MLflow Observability for Google ADK

Learn how to use MLflow Tracing to observe and debug Google ADK agents and tools using OpenTelemetry.

Posted on: 2026-03-07 by AI Assistant

MLflow Observability for Google ADK

Observability is a critical component when building applications with Large Language Models (LLMs). As AI agents become more autonomous and complex, understanding their decision-making processes, tool usage, and performance bottlenecks is essential.

MLflow Tracing provides robust, first-class support for ingesting OpenTelemetry (OTel) traces. Fortunately, Google ADK emits OTel spans for agent runs, tool calls, and model requests natively, allowing you to send them directly to an MLflow Tracking Server for seamless analysis and debugging.

In this guide, we’ll walk through how to set up MLflow tracing for your Google ADK agents.

Prerequisites

Before diving in, ensure you have the following ready:

MLflow 3.6.0+: OpenTelemetry ingestion is supported only in version 3.6.0 and newer.
A SQL-based Backend Store: You’ll need a database like SQLite, PostgreSQL, or MySQL. File-based stores do not support OTLP ingestion.
Google ADK: Make sure the ADK is installed in your development environment.

1. Install Dependencies

First, let’s install the necessary packages. We need MLflow, the Google ADK, and the required OpenTelemetry SDK and exporter:

pip install "mlflow>=3.6.0" google-adk opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

2. Start the MLflow Tracking Server

Next, initialize the MLflow tracking server using a SQL backend. In this example, we’ll use a local SQLite database and bind the server to port 5000:

mlflow server --backend-store-uri sqlite:///mlflow.db --port 5000

Note: You can easily substitute --backend-store-uri to point to PostgreSQL, MySQL, or MSSQL for production deployments. Remember, OTLP ingestion won’t work with file-based backends!

3. Configure OpenTelemetry

For spans to be correctly emitted to MLflow, it is crucial to configure an OTLP exporter and set a global tracer provider before interacting with any ADK components.

Add the following initialization code at the very top of your application script:

# my_agent/agent.py
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

# 1. Initialize the OTLP Exporter
exporter = OTLPSpanExporter(
    endpoint="http://localhost:5000/v1/traces",
    headers={"x-mlflow-experiment-id": "123"}  # Replace with your actual experiment ID
)

# 2. Set the Global Tracer Provider
provider = TracerProvider()
provider.add_span_processor(SimpleSpanProcessor(exporter))
trace.set_tracer_provider(provider)  # CRITICAL: Set this BEFORE importing/using ADK

This ensures the OpenTelemetry pipeline is properly configured to route ADK spans to your MLflow instance.

4. Trace an ADK Agent in Action

With OpenTelemetry configured, we can now define our agent. Let’s create a simple math agent equipped with a calculator tool to demonstrate tracing:

# my_agent/agent.py
from google.adk.agents import LlmAgent
from google.adk.tools import FunctionTool

def calculator(a: float, b: float) -> str:
    """Add two numbers and return the result."""
    return str(a + b)

calculator_tool = FunctionTool(func=calculator)

root_agent = LlmAgent(
    name="MathAgent",
    model="gemini-2.5-flash",
    instruction=(
        "You are a helpful assistant that can do math. "
        "When asked a math problem, use the calculator tool to solve it."
    ),
    tools=[calculator_tool],
)

You can now run this agent using the ADK CLI:

adk run my_agent

Try asking it a question:

> What is 12 + 34?
[MathAgent]: The answer is 46.

5. View Your Traces in MLflow

Head over to the MLflow UI by navigating to http://localhost:5000 in your web browser. Select your experiment (e.g., ID 123) to view the captured traces. You’ll be able to inspect the trace tree, delve into individual spans, view the exact prompts sent to the model, and monitor the inputs and outputs of your calculator tool.

MLflow Traces

Best Practices & Tips

Strict Execution Order: Always configure the tracer provider before importing or initializing Google ADK objects. Failing to do so will result in missing spans.
Remote MLflow Servers: If you are running MLflow behind a proxy or on a separate remote host, ensure you update the endpoint in OTLPSpanExporter from http://localhost:5000 to the correct remote address.

By integrating MLflow Observability with Google ADK, you gain the visibility required to confidently build, debug, and scale your AI agent workflows.

mlflow google-adk observability opentelemetry