PydanticAI

Code-First Frameworks

Strict Type-Safe Agent Pipelines

Maintained by Pydantic

Core Architecture

PydanticAI leverages Python type annotations and Pydantic models to guarantee strict type-safety for AI agent inputs, outputs, and intermediate tool calls. It integrates natively with type checkers (mypy/pyright) and structured generation libraries to ensure that model outputs map precisely to Python classes before execution continues.

How to Use & Configuration

code_example.pypython

from pydantic import BaseModel
from pydantic_ai import Agent, RunContext

class UserProfile(BaseModel):
    name: str
    skills: list[str]

agent = Agent(
    'openai:gpt-4o',
    result_type=UserProfile,
    system_prompt="Extract user details."
)

result = agent.run_sync("Hi, I am Shivam, an expert in Next.js and PyTorch.")
profile = result.data

Technology Payment Plans

Open Source CoreFree

Completely free and open-source under the MIT license, with no usage limits.

Enterprise SupportCustom

Optional production support SLA plans from the core Pydantic maintenance team.

Key Advantages

•Guarantees 100% structured data validation on model responses
•Eliminates python runtime type errors in multi-agent tool pipelines
•Lightweight, clean code structure with minimal dependencies

Comparison Analysis

Technology	Primary Use Case & Engineering Focus
PydanticAI	Strict data validation, Python type safety, and minimal dependencies
LangGraph	LangGraph is better suited for mapping complex, cyclic state transitions rather than data structures.