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The Google Agent Development Kit (ADK) streamlines building, orchestrating, and tracing generative-AI agents out of the box, letting you move from prototype to production far faster than wiring everything yourself. For more details on ADK, refer to the official Google ADK documentation. LangWatch can capture traces generated by Google ADK by leveraging its built-in OpenTelemetry support. This guide will show you how to set it up.

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Prerequisites

  1. Install LangWatch SDK: 
    pip install langwatch
  2. Install Google ADK and OpenInference instrumentor: 
    pip install google-adk openinference-instrumentation-google-adk
  3. Set up Google Cloud authentication: You’ll need to authenticate with Google Cloud. You can either:
    • Set the GOOGLE_API_KEY environment variable for Gemini API access
    • Use Application Default Credentials (ADC) if running on Google Cloud
    • Use service account keys for production deployments

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Instrumentation with OpenInference

LangWatch supports seamless observability for Google ADK agents using the OpenInference Google ADK instrumentor. This approach automatically captures traces from your ADK agents and sends them to LangWatch.

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Basic Setup (Automatic Tracing)

Here’s the simplest way to instrument your application: 
import langwatch
from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from openinference.instrumentation.google_adk import GoogleADKInstrumentor
import os

# Initialize LangWatch with the Google ADK instrumentor
langwatch.setup(
    instrumentors=[GoogleADKInstrumentor()]
)

# Set up environment variables
os.environ["GOOGLE_API_KEY"] = "your-gemini-api-key"

# Define your agent tools
def say_hello():
    return {"greeting": "Hello LangWatch 👋"}

def get_weather(location: str):
    return {"location": location, "temperature": "22°C", "condition": "sunny"}

# Create your agent
agent = Agent(
    name="hello_agent",
    model="gemini-2.0-flash",
    instruction="Always greet using the say_hello tool and provide weather information when asked.",
    tools=[say_hello, get_weather],
)

# Set up session service and runner
session_service = InMemorySessionService()
session_service.create_session(
    app_name="hello_app", user_id="demo-user", session_id="demo-session"
)

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

# Use the agent as usual—traces will be sent to LangWatch automatically
def run_agent_interaction(user_message: str):
    user_msg = types.Content(role="user", parts=[types.Part(text=user_message)])
    
    for event in runner.run(user_id="demo-user", session_id="demo-session", new_message=user_msg):
        if event.is_final_response():
            return event.content.parts[0].text
    
    return "No response generated"

# Example usage
if __name__ == "__main__":
    user_prompt = "hi"
    response = run_agent_interaction(user_prompt)
    print(f"User: {user_prompt}")
    print(f"Agent: {response}")
That’s it! All Google ADK agent activity will now be traced and sent to your LangWatch dashboard automatically.

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Optional: Using Decorators for Additional Context

If you want to add additional context or metadata to your traces, you can optionally use the @langwatch.trace() decorator: 
import langwatch
from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from openinference.instrumentation.google_adk import GoogleADKInstrumentor
import os

langwatch.setup(
    instrumentors=[GoogleADKInstrumentor()]
)

# ... agent setup code ...

@langwatch.trace(name="Google ADK Agent Run")
def run_agent_interaction(user_message: str):
    # Update the current trace with additional metadata
    current_trace = langwatch.get_current_trace()
    if current_trace:
        current_trace.update(
            metadata={
                "user_id": "user_123",
                "session_id": "session_abc",
                "agent_name": "hello_agent",
                "model": "gemini-2.0-flash"
            }
        )
    
    user_msg = types.Content(role="user", parts=[types.Part(text=user_message)])
    
    for event in runner.run(user_id="demo-user", session_id="demo-session", new_message=user_msg):
        if event.is_final_response():
            return event.content.parts[0].text
    
    return "No response generated"

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How it Works

  1. langwatch.setup(): Initializes the LangWatch SDK, which includes setting up an OpenTelemetry trace exporter. This exporter is ready to receive spans from any OpenTelemetry-instrumented library in your application.
  2. GoogleADKInstrumentor(): The OpenInference instrumentor automatically patches Google ADK components to create OpenTelemetry spans for their operations, including:
    • Agent initialization
    • Tool calls
    • Model completions
    • Session management
  3. Optional Decorators: You can optionally use @langwatch.trace() to add additional context and metadata to your traces, but it’s not required for basic functionality.
With this setup, all agent interactions, tool calls, and model completions will be automatically traced and sent to LangWatch, providing comprehensive visibility into your ADK-powered applications.

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Notes

  • You do not need to set any OpenTelemetry environment variables or configure exporters manually—langwatch.setup() handles everything.
  • You can combine Google ADK instrumentation with other instrumentors (e.g., OpenAI, LangChain) by adding them to the instrumentors list.
  • The @langwatch.trace() decorator is optional - the OpenInference instrumentor will capture all ADK activity automatically.
  • For advanced configuration (custom attributes, endpoint, etc.), see the Python integration guide.

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Troubleshooting

  • Make sure your LANGWATCH_API_KEY is set in the environment.
  • If you see no traces in LangWatch, check that the instrumentor is included in langwatch.setup() and that your agent code is being executed.
  • Ensure you have the correct Google API key set for Gemini access.

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Interoperability with LangWatch SDK

You can use this integration together with the LangWatch Python SDK to add additional attributes to the trace: 
import langwatch
from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from openinference.instrumentation.google_adk import GoogleADKInstrumentor

langwatch.setup(
    instrumentors=[GoogleADKInstrumentor()]
)

@langwatch.trace(name="Custom ADK Agent")
def my_custom_agent(input_message: str):
    # Your ADK agent code here
    agent = Agent(
        name="custom_agent",
        model="gemini-2.0-flash",
        instruction="Your custom instructions",
        tools=[your_custom_tools]
    )
    
    # Update the current trace with additional metadata
    current_trace = langwatch.get_current_trace()
    if current_trace:
        current_trace.update(
            metadata={
                "user_id": "user_123",
                "session_id": "session_abc",
                "agent_name": "custom_agent",
                "model": "gemini-2.0-flash"
            }
        )
    
    # Run your agent
    # ... agent execution code ...
    
    return "Agent response"
This approach allows you to combine the automatic tracing capabilities of Google ADK with the rich metadata and custom attributes provided by LangWatch.
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