openinference-instrumentation-google-adk 0.1.9
OpenInference Google ADK Instrumentation
Requires Python: <3.15,>=3.9

# OpenInference Google ADK Instrumentation

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Python auto-instrumentation library for Google ADK.

The traces emitted by this instrumentation are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as [`arize-phoenix`](https://github.com/Arize-ai/phoenix)

## Installation

```shell
pip install openinference-instrumentation-google-adk
```

## Quickstart

In this example we will instrument a small program that uses Gemini and observe the traces via [`arize-phoenix`](https://github.com/Arize-ai/phoenix).

Install packages.

```shell
pip install openinference-instrumentation-google-adk arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp
```

Start the phoenix server so that it is ready to collect traces.
The Phoenix server runs entirely on your machine and does not send data over the internet.

```shell
phoenix serve
```

In a python file, set up the `GoogleADKInstrumentor` and configure the tracer to send traces to Phoenix.

```python
import asyncio

from google.adk.agents import Agent
from google.adk.runners import InMemoryRunner
from google.genai import types

from openinference.instrumentation.google_adk import GoogleADKInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
# Optionally, you can also print the spans to the console.
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

GoogleADKInstrumentor().instrument(tracer_provider=tracer_provider)

def get_weather(city: str) -> dict:
    """Retrieves the current weather report for a specified city.

    Args:
        city (str): The name of the city for which to retrieve the weather report.

    Returns:
        dict: status and result or error msg.
    """
    if city.lower() == "new york":
        return {
            "status": "success",
            "report": (
                "The weather in New York is sunny with a temperature of 25 degrees"
                " Celsius (77 degrees Fahrenheit)."
            ),
        }
    else:
        return {
            "status": "error",
            "error_message": f"Weather information for '{city}' is not available.",
        }

agent = Agent(
   name="test_agent",
   model="gemini-2.0-flash-exp",
   description="Agent to answer questions using tools.",
   instruction="You must use the available tools to find an answer.",
   tools=[get_weather]
)

async def main():
    app_name = "test_instrumentation"
    user_id = "test_user"
    session_id = "test_session"
    runner = InMemoryRunner(agent=agent, app_name=app_name)
    session_service = runner.session_service
    await session_service.create_session(
        app_name=app_name,
        user_id=user_id,
        session_id=session_id
    )
    async for event in runner.run_async(
        user_id=user_id,
        session_id=session_id,
        new_message=types.Content(role="user", parts=[
            types.Part(text="What is the weather in New York?")]
        )
    ):
        if event.is_final_response():
            print(event.content.parts[0].text.strip())

if __name__ == "__main__":
    asyncio.run(main())
```

Since we are using Gemini, we must set the `GOOGLE_API_KEY` environment variable to authenticate with the Gemini API.

```shell
export GOOGLE_API_KEY=your-api-key
```

Now simply run the python file and observe the traces in Phoenix.

```shell
python your_file.py
```