Building A2A Protocol Agents with Python and FastAPI

The Agent-to-Agent (A2A) protocol is revolutionizing how AI agents communicate across different platforms. While frameworks like Mastra provide excellent TypeScript implementations, Python developers have equally powerful tools at their disposal. In this guide, I'll walk you through building a production-ready A2A agent using Python and FastAPI.

Why Python for A2A Agents?

Python has become the de facto language for AI and machine learning, offering:

• Rich AI Ecosystem: Libraries like Pydantic AI, LangChain, and transformers
• FastAPI Performance: Async/await support with near-native speed
• Type Safety: Modern Python with Pydantic for robust data validation
• Easy Integration: Simple connection to ML models, databases, and external APIs

What is the A2A Protocol?

The A2A protocol is a standardized communication layer based on JSON-RPC 2.0 that allows AI agents to interact seamlessly. It defines:

• Message format and structure
• Task lifecycle management
• Artifact handling and history tracking
• Error handling and status reporting

Project Setup

Let's start by setting up our Python project. We'll use modern Python 3.13+ with FastAPI and related libraries.

1. Initialize the Project

2. Configure Dependencies

Create your pyproject.toml:

3. Install Dependencies

Building the Core A2A Server

1. Define A2A Message Models

First, let's create Pydantic models for the A2A protocol:

2. Create the FastAPI Application

3. Implement the Chess Agent

Environment Configuration

Create a .env file:

Running the Agent

1. Start Required Services

2. Run the FastAPI Server

Your A2A agent is now running at http://localhost:5001!

Testing the A2A Endpoint

Test with cURL

Expected Response

Integration with Telex

Once your Python A2A agent is deployed, you can integrate it with Telex just like any other A2A agent:

Advanced Features

1. Webhook Support for Async Processing

For long-running operations, implement webhook notifications:

2. Redis Session Storage

Store conversation state in Redis:

3. Error Handling and Validation

Implement robust error handling:

Deployment

Deploy to Production

1. Containerize with Docker:

2. Deploy to Cloud Platform:

Key Takeaways

1. Python + FastAPI is Perfect for A2A: Modern async support, type safety, and rich AI ecosystem
2. Pydantic Models Ensure Type Safety: Validate all A2A messages at runtime
3. Stateful Agents Need Session Storage: Use Redis or databases for conversation context
4. Artifacts Enable Rich Responses: Return both text and visual/data artifacts
5. JSON-RPC 2.0 Compliance is Critical: Follow the spec for interoperability

Comparison: Python vs TypeScript for A2A

Next Steps

Build your own A2A agents:

• Translation Agent: Multi-language support with OpenAI
• Data Analysis Agent: Process datasets with Pandas
• Vision Agent: Image analysis with computer vision
• RAG Agent: Knowledge retrieval with vector databases

The A2A protocol opens up endless possibilities. Whether you choose Python or TypeScript, you're building the future of agent communication!

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Resources:

• FastAPI Documentation
• A2A Protocol Specification
• Chess Agent Example
• Pydantic AI

Built with Python 3.13, FastAPI 0.115, and deployed October 2025.