> ## 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.

# Agent with Structured Outputs

> Return a Pydantic MovieScript object from a Llama agent.

## Code

```python structured_output.py theme={null}
from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.meta import Llama
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 that uses a JSON schema output
json_schema_output_agent = Agent(
    model=Llama(id="Llama-4-Maverick-17B-128E-Instruct-FP8", temperature=0.1),
    output_schema=MovieScript,
)

json_schema_output_agent.print_response("New York")
```

## Usage

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

**1. Set your LLAMA API key**

```bash theme={null}
export LLAMA_API_KEY=YOUR_API_KEY
```

**2. Install dependencies**

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

**3. Run Agent**

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

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


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