Pydantic-AI: The Future of Building Production-Ready AI Applications with Pydantic

Want to build powerful AI applications with the ease and efficiency of Python's best practices? Pydantic-AI steps in as the agent framework that bridges the gap between Pydantic's robust validation and the dynamic world of Large Language Models (LLMs). Built by the creators of Pydantic, this library promises a seamless experience for developers aiming to create production-grade GenAI applications.

What is Pydantic-AI and Why Should You Care?

Pydantic-AI is engineered to bring the "FastAPI feeling" to GenAI development. It leverages Pydantic's established validation capabilities to ensure your AI application's inputs and outputs are strongly typed, consistent, and reliable. Pydantic AI simplifies interactions with LLMs, making it easier to build, test, and deploy AI-powered features.

Pydantic-AI offers:

• Model-Agnostic Design: Works with various LLMs like OpenAI, Gemini, Anthropic, Deepseek, and more. You're not locked into a single provider!
• Real-Time Debugging: Seamlessly integrates with Pydantic Logfire for enhanced monitoring and debugging.
• Structured Responses: Guarantees model outputs adhere to predefined Pydantic models, ensuring data consistency.
• Dependency Injection: Facilitates testing and iterative development through flexible dependency management.

Unleash the Power of Pydantic for AI Development

Pydantic-AI leverages the strengths of Pydantic, the validation layer used by major AI libraries. This includes those developed by OpenAI, Anthropic, LangChain, and LlamaIndex. By building on this foundation, Pydantic-AI offers several key advantages. You can ensure the data used by your LLM applications is correctly formatted and adheres to declared types. Validate and structure model outputs, ensuring responses are consistent across runs. You can also catch errors early through informative type checking.

Getting Started with Pydantic-AI: A "Hello World" Example

Let's dive into a basic example to illustrate the simplicity of Pydantic-AI.

from pydantic_ai import Agent

# Define a simple agent with a system prompt
agent = Agent(
  'google-gla:gemini-1.5-flash',
  system_prompt='Be concise, reply with one sentence.',
)

# Run the agent synchronously
result = agent.run_sync('Where does "hello world" come from?')
print(result.data)

This example demonstrates how easy it is to create an agent set a system prompt, and interact with an LLM.

Advanced Features: Tools and Dependency Injection

Pydantic-AI truly shines when you start utilizing its advanced features like tools and dependency injection. These features allow you to build more complex and robust AI applications.

Tools: Register functions that the LLM can call during its response generation. This enables you to provide the LLM with access to external data sources or functionalities.

Dependency Injection: Pass data, connections, and logic into your system prompts and tool functions, making your agents highly customizable and testable.

Here's a sneak peek at a support agent example:

from dataclasses import dataclass
from pydantic import BaseModel, Field
from pydantic_ai import Agent, RunContext

# Dependency Injection
@dataclass
class SupportDependencies:
    customer_id: int
    # The Pydantic model defines our final output structure
class SupportResult(BaseModel):
    support_advice: str = Field(description='Advice returned to the customer.')
    block_card: bool = Field(description="Whether to block the customer's card.")
    risk: int = Field(description='Risk level of query', ge=0, le=10)

# Initialize the Agent
support_agent = Agent(
    'openai:gpt-4o',
    deps_type=SupportDependencies,
    result_type=SupportResult,
    system_prompt=('You are a support agent in our bank, give the customer support and judge the risk level of their query.'),
)

This code snippet showcases how to define dependencies and a structured result type for your agent, ensuring type safety and consistent output. Pydantic-AI helps in applications where standard control flow can degrade into spaghetti code via graph support.

Why Pydantic-AI Stands Out

• Python-Centric Design: Uses familiar Python constructs for building AI agents.
• Type-Safe: Maximizes the power and effectiveness of type checking.
• Streamed Responses: Provides immediate validation with continuous LLM output streaming.

Dive Deeper into Pydantic-AI

Pydantic-AI is currently in beta, so the team welcomes your feedback and suggestions! If you are looking for agent frameworks to build upon pydantic, this might be the tool for you. To get started, follow the examples. Explore the documentation and API reference to unlock the full potential of Pydantic-AI.