A2A Client in Java: Bridging AI Agents with Java

Understanding Google's A2A Protocol

The Google A2A (Agent-to-Agent) protocol represents a significant leap forward in AI agent communication. At its core, A2A serves as a standardized protocol enabling AI agents to interact, collaborate, and share information seamlessly - think of it as the "HTTP of the AI world."

Why A2A Matters

Imagine a world where AI agents can:

🤝 Collaborate on complex tasks
📊 Share data and insights
🔄 Coordinate workflows
🎯 Achieve goals collectively

This is exactly what A2A enables, and my Java implementation makes this power accessible to everyone.

Why Java for A2A?

In the expanding landscape of AI development, Java stands out as a crucial platform for A2A implementation:

Introducing A2A Java Implementation

I've created a pure Java implementation that unifies both the Google A2A protocol and MCP (Model Context Protocol), creating a powerful framework for building sophisticated agent-based applications. This dual-protocol support opens up exciting possibilities for agent interactions and tool integration.

🌟 Core Capabilities

This implementation brings together the best of both worlds:

A2A Protocol: Enables standardized agent-to-agent communication
MCP Support: Provides access to a rich ecosystem of tools and resources
WebSocket Integration: Ensures real-time, efficient communication

🚀 Key Features and Benefits

Cross-Protocol Support: A2AJava natively supports both A2A and MCP in the same codebase, allowing developers to build applications where agents can communicate with each other (A2A) and also access external tools or services (MCP).
Cross-Platform and Cross-Language: Runs on any operating system with JVM support and is compatible with clients built in JavaScript, Python, and other languages, as well as LLM agents like Claude and Google A2A clients.
Flexible Integration: Can be used with Spring Boot, Quarkus, or pure Java/Kotlin, making it easy to integrate into any project.
Automatic Tool and Resource Registration: Uses Java annotations (such as @agent and @Action) to automatically expose methods as A2A tasks or MCP tools, simplifying agent and tool development.
Lightweight and Extensible: Designed for minimal overhead and maximum flexibility, supporting both local and remote tool execution, JSON-RPC, schema validation, and callback mapping
Technical Advantages
- Clean, maintainable codebase
- Efficient threading model
- Strong type safety
- Robust error handling
Rich Ecosystem
- Spring Boot integration
- Modern frameworks
- Extensive libraries
- Developer-friendly tools
Performance
- JVM optimization
- WebSocket efficiency
- Concurrent processing
- Low latency operations

The Power of WebSocket in A2A

The implementation leverages WebSocket technology to provide:

⚡ Real-time bidirectional communication
📈 Minimal latency overhead
🔄 Persistent connections
🎯 Event-driven architecture

🔥 Live Implementation

Try our live demo: A2A Java Client

Features in Action:

@Agent(name = "calculation-agent")
public class CalculationAgent {
    @Action(name = "perform-calculation")
    public Result calculate(Expression expr) {
        // Real-time calculation logic
        return evaluator.process(expr);
    }
}

🚀 Future Roadmap

Agentic Mesh
- Distributed agent networks
- Dynamic routing capabilities
- Load balancing support
Agentic RAG
- Knowledge integration
- Context-aware responses
- Dynamic information retrieval
Enhanced Workflows
- Complex task orchestration
- Multi-agent collaboration
- State management