Agent-to-Agent (A2A) Communication

The A2A system enables seamless communication between AI agents using JSON-RPC 2.0 over an event bus architecture. This allows agents to collaborate, delegate tasks, and share information regardless of their underlying framework or hosting platform.

Why A2A?

Traditional agent communication relies on HTTP APIs, which have limitations:

Blocking operations that reduce efficiency
No built-in streaming for real-time updates
Complex error handling across different platforms
Scalability challenges with direct connections

A2A solves these problems with:

Asynchronous messaging via event bus
Built-in streaming support for real-time collaboration
Standardized error handling with JSON-RPC
Scalable architecture that handles thousands of agents

Key Features

🚀 Task Management

Send tasks to other agents and track their progress in real-time.

1 response = await client.send_task({
2     "id": "analysis-task-123",
3     "message": {
4         "role": "user",
5         "parts": [{"type": "text", "text": "Analyze this market data"}]
6     }
7 })

📡 Streaming Responses

Receive real-time updates as agents process tasks.

1 async for update in client.send_task_streaming(task_params):
2     print(f"Status: {update.result['status']['state']}")
3     if update.result.get('final'):
4         break

🔍 Agent Discovery

Find and connect with agents that have the capabilities you need.

1 agents = await client.discover_agents({
2     "capabilities": ["data-analysis", "visualization"]
3 })

⚡ Event-Driven Architecture

Built on Redis Streams for high performance and reliability.

How It Works

Agent A sends a task request via JSON-RPC
Event Bus routes the message to the target agent
Agent B processes the task and sends updates
Agent A receives real-time progress updates

Use Cases

Multi-Agent Workflows

Chain multiple agents together for complex tasks:

Research Agent → Analysis Agent → Report Agent

Marketplace Integration

Connect your agent to multiple AI marketplaces:

ASI Marketplace
Agentverse
Custom Platforms

Real-time Collaboration

Enable agents to work together on shared objectives:

Collaborative document editing
Distributed problem solving
Resource sharing

1	response = await client.send_task({
2	"id": "analysis-task-123",
3	"message": {
4	"role": "user",
5	"parts": [{"type": "text", "text": "Analyze this market data"}]
6	}
7	})

1	async for update in client.send_task_streaming(task_params):
2	print(f"Status: {update.result['status']['state']}")
3	if update.result.get('final'):
4	break

1	agents = await client.discover_agents({
2	"capabilities": ["data-analysis", "visualization"]
3	})