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

# Google GenAI

> Trace Google Gen AI Python SDK calls (Gemini API) with OpenInference and send spans to Arize AX for LLM observability.

[Google Gen AI](https://ai.google.dev/) provides the Gemini family of large language models through the [Google Gen AI Python SDK](https://github.com/googleapis/python-genai). Arize AX captures every Gemini API call — chat completions, tool calls, and token usage — via the [`openinference-instrumentation-google-genai`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-google-genai) package. The same instrumentor also covers calls routed through Vertex AI when the SDK is configured against Vertex.

## Prerequisites

* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* A `GEMINI_API_KEY` from [Google AI Studio](https://aistudio.google.com/app/apikey)

## Launch Arize AX

1. Sign in to your [Arize AX account](https://app.arize.com/).
2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below.

## Install

```bash theme={null}
pip install arize-otel openinference-instrumentation-google-genai google-genai
```

## Configure credentials

```bash theme={null}
export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="google-genai-tracing-example"
export GEMINI_API_KEY="<your-gemini-api-key>"
```

## Setup tracing

```python theme={null}
# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

GoogleGenAIInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for Google GenAI.")
```

## Run Google GenAI

```python theme={null}
# example.py

# Importing instrumentation first ensures tracing is set up
# before `google.genai` is imported.
from instrumentation import tracer_provider

from google import genai

# The client reads GEMINI_API_KEY from the environment.
client = genai.Client()

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Why is the ocean salty? Answer in two sentences.",
)

print(response.text)
```

### Expected output

```text wrap theme={null}
Arize AX tracing initialized for Google GenAI.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.
```

## Verify in Arize AX

1. Open your Arize AX space and select project **`google-genai-tracing-example`**.
2. You should see a new trace within \~30 seconds containing a `GenerateContent` LLM span with the prompt, response, and token usage attached.
3. If no traces appear, see [Troubleshooting](#troubleshooting).

### Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

<Tabs>
  <Tab title="Arize skill (agent)">
    Install the [Arize Skills](https://github.com/Arize-ai/arize-skills) plugin and let your coding agent check for you:

    ```bash theme={null}
    npx skills add Arize-ai/arize-skills
    ```

    Then prompt your agent:

    > Use the `arize-trace` skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.
  </Tab>

  <Tab title="AX CLI">
    Export recent spans for your project — any rows mean traces are landing:

    ```bash theme={null}
    ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
      --limit 5 --stdout | jq 'length'
    ```

    A non-zero count confirms spans reached Arize AX. Run `ax auth login` first if you have not authenticated. See the [`ax spans` reference](/docs/api-clients/cli/spans).
  </Tab>

  <Tab title="SDK">
    Query the project's spans and check that at least one came back.

    <CodeGroup>
      ```python Python theme={null}
      import os
      from arize import ArizeClient

      client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
      resp = client.spans.list(
          project=os.environ["ARIZE_PROJECT_NAME"],
          space=os.environ["ARIZE_SPACE_ID"],
          limit=5,
      )
      count = len(resp.spans)
      print(
          f"{count} span(s) found" if count else "No spans yet — recheck setup"
      )
      ```

      ```typescript TypeScript theme={null}
      // Reads ARIZE_API_KEY from the environment.
      import { listSpans } from "@arizeai/ax-client";

      const { data: spans } = await listSpans({
        project: process.env.ARIZE_PROJECT_NAME!,
        space: process.env.ARIZE_SPACE_ID!,
        limit: 5,
      });
      const count = spans.length;
      console.log(
        count ? `${count} span(s) found` : "No spans yet — recheck setup",
      );
      ```

      ```go Go theme={null}
      client, err := arize.NewClient(
          arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
      )
      if err != nil {
          log.Fatal(err)
      }
      resp, err := client.Spans.List(ctx, spans.ListRequest{
          Project: os.Getenv("ARIZE_PROJECT_NAME"),
          Space:   os.Getenv("ARIZE_SPACE_ID"),
          Limit:   5,
      })
      if err != nil {
          log.Fatal(err)
      }
      fmt.Printf("%d span(s) found\n", len(resp.Spans))
      ```
    </CodeGroup>

    SDK span references: [Python](/docs/api-clients/python/version-8/client-resources/spans) · [TypeScript](/docs/api-clients/typescript/version-1/client-resources/spans) · [Go](/docs/api-clients/go/version-2/client-resources/spans).
  </Tab>
</Tabs>

## Troubleshooting

* **No traces in Arize AX.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`. Enable OpenTelemetry debug logs with `export OTEL_LOG_LEVEL=debug` and re-run.
* **Google GenAI spans missing but other spans present.** `GoogleGenAIInstrumentor().instrument(...)` must run before any `from google import genai` import. Make sure `instrumentation.py` is the first import in your entry point.
* **`401` / `403` from Gemini.** Verify `GEMINI_API_KEY` is set and has access to the model in the example. Swap `gemini-2.5-flash` for a model your key can call.
* **`404 NOT_FOUND` for the model.** Google occasionally retires older Gemini aliases for new users. If `gemini-2.5-flash` returns 404, list models with `client.models.list()` and pick a current one.
* **Using Vertex AI instead of the Gemini API.** Configure the SDK against Vertex per Google's [GenAI SDK docs](https://cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview) (set `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT`, `GOOGLE_CLOUD_LOCATION`). The same instrumentor captures Vertex calls — only the credential setup differs.

## Resources

<CardGroup>
  <Card icon="book-open" href="https://ai.google.dev/gemini-api/docs" title="Gemini API Documentation" horizontal />

  <Card icon="terminal" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-google-genai" title="OpenInference Google GenAI Instrumentor" horizontal />

  <Card icon="github" href="https://github.com/googleapis/python-genai" title="Google Gen AI Python SDK" horizontal />
</CardGroup>