> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini

> Learn how to use Gemini models in Agno.

Use Google's Gemini models through [Google AI Studio](https://ai.google.dev/gemini-api/docs) or [Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/overview) - 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](https://ai.google.dev/gemini-api/docs/models) and the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models) 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](https://ai.google.dev/gemini-api/docs/api-key).

<CodeGroup>
  ```bash Mac theme={null}
  export GOOGLE_API_KEY=***
  ```

  ```bash Windows theme={null}
  setx GOOGLE_API_KEY ***
  ```
</CodeGroup>

### Vertex AI

To use Vertex AI in Google Cloud:

1. Refer to the [Vertex AI documentation](https://cloud.google.com/vertex-ai/docs/start/cloud-environment) to set up a project and development environment.

2. Install the `gcloud` CLI and authenticate (refer to the [quickstart](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstarts/quickstart-multimodal) for more details):

```bash theme={null}
gcloud auth application-default login
```

3. 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:

```bash theme={null}
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:

```python theme={null}
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:

```python theme={null}
# 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`:

<CodeGroup>
  ```python agent.py theme={null}
  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.")
  ```
</CodeGroup>

<Note> View more examples [here](../examples/models/gemini). </Note>

## 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](https://ai.google.dev/gemini-api/docs/grounding?lang=python).

To enable these features, set the corresponding parameter when initializing the Gemini model:

To use grounding:

<CodeGroup>
  ```python theme={null}
  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?")
  ```
</CodeGroup>

To use search:

<CodeGroup>
  ```python theme={null}
  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?")
  ```
</CodeGroup>

<Tip> Combine `URL context` with `Google Search` to get a more in-depth analysis </Tip>

## Parameters

<Snippet file="model-google-params.mdx" />

`Gemini` is a subclass of the [Model](/reference/models/model) class and has access to the same params.