> ## Documentation Index
> Fetch the complete documentation index at: https://spacesail.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Async Agent with Structured Output

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

```python structured_output.py theme={null}
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

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U agno openai pydantic rich
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
          export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
          $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Create a Python file">
    Create a Python file and add the above code.

    ```bash theme={null}
    touch structured_output.py
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python structured_output.py
      ```

      ```bash Windows theme={null}
      python structured_output.py
      ```
    </CodeGroup>
  </Step>

  <Step title="Find All Cookbooks">
    Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

    <Link href="https://github.com/agno-agi/agno/tree/main/cookbook/agents/async" target="_blank">
      Agno Cookbooks on GitHub
    </Link>
  </Step>
</Steps>