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The Gemini API now supports GCS URIs natively (up to 2GB). No need to download or re-upload - just pass the gs:// URI directly. 
"""
Example: Analyze files directly from Google Cloud Storage (GCS).

The Gemini API now supports GCS URIs natively (up to 2GB).
No need to download or re-upload - just pass the gs:// URI directly.

Requirements:
- Vertex AI must be enabled (GCS URIs require OAuth, not API keys)
- Run: gcloud auth application-default login
- Set environment variables: GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION
- Your GCS bucket must be accessible to your credentials

Supported formats: PDF, JSON, HTML, CSS, XML, images (PNG, JPEG, WebP, GIF)
"""

from agno.agent import Agent
from agno.media import File
from agno.models.google import Gemini

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

# GCS requires Vertex AI (OAuth credentials), not API keys
# Set GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION env vars
agent = Agent(
    model=Gemini(
        id="gemini-3-flash-preview",
        vertexai=True,
    ),
    markdown=True,
)

# Pass GCS URI directly - no download or re-upload needed
agent.print_response(
    "Summarize this document and extract key insights.",
    files=[
        File(
            url="gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf",  # Sample PDF
            mime_type="application/pdf",
        )
    ],
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

​
Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/90_models/google/gemini

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python gcs_file_input.py