> ## 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.

# vLLM Structured Output

> Return a typed MovieScript object from a vLLM agent with a Pydantic output schema.

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

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

from typing import List

from agno.agent import Agent
from agno.models.vllm import VLLM
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=VLLM(id="Qwen/Qwen2.5-7B-Instruct", top_k=20, enable_thinking=False),
    description="You write movie scripts.",
    output_schema=MovieScript,
)

agent.print_response("Llamas ruling the world")

# ---------------------------------------------------------------------------
# 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 openai
```

**2. Export your vLLM API key**

```bash Mac/Linux theme={null}
export VLLM_API_KEY="your_vllm_api_key_here"
```

```bash Windows theme={null}
$Env:VLLM_API_KEY="your_vllm_api_key_here"
```

**3. Install vLLM**

Install vLLM in the environment that will serve the model:

```bash theme={null}
uv pip install -U vllm
```

**4. Start vLLM**

Serve the model used by this example:

```bash theme={null}
vllm serve Qwen/Qwen2.5-7B-Instruct
```

**5. 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/vllm/structured\_output.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/90_models/vllm/structured_output.py)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.