Tutor that remembers the learner

The scenario

A tutor that forgets is a tutor that re-teaches. It re-explains what the student already has, misses that they have failed the same kind of problem four times, and cannot tell you in March what they were struggling with in January. Session memory does not fix this. The gap between sessions is where the teaching signal lives.

Step 1 — Record what happened, not just what was said

client.record(
    space_id="tutoring",
    content="Priya solved quadratics by factoring but could not start one needing the formula.",
    user_id="student-4417",
    timestamp="2026-01-18T16:00:00Z",
)

timestamp is when the thing happened, which is not always when you are writing it down. Getting it right is what makes the temporal questions below answerable at all.

Step 2 — Open the next session already knowing

profile = client.get_user_profile(space_id="tutoring", user_id="student-4417")
print(profile["context"])

One call returns what is known about that learner, already formatted for a prompt. Put it in the system message and the first minute of the session stops being an interview.

Step 3 — Ask a question about one student

print(client.ask_about_user(
    space_id="tutoring",
    user_id="student-4417",
    query="Which topics has she needed re-explaining more than once?",
))

Step 4 — Show progress over a real interval

client.retrieve(
    space_id="tutoring",
    query="quadratics",
    user_id="student-4417",
    occurred_after="2026-01-01T00:00:00Z",
    occurred_before="2026-03-01T00:00:00Z",
)

That is the parents’ evening answer, and it is the one no chat history can give you.

Evals

Record five sessions for one student across three months.
Ask ask_about_user for recurring difficulties. Check it names the one you planted deliberately.
Run the same question with a two-week occurred_after window and confirm the answer narrows rather than repeating itself.
Ask about a student with no memories. It should say so, not improvise.

Guardrails

This is data about a child, in many deployments. Scope every write, keep the space out of reach of other learners’ guardians, and have the deletion runbook ready before you need it — not after a parent asks.

memory_count on a profile is non-monotonic. Consolidation merges related memories, so the number can go down while the system knows strictly more. Do not show it to a parent as “things learned”.
Don’t record judgements you would not say out loud. Anything stored is retrievable, and “seems lazy” reads very differently in an export than it did in the moment.
Set timestamp yourself for anything you are backfilling. Without it the event time is the write time, and a year of history imported on Tuesday all happened on Tuesday.

Built from

Both are complete, runnable scripts: user_profiles.py, time_travel.py.

Getting Started

API Essentials

Memory (data plane)

LLM Proxy (drop-in)

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Changelog

Tutor that remembers the learner

The scenario

Step 1 — Record what happened, not just what was said

Step 2 — Open the next session already knowing

Step 3 — Ask a question about one student

Step 4 — Show progress over a real interval

Evals

Guardrails

Built from

​The scenario

​Step 1 — Record what happened, not just what was said

​Step 2 — Open the next session already knowing

​Step 3 — Ask a question about one student

​Step 4 — Show progress over a real interval

​Evals

​Guardrails

​Built from

The scenario

Step 1 — Record what happened, not just what was said

Step 2 — Open the next session already knowing

Step 3 — Ask a question about one student

Step 4 — Show progress over a real interval

Evals

Guardrails

Built from