PydanticAI — Type-Safe AI Agent Framework

What it is

PydanticAI is an agent framework built by the creators of Pydantic and FastAPI. It brings type safety, structured outputs, and dependency injection to AI agent development. Agents return validated Pydantic models instead of raw strings, and the framework handles tool definitions, system prompts, and conversation flow with the same type-driven approach that made Pydantic popular in the Python ecosystem.

The framework targets Python developers building production AI applications who want compile-time guarantees on their agent outputs. It supports multiple LLM providers including Anthropic, OpenAI, and Google.

How it saves time or tokens

PydanticAI's structured outputs eliminate post-processing parsing. When an agent returns a Pydantic model, the output is validated automatically. Failed validations trigger automatic retries with error context, so the LLM corrects its output format without manual intervention. This reduces the token overhead of format correction loops and removes the need for brittle regex parsing of LLM responses.

How to use

1. Install PydanticAI:

pip install pydantic-ai

1. Create a simple agent:

from pydantic_ai import Agent

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Be concise, reply with one sentence.',
)
result = agent.run_sync('Where does hello world come from?')
print(result.output)

1. Add structured outputs and tools for production use cases.

Example

Agent with structured output and tool use:

from pydantic import BaseModel
from pydantic_ai import Agent, tool

class CityInfo(BaseModel):
    name: str
    country: str
    population: int
    notable_fact: str

@tool
def search_database(city: str) -> str:
    'Look up city information from the database.'
    return f'{city}: population 8.3M, known for finance'

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    output_type=CityInfo,
    tools=[search_database],
)
result = agent.run_sync('Tell me about New York')
print(result.output.population)  # 8300000 - typed int

The output is a validated Pydantic model with typed fields.

Related on TokRepo

• AI Tools for Agents — Compare agent frameworks for building AI applications
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Common pitfalls

• Structured outputs add token overhead because the model must generate valid JSON. For simple text responses, use string output type instead of Pydantic models.
• Tool functions must have type annotations and docstrings. PydanticAI uses these to generate the tool schema sent to the LLM.
• Retry logic for failed validations consumes additional tokens. Set a max_retries limit to prevent runaway costs on consistently malformed outputs.
• Always check the official documentation for the latest version-specific changes and migration guides before upgrading in production environments.
• For team deployments, establish clear guidelines on configuration and usage patterns to ensure consistency across developers.