A shelf of standing questions

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The scenario

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Step 1 — See the shelf

shelf = client.list_memory_models(space_id="accounts", limit=50)
for m in shelf["items"]:
    flag = "stale" if m["is_stale"] else "current"
    print(f"{m['name']:30} {flag:8} {m['last_refreshed_at']}")

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Step 2 — Group them with tags

client.list_memory_models(space_id="accounts", tags=["quarterly"])

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Step 3 — Sharpen the question without losing the answer

client.update_memory_model(
    space_id="accounts",
    model_id=mid,
    query="What is blocking the Acme renewal, and who owns each blocker?",
)

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Step 4 — Clear content, keep the definition

client.clear_memory_model(space_id="accounts", model_id=mid)

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Step 5 — Retire it

client.delete_memory_model(space_id="accounts", model_id=mid)

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Evals

1. Create three models, refresh them, confirm all three read current.
2. Write a memory that contradicts one. Only that one should turn is_stale.
3. Clear it, then read it — content gone, name and query intact.
4. Refresh and confirm the new text reflects the correction rather than blending it with the old answer. Blending is the failure that clearing exists to prevent.

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Guardrails

• A newly created model has no content until its first refresh completes. Render that state rather than an empty string that reads like an answer.
• Deleting is owner-only, like every other space-configuration call.
• Stale is not wrong. It means the memories moved, not that the text is false — often it is still the best answer you have.