Gemini

uv pip install google-genai agno

gcloud auth application-default login

export GOOGLE_GENAI_USE_VERTEXAI="true"
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="us-central1"

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

agent = Agent(
    model=Gemini(
        id="gemini-3.5-flash",
        vertexai=True,
        project_id="your-project-id",
        location="us-central1",
    ),
)

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

agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    markdown=True,
)

agent.print_response(
    "Tell me about this image.",
    images=[Image(url="https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg")],
)

from io import BytesIO
from agno.agent import Agent, RunOutput
from agno.models.google import Gemini
from PIL import Image

agent = Agent(
    model=Gemini(
        id="gemini-2.5-flash-image",
        response_modalities=["Text", "Image"],
    )
)

run_response = agent.run("Make me an image of a cat in a tree.")

if run_response and isinstance(run_response, RunOutput) and run_response.images:
    for image_response in run_response.images:
        image_bytes = image_response.content
        if image_bytes:
            image = Image.open(BytesIO(image_bytes))
            image.save("generated_image.png")

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

agent = Agent(
    model=Gemini(id="gemini-3.5-flash", search=True),
    markdown=True,
)

agent.print_response("What are the latest developments in AI?")

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

datastore_id = "projects/your-project-id/locations/global/collections/default_collection/dataStores/your-datastore-id"

agent = Agent(
    model=Gemini(
        id="gemini-2.5-flash",
        vertexai=True,
        vertexai_search=True,
        vertexai_search_datastore=datastore_id,
    ),
    markdown=True,
)

agent.print_response("What are our company's policies regarding remote work?")

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

agent = Agent(
    model=Gemini(id="gemini-2.5-flash", url_context=True),
    markdown=True,
)

url1 = "https://www.foodnetwork.com/recipes/ina-garten/perfect-roast-chicken-recipe-1940592"
url2 = "https://www.allrecipes.com/recipe/83557/juicy-roasted-chicken/"

agent.print_response(
    f"Compare the ingredients and cooking times from the recipes at {url1} and {url2}"
)

from pathlib import Path
from agno.agent import Agent
from agno.models.google import Gemini

model = Gemini(id="gemini-2.5-flash")
agent = Agent(model=model, markdown=True)

# Create a File Search store and upload documents
store = model.create_file_search_store(display_name="My Docs")
operation = model.upload_to_file_search_store(
    file_path=Path("documents/sample.txt"),
    store_name=store.name,
    display_name="Sample Document",
)
model.wait_for_operation(operation)

# Configure model to use File Search
model.file_search_store_names = [store.name]

# Query the documents
run = agent.run("What are the key points in the document?")
print(run.content)

# Cleanup
model.delete_file_search_store(store.name, force=True)

from agno.agent import Agent
from agno.models.google import Gemini
from agno.utils.audio import write_wav_audio_to_file

agent = Agent(
    model=Gemini(
        id="gemini-2.5-flash-preview-tts",
        response_modalities=["AUDIO"],
        speech_config={
            "voice_config": {"prebuilt_voice_config": {"voice_name": "Kore"}}
        },
    )
)

run_output = agent.run("Say cheerfully: Have a wonderful day!")

if run_output.response_audio is not None:
    audio_data = run_output.response_audio.content
    write_wav_audio_to_file("tmp/cheerful_greeting.wav", audio_data)

from agno.agent import Agent
from agno.models.google import Gemini
from google import genai

client = genai.Client()

# Upload file and create cache
txt_file = client.files.upload(file="large_document.txt")
cache = client.caches.create(
    model="gemini-3.5-flash",
    config={
        "system_instruction": "You are an expert at analyzing transcripts.",
        "contents": [txt_file],
        "ttl": "300s",
    },
)

# Use the cached content - no need to resend the file
agent = Agent(
    model=Gemini(id="gemini-3.5-flash", cached_content=cache.name),
)
run_output = agent.run("Find a lighthearted moment from this transcript")

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

agent = Agent(
    model=Gemini(id="gemini-2.5-pro", thinking_budget=1280, include_thoughts=True),
    markdown=True,
)

agent.print_response("Solve this logic puzzle...")

agent = Agent(
    model=Gemini(id="gemini-3.1-pro-preview", thinking_level="low"),  # Supported levels depend on the model
    markdown=True,
)

from agno.agent import Agent
from agno.models.google import Gemini
from pydantic import BaseModel

class MovieScript(BaseModel):
    name: str
    genre: str
    storyline: str

agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    output_schema=MovieScript,
)

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.hackernews import HackerNewsTools

agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    tools=[HackerNewsTools()],
    markdown=True,
)

agent.print_response("Summarize the top stories on HackerNews.")