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"""Structured output example using Ollama with the OpenAI Responses API.

This demonstrates using Pydantic models for structured output with Ollama's
Responses API endpoint.

Requirements:
- Ollama v0.13.3 or later running locally
- Run: ollama pull llama3.1:8b
"""

from typing import List

from agno.agent import Agent
from agno.models.ollama import OllamaResponses
from pydantic import BaseModel, Field

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


class MovieScript(BaseModel):
    name: str = Field(..., description="Give a name to this movie")
    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.",
    )
    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=OllamaResponses(id="gpt-oss:20b"),
    description="You write movie scripts.",
    output_schema=MovieScript,
)

agent.print_response("New York")

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

if __name__ == "__main__":
    pass

​
Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/90_models/ollama/responses

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python structured_output.py