Arize AX observability for ADK¶

Arize AX is the full-featured AI observability and evaluation platform from Arize AI for production teams, AI-native companies, and enterprises. It is available as managed cloud or enterprise self-hosted deployment and provides comprehensive tracing, evaluation, and monitoring capabilities for Google ADK applications. To get started, sign up for a free account.

For an open-source path for local development, experimentation, or single-container self-hosting, check out Arize Phoenix. Arize's agent evaluation guide and LLM evaluation guide show how teams use traces to evaluate agent decisions, tool calls, and model behavior.

Overview¶

Arize AX can automatically collect traces from Google ADK using OpenInference instrumentation, allowing you to:

Trace agent interactions - Automatically capture every agent run, tool call, model request, and response with context and metadata
Evaluate performance - Assess agent behavior using custom or pre-built evaluators and run experiments to test agent configurations
Monitor in production - Set up real-time dashboards and alerts to track performance
Debug issues - Analyze detailed traces to quickly identify bottlenecks, failed tool calls, and any unexpected agent behavior

Agent Traces

Installation¶

Install the required packages:

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

Setup¶

1. Configure Environment Variables¶

Set your Google API key:

export GOOGLE_API_KEY=[your_key_here]

2. Connect your application to Arize AX¶

from arize.otel import register

# Register with Arize AX
tracer_provider = register(
    space_id="your-space-id",      # Found in app space settings page
    api_key="your-api-key",        # Found in app space settings page
    project_name="your-project-name"  # Name this whatever you prefer
)

# Import and configure the automatic instrumentor from OpenInference
from openinference.instrumentation.google_adk import GoogleADKInstrumentor

# Finish automatic instrumentation
GoogleADKInstrumentor().instrument(tracer_provider=tracer_provider)

Observe¶

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

import nest_asyncio
nest_asyncio.apply()

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

# Define a tool function
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.",
        }

# Create an agent with tools
agent = Agent(
    name="weather_agent",
    model="gemini-flash-latest",
    description="Agent to answer questions using weather tools.",
    instruction="You must use the available tools to find an answer.",
    tools=[get_weather]
)

app_name = "weather_app"
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
)

# Run the agent (all interactions will be traced)
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())

View Results in Arize AX¶

Traces in Arize AX Agent Visualization Agent Experiments