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

# Gemini Interactions - Deep Research with multimodal input

> Deep Research accepts images and documents (PDFs) as input, then conducts web-based research grounded in that content.

Deep Research accepts images and documents (PDFs) as input, then conducts web-based research grounded in that content. Pass them as Agno `Image` / `File` objects with a URL (GCS / Gemini URIs pass through; regular HTTP URLs are downloaded and base64-encoded automatically).

```python deep_research_multimodal.py theme={null}
"""
Gemini Interactions - Deep Research with multimodal input
==========================================================

Deep Research accepts images and documents (PDFs) as input, then conducts
web-based research grounded in that content. Pass them as Agno `Image` /
`File` objects with a URL (GCS / Gemini URIs pass through; regular HTTP
URLs are downloaded and base64-encoded automatically).
"""

from agno.agent import Agent
from agno.media import File, Image
from agno.models.google import GeminiInteractions

agent = Agent(
    model=GeminiInteractions(
        agent="deep-research-preview-04-2026",
        thinking_summaries="auto",
    ),
    markdown=True,
)

if __name__ == "__main__":
    # --- Image-grounded research ---
    agent.print_response(
        "Analyze the interspecies dynamics in this image and research the "
        "symbiotic relationships shown.",
        images=[
            Image(
                url="https://storage.googleapis.com/generativeai-downloads/images/generated_elephants_giraffes_zebras_sunset.jpg"
            )
        ],
    )

    # --- Document-grounded research ---
    agent.print_response(
        "What is this document about, and how does it relate to current "
        "research trends?",
        files=[File(url="https://arxiv.org/pdf/1706.03762")],
    )
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno google-genai
    ```
  </Step>

  <Step title="Export your Google API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export GOOGLE_API_KEY="your_google_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:GOOGLE_API_KEY="your_google_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `deep_research_multimodal.py`, then run:

    ```bash theme={null}
    python deep_research_multimodal.py
    ```
  </Step>
</Steps>

Full source: [cookbook/90\_models/google/gemini\_interactions/deep\_research\_multimodal.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/google/gemini_interactions/deep_research_multimodal.py)