Structured Outputs

Structured outputs let you define the exact shape of agent responses using Pydantic models. The agent returns validated, type-safe data instead of free-form text.

Quick Start

from lyzr import Studio
from pydantic import BaseModel, Field

studio = Studio(api_key="your-api-key")

# Define your output structure
class ProductInfo(BaseModel):
    name: str = Field(description="Product name")
    price: float = Field(description="Price in USD")
    in_stock: bool = Field(description="Whether item is in stock")
    features: list[str] = Field(description="List of key features")

# Create agent
agent = studio.create_agent(
    name="Product Analyzer",
    provider="gpt-4o",
    role="Product information extractor",
    goal="Extract structured product data"
)

# Run with structured output
response = agent.run(
    "The iPhone 15 Pro costs $999 and has a titanium design, A17 chip, and USB-C. Currently available.",
    response_format=ProductInfo
)

# Access typed data
product = response.structured_output
print(f"Name: {product.name}")        # "iPhone 15 Pro"
print(f"Price: ${product.price}")     # "$999.0"
print(f"In Stock: {product.in_stock}")  # True
print(f"Features: {product.features}")  # ["titanium design", "A17 chip", "USB-C"]

Defining Output Models

Basic Model

from pydantic import BaseModel, Field

class Person(BaseModel):
    name: str = Field(description="Full name")
    age: int = Field(description="Age in years")
    email: str = Field(description="Email address")

With Descriptions

Use Field(description=...) to guide the model:

class OrderSummary(BaseModel):
    order_id: str = Field(description="Unique order identifier")
    total: float = Field(description="Total amount in USD")
    items: int = Field(description="Number of items ordered")
    status: str = Field(description="Current order status: pending, shipped, or delivered")

With Default Values

class UserPreferences(BaseModel):
    theme: str = Field(default="light", description="UI theme")
    notifications: bool = Field(default=True, description="Enable notifications")
    language: str = Field(default="en", description="Preferred language code")

Nested Models

class Address(BaseModel):
    street: str = Field(description="Street address")
    city: str = Field(description="City name")
    country: str = Field(description="Country name")
    postal_code: str = Field(description="Postal/ZIP code")

class Company(BaseModel):
    name: str = Field(description="Company name")
    industry: str = Field(description="Industry sector")
    headquarters: Address = Field(description="HQ address")
    employee_count: int = Field(description="Number of employees")

With Enums

from enum import Enum

class Priority(str, Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    URGENT = "urgent"

class Ticket(BaseModel):
    title: str = Field(description="Ticket title")
    description: str = Field(description="Issue description")
    priority: Priority = Field(description="Priority level")

With Lists

class Article(BaseModel):
    title: str = Field(description="Article title")
    author: str = Field(description="Author name")
    tags: list[str] = Field(description="Article tags")
    sections: list[str] = Field(description="Main section headings")

Optional Fields

from typing import Optional

class Review(BaseModel):
    product: str = Field(description="Product name")
    rating: int = Field(description="Rating 1-5")
    comment: Optional[str] = Field(default=None, description="Optional comment")
    verified_purchase: Optional[bool] = Field(default=None, description="Verified purchase flag")

Using Structured Outputs

With agent.run()

response = agent.run(
    "Extract information from this text...",
    response_format=MyModel
)

# Access the structured data
data = response.structured_output  # Type: MyModel

With Knowledge Bases

class Answer(BaseModel):
    answer: str = Field(description="The answer to the question")
    confidence: float = Field(description="Confidence score 0-1")
    sources: list[str] = Field(description="Source documents used")

response = agent.run(
    "What is our return policy?",
    knowledge_bases=[kb],
    response_format=Answer
)

answer = response.structured_output
print(f"Answer: {answer.answer}")
print(f"Confidence: {answer.confidence}")
print(f"Sources: {answer.sources}")

With Sessions (Memory)

class ConversationSummary(BaseModel):
    topic: str = Field(description="Main conversation topic")
    key_points: list[str] = Field(description="Key points discussed")
    action_items: list[str] = Field(description="Action items identified")
    sentiment: str = Field(description="Overall sentiment: positive, neutral, negative")

# Conversation with memory
agent.run("Let's discuss the Q1 roadmap", session_id="planning_session")
agent.run("We need to prioritize the mobile app", session_id="planning_session")
agent.run("John will handle the backend", session_id="planning_session")

# Get structured summary
response = agent.run(
    "Summarize our discussion",
    session_id="planning_session",
    response_format=ConversationSummary
)

summary = response.structured_output

Examples

Data Extraction

class ContactInfo(BaseModel):
    name: str = Field(description="Person's full name")
    email: str = Field(description="Email address")
    phone: str = Field(description="Phone number")
    company: str = Field(description="Company name")
    title: str = Field(description="Job title")

agent = studio.create_agent(
    name="Contact Extractor",
    provider="gpt-4o",
    role="Extract contact information from text"
)

text = """
Hi, I'm Sarah Johnson, VP of Sales at TechCorp.
You can reach me at sarah.j@techcorp.com or call 555-123-4567.
"""

response = agent.run(text, response_format=ContactInfo)
contact = response.structured_output

print(f"Name: {contact.name}")      # Sarah Johnson
print(f"Email: {contact.email}")    # sarah.j@techcorp.com
print(f"Company: {contact.company}")  # TechCorp

Sentiment Analysis

class SentimentAnalysis(BaseModel):
    sentiment: str = Field(description="Overall sentiment: positive, negative, or neutral")
    confidence: float = Field(description="Confidence score 0-1")
    key_phrases: list[str] = Field(description="Phrases indicating sentiment")
    summary: str = Field(description="Brief summary of the content")

agent = studio.create_agent(
    name="Sentiment Analyzer",
    provider="gpt-4o",
    role="Analyze sentiment in text"
)

review = "This product exceeded my expectations! Great quality and fast shipping."

response = agent.run(review, response_format=SentimentAnalysis)
analysis = response.structured_output

print(f"Sentiment: {analysis.sentiment}")  # positive
print(f"Confidence: {analysis.confidence}")  # 0.95

Content Classification

class ContentClassification(BaseModel):
    category: str = Field(description="Primary category")
    subcategories: list[str] = Field(description="Subcategories")
    topics: list[str] = Field(description="Main topics covered")
    audience: str = Field(description="Target audience")
    content_type: str = Field(description="Type: article, tutorial, review, etc.")

agent = studio.create_agent(
    name="Content Classifier",
    provider="gpt-4o",
    role="Classify content into categories"
)

article = "10 Python Tips for Data Scientists: From pandas tricks to visualization..."

response = agent.run(article, response_format=ContentClassification)
classification = response.structured_output

Entity Extraction

class Entity(BaseModel):
    name: str = Field(description="Entity name")
    type: str = Field(description="Entity type: person, organization, location, date, etc.")
    context: str = Field(description="Context in which entity appears")

class EntityExtraction(BaseModel):
    entities: list[Entity] = Field(description="All entities found in text")
    summary: str = Field(description="Brief summary of the document")

agent = studio.create_agent(
    name="Entity Extractor",
    provider="gpt-4o",
    role="Extract named entities from text"
)

news = "Apple CEO Tim Cook announced new products at the Cupertino headquarters yesterday."

response = agent.run(news, response_format=EntityExtraction)
extraction = response.structured_output

for entity in extraction.entities:
    print(f"{entity.name} ({entity.type})")

Task Breakdown

class Task(BaseModel):
    title: str = Field(description="Task title")
    description: str = Field(description="Task description")
    priority: str = Field(description="Priority: high, medium, low")
    estimated_hours: float = Field(description="Estimated hours to complete")
    dependencies: list[str] = Field(description="Task dependencies")

class ProjectPlan(BaseModel):
    project_name: str = Field(description="Project name")
    tasks: list[Task] = Field(description="List of tasks")
    total_hours: float = Field(description="Total estimated hours")
    critical_path: list[str] = Field(description="Critical path task titles")

agent = studio.create_agent(
    name="Project Planner",
    provider="gpt-4o",
    role="Break down projects into tasks"
)

request = "Build a user authentication system with login, registration, and password reset"

response = agent.run(request, response_format=ProjectPlan)
plan = response.structured_output

print(f"Project: {plan.project_name}")
print(f"Total Hours: {plan.total_hours}")
for task in plan.tasks:
    print(f"  - {task.title} ({task.priority}): {task.estimated_hours}h")

Best Practices

Use Descriptive Field Names

# Good
class Order(BaseModel):
    order_id: str
    customer_name: str
    total_amount: float

# Avoid
class Order(BaseModel):
    id: str
    name: str
    amount: float

Add Clear Descriptions

# Good
price: float = Field(description="Price in USD, excluding tax")

# Avoid
price: float = Field(description="Price")

Use Appropriate Types

# Good
age: int  # Integer for whole numbers
price: float  # Float for decimals
is_active: bool  # Boolean for yes/no
tags: list[str]  # List for multiple values

# Avoid
age: str  # Don't use string for numbers
is_active: str  # Don't use "yes"/"no" strings

Constrain Values Where Possible

from pydantic import Field

class Rating(BaseModel):
    score: int = Field(ge=1, le=5, description="Rating from 1 to 5")
    confidence: float = Field(ge=0, le=1, description="Confidence 0-1")

Handle Optional Data

from typing import Optional

class Review(BaseModel):
    rating: int  # Required
    title: Optional[str] = None  # Optional
    comment: Optional[str] = None  # Optional

Error Handling

from pydantic import ValidationError

try:
    response = agent.run(text, response_format=MyModel)
    data = response.structured_output
except ValidationError as e:
    print(f"Validation error: {e}")
except Exception as e:
    print(f"Error: {e}")

Supported Providers

Structured outputs work best with:

Provider	Model	Support
OpenAI	gpt-4o, gpt-4o-mini	Excellent
OpenAI	gpt-4-turbo	Good
Anthropic	claude-3.5-sonnet	Good
Google	gemini-1.5-pro	Good

Some models handle structured outputs better than others. GPT-4o provides the most reliable structured output support.

Getting Started

Agents

Knowledge Bases

Memory

Tools

Contexts

RAI Guardrails

Structured Outputs

File & Image Output

Streaming

Reference

Structured Outputs

Quick Start

Defining Output Models

Basic Model

With Descriptions

With Default Values

Nested Models

With Enums

With Lists

Optional Fields

Using Structured Outputs

With agent.run()

With Knowledge Bases

With Sessions (Memory)

Examples

Data Extraction

Sentiment Analysis

Content Classification

Entity Extraction

Task Breakdown

Best Practices

Use Descriptive Field Names

Add Clear Descriptions

Use Appropriate Types

Constrain Values Where Possible

Handle Optional Data

Error Handling

Supported Providers

​Quick Start

​Defining Output Models

​Basic Model

​With Descriptions

​With Default Values

​Nested Models

​With Enums

​With Lists

​Optional Fields

​Using Structured Outputs

​With agent.run()

​With Knowledge Bases

​With Sessions (Memory)

​Examples

​Data Extraction

​Sentiment Analysis

​Content Classification

​Entity Extraction

​Task Breakdown

​Best Practices

​Use Descriptive Field Names

​Add Clear Descriptions

​Use Appropriate Types

​Constrain Values Where Possible

​Handle Optional Data

​Error Handling

​Supported Providers

Quick Start

Defining Output Models

Basic Model

With Descriptions

With Default Values

Nested Models

With Enums

With Lists

Optional Fields

Using Structured Outputs

With agent.run()

With Knowledge Bases

With Sessions (Memory)

Examples

Data Extraction

Sentiment Analysis

Content Classification

Entity Extraction

Task Breakdown

Best Practices

Use Descriptive Field Names

Add Clear Descriptions

Use Appropriate Types

Constrain Values Where Possible

Handle Optional Data

Error Handling

Supported Providers