#a2a

A2A-MCP official website with modern UI and dark mode support

An extensible AI agents framework

Agent-to-Agent (A2A) communication protocol for inter-agent coordination and a Model Context Protocol (MCP)-inspired architecture for interacting with external tool servers.

Build and deploy an autonomous Devto Agent capable of interacting with the Dev.to platform, powered by A2A (Agent-to-Agent) and MCP (Model Context Protocol)

An agent playground (without authentication) in a single repo for fastest turn around cycles during experiments.

ScaledMCP is a horizontally scalabled MCP and A2A Server. You know, for AI.

Instead of dumping 1000+ tools into a model’s prompt and expecting it to choose wisely, the Unified MCP Tool Graph equips your LLM with structure, clarity, and relevance. It fixes tool confusion, prevents infinite loops, and enables modular, intelligent agent workflows.

Example implementation of the Google A2A protocol with a Flask server and Python client. Includes web search integration via MCP

Solana Protocol Design for Agent and MCP Server Registries

Atlassian (Jira/Confluence) AI Agent powered by 1st Party MCP Server using OpenAPI Codegen, LangGraph and LangChain MCP Adapters. Agent is exposed on various agent transport protocols (AGNTCY ACP, Google A2A, MCP Server)

it built itself

Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, CrewAI, RAG, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.