Memory Retrieval

Storing memory is useless if the agent can't find the right piece at the right moment. Retrieval quality directly impacts answer accuracy.

Memory retrieval is a librarian fetching the right shelf — not dumping the entire library on your desk.

Scenario

User asks 'What did we decide about the database?' Retrieval fetches the semantic memory entry from last week's architecture discussion, not unrelated chat about lunch.

Solution

In Agent Memory, apply Memory Retrieval to this scenario: User asks 'What did we decide about the database?' Retrieval fetches the semantic memory entry from last week's architecture discussion, not unrelated chat about lunch. 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 Retrieval (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 Retrieval happens in the code.

1def retrieve(query, store, user_id, k=5):  # define a reusable function2    return store.similarity_search(  # return the result3        query,4        k=k,5        filter={"user_id": user_id},6        score_threshold=0.7,7    )

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

Quick recap — the most important points from this module.