Memory Stores

The store choice affects latency, scalability, query patterns, and cost. Pick based on retention needs, not hype.

Memory stores are filing systems — sticky notes (in-memory) for demos, a warehouse (Postgres + pgvector) for production.

Scenario

A B2B SaaS agent uses Postgres for user profiles (structured) and Qdrant for semantic memory (unstructured facts and episodes).

Solution

In Agent Memory, apply Memory Stores to this scenario: A B2B SaaS agent uses Postgres for user profiles (structured) and Qdrant for semantic memory (unstructured facts and episodes). 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 Stores (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 Stores happens in the code.

1# Memory Stores — 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 Stores8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain memory stores clearly."},11        {"role": "user", "content": f"What is memory stores?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

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

Quick recap — the most important points from this module.