Quick Start

This guide will help you get started with Python A2A quickly. We’ll cover the basics of creating and connecting to A2A agents.

Creating a Simple A2A Agent

Let’s create a simple A2A agent that responds to weather queries:

from python_a2a import A2AServer, skill, agent, run_server
from python_a2a import TaskStatus, TaskState

@agent(
    name="Weather Agent",
    description="Provides weather information",
    version="1.0.0"
)
class WeatherAgent(A2AServer):

    @skill(
        name="Get Weather",
        description="Get current weather for a location",
        tags=["weather", "forecast"]
    )
    def get_weather(self, location):
        """Get weather for a location."""
        # Mock implementation
        return f"It's sunny and 75°F in {location}"

    def handle_task(self, task):
        # Extract location from message
        message_data = task.message or {}
        content = message_data.get("content", {})
        text = content.get("text", "") if isinstance(content, dict) else ""

        if "weather" in text.lower() and "in" in text.lower():
            location = text.split("in", 1)[1].strip().rstrip("?.")

            # Get weather and create response
            weather_text = self.get_weather(location)
            task.artifacts = [{
                "parts": [{"type": "text", "text": weather_text}]
            }]
            task.status = TaskStatus(state=TaskState.COMPLETED)
        else:
            task.status = TaskStatus(
                state=TaskState.INPUT_REQUIRED,
                message={"role": "agent", "content": {"type": "text",
                        "text": "Please ask about weather in a specific location."}}
            )
        return task

# Run the server
if __name__ == "__main__":
    agent = WeatherAgent()
    run_server(agent, port=5000)

Save this code in a file (e.g., weather_agent.py) and run it with python weather_agent.py. This will start an A2A server on port 5000.

Connecting to an A2A Agent

Now let’s create a client to connect to our agent:

from python_a2a import A2AClient

# Create a client connected to an A2A-compatible agent
client = A2AClient("http://localhost:5000")

# View agent information
print(f"Connected to: {client.agent_card.name}")
print(f"Description: {client.agent_card.description}")
print(f"Skills: {[skill.name for skill in client.agent_card.skills]}")

# Ask a question
response = client.ask("What's the weather in Paris?")
print(f"Response: {response}")

Save this code in a file (e.g., weather_client.py) and run it with python weather_client.py while the agent is running.

Creating an LLM-Powered Agent

Let’s create an agent powered by a large language model:

import os
from python_a2a import OpenAIA2AServer, run_server

# Create an agent powered by OpenAI
agent = OpenAIA2AServer(
    api_key=os.environ["OPENAI_API_KEY"],
    model="gpt-4",
    system_prompt="You are a helpful AI assistant specialized in explaining complex topics simply."
)

# Run the server
if __name__ == "__main__":
    run_server(agent, host="0.0.0.0", port=5000)

Save this code in a file (e.g., llm_agent.py), set your OpenAI API key as an environment variable, and run it with python llm_agent.py.

Using Decorators

Python A2A provides decorators for easy agent and skill creation:

from python_a2a import agent, skill, A2AServer, run_server
from python_a2a import TaskStatus, TaskState

@agent(
    name="Calculator",
    description="Performs calculations",
    version="1.0.0"
)
class CalculatorAgent(A2AServer):

    @skill(
        name="Add",
        description="Add two numbers",
        tags=["math", "addition"]
    )
    def add(self, a, b):
        """
        Add two numbers together.

        Examples:
            "What is 5 + 3?"
            "Add 10 and 20"
        """
        return float(a) + float(b)

    def handle_task(self, task):
        # Implementation details...
        pass

# Run the server
if __name__ == "__main__":
    calculator = CalculatorAgent()
    run_server(calculator, port=5000)

Using the Agent Flow UI

Python A2A includes a visual workflow editor for building agent networks:

# Start the Agent Flow UI
a2a ui

The UI will open in your browser at http://localhost:8080.

You can use the UI to:

• Discover and connect to agents

• Build visual workflows with drag-and-drop

• Run and monitor workflows

• Save and load workflows

For more details on using the Agent Flow UI, see Agent Flow UI.

Next Steps

Now that you’ve seen the basics, check out the following sections to learn more:

• User Guides - For more detailed explanations

• Agent Flow UI - For details on using the Agent Flow UI

• api/index - For API reference

• Examples - For more complete examples