Memory Fundamentals

Without memory, every message is a first meeting. Users expect agents to remember preferences, past decisions, and project context.

Memory is the difference between talking to a stranger on every call versus a colleague who remembers your last three projects.

Scenario

A project-management agent remembers you prefer Kanban boards, that sprint 12 slipped, and that your team uses Jira — retrieved at the start of each session.

Solution

In Agent Memory, apply Memory Fundamentals to this scenario: A project-management agent remembers you prefer Kanban boards, that sprint 12 slipped, and that your team uses Jira — retrieved at the start of each session. 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 Memory Fundamentals (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 Memory Fundamentals happens in the code.

1# Memory Fundamentals — 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 Memory Fundamentals8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain memory fundamentals clearly."},11        {"role": "user", "content": f"What is memory fundamentals?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating Memory Fundamentals as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for memory fundamentals

Quick recap — the most important points from this module.