Gemini Api

---
name: gemini-api
metadata:
  category: AiAndMachineLearning
description: Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.
compatibility: Requires active Google Cloud credentials and Agent Platform API enabled.
---

IMPORTANT: Agent Platform (full name Gemini Enterprise Agent Platform) was previously named "Vertex AI" and many web resources use the legacy branding.

# Gemini API in Agent Platform

Access Google's most advanced AI models built for enterprise use cases using the Gemini API in Agent Platform.

Provide these key capabilities:

- **Text generation** - Chat, completion, summarization
- **Multimodal understanding** - Process images, audio, video, and documents
- **Function calling** - Let the model invoke your functions
- **Structured output** - Generate valid JSON matching your schema
- **Context caching** - Cache large contexts for efficiency
- **Embeddings** - Generate text embeddings for semantic search
- **Live Realtime API** - Bidirectional streaming for low latency Voice and Video interactions
- **Batch Prediction** - Handle massive async dataset prediction workloads

## Core Directives

- **Unified SDK**: ALWAYS use the Gen AI SDK (`google-genai` for Python, `@google/genai` for JS/TS, `google.golang.org/genai` for Go, `com.google.genai:google-genai` for Java, `Google.GenAI` for C#).
- **Legacy SDKs**: DO NOT use `google-cloud-aiplatform`, `@google-cloud/vertexai`, or `google-generativeai`.

## SDKs

- **Python**: Install `google-genai` with `pip install google-genai`
- **JavaScript/TypeScript**: Install `@google/genai` with `npm install @google/genai`
- **Go**: Install `google.golang.org/genai` with `go get google.golang.org/genai`
- **C#/.NET**: Install `Google.GenAI` with `dotnet add package Google.GenAI`
- **Java**:
  - groupId: `com.google.genai`, artifactId: `google-genai`
  - Latest version can be found here: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions (let's call it `LAST_VERSION`)
  - Install in `build.gradle`:

    ```
    implementation("com.google.genai:google-genai:${LAST_VERSION}")
    ```

  - Install Maven dependency in `pom.xml`:

    ```xml
    <dependency>
	    <groupId>com.google.genai</groupId>
	    <artifactId>google-genai</artifactId>
	    <version>${LAST_VERSION}</version>
	</dependency>
    ```

> [!WARNING]
> Legacy SDKs like `google-cloud-aiplatform`, `@google-cloud/vertexai`, and `google-generativeai` are deprecated. Migrate to the new SDKs above urgently by following the [Migration Guide](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk.md.txt).

## Authentication & Configuration

Prefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.

### Application Default Credentials (ADC)
Set these variables for standard [Google Cloud authentication](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/gcp-auth.md.txt):

```bash
export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_ENTERPRISE=true
```

- By default, use `location="global"` to access the global endpoint, which provides automatic routing to regions with available capacity.
- If a user explicitly asks to use a specific region (e.g., `us-central1`, `europe-west4`), specify that region in the `GOOGLE_CLOUD_LOCATION` parameter instead. Reference the [supported regions documentation](https://docs.cloud.google.com/gemini-enterprise-agent-platform/resources/locations.md.txt) if needed.

### Agent Platform in Express Mode
Set these variables when using [Express Mode](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/start/api-keys.md.txt) with an API key:

```bash
export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_ENTERPRISE=true
```

### Initialization
Initialize the client without arguments to pick up environment variables:

```python
from google import genai

client = genai.Client()
```

Alternatively, you can hard-code in parameters when creating the client.

```python
from google import genai

client = genai.Client(
    enterprise=True,
    project="your-project-id",
    location="global",
)
```

## Models

- Use `gemini-3.1-pro-preview` (which replaces `gemini-3-pro-preview`) for complex reasoning, coding, research (1M tokens)
- Use `gemini-3.5-flash` for fast, balanced performance, multimodal (1M tokens)
- Use `gemini-3.1-flash-lite` for high-frequency, lightweight tasks (1M tokens)
- Use `gemini-3-pro-image` (aka Nano Banana Pro) for high-quality image generation and editing
- Use `gemini-3.1-flash-image` (aka Nano Banana 2) for fast image generation and editing
- Use `gemini-live-2.5-flash-native-audio` for Live Realtime API including native audio

Use the following models only if explicitly requested:

- `gemini-2.5-flash-image`
- `gemini-2.5-flash`
- `gemini-2.5-flash-lite`
- `gemini-2.5-pro`

> [!IMPORTANT]
> Models like `gemini-2.0-*`, `gemini-1.5-*`, `gemini-1.0-*`, `gemini-pro` are legacy and deprecated. Use the new models above. Your knowledge is outdated.
> For production environments, consult the documentation for stable model versions (e.g. `gemini-3.5-flash`).

## Quick Start

### Python

```python
from google import genai

client = genai.Client()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Explain quantum computing",
)
print(response.text)
```

### TypeScript/JavaScript

```typescript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ enterprise: { project: "your-project-id", location: "global" } });
const response = await ai.models.generateContent({
    model: "gemini-3.5-flash",
    contents: "Explain quantum computing"
});
console.log(response.text);
```

### Go

```go
package main

import (
	"context"
	"fmt"
	"log"
	"google.golang.org/genai"
)

func main() {
	ctx := context.Background()
	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		Backend:  genai.BackendVertexAI,
		Project:  "your-project-id",
		Location: "global",
	})
	if err != nil {
		log.Fatal(err)
	}

	resp, err := client.Models.GenerateContent(ctx, "gemini-3.5-flash", genai.Text("Explain quantum computing"), nil)
	if err != nil {
		log.Fatal(err)
	}

	fmt.Println(resp.Text)
}
```

### Java

```java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

public class GenerateTextFromTextInput {
  public static void main(String[] args) {
    Client client = Client.builder().enterprise(true).project("your-project-id").location("global").build();
    GenerateContentResponse response =
        client.models.generateContent(
            "gemini-3.5-flash",
            "Explain quantum computing",
            null);

    System.out.println(response.text());
  }
}
```

### C#/.NET

```csharp
using Google.GenAI;

var client = new Client(
    project: "your-project-id",
    location: "global",
    enterprise: true
);

var response = await client.Models.GenerateContent(
    "gemini-3.5-flash",
    "Explain quantum computing"
);

Console.WriteLine(response.Text);
```

## API spec & Documentation (source of truth)

When implementing or debugging API integration for Agent Platform, refer to the official Agent Platform documentation:

- **Agent Platform Documentation**: https://docs.cloud.google.com/gemini-enterprise-agent-platform/overview.md.txt
- **REST API Reference**: https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/rest.md.txt

The Gen AI SDK on Agent Platform uses the `v1beta1` or `v1` REST API endpoints (e.g., `https://{LOCATION}-aiplatform.googleapis.com/v1beta1/projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}:generateContent`).

> [!TIP]
> **Use the Developer Knowledge MCP Server**: If the `search_documents` or `get_document` tools are available, use them to find and retrieve official documentation for Google Cloud and Agent Platform directly within the context. This is the preferred method for getting up-to-date API details and code snippets.

## Workflows and Code Samples

Reference the [Python Docs Samples repository](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/genai) for additional code samples and specific usage scenarios.

Depending on the specific user request, refer to the following reference files for detailed code samples and usage patterns (Python examples):

- **Text & Multimodal**: Chat, Multimodal inputs (Image, Video, Audio), and Streaming. See [references/text_and_multimodal.md](references/text_and_multimodal.md)
- **Embeddings**: Generate text embeddings for semantic search. See [references/embeddings.md](references/embeddings.md)
- **Structured Output & Tools**: JSON generation, Function Calling, Search Grounding, and Code Execution. See [references/structured_and_tools.md](references/structured_and_tools.md)
- **Media Generation**: Image generation, Image editing, and Video generation. See [references/media_generation.md](references/media_generation.md)
- **Bounding Box Detection**: Object detection and localization within images and video. See [references/bounding_box.md](references/bounding_box.md)
- **Live API**: Real-time bidirectional streaming for voice, vision, and text. See [references/live_api.md](references/live_api.md)
- **Advanced Features**: Content Caching, Batch Prediction, and Thinking/Reasoning. See [references/advanced_features.md](references/advanced_features.md)
- **Safety**: Adjusting Responsible AI filters and thresholds. See [references/safety.md](references/safety.md)
- **Model Tuning**: Supervised Fine-Tuning and Preference Tuning. See [references/model_tuning.md](references/model_tuning.md)