Pydantic AI Integration
Last updated September 11, 2025
Table of Contents
Pydantic AI is a Python agent framework that enables developers to build production-grade applications and agents with generative AI. You can use Heroku’s Pydantic AI integration for various use cases, including advanced tooling, Model Context Protocol (MCP) and Agent2Agent protocol (A2A), and modular, agentic workflows that support enterprise demands.
Setup and Configuration
Set the INFERENCE_KEY to the API key and INFERENCE_URL to the base URL with environment variables:
export INFERENCE_KEY='your-heroku-api-key'
export INFERENCE_URL='https://us.inference.heroku.com'
Then, configure the Pydantic AI agent:
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.heroku import HerokuProvider
model = OpenAIModel(
'claude-4-sonnet',
provider=HerokuProvider(api_key='your-heroku-inference-key'),
)
agent = Agent(model)
MCP
You can use Heroku Managed Inference and Agents and Pydantic AI to create agentic workflows with built-in tools and tool calling with MCP.
MCP Client: Pydantic AI agents can act as an MCP Client, connecting to MCP servers to use their tools.
MCP Server: Agents can be exposed as MCP servers, allowing other agents to use them as tools.
A2A
Pydantic’s FastA2A library simplifies implementation of A2A protocol in Python. To expose a Pydantic AI agent as an A2A server:
from pydantic_ai import Agent
agent = Agent('HerokuProvider:claude-4-sonnet', instructions='Be fun!')
app = agent.to_a2a()
Then, run the example with uvicorn:
uvicorn agent_to_a2a:app --host 0.0.0.0 --port 8000