Google ADK Tracing

Instrument LLM calls made using the Google ADK Python SDK

Agent Development Kit

google.github.io

Launch Phoenix

Phoenix Cloud

Sign up for Phoenix:

1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login

2. Click Create Space, then follow the prompts to create and launch your space.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint and API Key:

From your new Phoenix Space

1. Create your API key from the Settings page

2. Copy your Hostname from the Settings page

3. In your code, set your endpoint and API key:

import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"

Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

Install

pip install openinference-instrumentation-google-adk google-adk arize-phoenix-otel

Setup

Set the GOOGLE_API_KEY environment variable. Refer to Google’s ADK documentation for more details on authentication and environment variables.

export GOOGLE_API_KEY=[your_key_here]

Use the register function to connect your application to Phoenix.

from phoenix.otel import register

# Configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

Observe

Now that you have tracing setup, all Google ADK SDK requests will be streamed to Phoenix for observability and evaluation.

import asyncio

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

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())

Agent Engine Deployment

When using Vertex AI Agent Engine for remote deployment, instrumentation must be configured within the remote agent module, not in the main application code.

For Agent Engine deployment, include the instrumentation packages in your requirements and set up instrumentation in your agent module:

Main Application:

remote_agent = agent_engines.create(
    agent_engine=ModuleAgent(module_name="adk_agent", agent_name="app"),
    requirements=[
        "google-cloud-aiplatform[agent_engines,adk]",
        "arize-otel",
        "openinference-instrumentation-google-adk",
    ],
    extra_packages=["adk_agent.py"],
    env_vars={
        "OTEL_EXPORTER_OTLP_ENDPOINT": "https://otlp.arize.com/v1",
        "OTEL_EXPORTER_OTLP_TIMEOUT": "60000",  # Optional, can prevent "context deadline exceeded" errors
    },
)

Agent Module (adk_agent.py):

from phoenix.otel import register
from openinference.instrumentation.google_adk import GoogleADKInstrumentor

# Configure instrumentation within the remote agent
tracer_provider = register(
    project_name="adk-agent",
)
GoogleADKInstrumentor().instrument(tracer_provider=tracer_provider)

# Your agent code here...

Resources:

OpenInference Package

Google ADK documentation