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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Python A2A is a powerful, easy-to-use library for implementing Google's [Agent-to-Agent (A2A) protocol](https://google.github.io/A2A/). It enables seamless communication between AI agents, creating interoperable agent ecosystems that can collaborate to solve complex problems.
| Date | Stars |
|---|---|
| 2026-07-31 | 1005 |
| 2026-08-01 | 1005 |
| 2026-08-06 | 1005 |
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# Python A2A
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[](https://pypi.org/project/python-a2a/)
[](https://pypi.org/project/python-a2a/)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/python-a2a)
[](https://python-a2a.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
[](https://github.com/astral-sh/uv)
[](https://github.com/themanojdesai/python-a2a/stargazers)
<p>
<a href="README.md">English</a> | <a href="README_zh.md">简体中文</a> | <a href="README_ja.md">日本語</a> | <a href="README_es.md">Español</a> | <a href="README_de.md">Deutsch</a> | <a href="README_fr.md">Français</a>
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</p>
**The Definitive Python Implementation of Google's Agent-to-Agent (A2A) Protocol with Model Context Protocol (MCP) Integration**
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## 🌟 Overview
Python A2A is a comprehensive, production-ready library for implementing Google's [Agent-to-Agent (A2A) protocol](https://google.github.io/A2A/) with full support for the [Model Context Protocol (MCP)](https://contextual.ai/introducing-mcp/). It provides everything you need to build interoperable AI agent ecosystems that can collaborate seamlessly to solve complex problems.
The A2A protocol establishes a standard communication format that enables AI agents to interact regardless of their underlying implementation, while MCP extends this capability by providing a standardized way for agents to access external tools and data sources. Python A2A makes these protocols accessible with an intuitive API that developers of all skill levels can use to build sophisticated multi-agent systems.
## 📋 What's New in v0.5.X
- **🔌 MCP v2.0 Complete Rewrite**: Rebuilt MCP implementation from scratch following JSON-RPC 2.0 specification
- **🏗️ Provider Architecture**: New provider-based architecture for external MCP servers with GitHub, Browserbase, and Filesystem providers
- **🚀 Real-World MCP Examples**: Production-ready examples with actual services (no mocks!) including GitHub, browser automation, and file management
- **🛡️ Enterprise MCP Support**: Robust transport abstraction supporting stdio and SSE for production deployments
- **🔄 Backward Compatible Migration**: Seamless upgrade path from previous MCP implementations
- **Agent Flow UI**: Visual workflow editor for building and managing agent networks with drag-and-drop interface
- **Agent Discovery**: Built-in support for agent registry and discovery with full Google A2A protocol compatibility
- **LangChain Integration**: Seamless integration with LangChain's tools and agents
- **Expanded Tool Ecosystem**: Use tools from both LangChain and MCP in any agent
- **Enhanced Agent Interoperability**: Convert between A2A agents and LangChain agents
- **Mixed Workflow Engine**: Build workflows combining both ecosystems
- **Simplified Agent Development**: Access thousands of pre-built tools instantly
- **Advanced Streaming Architecture**: Enhanced streaming with Server-Sent Events (SSE), better error handling, and robust fallback mechanisms
- **Task-Based Streaming**: New `tasks_send_subscribe` method for streaming task updates in real-time
- **Streaming Chunks API**: Improved chunk processing with the `StreamingChunk` class for structured strExcerpt of 41,156 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1ddebf20b808030c, desc:ai agents