Pydantic-AI: The GenAI App Framework Built by the Pydantic Team

Want to build powerful AI applications with the ease and elegance of FastAPI? Pydantic-AI brings the familiar Pydantic experience to the world of Generative AI, simplifying how you build production-ready AI solutions. This Python agent framework lets you harness the power of LLMs with structured responses, dependency injection, and more.

Why Pydantic-AI is a Game Changer for GenAI Development

• Effortless Integration: Built by the creators of Pydantic, it seamlessly connects with your existing Pydantic workflows.
• Supports Multiple Models: OpenAI, Gemini, Anthropic, and more – Pydantic-AI is model-agnostic, giving you flexibility.
• Pydantic Logfire Integration: Real-time debugging and performance monitoring? Yes, please! Track your LLM app's behavior.
• Structured Responses: Validate and structure model outputs using Pydantic, ensuring consistent and reliable results. Use cases includes pydantic ai agent for banking industry.

Python-Centric Design for AI Solutions

Leverage Python's best practices for AI app development. Build AI-driven projects with familiar control flow and agent composition.

Key Features That Make Pydantic-AI Stand Out

• Type Safety: Powerful type checking makes your code more robust and informative.
• Dependency Injection: Test and iterate with ease using optional dependency injection for system prompts, tools, and validators.
• Streamed Responses: Get rapid and accurate results with immediate, validated LLM output streaming.
• Graph Support: Define graphs with typing hints for complex apps where standard control flow falters.

Get Started with Pydantic-AI

"Hello, World" Example

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.data)

Tools & Dependency Injection Example: Bank Support Agent

Build powerful agents like a bank support agent with Pydantic-AI's tools and dependency injection features.

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

@dataclass
class SupportDependencies:
 customer_id: int
 db: DatabaseConn

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)

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.'
),
)

@support_agent.system_prompt
async def add_customer_name(ctx: RunContext[SupportDependencies]) -> str:
 customer_name = await ctx.deps.db.customer_name(id=ctx.deps.customer_id)
 return f"The customer's name is {customer_name!r}"

@support_agent.tool
async def customer_balance(
 ctx: RunContext[SupportDependencies], include_pending: bool
) -> float:
 balance = await ctx.deps.db.customer_balance(
 id=ctx.deps.customer_id,
 include_pending=include_pending,
)
 return balance

... # Add more tools and prompts

async def main():
 deps = SupportDependencies(customer_id=123, db=DatabaseConn())
 result = await support_agent.run('What is my balance?', deps=deps)
 print(result.data)

 result = await support_agent.run('I just lost my card!', deps=deps)
 print(result.data)

Take Your GenAI Development to the Next Level:

Explore the examples and dive into the documentation to unleash the full potential of Pydantic-AI. Build robust, scalable, and maintainable AI applications today! Use cases includes pydantic ai tools for banking applications.