> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-update-1770920925-e15dbde.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Trace Google ADK applications
This guide shows you how to trace [Google Agent Development Kit (ADK)](https://github.com/google/adk-python) 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:
```bash pip theme={null}
pip install langsmith google-adk
```
```bash uv theme={null}
uv add langsmith google-adk
```
## Setup
Set your [API keys](/langsmith/create-account-api-key):
```bash theme={null}
export LANGSMITH_API_KEY=
export GOOGLE_API_KEY=
```
To create a Google API key, refer to [Google AI Studio](https://aistudio.google.com/api-keys).
## 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:
```python theme={null}
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:
```python theme={null}
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](https://smith.langchain.com) 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:
```python theme={null}
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:
```python theme={null}
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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