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"""
In this example, we upload a text file to Google and then create a cache.

This greatly saves on tokens during normal prompting.
"""

from pathlib import Path
from time import sleep

import requests
from agno.agent import Agent
from agno.models.google import Gemini
from google import genai
from google.genai.types import UploadFileConfig

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

client = genai.Client()

# Download txt file
url = "https://storage.googleapis.com/generativeai-downloads/data/a11.txt"
path_to_txt_file = Path(__file__).parent.joinpath("a11.txt")
if not path_to_txt_file.exists():
    print("Downloading txt file...")
    with path_to_txt_file.open("wb") as wf:
        response = requests.get(url, stream=True)
        for chunk in response.iter_content(chunk_size=32768):
            wf.write(chunk)

# Upload the txt file using the Files API
remote_file_path = Path("a11.txt")
remote_file_name = f"files/{remote_file_path.stem.lower().replace('_', '-')}"

txt_file = None
try:
    txt_file = client.files.get(name=remote_file_name)
    print(f"Txt file exists: {txt_file.uri}")
except Exception:
    pass

if not txt_file:
    print("Uploading txt file...")
    txt_file = client.files.upload(
        file=path_to_txt_file, config=UploadFileConfig(name=remote_file_name)
    )

    # Wait for the file to finish processing
    while txt_file and txt_file.state and txt_file.state.name == "PROCESSING":
        print("Waiting for txt file to be processed.")
        sleep(2)
        txt_file = client.files.get(name=remote_file_name)

    print(f"Txt file processing complete: {txt_file.uri}")

# Create a cache with 5min TTL
cache = client.caches.create(
    model="gemini-3-flash-preview",
    config={
        "system_instruction": "You are an expert at analyzing transcripts.",
        "contents": [txt_file],
        "ttl": "300s",
    },
)

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

if __name__ == "__main__":
    agent = Agent(
        model=Gemini(id="gemini-3-flash-preview", cached_content=cache.name),
    )
    run_output = agent.run(
        "Find a lighthearted moment from this transcript",  # No need to pass the txt file
    )
    print("Metrics: ", run_output.metrics)

​
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 file_upload_with_cache.py