# Google ADK

Google's Agent Development Kit (ADK) accelerates the path from prototype to production for generative AI agents. The framework bundles orchestration, tracing, and agent management into a unified package, reducing the infrastructure work typically required when building agent systems.

This guide covers capturing telemetry from ADK applications using InteractiveAI and OpenTelemetry instrumentation.

### Prerequisites

* InteractiveAI account with API credentials
* Google Gemini API key

***

### Installation

```bash
pip install interactiveai google-adk openinference-instrumentation-google-adk
```

***

### Configuration

Set your API credentials as environment variables:

```python
import os

# InteractiveAI credentials
os.environ["INTERACTIVEAI_PUBLIC_KEY"] = "pk-..."
os.environ["INTERACTIVEAI_SECRET_KEY"] = "sk-..."

# Google API key
os.environ["GOOGLE_API_KEY"] = "..."
```

Initialize the client and confirm connectivity:

```python
from interactiveai import Interactive

interactiveai = Interactive(
    public_key=os.environ["INTERACTIVEAI_PUBLIC_KEY"],
    secret_key=os.environ["INTERACTIVEAI_SECRET_KEY"],
)

if interactiveai.auth_check():
    print("Connection established")
else:
    print("Authentication failed - verify credentials")
```

***

### Enabling Trace Capture

Google ADK has a dedicated OpenInference instrumentor that captures agent operations automatically:

```python
from openinference.instrumentation.google_adk import GoogleADKInstrumentor

GoogleADKInstrumentor().instrument()
```

Once activated, tool calls and model completions generate spans that route to InteractiveAI.

***

### Running an ADK Agent

Here's a functional example with a simple tool-equipped agent:

```python
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

def get_current_time():
    return {"time": "14:32 UTC"}

agent = Agent(
    name="time_agent",
    model="gemini-2.0-flash",
    instruction="Always respond with the current time using the get_current_time tool.",
    tools=[get_current_time],
)

APP_NAME = "time_app"
USER_ID = "demo-user"
SESSION_ID = "demo-session"

session_service = InMemorySessionService()
await session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)

runner = Runner(agent=agent, app_name=APP_NAME, session_service=session_service)

user_msg = types.Content(role="user", parts=[types.Part(text="What time is it?")])
for event in runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=user_msg):
    if event.is_final_response():
        print(event.content.parts[0].text)
```

Each tool invocation and model response appears as a distinct span in InteractiveAI.

***

### Enriching Traces with Context

Combine OpenInference instrumentation with the InteractiveAI SDK to attach identifiers and metadata:

<pre class="language-python"><code class="lang-python">from interactiveai import Interactive
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

interactiveai = Interactive(
    public_key=os.environ["INTERACTIVEAI_PUBLIC_KEY"],
    secret_key=os.environ["INTERACTIVEAI_SECRET_KEY"],
)

with interactiveai.start_as_current_span(name="adk-agent-task") as span:
    
<strong>    interactiveai.update_current_trace(
</strong><strong>        user_id="user_google_adk",
</strong><strong>        session_id="session_google_adk",
</strong><strong>        tags=["google-adk", "gemini"],
</strong><strong>        metadata={"experiment": "baseline", "environment": "production"}
</strong><strong>    )
</strong>    
    # ADK agent execution here
    # ...
    
    interactiveai.update_current_trace(
        input="User query",
        output="Agent response"
    )

interactiveai.flush()
</code></pre>

***

### Trace Visibility

The InteractiveAI dashboard displays:

* Agent execution flow and tool invocations
* Model requests with prompts and completions
* Token consumption and latency metrics
* Session and user context for each trace


---

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.interactive.ai/integrations/ai-frameworks/google-adk.md?ask=<question>
```

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The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.