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Lab 2: Environment Setup Challenge

Prerequisites​

Before you begin, ensure you have the following tools ready:

  • Code Editor (IDE): You need a good environment to write your code. We recommend:
  • Python: You must have Python installed, version 3.10 or higher.
  • pip: The Python package installer must be available.

Goal​

Your task is to prepare your local machine for agent development. Try to complete the steps below using your existing knowledge. If you get stuck, the lab-solution.md file provides a detailed, step-by-step walkthrough.

Requirements​

  1. Create a project directory named adk-training.
  2. Inside it, create and activate a Python virtual environment.
  3. Install the google-adk and python-dotenv packages.
  4. Create a .env file and configure your authentication method.
  5. Save the project dependencies to a requirements.txt file.
  6. Verify your setup by creating a verify_setup.py file with the code below and running it successfully.

Step 4: Create a .env file​

Create a file named .env in your adk-training directory. This file will securely store your authentication credentials. Choose one of the two options below.

Option A: Use a Google AI Studio API Key (Recommended for Beginners)

  1. Get your API key from Google AI Studio.
  2. Add the following line to your .env file: 
    GOOGLE_API_KEY="YOUR_API_KEY"

Option B: Use Vertex AI (Advanced)

  1. Authenticate with the gcloud CLI: gcloud auth application-default login
  2. Add the following lines to your .env file, replacing the placeholder values with your Google Cloud project details: 
    GOOGLE_GENAI_USE_VERTEXAI="1"
    GOOGLE_CLOUD_PROJECT="your-gcp-project-id"
    GOOGLE_CLOUD_LOCATION="us-central1"

Verification Script​

Create a file named verify_setup.py and add the following content:

# verify_setup.py
import asyncio
import os
from dotenv import load_dotenv
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

async def main():
    # Load environment variables from the .env file
    load_dotenv()

    try:
        print("✅ Google ADK is installed correctly.")
        print("Attempting to connect to the LLM service via an ADK agent...")

        # Define a simple ADK agent
        agent = LlmAgent(
            name="verify_agent",
            model="gemini-2.5-flash",
            instruction="You are a helpful assistant. Respond with a short confirmation."
        )

        # Use the ADK Runner to execute the agent
        runner = Runner(
            app_name="agents", # This must match the ADK's inferred app name
            agent=agent,
            session_service=InMemorySessionService()
        )
        session = await runner.session_service.create_session(
            app_name="agents", user_id="test_user"
        )
        message = types.Content(parts=[types.Part(text="hello")])

        # Stream the response from the agent
        final_response_text = "Agent did not produce a final response."
        async for event in runner.run_async(user_id="test_user", session_id=session.id, new_message=message):
            if event.is_final_response():
                if event.content and event.content.parts:
                    final_response_text = event.content.parts[0].text
                elif event.actions and event.actions.escalate:
                    final_response_text = f"Agent escalated: {event.error_message or 'No specific message.'}"
                break

        if final_response_text != "Agent did not produce a final response.":
            print("✅ Authentication successful: Connected to the LLM service via ADK agent.")
            print(f"ADK agent response: {final_response_text}")
        else:
            print("❌ Authentication failed: Could not connect to the LLM service via ADK agent.")

    except ImportError:
        print("❌ Installation error: The 'google-adk' package could not be found.")
    except Exception as e:
        print(f"❌ An unexpected error occurred: {e}")

if __name__ == "__main__":
    asyncio.run(main())

Self-Reflection Questions​

  • Why is it important to use a virtual environment instead of installing packages globally?
  • What are the security implications of storing API keys in a .env file versus hardcoding them in your script?
  • How does an IDE (like VS Code or Cloud Shell Editor) improve your productivity compared to a basic text editor?

🕵️ Hidden Solution 🕵️​

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMDItZW52aXJvbm1lbnQtc2V0dXAvbGFiLXNvbHV0aW9u

The direct link is: Lab Solution