Enabling Agent-to-Agent (A2A) Support in PydanticAI

Learn how to expose your PydanticAI agents as Agent2Agent (A2A) protocol compliant servers for seamless interoperability.

Posted on: 2026-03-01 by AI Assistant

Enabling Agent-to-Agent (A2A) Support in PydanticAI

PydanticAI leverages the Agent2Agent (A2A) Protocol, an open standard created by Google, to foster seamless interoperability and communication between different AI agents. With the help of the FastA2A library, PydanticAI makes it incredibly straightforward to expose your agents as A2A-compliant servers.

What is the A2A Protocol?

The A2A Protocol is designed to allow agents built on different frameworks or platforms to communicate with one another using a standardized format. In PydanticAI, this is handled through Tasks, which represent single executions, and a context_id, which maintains conversation continuity across multiple interactions.

Installation & Requirements

To enable A2A features, you need the fasta2a library and its associated dependencies (such as starlette, pydantic, and opentelemetry-api).

You can install the necessary packages using one of two methods:

Standalone installation:

pip install fasta2a

With PydanticAI:

pip install 'pydantic-ai-slim[a2a]'

How to Set It Up

PydanticAI provides a powerful convenience method called .to_a2a(). This method takes an existing Agent instance and converts it directly into an ASGI application that adheres to the A2A specification.

Here is the basic workflow:

1. Define the Agent: Create your PydanticAI agent as you normally would.
2. Convert to A2A: Call .to_a2a() on the agent instance.
3. Run the Server: Host the resulting application using an ASGI server like uvicorn.

Code Example

Let’s look at a practical example. Save the following code as agent_to_a2a.py:

from pydantic_ai import Agent

# 1. Define your agent
agent = Agent('openai:gpt-4o', instructions='Be a helpful assistant.')

# 2. Expose as an A2A ASGI app
app = agent.to_a2a()

To run this server, use the following command in your terminal:

uvicorn agent_to_a2a:app --host 0.0.0.0 --port 8000

Core Concepts to Understand

When using .to_a2a(), PydanticAI handles a lot of complexity behind the scenes. However, understanding these core concepts is beneficial:

• Tasks: Every time a client sends a message to your A2A server, a new task is created and runs until completion.
• Context (context_id): This is vital for conversation continuity. Any messages sharing the same context_id have access to the full message history of that specific thread.
• Automatic Handling: The .to_a2a() method automatically stores complete conversation history (including any tool calls) and persists results as A2A artifacts (e.g., TextPart for strings, DataPart for structured Pydantic models).

Custom A2A Setups

If you need to move beyond the convenience method and implement a custom A2A setup, you will need to provide three core components:

1. Storage: To persist tasks (in A2A format) and conversation context (in your agent’s specific format).
2. Broker: To manage task queuing and scheduling.
3. Worker: To execute the actual agent logic.

By adopting the A2A protocol through PydanticAI, you can build highly interoperable, scalable multi-agent systems that communicate seamlessly across different platforms.