Gemini

Use Google’s Gemini models through Google AI Studio or Google Cloud Vertex AI - platforms that provide access to large language models and other services. We recommend experimenting to find the best-suited model for your use case. Here are some general recommendations in the Gemini 2.x family of models:

gemini-2.0-flash is good for most use-cases.
gemini-2.0-flash-lite is the most cost-effective model.
gemini-2.5-pro-exp-03-25 is the strongest multi-modal model.

Refer to the Google AI Studio documentation and the Vertex AI documentation for information on available model versions.

Authentication

You can use Gemini models through either Google AI Studio or Google Cloud’s Vertex AI:

Google AI Studio

Set the GOOGLE_API_KEY environment variable. You can get one from Google AI Studio.

export GOOGLE_API_KEY=***

setx GOOGLE_API_KEY ***

Vertex AI

To use Vertex AI in Google Cloud:

Refer to the Vertex AI documentation to set up a project and development environment.
Install the gcloud CLI and authenticate (refer to the quickstart for more details):

gcloud auth application-default login

Enable Vertex AI API and set the project ID environment variable (alternatively, you can set project_id in the Agent config):

Export the following variables:

export GOOGLE_GENAI_USE_VERTEXAI="true"
export GOOGLE_CLOUD_PROJECT="your-gcloud-project-id"
export GOOGLE_CLOUD_LOCATION="your-gcloud-location"

Or update your Agent configuration:

agent = Agent(
    model=Gemini(
        id="gemini-1.5-flash",
        vertexai=True,
        project_id="your-gcloud-project-id",
        location="your-gcloud-location",
    ),
)

To use Vertex AI Search:

# Replace with your actual datastore ID
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_search=True,
        vertexai_search_datastore=datastore_id,
        vertexai=True,  # Required for Vertex AI Search
    ),
)

Example

Use Gemini with your Agent:

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

# Using Google AI Studio
agent = Agent(
    model=Gemini(id="gemini-2.0-flash"),
    markdown=True,
)

# Or using Vertex AI
agent = Agent(
    model=Gemini(
        id="gemini-2.0-flash",
        vertexai=True,
        project_id="your-project-id",  # Optional if GOOGLE_CLOUD_PROJECT is set
        location="us-central1",  # Optional
    ),
    markdown=True,
)

# Print the response in the terminal
agent.print_response("Share a 2 sentence horror story.")

View more examples here.

Grounding and Search

Gemini models support grounding and search capabilities through optional parameters. This automatically sends tools for grounding or search to Gemini. See more details here. To enable these features, set the corresponding parameter when initializing the Gemini model: To use grounding:

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

agent = Agent(
    model=Gemini(id="gemini-2.0-flash", grounding=True),
    show_tool_calls=True,
    markdown=True,
)

agent.print_response("Any news from USA?")

To use search:

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

agent = Agent(
    model=Gemini(id="gemini-2.0-flash", search=True),
    show_tool_calls=True,
    markdown=True,
)

agent.print_response("What's happening in France?")

Combine URL context with Google Search to get a more in-depth analysis

Parameters

Parameter	Type	Default	Description
`id`	`str`	`"gemini-2.0-flash-exp"`	The specific Gemini model ID to use.
`name`	`str`	`"Gemini"`	The name of this Gemini model instance.
`provider`	`str`	`"Google"`	The provider of the model.
`function_declarations`	`Optional[List[FunctionDeclaration]]`	`None`	List of function declarations for the model.
`generation_config`	`Optional[Any]`	`None`	Configuration for text generation.
`safety_settings`	`Optional[Any]`	`None`	Safety settings for the model.
`generative_model_kwargs`	`Optional[Dict[str, Any]]`	`None`	Additional keyword arguments for the generative model.
`grounding`	`bool`	`False`	Whether to use grounding.
`search`	`bool`	`False`	Whether to use search.
`grounding_dynamic_threshold`	`Optional[float]`	`None`	The dynamic threshold for grounding.
`url_context`	`bool`	`None`	Whether to use URL context for grounding.
`vertexai_search`	`bool`	`False`	Whether to use Vertex AI Search for private knowledge base grounding.
`vertexai_search_datastore`	`Optional[str]`	`None`	The Vertex AI Search datastore ID for private knowledge base search.
`api_key`	`Optional[str]`	`None`	API key for authentication.
`vertexai`	`bool`	`False`	Whether to use Vertex AI instead of Google AI Studio.
`project_id`	`Optional[str]`	`None`	Google Cloud project ID for Vertex AI.
`location`	`Optional[str]`	`None`	Google Cloud region for Vertex AI.
`client_params`	`Optional[Dict[str, Any]]`	`None`	Additional parameters for the client.
`client`	`Optional[GeminiClient]`	`None`	The underlying generative model client.
`temperature`	`Optional[float]`	`None`	Controls randomness in the output. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic.
`top_p`	`Optional[float]`	`None`	Nucleus sampling parameter. Only consider tokens whose cumulative probability exceeds this value.
`top_k`	`Optional[int]`	`None`	Only consider the top k tokens for text generation.
`max_output_tokens`	`Optional[int]`	`None`	The maximum number of tokens to generate in the response.
`stop_sequences`	`Optional[list[str]]`	`None`	List of sequences where the model should stop generating further tokens.
`logprobs`	`Optional[bool]`	`None`	Whether to return log probabilities of the output tokens.
`presence_penalty`	`Optional[float]`	`None`	Penalizes new tokens based on whether they appear in the text so far.
`frequency_penalty`	`Optional[float]`	`None`	Penalizes new tokens based on their frequency in the text so far.
`seed`	`Optional[int]`	`None`	Random seed for deterministic text generation.
`request_params`	`Optional[Dict[str, Any]]`	`None`	Additional parameters for the request.

Gemini is a subclass of the Model class and has access to the same params.

Introduction

Concepts

Other

How to

Authentication

Google AI Studio

Vertex AI

Example

Grounding and Search

Parameters

​Authentication

​Google AI Studio

​Vertex AI

​Example

​Grounding and Search

​Parameters

Authentication

Google AI Studio

Vertex AI

Example

Grounding and Search

Parameters