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This example demonstrates how to use async agents with structured output schemas, comparing structured output mode versus JSON mode for generating movie scripts with defined data models.

Code

structured_output.py
import asyncio
from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.openai import OpenAIChat
from pydantic import BaseModel, Field
from rich.pretty import pprint  # noqa


class MovieScript(BaseModel):
    setting: str = Field(
        ..., description="Provide a nice setting for a blockbuster movie."
    )
    ending: str = Field(
        ...,
        description="Ending of the movie. If not available, provide a happy ending.",
    )
    genre: str = Field(
        ...,
        description="Genre of the movie. If not available, select action, thriller or romantic comedy.",
    )
    name: str = Field(..., description="Give a name to this movie")
    characters: List[str] = Field(..., description="Name of characters for this movie.")
    storyline: str = Field(
        ..., description="3 sentence storyline for the movie. Make it exciting!"
    )


# Agent that uses structured outputs
structured_output_agent = Agent(
    model=OpenAIChat(id="gpt-5-mini-2024-08-06"),
    description="You write movie scripts.",
    output_schema=MovieScript,
)

# Agent that uses JSON mode
json_mode_agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    description="You write movie scripts.",
    output_schema=MovieScript,
    use_json_mode=True,
)


# Get the response in a variable
# json_mode_response: RunOutput = json_mode_agent.arun("New York")
# pprint(json_mode_response.content)
# structured_output_response: RunOutput = structured_output_agent.arun("New York")
# pprint(structured_output_response.content)

asyncio.run(structured_output_agent.aprint_response("New York"))
asyncio.run(json_mode_agent.aprint_response("New York"))

Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U agno openai pydantic rich
3

Export your OpenAI API key

    export OPENAI_API_KEY="your_openai_api_key_here"
4

Create a Python file

Create a Python file and add the above code.
touch structured_output.py
5

Run Agent

python structured_output.py
6

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Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:Agno Cookbooks on GitHub
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