Skip to content

This deep dive explores key protocols and frameworks essential for building AI agents, including MCP, A2A, ACP, ADK, Arc, Quarkus, and LangChain4j. Attendees will learn their merits, practical implementation, and interoperability through detailed walkthroughs and demos, gaining clarity on their roles in effective LLM-based agent development.

License

Notifications You must be signed in to change notification settings

glaforge/ai-agent-protocols

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AI Agent Protocols and Agent Development Kit

This repository is a deep dive into the protocols and frameworks for building modern, interconnected AI agents. It showcases how different components can work together to create sophisticated, multi-agent systems.

The project demonstrates:

  • Agent Development: Using the Google Agent Development Kit (ADK) to build various types of agents, from simple chatbots to complex, collaborative workflows.
  • Remote Tool Usage: How an agent can use tools exposed by a remote server over the Model-Context-Protocol (MCP).
  • Agent-to-Agent Communication: How agents can delegate tasks and communicate with each other using the Agent-to-Agent (A2A) protocol.
  • Modern Java Frameworks: Building the remote servers with Quarkus for fast, efficient services.

📂 Project Structure

The repository is organized into three main sub-projects:

1. adk/

This directory contains a comprehensive collection of agent examples built with the Google ADK. It demonstrates a wide range of features, from basic agents and tool usage to advanced multi-agent collaboration patterns (sequential, parallel, and loops).

➡️ See the ADK README for a detailed list of all agent examples.

2. quarkus-db-store-mcp/

This is a Quarkus application that implements an MCP server. It exposes a simple tool that allows an agent to store data in a database. This server is used by the ProductStrategist agent in the adk project to show how agents can interact with external services via a standardized protocol.

➡️ See the MCP Server README for more details.

3. social-media-a2a-server/

This is a Quarkus application that implements an A2A agent server. It acts as a mock "social media" agent that can receive and process requests from other agents. It is used by the MarketingSpecialist agent in the adk project to demonstrate inter-agent communication.

➡️ See the A2A Server README for more details.

🚀 How to Run the Full Demo

The most comprehensive example is the ProductCampaign agent, which utilizes all parts of this repository. To run it, you first need to start the two companion servers.

Step 1: Start the MCP Tool Server

In a terminal, navigate to the quarkus-db-store-mcp directory and run:

./gradlew quarkusDev

This will start the MCP server on http://localhost:8080.

Step 2: Start the A2A Agent Server

In a second terminal, navigate to the social-media-a2a-server directory and run:

./mvnw quarkus:dev

This will start the A2A server on http://localhost:9999.

Step 3: Run the Product Campaign Agent

Open the adk project in your IDE. Navigate to the agents.media.ProductCampaign class and run its main method. This will start a command-line interface where you can interact with the agent.

Example interaction:

Tell the agent: Create a product campaign for a new brand of coffee for developers.

The ProductCampaign agent will then orchestrate the ProductStrategist and MarketingSpecialist sub-agents. You will see it call the MCP server to store the product brief, generate an image, and then call the A2A server to post social media updates.


License

This project is licensed under the Apache 2.0 License.

Disclaimer

This is not an official Google product.

About

This deep dive explores key protocols and frameworks essential for building AI agents, including MCP, A2A, ACP, ADK, Arc, Quarkus, and LangChain4j. Attendees will learn their merits, practical implementation, and interoperability through detailed walkthroughs and demos, gaining clarity on their roles in effective LLM-based agent development.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages