What is Pydantic AI
Pydantic AI is a Python agent framework that combines AI language models with Pydantic for structured, typed outputs and easy tool integration. It enables developers to build AI agents that produce validated, strongly typed results and interact with external tools using a clean, declarative API.Pydantic AI is a Python agent framework that integrates AI models with Pydantic to produce typed, structured outputs and enable tool use.How it works
Pydantic AI works by wrapping AI language models and leveraging Pydantic data models to define the expected structure of AI outputs. When you run a prompt through the agent, it parses the AI's response into strongly typed Python objects, ensuring data validity and ease of use. Additionally, Pydantic AI supports defining tools as Python functions that the agent can call during its reasoning process, enabling dynamic interaction with external APIs or logic.
Think of it as a bridge between the unstructured text generation of AI models and the structured, typed data your application needs, with built-in support for tool invocation to extend AI capabilities.
Concrete example
from pydantic_ai import Agent
from pydantic import BaseModel
# Define a Pydantic model for structured output
class Result(BaseModel):
answer: str
confidence: float
# Create an agent using OpenAI gpt-4o-mini
agent = Agent(
"openai:gpt-4o-mini",
result_type=Result,
system_prompt="You are a helpful assistant."
)
# Run the agent with a question
result = agent.run_sync("What is Retrieval-Augmented Generation?")
print(f"Answer: {result.data.answer}")
print(f"Confidence: {result.data.confidence}")Answer: Retrieval-Augmented Generation (RAG) is an AI technique that combines retrieval of relevant documents with language model generation. Confidence: 0.95
When to use it
Use Pydantic AI when you need AI-generated outputs to be strongly typed and validated, reducing errors from unstructured text parsing. It is ideal for applications requiring structured data extraction, such as form filling, data validation, or generating JSON-like responses.
Also use it when your AI agent needs to interact with external tools or APIs during its reasoning process, as Pydantic AI supports defining and invoking such tools seamlessly.
Do not use it if you only need simple text completions without structured output or tool integration, as it adds complexity unnecessary for basic use cases.
Key terms
| Term | Definition |
|---|---|
Pydantic | A Python library for data validation and settings management using Python type annotations. |
Agent | An AI interface in Pydantic AI that runs prompts and returns typed results. |
result_type | A Pydantic model class defining the expected structure of the AI output. |
tool | A Python function registered with the agent that can be called during AI reasoning. |
system_prompt | Initial instructions given to the AI model to guide its behavior. |
Key Takeaways
-
Pydantic AIensures AI outputs are validated and strongly typed usingPydanticmodels. - It supports defining and invoking external tools as Python functions within AI agent workflows.
- Use it to build reliable AI agents that produce structured data and interact with APIs or logic.
- Avoid it for simple text generation tasks without structured output needs.
- The framework integrates seamlessly with OpenAI and other AI models using a declarative API.