> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Trace Google Gemini applications

This guide shows you how to trace and log [Google's Gemini](https://ai.google.dev/gemini-api/docs) models in LangSmith. You'll instrument Gemini calls using the latest [`google-genai` SDK](https://googleapis.github.io/python-genai/) (Python) or [`@google/genai` SDK](https://googleapis.github.io/js-genai/release_docs/index.html) (JavaScript), wrap the Gemini client for tracing, and try examples including basic prompts, metadata tagging, and multi-turn conversations.

<Note>
  The LangSmith Gemini wrappers are in **beta**. The API may change in future releases.
</Note>

## Installation

Install the required packages using your preferred package manager:

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langsmith google-genai
  ```

  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install langsmith@latest @google/genai
  ```
</CodeGroup>

## Setup

Set your [API keys](/langsmith/create-account-api-key) and project name:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export LANGSMITH_API_KEY=<your_langsmith_api_key>
export LANGSMITH_PROJECT=<your_project_name>
export LANGSMITH_TRACING=true
export GOOGLE_API_KEY=<your_google_api_key>
```

To create a Google API key, refer to [Google AI Studio](https://aistudio.google.com/apikey).

## Configure tracing

To trace Gemini API calls, use LangSmith's [`wrap_gemini`](https://reference.langchain.com/python/langsmith/wrappers/_gemini/wrap_gemini) (Python) or [`wrapGemini`](https://reference.langchain.com/javascript/functions/langsmith.wrappers_gemini.wrapGemini.html) (JavaScript) wrapper function. This wrapper intercepts calls to the Gemini client and automatically logs them as traces in LangSmith. The wrapper preserves all of the original client's functionality while adding observability:

<CodeGroup>
  ```python Python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from google import genai
  from langsmith import wrappers

  def main():
      # genai.Client() reads GOOGLE_API_KEY / GEMINI_API_KEY from the environment
      gemini_client = genai.Client()

      # Wrap the Gemini client to enable LangSmith tracing
      client = wrappers.wrap_gemini(
          gemini_client,
          tracing_extra={
              "tags": ["gemini", "python"],
              "metadata": {
                  "integration": "google-genai",
              },
          },
      )

      # Make a traced Gemini call
      response = client.models.generate_content(
          model="gemini-2.5-flash",
          contents="Explain quantum computing in simple terms.",
      )

      print(response.text)


  if __name__ == "__main__":
      main()
  ```

  ```javascript JavaScript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  import { GoogleGenAI } from "@google/genai";
  import { wrapGemini } from "langsmith/wrappers/gemini";

  const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

  // Initialize the Gemini client
  const geminiClient = new GoogleGenAI({ apiKey: GEMINI_API_KEY });

  // Wrap the client to enable LangSmith tracing
  // Configuration is applied to ALL calls made with this wrapped client
  const client = wrapGemini(geminiClient, {
    tags: ["gemini", "javascript"],
    metadata: {
      integration: "google-genai",
    },
  });

  // Make a traced call - tracing happens automatically
  const response = await client.models.generateContent({
    model: "gemini-2.5-flash",
    contents: "Explain quantum computing in simple terms.",
  });

  console.log(response.text);
  ```
</CodeGroup>

<Tabs>
  <Tab title="Python" icon="brand-python">
    You can customize tracing by passing [`tracing_extra`](https://reference.langchain.com/python/langsmith/wrappers/_gemini/wrap_gemini) when calling `wrap_gemini()`. This parameter applies to all subsequent requests you make with that wrapped client, which allows you to attach tags and metadata for filtering and organizing traces in the [LangSmith UI](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=langsmith-trace-with-google-gemini). The `tracing_extra` parameter accepts:

    * `tags`: A list of strings to categorize traces (for example, `["production", "gemini"]`).
    * `metadata`: A dictionary of key-value pairs for additional context (for example, `{"team": "ml-research", "integration": "google-genai"}`).
    * `client`: An optional custom LangSmith client instance.

    These settings apply consistently across all traces from the wrapped client, so that you can include environment-level tags or team metadata that should remain constant throughout your application.
  </Tab>

  <Tab title="JavaScript" icon="brand-javascript">
    You can customize tracing by passing configuration options to [`wrapGemini`](https://reference.langchain.com/javascript/functions/langsmith.wrappers_gemini.wrapGemini.html). These options apply to all subsequent requests you make with that wrapped client, which allows you to attach tags and metadata for filtering and organizing traces in the [LangSmith UI](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=langsmith-trace-with-google-gemini). The configuration accepts:

    * `tags`: An array of strings to categorize traces (for example, `["production", "gemini"]`).
    * `metadata`: An object with key-value pairs for additional context (for example, `{ team: "ml-research", integration: "google-genai" }`).
    * `client`: An optional custom LangSmith client instance.

    These settings apply consistently across all traces from the wrapped client, so that you can include environment-level tags or team metadata that should remain constant throughout your application.
  </Tab>
</Tabs>

## View traces in LangSmith

After running your application, you can view traces in the [LangSmith UI](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=langsmith-trace-with-google-gemini) that include:

* **Model requests**: Complete prompts sent to Gemini models
* **Model responses**: Generated text and structured outputs
* **Function calls**: Tool invocations and results when using function calling
* **Chat sessions**: Multi-turn conversation context
* **Performance metrics**: Latency and token usage information

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/langsmith/trace-with-google-gemini.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>