> ## 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.

# Structured Output

> Get structured output from a vLLM agent with a Pydantic output_schema.

## Code

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

from agno.agent import Agent
from agno.models.vllm import VLLM
from pydantic import BaseModel, Field

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")
```

## Usage

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

**1. Install dependencies**

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

**2. Start vLLM server**

```bash theme={null}
vllm serve Qwen/Qwen2.5-7B-Instruct \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --dtype float16 \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.9
```

**3. Set your API key**

```bash theme={null}
export VLLM_API_KEY=xxx
```

**4. Run Agent**

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

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


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