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The OpenInference Google GenAI instrumentor traces calls made with the Google Gen AI SDK (google-genai) — model, token usage, and content — as OTLP spans. Point the exporter at AgentMark and the traces arrive normalized.

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Setup

1

Install the instrumentor and the OTLP exporter

pip install openinference-instrumentation-google-genai google-genai \
  opentelemetry-sdk opentelemetry-exporter-otlp-proto-http
2

Point the exporter at AgentMark and instrument Google GenAI

Use your AgentMark API key and app id from project settings.
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://api.agentmark.co/v1/traces",
            headers={
                "Authorization": "<YOUR_API_KEY>",  # raw key, no "Bearer" prefix
                "X-Agentmark-App-Id": "<YOUR_APP_ID>",
            },
        )
    )
)

GoogleGenAIInstrumentor().instrument(tracer_provider=provider)
3

Run your calls

Use the Gen AI client as usual — client.models.generate_content(...). Each call arrives in AgentMark as a span, grouped into a trace. See Traces and logs.
from google import genai

client = genai.Client(api_key="<GEMINI_API_KEY>")
client.models.generate_content(
    model="gemini-2.0-flash",
    contents="What is the capital of France?",
)

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What AgentMark captures

Gemini spans use the OpenInference attribute conventions — model, token usage, input and output content, and span kind are all mapped onto AgentMark’s normalized trace fields, and token counts feed cost tracking. See OpenInference for the full attribute mapping.

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Next steps

OpenInference

How AgentMark reads OpenInference attributes

Traces and logs

Explore traces once they arrive

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Have questions?

Reach out any time: