Gemini API in Agent Platform

• 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.

Authentication & Configuration

Application Default Credentials (ADC)

export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_VERTEXAI=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 if needed.

Agent Platform in Express Mode

export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_VERTEXAI=true

Initialization

from google import genai
client = genai.Client()

from google import genai
client = genai.Client(vertexai=True, project="your-project-id", location="global")

Models

• Use

  gemini-3.1-pro-preview

  for complex reasoning, coding, research (1M tokens)
  • IMPORTANT: Do not use

    gemini-3-pro-preview

• Use

  gemini-3-flash-preview

  for fast, balanced performance, multimodal (1M tokens)
• Use

  gemini-3-pro-image-preview

  for Nano Banana Pro image generation and editing
• Use

  gemini-3.1-flash-image-preview

  for Nano Banana 2 image generation and editing
• Use

  gemini-live-2.5-flash-native-audio

  for Live Realtime API including native audio

• 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-flash

).

Quick Start

Python

from google import genai
client = genai.Client()
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Explain quantum computing"
)
print(response.text)

TypeScript/JavaScript

import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ vertexai: { project: "your-project-id", location: "global" } });
const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: "Explain quantum computing"
});
console.log(response.text);

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-flash-preview", genai.Text("Explain quantum computing"), nil)
	if err != nil {
		log.Fatal(err)
	}

	fmt.Println(resp.Text)
}

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().vertexAi(true).project("your-project-id").location("global").build();
    GenerateContentResponse response =
        client.models.generateContent(
            "gemini-3-flash-preview",
            "Explain quantum computing",
            null);

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

C#/.NET

using Google.GenAI;

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

var response = await client.Models.GenerateContent(
    "gemini-3-flash-preview",
    "Explain quantum computing"
);

Console.WriteLine(response.Text);

API spec & Documentation (source of truth)

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

v1beta1

v1

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

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