> ## Documentation Index
> Fetch the complete documentation index at: https://spacesail.mintlify.site/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).
```bash Mac theme={null}
export GOOGLE_API_KEY=***
```
```bash Windows theme={null}
setx GOOGLE_API_KEY ***
```
### 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",
),
)
```
## Example
Use `Gemini` with your `Agent`:
```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.")
```
View more examples [here](/examples/models/gemini/basic).
## 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:
```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),
markdown=True,
)
agent.print_response("Any news from USA?")
```
To use search:
```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),
markdown=True,
)
agent.print_response("What's happening in France?")
```
## Parameters
| Parameter | Type | Default | Description |
| ----------------------------- | ------------------------------------- | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `id` | `str` | `"gemini-2.0-flash-001"` | 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` | `False` | Whether to include URL context in responses. |
| `vertexai_search` | `bool` | `False` | Whether to use Vertex AI search capabilities. |
| `vertexai_search_datastore` | `Optional[str]` | `None` | The Vertex AI search datastore to use. |
| `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. |
| `response_modalities` | `Optional[list[str]]` | `None` | List of response modalities (e.g., "TEXT", "IMAGE", "AUDIO"). |
| `speech_config` | `Optional[dict[str, Any]]` | `None` | Configuration for speech synthesis. |
| `cached_content` | `Optional[Any]` | `None` | Reference to cached content for context caching. |
| `thinking_budget` | `Optional[int]` | `None` | Thinking budget for Gemini 2.5 models (reasoning tokens). |
| `include_thoughts` | `Optional[bool]` | `None` | Whether to include thought summaries in response. |
| `request_params` | `Optional[Dict[str, Any]]` | `None` | Additional parameters for the request. |
`Gemini` is a subclass of the [Model](/reference/models/model) class and has access to the same params.