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python a2a

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Basic Information

Python A2A is a production-ready Python library that implements Google's Agent-to-Agent (A2A) protocol with first-class support for the Model Context Protocol (MCP). It is designed to help developers build interoperable multi-agent systems, A2A-compatible agents, MCP tool servers and clients, and visual workflows. The project provides server and client components, agent discovery and registry facilities, an AgentNetwork abstraction for managing multiple agents, a workflow engine for orchestrating complex multi-agent interactions, streaming clients for real-time responses, and a command-line interface and Agent Flow UI for visual workflow construction and management. The README and examples show how to run agents, compose networks, wire MCP providers, integrate LLM backends, and deploy in production environments.

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App URL
https://github.com/themanojdesai/python-a2a
Github Repository
https://github.com/themanojdesai/python-a2a/blob/main/README.md

App Details

Features
Key features include a complete implementation of the A2A specification and a rebuilt MCP v2.0 that follows JSON-RPC 2.0. The library offers a provider architecture with production-ready MCP providers for GitHub, Browserbase (browser automation), and Filesystem, and transport abstractions supporting stdio and Server-Sent Events. It includes AgentNetwork management, an AI-powered router for intelligent routing, a Workflow engine with conditional branching and parallel execution, real-time streaming via StreamingClient and StreamingChunk support, LangChain bridge utilities to convert between LangChain and A2A components, an Agent Flow UI for drag-and-drop workflows, a CLI for common tasks, examples for real-world integrations, and type hints and documentation for developer experience.
Use Cases
Python A2A makes it practical to build, orchestrate, and deploy ecosystems of collaborating AI agents while ensuring interoperability with the A2A and MCP standards. Developers can attach tool-capable MCP servers to agents, discover and register agents centrally, route queries to specialized agents, and compose multi-step workflows that run in parallel or conditionally. LangChain integration enables reuse of existing tools and LLMs, and the provider architecture offers ready integrations for GitHub, browser automation, and file operations. Real-time streaming and an Agent Flow UI support responsive user interfaces and visual process design, and the CLI and examples accelerate experimentation, R&D, enterprise deployments, customer-facing assistants, and educational simulations.

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