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

# Llama OpenAI Structured Output

> Return a MovieScript Pydantic model from Llama 4 Maverick through the OpenAI-compatible client.

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

Cookbook example for `meta/llama_openai/structured_output.py`.
"""

from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.meta import LlamaOpenAI
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 a JSON schema output
json_schema_output_agent = Agent(
    model=LlamaOpenAI(id="Llama-4-Maverick-17B-128E-Instruct-FP8", temperature=0.1),
    description="You are a helpful assistant. Summarize the movie script based on the location in a JSON object.",
    output_schema=MovieScript,
)

json_schema_output_agent.print_response("New York")

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

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

## Run the Example

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

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno llama-api-client openai
```

**2. Export your Meta Llama API key**

```bash Mac/Linux theme={null}
export LLAMA_API_KEY="your_llama_api_key_here"
```

```bash Windows theme={null}
$Env:LLAMA_API_KEY="your_llama_api_key_here"
```

**3. Run the example**

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

```bash theme={null}
python structured_output.py
```

Full source: [cookbook/90\_models/meta/llama\_openai/structured\_output.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/90_models/meta/llama_openai/structured_output.py)


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