> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orq.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Google ADK
> Connect Google Agent Development Kit to Orq.ai via OpenTelemetry. Trace agent workflows, tool calls, and Gemini model interactions.
Instrument your code with OpenTelemetry to capture traces, logs, and metrics for every LLM call, agent step, and tool use.
## Observability
### Getting Started
Google Agent Development Kit (ADK) is a flexible, model-agnostic framework for developing and deploying AI agents. ADK simplifies building complex agent architectures with workflow orchestration, tool integration, and multi-agent collaboration. Integrate with Orq.ai to monitor agent behavior, track tool usage, analyze workflows, and optimize your agent systems.
### Prerequisites
Before you begin, ensure you have:
* An Orq.ai account and [API Key](/docs/ai-studio/organization/api-keys)
* Google API key (for model access)
* Python 3.8+
* Google ADK installed in your project
### Install Dependencies
**Python:**
```bash Bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Google ADK
pip install google-adk
# OpenTelemetry packages
pip install opentelemetry-sdk opentelemetry-exporter-otlp
# OpenInference instrumentation for ADK (if available)
pip install openinference-instrumentation-google-adk
```
**Java:**
```bash Bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
com.google.ai
google-adk
latest
```
```bash Bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
// For Gradle
implementation 'com.google.ai:google-adk:latest'
```
### Configure Orq.ai
Set up your environment variables to connect to Orq.ai's OpenTelemetry collector:
**Unix/Linux/macOS:**
```bash Bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.orq.ai/v2/otel"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer "
export OTEL_RESOURCE_ATTRIBUTES="service.name=google-adk-app,service.version=1.0.0"
export GOOGLE_API_KEY="your-google-api-key"
```
**Windows (PowerShell):**
```bash Bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
$env:OTEL_EXPORTER_OTLP_ENDPOINT = "https://api.orq.ai/v2/otel"
$env:OTEL_EXPORTER_OTLP_HEADERS = "Authorization=Bearer "
$env:OTEL_RESOURCE_ATTRIBUTES = "service.name=google-adk-app,service.version=1.0.0"
$env:GOOGLE_API_KEY = "your-google-api-key"
```
**Using .env file:**
```bash Bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.orq.ai/v2/otel
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer
OTEL_RESOURCE_ATTRIBUTES=service.name=google-adk-app,service.version=1.0.0
GOOGLE_API_KEY=your-google-api-key
```
### Integrations
Choose your preferred OpenTelemetry framework for collecting traces:
### OpenInference
**Best for**: Comprehensive ADK instrumentation with automatic tool and model tracking
```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from openinference.instrumentation.google_adk import GoogleADKInstrumentor
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 BatchSpanProcessor
provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
trace.set_tracer_provider(provider)
# Instrument Google ADK
GoogleADKInstrumentor().instrument()
# Now use ADK normally - all agent operations will be traced
import google.adk as adk
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
import asyncio
def get_weather(location: str) -> str:
"""Return current weather for a location."""
return f"Sunny in {location}, 22C"
async def main():
# Create an agent with a tool
agent = adk.Agent(
name="Helper",
instruction="Use the provided tool to answer questions briefly.",
model="gemini-2.5-flash",
tools=[get_weather],
)
# Create session and runner
session_service = InMemorySessionService()
runner = Runner(
agent=agent,
app_name="simple-helper",
session_service=session_service
)
# Create a session (await the async call)
session = await session_service.create_session(
app_name="simple-helper",
user_id="user1"
)
# Simple one-shot example
question = "What's the weather in Paris?"
content = types.Content(role='user', parts=[types.Part(text=question)])
# Run the agent (this is also async)
events = runner.run_async(
user_id="user1",
session_id=session.id,
new_message=content
)
# Get the response
async for event in events:
if event.is_final_response():
response = event.content.parts[0].text
print(f"Q: {question}")
print(f"A: {response}")
# Run the async main function
if __name__ == "__main__":
try:
asyncio.run(main())
finally:
provider.force_flush()
provider.shutdown()
```
To view more examples of ADK implementation, browse the [Google ADK Samples repository](https://github.com/google/adk-samples)
### Next Steps
**Verify your Traces** in the **orq.ai** Studio.
To learn more about the capabilities of Traces in orq.ai, see [Traces](/docs/ai-studio/observability/traces)
## Evaluations & Experiments
Once your agents are running, use **Evaluatorq** to score outputs across a dataset and **Experiments** to compare configurations side-by-side.
Run parallel evaluations across your agents and compare results.
Compare agent configurations and view results in the AI Studio.