is A2A? A2A (Agent2Agent) is an open protocol created by Google that enables different AI agents to communicate and collaborate with each other using a standardized interface. The protocol allows agents to discover capabilities, submit tasks for execution, monitor task progress, and receive task results in a unified way. Clients AI clients that can interact with A2A servers: Claude from Anthropic Gemini from Google Custom A2A-compatible clients Tutorials Getting Started with A2A Protocol Building Your First A2A Server Community Google A2A GitHub Legend 🎖️ – official implementation programming language 🐍 – Python codebase 📇 – TypeScript/JavaScript codebase

a2aagentagent2agentaimcp

Quick Facts

Stars169
Forks38
CategoryMCP Server
LicenseMIT
Quality Score42.712/100
Open Issues5
Last Updated2026-05-03
Created2025-04-09
Platformscli, mcp
Est. Tokens~13k

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Frequently Asked Questions

What is awesome-a2a?

awesome-a2a is Agent2Agent (A2A) – awesome A2A agents, tools, servers & clients, all in one place.. It is categorized as a MCP Server with 169 GitHub stars.

How do I install or use awesome-a2a?

You can find installation instructions and usage details in the awesome-a2a GitHub repository at github.com/pab1it0/awesome-a2a. The project has 169 stars and 38 forks, indicating an active community.

What license does awesome-a2a use?

awesome-a2a is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to awesome-a2a?

The top alternatives to awesome-a2a on Agent Skills Hub include awesome-a2a, swival, nuwax. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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