Results

Scores on LongMemEval and LoCoMo

We ran every question in both benchmarks, once, and publish every answer.

LongMemEval-S

500 questions

Answered by gpt-6-luna. Graded by gpt-6.1-sol with LongMemEval's grading prompts.

Each question comes with about 50 past chats with an assistant, around 115,000 tokens. Ormem has to find the right ones itself.

459 of 500 right. Ormem sends about 6,200 tokens of memory per question.

By kind of question

All questions459 of 50091.8%
What the assistant said earlier55 of 5698.2%
What the person said68 of 7097.1%
Noticing when things changed75 of 7896.2%
Dates and timing121 of 13391.0%
Likes and preferences26 of 3086.7%
Combining several chats114 of 13385.7%

LoCoMo

1,540 questions

Answered by gpt-6-luna. Graded by gpt-6.1-sol with the same prompts.

Ten very long conversations between two people, each spread over many sessions.

1,369 of 1,540 right. Ormem sends about 5,300 tokens of memory per question.

By kind of question

All questions1,369 of 1,54088.9%
A fact from one session772 of 84191.8%
Combining several sessions252 of 28289.4%
Dates and timing282 of 32187.9%
Guessing what a person would do or think63 of 9665.6%

How we tested

The hard version of LongMemEval. Every question comes with the full chat history, not the "oracle" version that hands over only the chats with the answer.
Every question, one run. No answers combined from several runs, and no questions left out.
A low-cost answering model, gpt-6-luna. Grading by gpt-6.1-sol, since OpenAI is retiring gpt-4o, which many published scores use.
Tuned on 90 of the 500 LongMemEval questions. On the other 410, which we never tuned on, Ormem scored 91.2%.
Every answer is published: LongMemEval-S · LoCoMo. Each line has the question ID, Ormem's answer and the grade; the IDs match the official datasets.

Small context

Memory text sent to the AI per question (LongMemEval-S)

Pricing

Which plan fits

Pay for the messages you store and the questions you ask each month. Type your usage to see the plan that fits and what it would cost. Free for everyone during the invite-only beta; paid plans start later, and nobody is charged without notice.

Conversations a month Messages each, on average Questions a month

Free stops at its limits. Paid plans don't stop: extra messages are $1 per 1,000, extra questions $5 per 1,000. Bringing in past chats counts as messages in the month you import them. Need more, or your own deployment? Talk to us.

How it works

Two API calls: store and recall

Ormem searches the stored memories and sends the model only the relevant ones, about 6,000 tokens per question.

Outer rings are newer memories

Question

Memories found

Answer

Example. The API returns the same sources as JSON.

Send what happens

Messages, notes and documents, as they arrive. Or import past chats from ChatGPT, Claude or WhatsApp in one go.

Ask in plain language

"What did Maya say about her mum's appointment?" Ormem finds the best passages, ranks them, and asks the model.

Get an answer with sources

Every answer lists the exact memories it used, with dates. When Ormem doesn't know, it says so instead of guessing.

Store a memory

# One key per customer, sent with every request
curl $ORMEM_URL/v1/memories \
  -H "Authorization: Bearer $ORMEM_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "owner": "maya",
    "memories": [{
      "body": "Mum's cardiology check moved to Friday 3pm.",
      "speaker": "maya",
      "happened_at": "2026-10-02T09:14:00Z"
    }]
  }'

Ask a question

curl $ORMEM_URL/v1/recall \
  -H "Authorization: Bearer $ORMEM_KEY" \
  -H "Content-Type: application/json" \
  -d '{"owner": "maya", "question": "When is Mum'\''s heart check?"}'

# Returns
{
  "answer": "Friday at 3pm. It was moved there on 2 Oct.",
  "evidence_status": "supported",
  "citations": [{ "text": "Mum's cardiology check moved…",
                  "when": "2026-10-02T09:14:00Z" }]
}

Features

What it stores

Conversations, in order

Who said what, and when. The assistant's own past replies too, so "what was that recipe you gave me?" just works.

Facts that change over time

"Moved to Berlin" replaces "lives in Paris", and Ormem can still answer what was true back then.

Likes and preferences

Remembers what someone likes, dislikes and prefers, and uses it when they ask for a suggestion: "somewhere for dinner" leaves out the place they said they didn't enjoy.

Documents with versions

Care plans, policies, manuals. A newer version replaces the old one; if two sources disagree, both are shown.

Bring past chats along

Import exports from ChatGPT, Claude and WhatsApp. Re-importing the same file adds nothing twice.

Hide, fade and forget

Hide a memory so it only surfaces when its owner asks. Let old small talk fade to a summary. Delete permanently on request.

Trust

Security and data

Every answer shows its sources with dates, and says when it can't find an answer.
Each person's memories are kept in their own store. One customer's key can never reach another's.
Memory text is never written to logs, and text inside a memory can't give the AI instructions.
Export or erase everything about a person at any time.
Answers from OpenAI's gpt-6-luna, a fast, low-cost model.
Private deployment for larger teams with strict data rules. Talk to us.

The Ormem API is a separate product for developers building their own apps. The Ormem iPhone app is unchanged: your notes stay on your iPhone and never touch an Ormem server.

The fine print

Each score is one full run. Repeat runs of the same setup usually land within about a point.
Scores from other memory products use different answering models, graders or benchmark versions, so they aren't directly comparable with these.
Token counts are estimated (characters ÷ 4) and include only the memory text.

Long-term memory for your AI.