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# Observability for Google Gemini (TypeScript) with Opik

> Start here to integrate Opik into your Google Gemini-based genai application for end-to-end LLM observability, unit testing, and optimization.

Opik provides seamless integration with the [Google Generative AI Node.js SDK](https://github.com/googleapis/js-genai) (`@google/genai`) through the `opik-gemini` package, allowing you to trace, monitor, and debug your Gemini API calls.

## Features

* **Comprehensive Tracing**: Automatically trace Gemini API calls, including text generation, chat, and multimodal interactions
* **Hierarchical Visualization**: View your Gemini requests as structured traces with parent-child relationships
* **Detailed Metadata Capture**: Record model names, prompts, completions, token usage, and custom metadata
* **Error Handling**: Capture and visualize errors encountered during Gemini API interactions
* **Custom Tagging**: Add custom tags to organize and filter your traces
* **Streaming Support**: Full support for streamed responses with token-by-token tracing
* **VertexAI Support**: Works with both Google AI Studio and Vertex AI endpoints

![Gemini TypeScript Integration](/docs/opik/_fern-img/57c2d96d89397429136725e10c67cb883ed1b6b63c2d12af87c56ccd7e582360.webp)

## Installation

### Option 1: Using npm

```bash
npm install opik-gemini @google/genai
```

### Option 2: Using yarn

```bash
yarn add opik-gemini @google/genai
```

### Requirements

* Node.js ≥ 18
* Google Generative AI SDK (`@google/genai` ≥ 1.0.0)
* Opik SDK (automatically installed as a dependency)

**Note**: The official Google GenAI SDK package is `@google/genai` (not `@google/generative-ai`). This is Google Deepmind's unified SDK for both Gemini Developer API and Vertex AI.

## Basic Usage

### Using with Google Generative AI Client

To trace your Gemini API calls, you need to wrap your Gemini client instance with the `trackGemini` function:

```typescript
import { GoogleGenAI } from "@google/genai";
import { trackGemini } from "opik-gemini";

// Initialize the original Gemini client
const genAI = new GoogleGenAI({
  apiKey: process.env.GEMINI_API_KEY,
});

// Wrap the client with Opik tracking
const trackedGenAI = trackGemini(genAI);

// Generate content
const response = await trackedGenAI.models.generateContent({
  model: "gemini-2.0-flash-001",
  contents: "Hello, how can you help me today?",
});

console.log(response.text);

// Ensure all traces are sent before your app terminates
await trackedGenAI.flush();
```

### Using with Streaming Responses

The integration fully supports Gemini's streaming responses:

```typescript
import { GoogleGenAI } from "@google/genai";
import { trackGemini } from "opik-gemini";

const genAI = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const trackedGenAI = trackGemini(genAI);

async function streamingExample() {
  // Create a streaming generation
  const response = await trackedGenAI.models.generateContentStream({
    model: "gemini-2.0-flash-001",
    contents: "Write a short story about AI observability",
  });

  // Process the stream
  let streamedContent = "";
  for await (const chunk of response) {
    const chunkText = chunk.text;
    if (chunkText) {
      process.stdout.write(chunkText);
      streamedContent += chunkText;
    }
  }

  console.log("\nStreaming complete!");

  // Don't forget to flush when done
  await trackedGenAI.flush();
}

streamingExample();
```

## Advanced Configuration

The `trackGemini` function accepts an optional configuration object to customize the integration:

```typescript
import { GoogleGenAI } from "@google/genai";
import { trackGemini } from "opik-gemini";
import { Opik } from "opik";

// Optional: Create a custom Opik client
const customOpikClient = new Opik({
  apiKey: "YOUR_OPIK_API_KEY", // If not using environment variables
  projectName: "gemini-integration-project",
});

const existingOpikTrace = customOpikClient.trace({
  name: `Trace`,
  input: {
    prompt: `Hello, world!`,
  },
  output: {
    response: `Hello, world!`,
  },
});

const genAI = new GoogleGenAI({
  apiKey: process.env.GEMINI_API_KEY,
});

// Configure the tracked client with options
const trackedGenAI = trackGemini(genAI, {
  // Optional array of tags to apply to all traces
  traceMetadata: {
    tags: ["gemini", "production", "user-query"],

    // Optional metadata to include with all traces
    environment: "production",
    version: "1.2.3",
    component: "story-generator",
  },

  // Optional custom name for the generation/trace
  generationName: "StoryGenerationService",

  // Optional pre-configured Opik client
  // If not provided, a singleton instance will be used
  client: customOpikClient,

  // Optional parent trace for hierarchical relationships
  parent: existingOpikTrace,
});

// Use the tracked client with your configured options
const response = await trackedGenAI.models.generateContent({
  model: "gemini-2.0-flash-001",
  contents: "Generate a creative story",
});

console.log(response.text);

// Close the existing trace
existingOpikTrace.end();

// Flush before your application exits
await trackedGenAI.flush();
```

## Using with VertexAI

The integration also supports Google's VertexAI platform. Simply configure your Gemini client for VertexAI and wrap it with `trackGemini`:

```typescript
import { GoogleGenAI } from "@google/genai";
import { trackGemini } from "opik-gemini";

// Configure for VertexAI
const genAI = new GoogleGenAI({
  vertexai: true,
  project: "your-project-id",
  location: "us-central1",
});

const trackedGenAI = trackGemini(genAI);

const response = await trackedGenAI.models.generateContent({
  model: "gemini-2.0-flash-001",
  contents: "Write a short story about AI observability",
});

console.log(response.text);

// Flush before your application exits
await trackedGenAI.flush();
```

## Chat Conversations

Track multi-turn chat conversations with Gemini:

```typescript
import { GoogleGenAI } from "@google/genai";
import { trackGemini } from "opik-gemini";

const genAI = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const trackedGenAI = trackGemini(genAI);

async function chatExample() {
  // Multi-turn conversation using generateContent with history
  const response = await trackedGenAI.models.generateContent({
    model: "gemini-2.0-flash-001",
    contents: [
      {
        role: "user",
        parts: [{ text: "Hello, I want to learn about AI observability." }],
      },
      {
        role: "model",
        parts: [
          {
            text: "Great! AI observability helps track and debug LLM applications.",
          },
        ],
      },
      {
        role: "user",
        parts: [{ text: "What are the key benefits of using Opik?" }],
      },
    ],
  });

  console.log(response.text);

  await trackedGenAI.flush();
}

chatExample();
```

## Troubleshooting

**Missing Traces**: Ensure your Gemini and Opik API keys are correct and that you're calling `await trackedGenAI.flush()` before your application exits.

**Incomplete Data**: For streaming responses, make sure you're consuming the entire stream before ending your application.

**Hierarchical Traces**: To create proper parent-child relationships, use the `parent` option in the configuration when you want Gemini calls to be children of another trace.

**Performance Impact**: The Opik integration adds minimal overhead to your Gemini API calls.

**VertexAI Authentication**: When using VertexAI, ensure you have properly configured your Google Cloud project credentials.

> Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.