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Documentation Index

Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt

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Tasks support various response formats to structure the output according to your needs. You can specify the expected format using the response_format parameter.

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String Response (Default)

from upsonic import Agent, Task

# Create agent
agent = Agent(model="anthropic/claude-sonnet-4-5")

# Default string response
task = Task(
    description="What is the capital of Japan?",
    response_format=str
)

# Execute and print result
agent.print_do(task)

​
Pydantic Model Response

from upsonic import Agent, Task
from pydantic import BaseModel

class AnalysisResult(BaseModel):
    summary: str
    confidence: float
    recommendations: list[str]
    key_metrics: dict[str, float]

# Create agent
agent = Agent(model="anthropic/claude-sonnet-4-5")

# Task with structured response
task = Task(
    description="Analyze the current state of electric vehicle market and provide structured results",
    response_format=AnalysisResult
)

# Execute and access structured result
result = agent.print_do(task)
print(f"Summary: {result.summary}")
print(f"Confidence: {result.confidence}")
print(f"Recommendations: {result.recommendations}")
print(f"Key Metrics: {result.key_metrics}")

​
Complex Nested Models

from upsonic import Agent, Task
from pydantic import BaseModel
from typing import List, Optional

class Metric(BaseModel):
    name: str
    value: float
    unit: str

class Recommendation(BaseModel):
    title: str
    description: str
    priority: str
    estimated_impact: float

class DetailedAnalysis(BaseModel):
    summary: str
    confidence: float
    metrics: List[Metric]
    recommendations: List[Recommendation]
    risk_factors: Optional[List[str]] = None

# Create agent
agent = Agent(model="anthropic/claude-sonnet-4-5")

# Task with complex structured response
task = Task(
    description="Perform comprehensive analysis of the renewable energy sector with detailed metrics and recommendations",
    response_format=DetailedAnalysis
)

# Execute and access nested structured result
result = agent.print_do(task)
print(f"Summary: {result.summary}")
print(f"Confidence: {result.confidence}")
print(f"Metrics: {result.metrics}")
print(f"Recommendations: {result.recommendations}")

​
Response Format Types

TypeDescriptionUse Case
strSimple text responseBasic questions, summaries
BaseModelStructured Pydantic modelComplex data, analysis results
NoneNo format constraintFlexible responses

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Best Practices

  • Structured Data: Use Pydantic models for complex, structured responses
  • Field Validation: Leverage Pydantic’s built-in validation for data integrity
  • Optional Fields: Use Optional types for fields that might not always be present
  • Nested Models: Break down complex responses into smaller, reusable models
  • Type Hints: Always provide clear type hints for better IDE support and validation
  • Default Values: Set appropriate default values for optional fields