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

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.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U agno openai pydantic rich
   
3. Export your OpenAI API key

   Mac/Linux

       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

   Mac

   python structured_output.py
   
6. Find All Cookbooks

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

   Agno Cookbooks on GitHub