Pydantic AI

Pydantic AI is a powerful Python agent framework developed by the team behind the renowned Pydantic validation library. Its core mission is to streamline the development of production-ready applications powered by Generative AI, providing a developer experience that is as intuitive and efficient as FastAPI. By integrating seamlessly with the Python ecosystem, it allows developers to build complex AI agents using familiar control flows, type safety, and best practices, moving beyond simple scripts to create robust, maintainable systems.

The framework is fundamentally model-agnostic, offering out-of-the-box support for a wide array of Large Language Models (LLMs), including those from OpenAI, Anthropic, Google (Gemini), Deepseek, Ollama, Groq, Cohere, and Mistral. This flexibility ensures that developers are not locked into a single provider and can choose the best model for their specific needs. At its heart, Pydantic AI harnesses the power of Pydantic Validation to enforce structured, reliable, and consistent outputs from LLMs, which is a critical requirement for production applications.

How to use Pydantic AI

Getting started with Pydantic AI is straightforward for any Python developer. The process involves defining an agent, its tools, and its expected output structure.

1. Installation: First, install the library using pip: pip install pydantic-ai

2. Create a Simple Agent: For a basic interaction, you can instantiate an `Agent` and run it with a prompt. The framework handles the communication with the specified LLM.

from Pydantic AI import Agent

agent = Agent(
    'google-gla:gemini-1.5-flash',
    system_prompt='Be concise, reply with one sentence.',
)

result = agent.run_sync('Where does "hello world" come from?')
print(result.output)

3. Build an Advanced Agent with Tools: For more complex tasks, you can define structured outputs using Pydantic's `BaseModel`, create tools that the agent can use, and inject dependencies. For example, a bank support agent can be equipped with a tool to check a customer's balance.

from pydantic import BaseModel, Field
from Pydantic AI import Agent, RunContext

# Define the structured output
class SupportOutput(BaseModel):
    support_advice: str = Field(description='Advice returned to the customer')
    block_card: bool = Field(description="Whether to block the customer's card")

# Create the agent
support_agent = Agent(
    'openai:gpt-4o',
    output_type=SupportOutput,
    system_prompt='You are a support agent in our bank...'
)

# Define a tool the agent can use
@support_agent.tool
async def customer_balance(ctx: RunContext, include_pending: bool) -> float:
    """Returns the customer's current account balance."""
    # ... logic to fetch balance from a database ...
    return 123.45

# Run the agent
result = await support_agent.run('What is my balance?')
print(result.output)

4. Monitoring and Debugging: Pydantic AI integrates seamlessly with Pydantic Logfire, allowing you to monitor, debug, and track the performance and behavior of your LLM applications in real-time with minimal setup.

Core Features of Pydantic AI

• Model-Agnostic: Supports a wide range of LLMs including OpenAI, Anthropic, Gemini, Ollama, Groq, and more, with a simple interface to add custom models.
• Structured Responses: Utilizes Pydantic Validation to ensure that LLM outputs are consistently structured and validated against a defined schema.
• Type-Safe Design: Leverages Python's type hints to provide a powerful and informative type-checking experience, reducing errors.
• Python-Centric Design: Enables building AI agents using standard Python control flow and composition, making code more readable and maintainable.
• Dependency Injection: An optional system to provide data and services (like database connections) to agents, simplifying testing and development.
• Streamed Responses: Supports continuous streaming of LLM responses with immediate validation, enabling real-time applications.
• Pydantic Logfire Integration: Offers seamless observability for real-time debugging, performance monitoring, and behavior tracking.
• Graph Support: Includes Pydantic Graph to define complex application flows and state machines, preventing spaghetti code in sophisticated agents.

Use Cases for Pydantic AI

Pydantic AI is ideal for building a variety of production-grade AI applications:

• Customer Support Automation: Create intelligent chatbots that can understand user queries, use tools to access customer data (e.g., order status, account balance), and perform actions like blocking a credit card or creating a support ticket.
• Internal Data Tools: Build agents that allow non-technical users to query databases or internal APIs using natural language, generating reports or fetching specific information.
• Automated Workflow Agents: Develop systems that can process unstructured data (like emails or documents), extract relevant information, and trigger actions in other systems (e.g., creating a task in a project management tool).
• Complex Multi-Step Reasoning: Implement agents that can break down a complex problem into smaller steps, use different tools for each step, and combine the results to provide a comprehensive answer.

Advantages of Pydantic AI

The primary advantage of Pydantic AI is its focus on production-readiness and developer experience. By being built on the solid foundation of Pydantic, it brings reliability, predictability, and maintainability to AI development. Its Python-centric approach means developers don't have to learn a new DSL or paradigm, leading to faster development cycles. The model-agnostic design provides crucial flexibility, while the tight integration with Pydantic Logfire makes observability a first-class citizen, which is essential for managing complex AI systems in production.

Pricing and Plans

Pydantic AI is a free, open-source Python library. There are no direct costs, subscriptions, or licensing fees associated with using the framework itself. Users are only responsible for the costs incurred from the API usage of the underlying Large Language Models (e.g., OpenAI API, Google AI Platform) they choose to integrate with their applications.