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PydanticAI is a Python agent framework designed to make it less painful to build production-grade applications with Generative AI. Built by the team behind Pydantic, it provides a clean, type-safe way to build AI agents with structured outputs.

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Launch Phoenix

  • Phoenix Cloud
  • Command Line
  • Docker
  • Notebook
Sign up for Phoenix:
  1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
  2. Click Create Space, then follow the prompts to create and launch your space.
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint and API Key:From your new Phoenix Space
  1. Create your API key from the Settings page
  2. Copy your Hostname from the Settings page
  3. In your code, set your endpoint and API key: 
import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"
Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

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Install

pip install openinference-instrumentation-pydantic-ai pydantic-ai opentelemetry-sdk opentelemetry-exporter-otlp opentelemetry-api

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Setup

Set up tracing using OpenTelemetry and the PydanticAI instrumentation: 
import os
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from openinference.instrumentation.pydantic_ai import OpenInferenceSpanProcessor
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

# Set up the tracer provider
tracer_provider = TracerProvider()
trace.set_tracer_provider(tracer_provider)

# Add the OpenInference span processor
endpoint = f"{os.environ['PHOENIX_COLLECTOR_ENDPOINT']}/v1/traces"

# If you are using a local instance without auth, ignore these headers
headers = {"Authorization": f"Bearer {os.environ['PHOENIX_API_KEY']}"}
exporter = OTLPSpanExporter(endpoint=endpoint, headers=headers)

tracer_provider.add_span_processor(OpenInferenceSpanProcessor())
tracer_provider.add_span_processor(SimpleSpanProcessor(exporter))

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Basic Usage

Here’s a simple example using PydanticAI with automatic tracing: 
from pydantic import BaseModel
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
import nest_asyncio
nest_asyncio.apply()

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define your Pydantic model
class LocationModel(BaseModel):
    city: str
    country: str

# Create and configure the agent
model = OpenAIModel("gpt-4", provider='openai')
agent = Agent(model, output_type=LocationModel, instrument=True)

# Run the agent
result = agent.run_sync("The windy city in the US of A.")
print(result)

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Advanced Usage

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Agent with System Prompts and Tools

from pydantic import BaseModel, Field
from pydantic_ai import Agent, RunContext
from pydantic_ai.models.openai import OpenAIModel
from typing import List
import httpx

class WeatherInfo(BaseModel):
    location: str
    temperature: float = Field(description="Temperature in Celsius")
    condition: str
    humidity: int = Field(description="Humidity percentage")

# Create an agent with system prompts and tools
weather_agent = Agent(
    model=OpenAIModel("gpt-4"),
    output_type=WeatherInfo,
    system_prompt="You are a helpful weather assistant. Always provide accurate weather information.",
    instrument=True
)

@weather_agent.tool
async def get_weather_data(ctx: RunContext[None], location: str) -> str:
    """Get current weather data for a location."""
    # Mock weather API call - replace with actual weather service
    async with httpx.AsyncClient() as client:
        # This is a placeholder - use a real weather API
        mock_data = {
            "temperature": 22.5,
            "condition": "partly cloudy",
            "humidity": 65
        }
        return f"Weather in {location}: {mock_data}"

# Run the agent with tool usage
result = weather_agent.run_sync("What's the weather like in Paris?")
print(result)

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Observe

Now that you have tracing setup, all PydanticAI agent operations will be streamed to your running Phoenix instance for observability and evaluation. You’ll be able to see:
  • Agent interactions: Complete conversations between your application and the AI model
  • Structured outputs: Pydantic model validation and parsing results
  • Tool usage: When agents call external tools and their responses
  • Performance metrics: Response times, token usage, and success rates
  • Error handling: Validation errors, API failures, and retry attempts
  • Multi-agent workflows: Complex interactions between multiple agents
The traces will provide detailed insights into your AI agent behaviors, making it easier to debug issues, optimize performance, and ensure reliability in production.

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Resources