Trace Google ADK applications

This guide shows you how to trace Google Agent Development Kit (ADK) agents in LangSmith. You’ll configure automatic tracing for your ADK applications to capture agent invocations, tool calls, and LLM interactions.

Installation

Install the required packages using your preferred package manager:

pip

pip install langsmith google-adk

Setup

Set your API keys:

export LANGSMITH_API_KEY=<your_langsmith_api_key>
export GOOGLE_API_KEY=<your_google_api_key>

To create a Google API key, refer to Google AI Studio.

Configure tracing

To trace ADK agents, use configure_google_adk() from the LangSmith SDK. Call this function once at the start of your application before creating any ADK agents:

from langsmith.integrations.google_adk import configure_google_adk

configure_google_adk(
    project_name="my-adk-project",  # Optional: defaults to LANGSMITH_PROJECT env var
)

The function accepts the following optional parameters:

• project_name: LangSmith project to send traces to. Defaults to the LANGSMITH_PROJECT environment variable.
• name: Name for the root trace. Defaults to "google_adk.session".
• metadata: Dictionary of key-value pairs for additional context.
• tags: List of strings to categorize traces.

Example

This example creates a weather agent with a tool, then runs it with tracing enabled:

from langsmith.integrations.google_adk import configure_google_adk
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

# Configure LangSmith tracing
configure_google_adk(project_name="weather-assistant")

# Define a tool
def get_weather(city: str) -> dict:
    """Get weather for a city."""
    return {"city": city, "temperature": "72°F", "conditions": "Sunny"}

# Create the agent
agent = Agent(
    name="weather_agent",
    model="gemini-2.0-flash",
    description="Provides weather information.",
    instruction="Use the get_weather tool to answer weather questions.",
    tools=[get_weather],
)

# Set up session and runner
session_service = InMemorySessionService()
session_service.create_session_sync(
    app_name="weather_app",
    user_id="user_123",
    session_id="session_456",
)

runner = Runner(
    agent=agent,
    app_name="weather_app",
    session_service=session_service,
)

# Run the agent
events = runner.run(
    user_id="user_123",
    session_id="session_456",
    new_message=types.Content(
        role="user",
        parts=[types.Part(text="What's the weather in San Francisco?")],
    ),
)

for event in events:
    if event.is_final_response():
        print(event.content.parts[0].text)

View traces in LangSmith

After running your application, you can view traces in the LangSmith UI that include:

• Agent invocations: Complete flows through your ADK agents
• Tool calls: Individual function calls made by agents
• LLM interactions: Requests and responses from Gemini models
• Multi-agent workflows: Traces from sequential and parallel agent compositions

Custom metadata and tags

Add metadata and tags when configuring tracing to categorize and filter traces:

from langsmith.integrations.google_adk import configure_google_adk

configure_google_adk(
    project_name="production-agents",
    metadata={
        "environment": "production",
        "team": "ml-platform",
    },
    tags=["adk", "weather", "v2"],
)

Multi-agent workflows

The integration automatically traces multi-agent workflows including sequential and parallel agent compositions:

from langsmith.integrations.google_adk import configure_google_adk
from google.adk.agents import Agent, SequentialAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

configure_google_adk(project_name="multi-agent-demo")

# Create sub-agents
translator = Agent(
    name="translator",
    model="gemini-2.0-flash",
    description="Translates text to English.",
)

summarizer = Agent(
    name="summarizer",
    model="gemini-2.0-flash",
    description="Summarizes text concisely.",
)

# Create a sequential agent that runs sub-agents in order
pipeline = SequentialAgent(
    name="translate_and_summarize",
    sub_agents=[translator, summarizer],
    description="Translates text then summarizes it.",
)

# Set up and run
session_service = InMemorySessionService()
session_service.create_session_sync(
    app_name="pipeline_app",
    user_id="user_123",
    session_id="session_456",
)

runner = Runner(
    agent=pipeline,
    app_name="pipeline_app",
    session_service=session_service,
)

events = runner.run(
    user_id="user_123",
    session_id="session_456",
    new_message=types.Content(
        role="user",
        parts=[types.Part(text="Bonjour, comment allez-vous aujourd'hui?")],
    ),
)

for event in events:
    print(event)

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