Aimlapi Structured Output

These retained examples come from the source revision linked below. Use the installation and model configuration in the AI/ML API guide, or use the compatible OpenAILike adaptation below with an older package.

structured_output.py

"""
Aimlapi Structured Output
=========================

Cookbook example for `aimlapi/structured_output.py`.
"""

from typing import List

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

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------


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!"
    )


json_mode_agent = Agent(
    model=AIMLAPI(id="gpt-5.2"),
    description="You help people write movie scripts.",
    output_schema=MovieScript,
    use_json_mode=True,
)

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

# json_mode_agent.print_response("New York")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

This recipe uses JSON Object mode and local Pydantic validation.

output_schema describes the expected type. If parsing or validation fails, result.content can remain a string. Before accessing schema fields in a run result, use isinstance(result.content, YourSchema), replacing YourSchema with the class you passed as output_schema.

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno openai

Export your AI/ML API key

export AIMLAPI_API_KEY="your_aimlapi_api_key_here"

Use the working gateway adapter

In the saved Python file, replace the AIMLAPI import with:

from os import environ
from agno.models.openai.like import OpenAILike

Replace the entire AIMLAPI(...) model expression with:

OpenAILike(
    id="openai/gpt-5-2",
    api_key=environ["AIMLAPI_API_KEY"],
    base_url="https://api.aimlapi.com/v1",
)

Keep the surrounding Agent(...) settings and run calls. This route supports image input as well as text; use the provider's exact IDs when choosing another model.

Run the example

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

python structured_output.py

Full source: cookbook/90_models/aimlapi/structured_output.py