Graph Runtime

• Key points are in the visual diagram above.

Understanding Graph Runtime helps you build reliable, scalable agent applications instead of fragile demos.

Think of Graph Runtime as a specialized capability in your Google ADK engineering toolkit.

Scenario

A production team in Google ADK uses Graph Runtime to handle a real user request — reducing manual work and improving response quality with proper validation and logging.

Solution

In Google ADK, apply Graph Runtime to this scenario: A production team in Google ADK uses Graph Runtime to handle a real user request — reducing manual work and improving response quality with proper validation and logging. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Graph Runtime (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Graph Runtime happens in the code.

1# Graph Runtime — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Graph Runtime8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain graph runtime clearly."},11        {"role": "user", "content": f"What is graph runtime?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Graph Runtime before production
• No logging or tracing around adk graph runtime steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.