Agent with Structured Outputs

Code

from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.azure import AzureOpenAI
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 = Agent(
    model=AzureOpenAI(id="gpt-5.2"),
    description="You help people write movie scripts.",
    output_schema=MovieScript,
)

# Get the response in a variable
run: RunOutput = agent.run("New York")
pprint(run.content)

# agent.print_response("New York")

Create or select a gpt-5.2 chat deployment. Set AZURE_OPENAI_DEPLOYMENT to its actual deployment name, and use the key and resource endpoint for that deployment. You may omit the deployment setting only when an existing deployment is named exactly like id. Keep id aligned with the deployed model family.

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Set your API key

export AZURE_OPENAI_API_KEY=xxx
export AZURE_OPENAI_ENDPOINT=xxx
export AZURE_OPENAI_DEPLOYMENT="your_chat_deployment"

Install dependencies

uv pip install -U openai agno

Run Agent

Save the code above as structured_output.py, then run:

python structured_output.py