Overview The Narada Python SDK uses Pydantic models to define structured output schemas, ensuring consistent and type-safe data extraction from automation tasks.
Using Structured Output Define your data structure using Pydantic models and pass it to the SDK:
import asyncio from narada import Agent, BrowserEnvironment from pydantic import BaseModel, Field # Define your data structure class JobListing ( BaseModel ): title: str = Field( description = "Job title" ) company: str = Field( description = "Company name" ) location: str = Field( description = "Job location" ) salary: str | None = Field( description = "Salary range if available" ) remote: bool = Field( description = "Whether the job is remote" ) async def main () -> None : env = BrowserEnvironment() agent = Agent( environment = env) try : # Use structured output to extract job data response = await agent.run( prompt = 'search for "Python developer" jobs on LinkedIn and extract details of the first job listing' , output_schema = JobListing ) # Access structured data job = response.structured_output assert job is not None print ( f "Found job: { job.title } at { job.company } " ) print ( f "Location: { job.location } " ) if job.salary: print ( f "Salary: { job.salary } " ) print ( f "Remote: { 'Yes' if job.remote else 'No' } " ) finally : await env.close() if __name__ == "__main__" : asyncio.run(main()) Supported Data Types Basic Types
Optional Fields
Lists
Nested Models
from pydantic import BaseModel, Field class UserProfile ( BaseModel ): name: str = Field( description = "User's full name" ) age: int = Field( description = "User's age" ) email: str = Field( description = "User's email address" ) active: bool = Field( description = "Whether user is active" )
from typing import Optional class Product ( BaseModel ): name: str = Field( description = "Product name" ) price: float = Field( description = "Product price" ) description: str | None = Field( description = "Product description" ) stock: int | None = Field( description = "Units in stock" )
from typing import List class SearchResults ( BaseModel ): top_links: list[ str ] = Field( description = "List of top result URLs" )
class Address ( BaseModel ): street: str = Field( description = "Street address" ) city: str = Field( description = "City name" ) country: str = Field( description = "Country name" ) class Company ( BaseModel ): name: str = Field( description = "Company name" ) address: Address = Field( description = "Company address" ) employee_count: int = Field( description = "Number of employees" )
Best Practices
Clear Descriptions Add descriptive Field annotations to guide data extraction
Type Safety Use appropriate types and Optional fields for reliable data
Modular Models Break complex schemas into smaller, reusable models
Validation Let Pydantic handle data validation automatically
Use structured output whenever you need to extract specific data points from web pages. The SDK will ensure the data matches your schema exactly.
