Structured Output

Introduction

Structured output enables you to get type-safe, validated responses from language models using schema definitions. Instead of receiving raw text that you need to parse, you can define the exact structure you want and receive a validated object that matches your schema. This transforms unstructured LLM outputs into reliable, program-friendly data structures that integrate seamlessly with your application’s type system and validation rules.

In Python, structured output uses Pydantic models. In TypeScript, it uses Zod schemas for runtime validation and type inference.

flowchart LR
    A[Schema Definition] --> B[Agent Invocation]
    B --> C[LLM] --> D[Validated Object]
    D --> E[AgentResult.structured_output]

Key benefits:

• Type Safety: Get typed objects instead of raw strings
• Automatic Validation: Schema validation ensures responses match your structure
• Clear Documentation: Schema serves as documentation of expected output
• IDE Support: IDE type hinting from LLM-generated responses
• Error Prevention: Catch malformed responses early

Basic Usage

Define an output structure using a schema. In Python, use a Pydantic model and pass it to structured_output_model. In TypeScript, use a Zod schema and pass it to structuredOutputSchema. Then, access the validated output from the AgentResult.

Python

from pydantic import BaseModel, Field
from strands import Agent

# 1) Define the Pydantic model
class PersonInfo(BaseModel):
    """Model that contains information about a Person"""
    name: str = Field(description="Name of the person")
    age: int = Field(description="Age of the person")
    occupation: str = Field(description="Occupation of the person")

# 2) Pass the model to the agent
agent = Agent()
result = agent(
    "John Smith is a 30 year-old software engineer",
    structured_output_model=PersonInfo
)

# 3) Access the `structured_output` from the result
person_info: PersonInfo = result.structured_output
print(f"Name: {person_info.name}")      # "John Smith"
print(f"Age: {person_info.age}")        # 30
print(f"Job: {person_info.occupation}") # "software engineer"

TypeScript

// 1) Define the Zod schema
const PersonSchema = z.object({
  name: z.string().describe('Name of the person'),
  age: z.number().describe('Age of the person'),
  occupation: z.string().describe('Occupation of the person'),
})

type Person = z.infer<typeof PersonSchema>

// 2) Pass the schema to the agent
const agent = new Agent({
  structuredOutputSchema: PersonSchema,
})

const result = await agent.invoke('John Smith is a 30 year-old software engineer')

// 3) Access the `structuredOutput` from the result
// TypeScript infers the type from the schema
const person = result.structuredOutput as Person
console.log(`Name: ${person.name}`) // "John Smith"
console.log(`Age: ${person.age}`) // 30
console.log(`Job: ${person.occupation}`) // "software engineer"

Async Support

Structured Output is supported with async in both Python and TypeScript:

Python

import asyncio
agent = Agent()
result = asyncio.run(
    agent.invoke_async(
        "John Smith is a 30 year-old software engineer",
        structured_output_model=PersonInfo
    )
)

TypeScript

// Agent.invoke() is already async in TypeScript
const agent = new Agent({ structuredOutputSchema: PersonSchema })
const result = await agent.invoke('John Smith is a 30 year-old software engineer')

More Information

How It Works

The structured output system converts your schema definitions into tool specifications that guide the language model to produce correctly formatted responses. All of the model providers supported in Strands can work with Structured Output.

In Python, Strands accepts the structured_output_model parameter in agent invocations, which manages the conversion, validation, and response processing automatically. In TypeScript, the structuredOutputSchema parameter (either at agent initialization or per-invocation) handles this process. The validated result is available in the AgentResult.structured_output (Python) or AgentResult.structuredOutput (TypeScript) field.

Error Handling

When structured output validation fails, Strands throws a custom StructuredOutputException that can be caught and handled appropriately:

Python

from pydantic import ValidationError
from strands.types.exceptions import StructuredOutputException

try:
    result = agent(prompt, structured_output_model=MyModel)
except StructuredOutputException as e:
    print(f"Structured output failed: {e}")

TypeScript

try {
  const result = await agent.invoke('some prompt')
} catch (error) {
  if (error instanceof StructuredOutputException) {
    console.log(`Structured output failed: ${error.message}`)
  }
}

Migration from Legacy API

Deprecated API (Python Only)

The Agent.structured_output() and Agent.structured_output_async() methods are deprecated in Python. Use the new structured_output_model parameter approach instead.

Before (Deprecated)

Python

# Old approach - deprecated
result = agent.structured_output(PersonInfo, "John is 30 years old")
print(result.name)  # Direct access to model fields

TypeScript

// No deprecated API in TypeScript

After (Recommended)

Python

# New approach - recommended
result = agent("John is 30 years old", structured_output_model=PersonInfo)
print(result.structured_output.name)  # Access via structured_output field

TypeScript

// TypeScript approach
const agent = new Agent({ structuredOutputSchema: PersonSchema })
const result = await agent.invoke('John is 30 years old')
console.log(result.structuredOutput.name)  // Access via structuredOutput field

Best Practices

• Keep schemas focused: Define specific schemas for clear purposes
• Use descriptive field names: Include helpful descriptions with field metadata
• Handle errors gracefully: Implement proper error handling strategies with fallbacks

Related Documentation

For Python, refer to Pydantic documentation:

• Models and schema definition
• Field types and constraints
• Custom validators

For TypeScript, refer to Zod documentation:

• Zod documentation
• Schema types
• Schema methods

Cookbook

Auto Retries with Validation

Automatically retry validation when initial extraction fails due to schema validation:

Python

from strands.agent import Agent
from pydantic import BaseModel, field_validator


class Name(BaseModel):
    first_name: str

    @field_validator("first_name")
    @classmethod
    def validate_first_name(cls, value: str) -> str:
        if not value.endswith('abc'):
            raise ValueError("You must append 'abc' to the end of my name")
        return value


agent = Agent()
result = agent("What is Aaron's name?", structured_output_model=Name)

TypeScript

const NameSchema = z.object({
  firstName: z.string().refine((val) => val.endsWith('abc'), {
    message: "You must append 'abc' to the end of my name",
  }),
})

const agent = new Agent({ structuredOutputSchema: NameSchema })
const result = await agent.invoke("What is Aaron's name?")

Streaming Structured Output

Stream agent execution while using structured output. The structured output is available in the final result:

Python

from strands import Agent
from pydantic import BaseModel, Field

class WeatherForecast(BaseModel):
    """Weather forecast data."""
    location: str
    temperature: int
    condition: str
    humidity: int
    wind_speed: int
    forecast_date: str

streaming_agent = Agent()

async for event in streaming_agent.stream_async(
    "Generate a weather forecast for Seattle: 68°F, partly cloudy, 55% humidity, 8 mph winds, for tomorrow",
    structured_output_model=WeatherForecast
):
    if "data" in event:
        print(event["data"], end="", flush=True)
    elif "result" in event:
        print(f'The forecast for today is: {event["result"].structured_output}')

TypeScript

const WeatherForecastSchema = z.object({
  location: z.string(),
  temperature: z.number(),
  condition: z.string(),
  humidity: z.number(),
  windSpeed: z.number(),
  forecastDate: z.string(),
})

type WeatherForecast = z.infer<typeof WeatherForecastSchema>

const agent = new Agent({ structuredOutputSchema: WeatherForecastSchema })

for await (const event of agent.stream(
  'Generate a weather forecast for Seattle: 68°F, partly cloudy, 55% humidity, 8 mph winds, for tomorrow'
)) {
  if (event.type === 'agentResultEvent') {
    const forecast = event.result.structuredOutput as WeatherForecast
    console.log(`The forecast is: ${JSON.stringify(forecast)}`)
  }
}

Combining with Tools

Combine structured output with tool usage to format tool execution results:

Python

from strands import Agent
from strands_tools import calculator
from pydantic import BaseModel, Field

class MathResult(BaseModel):
    operation: str = Field(description="the performed operation")
    result: int = Field(description="the result of the operation")

tool_agent = Agent(
    tools=[calculator]
)
res = tool_agent("What is 42 + 8", structured_output_model=MathResult)

TypeScript

const calculatorTool = tool({
  name: 'calculator',
  description: 'Perform basic arithmetic operations',
  inputSchema: z.object({
    operation: z.enum(['add', 'subtract', 'multiply', 'divide']),
    a: z.number(),
    b: z.number(),
  }),
  callback: (input) => {
    const ops = {
      add: input.a + input.b,
      subtract: input.a - input.b,
      multiply: input.a * input.b,
      divide: input.a / input.b,
    }
    return ops[input.operation]
  },
})

const MathResultSchema = z.object({
  operation: z.string().describe('the performed operation'),
  result: z.number().describe('the result of the operation'),
})

const agent = new Agent({
  tools: [calculatorTool],
  structuredOutputSchema: MathResultSchema,
})
const result = await agent.invoke('What is 42 + 8')

Multiple Output Types

Reuse a single agent instance with different structured output schemas for varied extraction tasks:

Python

from strands import Agent
from pydantic import BaseModel, Field
from typing import Optional

class Person(BaseModel):
    """A person's basic information"""
    name: str = Field(description="Full name")
    age: int = Field(description="Age in years", ge=0, le=150)
    email: str = Field(description="Email address")
    phone: Optional[str] = Field(description="Phone number", default=None)

class Task(BaseModel):
    """A task or todo item"""
    title: str = Field(description="Task title")
    description: str = Field(description="Detailed description")
    priority: str = Field(description="Priority level: low, medium, high")
    completed: bool = Field(description="Whether task is completed", default=False)


agent = Agent()
person_res = agent("Extract person: John Doe, 35, john@test.com", structured_output_model=Person)
task_res = agent("Create task: Review code, high priority, completed", structured_output_model=Task)

TypeScript

const PersonSchema = z.object({
  name: z.string().describe('Full name'),
  age: z.number().min(0).max(150).describe('Age in years'),
  email: z.string().email().describe('Email address'),
  phone: z.string().optional().describe('Phone number'),
})

const TaskSchema = z.object({
  title: z.string().describe('Task title'),
  description: z.string().describe('Detailed description'),
  priority: z.enum(['low', 'medium', 'high']).describe('Priority level'),
  completed: z.boolean().default(false).describe('Whether task is completed'),
})

type Person = z.infer<typeof PersonSchema>
type Task = z.infer<typeof TaskSchema>

const personAgent = new Agent({ structuredOutputSchema: PersonSchema })
const taskAgent = new Agent({ structuredOutputSchema: TaskSchema })

const personResult = await personAgent.invoke('Extract person: John Doe, 35, john@test.com')
const taskResult = await taskAgent.invoke('Create task: Review code, high priority, completed')

Using Conversation History

Extract structured information from prior conversation context without repeating questions:

Python

from strands import Agent
from pydantic import BaseModel
from typing import Optional

agent = Agent()

# Build up conversation context
agent("What do you know about Paris, France?")
agent("Tell me about the weather there in spring.")

class CityInfo(BaseModel):
    city: str
    country: str
    population: Optional[int] = None
    climate: str

# Extract structured information from the conversation
result = agent(
    "Extract structured information about Paris from our conversation",
    structured_output_model=CityInfo
)

print(f"City: {result.structured_output.city}")     # "Paris"
print(f"Country: {result.structured_output.country}") # "France"

TypeScript

const CityInfoSchema = z.object({
  city: z.string(),
  country: z.string(),
  population: z.number().optional(),
  climate: z.string(),
})

type CityInfo = z.infer<typeof CityInfoSchema>

const agent = new Agent({ structuredOutputSchema: CityInfoSchema })

// Build up conversation context
await agent.invoke('What do you know about Paris, France?')
await agent.invoke('Tell me about the weather there in spring.')

// Extract structured information from the conversation
const result = await agent.invoke('Extract structured information about Paris from our conversation')

const cityInfo = result.structuredOutput as CityInfo
console.log(`City: ${cityInfo.city}`) // "Paris"
console.log(`Country: ${cityInfo.country}`) // "France"

Agent-Level Defaults

You can also set a default structured output schema that applies to all agent invocations:

Python

class PersonInfo(BaseModel):
    name: str
    age: int
    occupation: str

# Set default structured output model for all invocations
agent = Agent(structured_output_model=PersonInfo)
result = agent("John Smith is a 30 year-old software engineer")

print(f"Name: {result.structured_output.name}")      # "John Smith"
print(f"Age: {result.structured_output.age}")        # 30
print(f"Job: {result.structured_output.occupation}") # "software engineer"

TypeScript

const PersonSchema = z.object({
  name: z.string(),
  age: z.number(),
  occupation: z.string(),
})

type Person = z.infer<typeof PersonSchema>

// Set default structured output schema for all invocations
const agent = new Agent({ structuredOutputSchema: PersonSchema })
const result = await agent.invoke('John Smith is a 30 year-old software engineer')

const person = result.structuredOutput as Person
console.log(`Name: ${person.name}`) // "John Smith"
console.log(`Age: ${person.age}`) // 30
console.log(`Job: ${person.occupation}`) // "software engineer"

Note

Since this is on the agent init level, not the invocation level, the expectation is that the agent will attempt structured output for each invocation.

Overriding Agent Defaults

Even when you set a default schema at the agent initialization level, you can override it for specific invocations:

Python

class PersonInfo(BaseModel):
    name: str
    age: int
    occupation: str

class CompanyInfo(BaseModel):
    name: str
    industry: str
    employees: int

# Agent with default PersonInfo model
agent = Agent(structured_output_model=PersonInfo)

# Override with CompanyInfo for this specific call
result = agent(
    "TechCorp is a software company with 500 employees",
    structured_output_model=CompanyInfo
)

print(f"Company: {result.structured_output.name}")      # "TechCorp"
print(f"Industry: {result.structured_output.industry}") # "software"
print(f"Size: {result.structured_output.employees}")    # 500

TypeScript

const PersonSchema = z.object({
  name: z.string(),
  age: z.number(),
  occupation: z.string(),
})

const CompanySchema = z.object({
  name: z.string(),
  industry: z.string(),
  employees: z.number(),
})

type Company = z.infer<typeof CompanySchema>

// Agent with default PersonInfo schema
const personAgent = new Agent({ structuredOutputSchema: PersonSchema })

// Create a new agent with CompanyInfo schema for this specific use case
const companyAgent = new Agent({ structuredOutputSchema: CompanySchema })
const result = await companyAgent.invoke('TechCorp is a software company with 500 employees')

const company = result.structuredOutput as Company
console.log(`Company: ${company.name}`) // "TechCorp"
console.log(`Industry: ${company.industry}`) // "software"
console.log(`Size: ${company.employees}`) // 500