Sessions

• Key points are in the visual diagram above.

Coding tasks span hours and process restarts.

A save game.

Scenario

Scenario (Claude Agent SDK): A user triggers a workflow that depends on Sessions. Sessions persist the thread so you can resume a job. Your implementation handles the request, logs the step for observability, validates the output, and returns a grounded response — e.g. cutting manual work from 20 minutes to under 30 seconds.

Solution

In Claude Agent SDK, apply Sessions to this scenario: Scenario (Claude Agent SDK): A user triggers a workflow that depends on Sessions. 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 Sessions (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 Sessions happens in the code.

1# Sessions — 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 Sessions8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain sessions clearly."},11        {"role": "user", "content": f"What is sessions?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Sessions before production
• No logging or tracing around claude sessions steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.