> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-service-account.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LiteLLM Structured Output

> Return a MovieScript Pydantic model via LiteLLM using JSON mode and native structured outputs.

```python structured_output.py theme={null}
"""
Litellm Structured Output
=========================

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

from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.litellm import LiteLLM
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!"
    )


# Agent that uses JSON mode
json_mode_agent = Agent(
    model=LiteLLM(id="gpt-4o"),
    description="You write movie scripts.",
    output_schema=MovieScript,
    use_json_mode=True,
)

# Agent that uses native structured outputs.
# Set supports_native_structured_outputs=True for the providers that support it.
structured_output_agent = Agent(
    model=LiteLLM(id="gpt-4o", supports_native_structured_outputs=True),
    description="You write movie scripts.",
    output_schema=MovieScript,
    structured_outputs=True,
)

json_mode_agent.print_response("New York")
structured_output_agent.print_response("New York")

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

if __name__ == "__main__":
    pass
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno litellm
    ```
  </Step>

  <Step title="Export your LiteLLM API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export LITELLM_API_KEY="your_litellm_api_key_here"
      ```

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

  <Step title="Run the example">
    Save the code above as `structured_output.py`, then run:

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

Full source: [cookbook/90\_models/litellm/structured\_output.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/litellm/structured_output.py)