Implementing Retry Logic with PydanticAI: A Step-by-Step Tutorial
We’re building a reliable data-fetching service that handles network interruptions like a pro — because nobody wants their app crashing over a simple timeout.
Prerequisites
- Python 3.11+
- pip install pydantic-ai
- Basic understanding of Pydantic models
- Knowledge of network request libraries (like requests or httpx)
Step 1: Setting Up Your Environment
Before implementing retry logic, we need to get our development environment set up. This will involve installing the PydanticAI package and ensuring we have all necessary libraries.
# Prepare your Python environment
# Install pydantic-ai and httpx libraries
!pip install pydantic-ai httpx
This command installs PydanticAI along with httpx, which is crucial for making HTTP requests. I chose httpx over requests because of its native support for async operations, which is a great advantage for performance. Now, if you’re coming from a requests background, be prepared for a slight learning curve, but trust me, it’s worth it.
Step 2: Creating a Basic Pydantic Model
With the environment ready, let’s create our first Pydantic model. A model in Pydantic defines the schema for your data – it’s like a blueprint. We’re going to create one that handles a simulated API response.
from pydantic import BaseModel
class ApiResponseModel(BaseModel):
status: str
data: dict
This model has two fields: status and data. The status field indicates if the request was successful, and data contains the response payload. The clever part here is that Pydantic will validate the structure of the API response against this model.
Step 3: Implementing the HTTP Request Logic
Now, let’s create a function to make an HTTP request. We will use Pydantic to validate the response. Here’s the kicker: we’re going to add retry logic, so if our request fails, we can try again instead of throwing a tantrum.
import httpx
import time
async def fetch_data(url: str) -> ApiResponseModel:
async with httpx.AsyncClient() as client:
response = await client.get(url)
response.raise_for_status()
return ApiResponseModel(**response.json())
This code fetches data from the specified URL. If it fails, we need to implement the retry logic. No one wants to deal with HTTP errors in production, right?
Step 4: Adding Retry Logic
It’s time to add that all-important retry logic. We’re going to implement a simple mechanism for retrying the request a specified number of times before finally giving up.
async def fetch_with_retries(url: str, retries: int = 3, delay: int = 2) -> ApiResponseModel:
for i in range(retries):
try:
return await fetch_data(url)
except httpx.HTTPStatusError as e:
print(f"Attempt {i + 1} failed: {e}. Retrying...")
time.sleep(delay) # Wait before retrying
except Exception as e:
print(f"Unexpected error: {e}. Retrying...")
time.sleep(delay)
raise Exception(f"Failed to fetch data after {retries} attempts.")
In this function, we’re taking an optional retries parameter to let us specify how many times to attempt the request before giving up. We also have a delay parameter, so if our request fails, we pause for a couple of seconds instead of bombarding the server with requests.
The Gotchas
So far, so good, but here are some pitfalls I’ve stumbled into time and again that can trip you up in production:
- Exponential Backoff: It’s crucial for avoiding overwhelming the server after a failure. Make sure you increase the delay each time you retry. Simple static delays won’t cut it when the system starts overwhelming the server.
- Handling Different Exceptions: You might want to retry only on transient issues. Not all exceptions warrant a retry, so refine your exception handling to catch specific errors.
- Logging Failures: Don’t forget to log details about the failures. It’s a lifesaver for diagnosing issues later.
- Async Context Managers: If you’re mixing sync and async code, watch out for context managers. They don’t work the same way, and it can lead to nasty bugs.
- Network Latency: Be aware that network latency can significantly affect your retries. Test under different conditions to structure your logic around realistic scenarios.
Full Code Example
Here’s a complete version of what we’ve built, fully operational. This will make it easy for you to copy over and use immediately. Just replace YOUR_API_URL_HERE with your target endpoint.
import httpx
import time
from pydantic import BaseModel
class ApiResponseModel(BaseModel):
status: str
data: dict
async def fetch_data(url: str) -> ApiResponseModel:
async with httpx.AsyncClient() as client:
response = await client.get(url)
response.raise_for_status()
return ApiResponseModel(**response.json())
async def fetch_with_retries(url: str, retries: int = 3, delay: int = 2) -> ApiResponseModel:
for i in range(retries):
try:
return await fetch_data(url)
except httpx.HTTPStatusError as e:
print(f"Attempt {i + 1} failed: {e}. Retrying...")
time.sleep(delay)
except Exception as e:
print(f"Unexpected error: {e}. Retrying...")
time.sleep(delay)
raise Exception(f"Failed to fetch data after {retries} attempts.")
# Example Usage
# Ensure this is run in an async context, like asyncio.run()
url = "YOUR_API_URL_HERE"
data = await fetch_with_retries(url)
print(data)
What’s Next?
Your next move? Experiment with adding custom logging and metrics to your retry logic. You’ll want to monitor how often requests fail, the time taken for retry attempts, etc. Having this data can drastically enhance your application’s reliability.
FAQ
Q: Why use PydanticAI over traditional options?
A: PydanticAI allows for structured data handling and automatic validation of your API responses, something that’s a pain to manage manually. With 15,593 stars on GitHub, PydanticAI has proven itself popular, which can mean better community support and resources.
Q: What if my API requires authentication?
A: You’ll need to extend the HTTP client to include authentication headers. This could be done by modifying the httpx.AsyncClient() initialization to include an auth parameter if your API supports basic auth, or using OAuth tokens as needed.
Q: Is there an alternative to retry logic in real-time applications?
A: While retry logic is essential, consider implementing circuit breaker patterns as an additional layer of resilience. This allows your app to respond to failures more gracefully and can reduce server load during outages.
Recommendations for Different Developer Personas
If you’re new to this world of data fetching:
- New Developer: Focus on mastering the basics, get comfortable with making HTTP requests, and pattern your models after real APIs.
- Intermediate Developer: Look into advanced retry mechanisms and performance optimization strategies. Consider building a custom retry method for better flexibility.
- Senior Developer: Spend time fine-tuning error handling and observer patterns that can provide better insights into your application’s behavior under load. Metrics and logging are your friends!
Data as of March 20, 2026. Sources: pydantic/pydantic-ai, Pydantic AI Retries, Add Custom Request Retry Behavior.
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