> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getnetra.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Google ADK
> Trace Google Agent Development Kit (ADK) workflows with Netra auto-instrumentation. Monitor agent execution, tool calls, and state across ADK apps.
## Installation
Install both the Netra SDK and Google ADK:
```bash Python theme={null}
pip install netra-sdk google-adk
```
## Usage
Initialize the Netra SDK to automatically trace all Google ADK operations:
```python Python theme={null}
from netra import Netra
from google.adk import Agent
import os
# Initialize Netra
Netra.init(
headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
trace_content=True
)
# Define agent - automatically traced
agent = Agent(
name="assistant",
model="gemini-1.5-pro",
instructions="You are a helpful assistant"
)
# Run agent
response = await agent.run("What is Google ADK?")
print(response.output)
```
### Getting Started
Trace basic agent execution:
```python Python theme={null}
from netra.decorators import task
from netra import SpanWrapper
from google.adk import Agent
@task()
async def run_agent(prompt: str) -> str:
span = SpanWrapper("adk-agent", {
"prompt": prompt
}).start()
agent = Agent(
name="assistant",
model="gemini-1.5-pro",
instructions="You are a helpful assistant"
)
response = await agent.run(prompt)
span.set_attribute("response", response.output)
span.end()
return response.output
```
### Agent Tools
Trace agents with custom tools:
```python Python theme={null}
from netra.decorators import agent
from netra import SpanWrapper
from google.adk import Agent, Tool
def get_weather(location: str) -> str:
"""Get weather information for a location."""
return f"Weather in {location}: Sunny, 72°F"
@agent()
async def agent_with_tools(query: str):
tool_span = SpanWrapper("adk-agent-tools", {
"query": query
}).start()
# Define tool
weather_tool = Tool(
name="get_weather",
description="Get weather information",
function=get_weather
)
# Create agent with tool
assistant = Agent(
name="weather-agent",
model="gemini-1.5-pro",
instructions="Help users with weather information",
tools=[weather_tool]
)
response = await assistant.run(query)
tool_span.set_attribute("response", response.output)
tool_span.end()
return response.output
```
### Multi-Agent Systems
Trace multi-agent orchestration:
```python Python theme={null}
from netra.decorators import workflow
from netra import SpanWrapper
from google.adk import Agent
@workflow()
async def multi_agent_system(task: str):
workflow_span = SpanWrapper("multi-agent-workflow", {
"task": task
}).start()
# Research agent
researcher = Agent(
name="researcher",
model="gemini-1.5-pro",
instructions="Research and gather information"
)
# Writer agent
writer = Agent(
name="writer",
model="gemini-1.5-pro",
instructions="Write based on research"
)
# Execute agents in sequence
research_result = await researcher.run(f"Research: {task}")
final_result = await writer.run(
f"Write based on: {research_result.output}"
)
workflow_span.set_attribute("result", final_result.output)
workflow_span.end()
return final_result.output
```
### Streaming Responses
Trace streaming agent outputs:
```python Python theme={null}
from netra.decorators import task
from netra import SpanWrapper
@task()
async def stream_agent_response(prompt: str):
stream_span = SpanWrapper("adk-stream").start()
agent = Agent(
name="assistant",
model="gemini-1.5-pro",
instructions="You are a helpful assistant"
)
full_response = ""
async for chunk in agent.stream(prompt):
print(chunk, end='', flush=True)
full_response += chunk
stream_span.set_attribute("response", full_response)
stream_span.end()
return full_response
```
### State Management
Trace agents with persistent state:
```python Python theme={null}
from netra.decorators import agent
from netra import SpanWrapper
from google.adk import Agent, Memory
@agent()
async def stateful_agent(user_id: str, message: str):
state_span = SpanWrapper("adk-stateful", {
"user_id": user_id,
"message": message
}).start()
# Create agent with memory
assistant = Agent(
name="assistant",
model="gemini-1.5-pro",
instructions="Remember context from previous interactions",
memory=Memory(user_id=user_id)
)
response = await assistant.run(message)
state_span.set_attribute("response", response.output)
state_span.end()
return response.output
```
## Configuration
Configure selective instrumentation:
```python Python theme={null}
from netra import Netra
from netra.instrumentation.instruments import InstrumentSet
# Enable Google ADK instrumentation
Netra.init(
headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
trace_content=True,
instruments={InstrumentSet.ADK}
)
```
## Next Steps
* [Quick Start Guide](https://docs.getnetra.ai/quick-start/python) - Complete setup and configuration
* [Decorators](https://docs.getnetra.ai/tracing/decorators) - Add custom tracing with `@workflow`, `@agent`, and `@task` decorators
* [Google ADK Documentation](https://google.github.io/adk-docs/) - Official Google ADK documentation