---
title: Google
tags: [vision]
integrationType: model-provider
redirectFrom:
  - docs/user-guide/concepts/model-providers/gemini
---

[Google Gemini](https://ai.google.dev/api) is Google's family of multimodal large language models designed for advanced reasoning, code generation, and creative tasks. The Strands Agents SDK implements a Google/Gemini provider, allowing you to run agents against the Gemini models available through Google's AI API.

## Installation

Gemini is configured as an optional dependency in Strands Agents.

To install it, run:

<Tabs>
<Tab label="Python">

```bash
pip install 'strands-agents[gemini]' strands-agents-tools
```
</Tab>
<Tab label="TypeScript">

```bash
npm install @strands-agents/sdk @google/genai
```
</Tab>
</Tabs>

## Usage

After installing dependencies, you can import and initialize the Strands Agents' Gemini provider as follows:

<Tabs>
<Tab label="Python">

```python
from strands import Agent
from strands.models.gemini import GeminiModel
from strands_tools import calculator

model = GeminiModel(
    client_args={
        "api_key": "<KEY>",
    },
    # **model_config
    model_id="gemini-2.5-flash",
    params={
        # some sample model parameters
        "temperature": 0.7,
        "max_output_tokens": 2048,
        "top_p": 0.9,
        "top_k": 40
    }
)

agent = Agent(model=model, tools=[calculator])
response = agent("What is 2+2")
print(response)
```
</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/concepts/model-providers/google_imports.ts:basic_usage_imports"

--8<-- "user-guide/concepts/model-providers/google.ts:basic_usage"
```
</Tab>
</Tabs>

## Configuration

### Client Configuration

<Tabs>
<Tab label="Python">

The `client_args` configure the underlying Google GenAI client. For a complete list of available arguments, please refer to the [Google GenAI documentation](https://googleapis.github.io/python-genai/).
</Tab>
<Tab label="TypeScript">

The `clientConfig` configures the underlying Google GenAI client. You can also pass a pre-configured `client` instance directly. For a complete list of available options, please refer to the [@google/genai documentation](https://github.com/googleapis/js-genai).
</Tab>
</Tabs>

### Model Configuration

<Tabs>
<Tab label="Python">

The `model_config` configures the underlying model selected for inference. The supported configurations are:

|  Parameter | Description | Example | Options |
|------------|-------------|---------|---------|
| `model_id` | ID of a Gemini model to use | `"gemini-2.5-flash"` | [Available models](#available-models) |
| `params` | Model specific parameters | `{"temperature": 0.7, "maxOutputTokens": 2048}` | [Parameter reference](#model-parameters) |
</Tab>
<Tab label="TypeScript">

|  Parameter | Description | Example | Options |
|------------|-------------|---------|---------|
| `modelId` | ID of a Gemini model to use | `'gemini-2.5-flash'` | [Available models](#available-models) |
| `params` | Model specific parameters | `{ temperature: 0.7, maxOutputTokens: 2048 }` | [Parameter reference](#model-parameters) |
</Tab>
</Tabs>

### Model Parameters

For a complete list of supported parameters, see the [Gemini API documentation](https://ai.google.dev/api/generate-content#generationconfig).

<Tabs>
<Tab label="Python">

| Parameter | Description | Type |
|-----------|-------------|------|
| `temperature` | Controls randomness in responses | `float` |
| `max_output_tokens` | Maximum tokens to generate | `int` |
| `top_p` | Nucleus sampling parameter | `float` |
| `top_k` | Top-k sampling parameter | `int` |
| `candidate_count` | Number of response candidates | `int` |
| `stop_sequences` | Custom stopping sequences | `list[str]` |

**Example:**

```python
params = {
    "temperature": 0.8,
    "max_output_tokens": 4096,
    "top_p": 0.95,
    "top_k": 40,
    "candidate_count": 1,
    "stop_sequences": ['STOP!']
}
```
</Tab>
<Tab label="TypeScript">

| Parameter | Description | Type |
|-----------|-------------|------|
| `temperature` | Controls randomness in responses | `number` |
| `maxOutputTokens` | Maximum tokens to generate | `number` |
| `topP` | Nucleus sampling parameter | `number` |
| `topK` | Top-k sampling parameter | `number` |
| `candidateCount` | Number of response candidates | `number` |
| `stopSequences` | Custom stopping sequences | `string[]` |

**Example:**

```typescript
--8<-- "user-guide/concepts/model-providers/google.ts:params_example"
```
</Tab>
</Tabs>

### Available Models

For a complete list of supported models, see the [Gemini API documentation](https://ai.google.dev/gemini-api/docs/models).

**Popular Models:**

- `gemini-2.5-pro` - Most advanced model for complex reasoning and thinking
- `gemini-2.5-flash` - Best balance of performance and cost
- `gemini-2.5-flash-lite` - Most cost-efficient option
- `gemini-2.0-flash` - Next-gen features with improved speed
- `gemini-2.0-flash-lite` - Cost-optimized version of 2.0

### Built-in Tools

<Tabs>
<Tab label="Python">

Google's built-in tools (Google Search, Code Execution, URL Context) can be passed via the `gemini_tools` config option. These are appended alongside any function tools registered on the agent.

```python
from google import genai
from strands import Agent
from strands.models.gemini import GeminiModel

model = GeminiModel(
    client_args={"api_key": "<KEY>"},
    model_id="gemini-2.5-flash",
    gemini_tools=[
        genai.types.Tool(google_search=genai.types.GoogleSearch()),
    ],
)

agent = Agent(model=model)
response = agent("What are the latest AI news today?")
print(response)
```
</Tab>
<Tab label="TypeScript">

Google's built-in tools (Google Search, Code Execution, URL Context) can be passed via the `builtInTools` config option. These are appended alongside any function tools registered on the agent.

```typescript
--8<-- "user-guide/concepts/model-providers/google.ts:builtin_tools"
```
</Tab>
</Tabs>

For available built-in tools, see the [Gemini tools documentation](https://ai.google.dev/gemini-api/docs/tools).

## Troubleshooting

### Module Not Found

<Tabs>
<Tab label="Python">

If you encounter the error `ModuleNotFoundError: No module named 'google.genai'`, this means the `google-genai` dependency hasn't been properly installed in your environment. To fix this, run `pip install 'strands-agents[gemini]'`.
</Tab>
<Tab label="TypeScript">

If you encounter import errors for `@google/genai`, ensure the package is installed: `npm install @google/genai`.
</Tab>
</Tabs>

### API Key Issues

Make sure your Google AI API key is properly set via `client_args` (Python) or `apiKey` (TypeScript), or as the `GOOGLE_API_KEY` / `GEMINI_API_KEY` environment variable. You can obtain an API key from the [Google AI Studio](https://aistudio.google.com/app/apikey).

### Rate Limiting and Safety Issues

The Gemini provider handles several types of errors automatically:

- **Safety/Content Policy**: When content is blocked due to safety concerns, the model will return a safety message
- **Rate Limiting**: When quota limits are exceeded, a `ModelThrottledException` is raised
- **Server Errors**: Temporary server issues are handled with appropriate error messages

<Tabs>
<Tab label="Python">

```python
from strands.types.exceptions import ModelThrottledException

try:
    response = agent("Your query here")
except ModelThrottledException as e:
    print(f"Rate limit exceeded: {e}")
    # Implement backoff strategy
```
</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/concepts/model-providers/google.ts:error_handling"
```
</Tab>
</Tabs>

## Advanced Features

### Structured Output

Gemini models support structured output through their native JSON schema capabilities. When you use [`Agent.structured_output()`](@api/python/strands.agent.agent#Agent.structured_output), the Strands SDK automatically converts your Pydantic models to Gemini's JSON schema format.

<Tabs>
<Tab label="Python">

```python
from pydantic import BaseModel, Field
from strands import Agent
from strands.models.gemini import GeminiModel

class MovieReview(BaseModel):
    """Analyze a movie review."""
    title: str = Field(description="Movie title")
    rating: int = Field(description="Rating from 1-10", ge=1, le=10)
    genre: str = Field(description="Primary genre")
    sentiment: str = Field(description="Overall sentiment: positive, negative, or neutral")
    summary: str = Field(description="Brief summary of the review")

model = GeminiModel(
    client_args={"api_key": "<KEY>"},
    model_id="gemini-2.5-flash",
    params={
        "temperature": 0.3,
        "max_output_tokens": 1024,
        "top_p": 0.85
    }
)

agent = Agent(model=model)

result = agent.structured_output(
    MovieReview,
    """
    Just watched "The Matrix" - what an incredible sci-fi masterpiece!
    The groundbreaking visual effects and philosophical themes make this
    a must-watch. Keanu Reeves delivers a solid performance. 9/10!
    """
)

print(f"Movie: {result.title}")
print(f"Rating: {result.rating}/10")
print(f"Genre: {result.genre}")
print(f"Sentiment: {result.sentiment}")
```
</Tab>
<Tab label="TypeScript">

```ts
// Structured output is not yet supported for Gemini in the TypeScript SDK
```
</Tab>
</Tabs>

### Custom client

Users can pass their own custom Gemini client to the GeminiModel for Strands Agents to use directly. Users are responsible for handling the lifecycle (e.g., closing) of the client.

<Tabs>
<Tab label="Python">

```python
from google import genai
from strands import Agent
from strands.models.gemini import GeminiModel
from strands_tools import calculator

client = genai.Client(api_key="<KEY>")

model = GeminiModel(
    client=client,
    # **model_config
    model_id="gemini-2.5-flash",
    params={
        # some sample model parameters
        "temperature": 0.7,
        "max_output_tokens": 2048,
        "top_p": 0.9,
        "top_k": 40
    }
)

agent = Agent(model=model, tools=[calculator])
response = agent("What is 2+2")
print(response)
```
</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/concepts/model-providers/google_imports.ts:custom_client_imports"

--8<-- "user-guide/concepts/model-providers/google.ts:custom_client"
```
</Tab>
</Tabs>

### Multimodal Capabilities

Gemini models support text, image, document, and video inputs, making them ideal for multimodal applications.

#### Image Input

<Tabs>
<Tab label="Python">

```python
from strands import Agent
from strands.models.gemini import GeminiModel

model = GeminiModel(
    client_args={"api_key": "<KEY>"},
    model_id="gemini-2.5-flash",
    params={
        "temperature": 0.5,
        "max_output_tokens": 2048,
        "top_p": 0.9
    }
)

agent = Agent(model=model)

# Process image with text
response = agent([
    {
        "role": "user",
        "content": [
            {"text": "What do you see in this image?"},
            {"image": {"format": "png", "source": {"bytes": image_bytes}}}
        ]
    }
])
```
</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/concepts/model-providers/google_imports.ts:image_imports"

--8<-- "user-guide/concepts/model-providers/google.ts:image_input"
```
</Tab>
</Tabs>

#### Document Input

<Tabs>
<Tab label="Python">

```python
response = agent([
    {
        "role": "user",
        "content": [
            {"text": "Summarize this document"},
            {"document": {"format": "pdf", "source": {"bytes": document_bytes}}}
        ]
    }
])
```
</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/concepts/model-providers/google_imports.ts:document_imports"

--8<-- "user-guide/concepts/model-providers/google.ts:document_input"
```
</Tab>
</Tabs>

#### Video Input

<Tabs>
<Tab label="Python">

```python
response = agent([
    {
        "role": "user",
        "content": [
            {"text": "Describe what happens in this video"},
            {"video": {"format": "mp4", "source": {"bytes": video_bytes}}}
        ]
    }
])
```
</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/concepts/model-providers/google_imports.ts:video_imports"

--8<-- "user-guide/concepts/model-providers/google.ts:video_input"
```
</Tab>
</Tabs>

**Supported formats:**

- **Images**: PNG, JPEG, GIF, WebP (automatically detected via MIME type)
- **Documents**: PDF and other binary formats (automatically detected via MIME type)
- **Video**: MP4 and other video formats (automatically detected via MIME type)

### Token Counting

Token counting is used by context management strategies to estimate input tokens before each model call.

<Tabs>
<Tab label="Python">

The Google provider can use the native `models.count_tokens()` API for message content. However, the Gemini API does not support counting system instructions or tool specifications natively. These are estimated separately using a character-based heuristic (characters ÷ 4 for text, characters ÷ 2 for JSON).

You can enable native token counting with:

```python
model = GoogleModel(
    model_id="gemini-2.5-flash",
    use_native_token_count=True,
)
```
</Tab>
<Tab label="TypeScript">

The Google provider can use the native `models.countTokens()` API for message content. However, the Gemini API does not support counting system instructions or tool specifications natively. These are estimated separately using a character-based heuristic (characters ÷ 4 for text, characters ÷ 2 for JSON).

You can enable native token counting with:

```typescript
const model = new GoogleModel({
  modelId: 'gemini-2.5-flash',
  useNativeTokenCount: true,
})
```

When disabled (or if the API call fails), falls back to estimation using the character-based heuristic.
</Tab>
</Tabs>

## References

- [Python API](@api/python/strands.models.model)
- [Google Gemini](https://ai.google.dev/api)
- [Google GenAI SDK documentation](https://googleapis.github.io/python-genai/)
- [Google AI Studio](https://aistudio.google.com/)
- [@google/genai TypeScript SDK](https://github.com/googleapis/js-genai)