> ## 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.

# LiteLLM PDF Input Bytes

> Pass a downloaded PDF as raw bytes to a LiteLLM agent and summarize its contents.

```python pdf_input_bytes.py theme={null}
"""
Litellm Pdf Input Bytes
=======================

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

from pathlib import Path

from agno.agent import Agent
from agno.media import File
from agno.models.litellm import LiteLLM
from agno.utils.media import download_file

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

pdf_path = Path(__file__).parent.joinpath("ThaiRecipes.pdf")

download_file(
    "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf", str(pdf_path)
)

agent = Agent(
    model=LiteLLM(id="openai/gpt-5.6-luna"),
    markdown=True,
)

agent.print_response(
    "Summarize the contents of the attached file.",
    files=[
        File(
            content=pdf_path.read_bytes(),
        ),
    ],
)

# ---------------------------------------------------------------------------
# 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 litellm
```

**2. Export your LiteLLM API key**

```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"
```

**3. Run the example**

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

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

Full source: [cookbook/90\_models/litellm/pdf\_input\_bytes.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/90_models/litellm/pdf_input_bytes.py)


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