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Google Gen AI provides the Gemini family of large language models through the Google Gen AI Python SDK. Arize AX captures every Gemini API call — chat completions, tool calls, and token usage — via the openinference-instrumentation-google-genai package. The same instrumentor also covers calls routed through Vertex AI when the SDK is configured against Vertex.

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Prerequisites

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Launch Arize AX

  1. Sign in to your Arize AX account.
  2. From Space Settings, copy your Space ID and API Key. You will set them as ARIZE_SPACE_ID and ARIZE_API_KEY below.

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Install

pip install arize-otel openinference-instrumentation-google-genai google-genai

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Configure credentials

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>"

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Setup tracing

# 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.")

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Run Google GenAI

# 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)

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Expected output

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.

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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.

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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 (set GOOGLE_GENAI_USE_VERTEXAI, GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION). The same instrumentor captures Vertex calls — only the credential setup differs.

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Resources

Gemini API Documentation

OpenInference Google GenAI Instrumentor

Google Gen AI Python SDK