Lab 6: Programmatic Execution: Apps and Runners

Goal

In this lab, you will move beyond the CLI and trigger your "Support Analyzer" agent from a Python script. You will learn how to wrap your agent in an App container and use an InMemoryRunner to handle multiple independent user sessions.

This will teach you the canonical way to execute an agent as part of a larger Python application (like a web server or a bot).

Step 1: Prepare the Project

1. Navigate to your adk-training/support_analyzer directory:

   cd /path/to/your/adk-training/support_analyzer

2. Create the execution script: In the same directory as your agent.py, create a new Python file named main.py. This is where you will write the code to run your agent.

Python Skeleton (main.py)

Complete the main.py script by following the comments.

import asyncio
import logging
from dotenv import load_dotenv

# --- Step 1: ADK Imports ---
# TODO: Import App from google.adk.apps
# TODO: Import InMemoryRunner from google.adk.runners
# TODO: Import root_agent from agent.py
from ... import ...

# Optional: Suppress noisy ADK/httpx logs
logging.getLogger("google.adk").setLevel(logging.WARNING)

load_dotenv()

# --- Step 2: Infrastructure Setup ---
# TODO: 1. Create the App instance named "support_app".
app = ...

# TODO: 2. Create the Runner instance using the app.
runner = ...

async def main():
    print("--- User A (Alice) ---")
    # --- Step 3: Run for Alice ---
    # TODO: Use the runner's debug method to send Alice's billing issue: 
    # "I was overcharged $50". Don't forget the user_id!
    events_a = ...
    
    # --- Step 4: Process Events ---
    # TODO: Loop through events_a and find the final response.
    # Hint: use event.is_final_response()
    for event in events_a:
        ...

    print("\n--- User B (Bob) ---")
    # --- Step 5: Run for Bob ---
    # TODO: Use the SAME runner instance to send Bob's technical issue:
    # "My wifi is slow". Use a different user_id.
    ...

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

Step 3: Run the Script

Once you have completed the script, you can execute it using uv run.

1. Run the script from your support_analyzer directory:

   uv run python main.py

Step 4: Observe the Output

If your script is correct, you will see two distinct interactions printed to your console—one for Alice's billing issue and one for Bob's technical issue. Observe how the same Runner managed both interactions correctly using their user_id.

Lab Summary

You have successfully run an agent programmatically using the modern App and Runner architecture. This is the foundation for integrating your agent into any larger Python application.

You have learned to:

• Wrap an agent in an App to manage infrastructure concerns.
• Instantiate an InMemoryRunner for easy local development.
• Use run_debug() to quickly execute agents and see their output.
• Use user_id to ensure that different users' conversations are isolated from one another.

Self-Reflection Questions

• Why is the App class considered "infrastructure" while the Agent is considered "intelligence"?
• What would happen if you didn't provide a user_id to the runner (or provided the same one for both Alice and Bob)?
• In a production web server (like FastAPI), where would you put the code to instantiate the Runner? Would you put it inside the request handler function or as a global variable? Why?

---

🕵️ Hidden Solution 🕵️

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMDYtcHJvZ3JhbW1hdGljLWV4ZWN1dGlvbi9sYWItc29sdXRpb24=

The direct link is: Lab Solution