I work at Wontopos, so I'm biased. About three months ago we launched a long-term memory API for AI agents, and honestly almost nobody has looked at it. I'm not asking you to sign up. I'd like a blunt answer: at this price and performance, would you use it?
What it is
You store a user's memories once. For each query, recall returns only the relevant ones, and you pass them into your model's prompt. Memory lives per user, so each end-user or agent gets an isolated space.
The core calls are store, recall, search, supersede (replace an outdated memory) and forget. SDKs for Python, TypeScript and Rust, plain HTTP works too, and there is an MCP server (beta) for Claude Code and Cursor.
What I think is different
- Same recall across languages. We measure it as a grid of 70 store-language by query-language pairs, and it averages 95.2% recall@5. A memory saved in Korean should be found by a query in Japanese.
- Bounded context. Recall works over histories of 1.4M tokens and still hands back a slice of roughly 1,470 tokens, so the prompt does not grow with the history.
- Images. A memory can carry an image with no caption or alt text, and it is found from a sentence in any language.
- Ask again. If one pass does not carry the answer, a second pass returns memories the first one did not.
- Deletion in one call. One call erases a single memory or an entire user, including images and revisions (GDPR).
- Your LLM key is yours. Keys are sent per request and never stored, and nothing is trained on your data.
Price
| Input / 1M tokens | Output / 1M tokens | |
|---|---|---|
| Tablet | $2 | $3 |
| Scroll | $4 | $8 |
Each request adds a flat $0.0001. No subscription, no storage rent, no memory caps. You add a card and top up at least $5, and there is a hard spend cap. If you bring your own LLM keys, the provider bills those.
Numbers (measured by us)
- Tablet 2: 95.7% on LongMemEval-S, 67.5% on BEAM-1M. The BEAM-1M number is averaged over 5 independent runs (σ 0.22%, none cherry-picked).
- Scroll 1.2: 92.3% on LongMemEval-S, about 2,800 tokens and about 450 ms per query. BEAM-1M is 58.6% with re-asking turned off.
- Where it is weak: low-resource languages. Swahili is 53.0% recall@5.
The harness and per-question records behind the BEAM-1M number are public, and the Tablet 2 paper reports three findings that run against us.
What I'd like to know
- Would you pay this for memory in your own app?
- What would make you pick this over Mem0, Zep, or just keeping context in a file?
- Which number looks wrong or unbelievable?
Details
You don't need to sign up, you can just read the information. Knowing what I'm talking about should let you give a more accurate answer than my summary, so please take a look if you want the details: wontopos.com and the research page.
You can answer here in the comments. Even better, there is a short survey on the site: click the Survey button in the top menu of the main page. No sign-up is needed for that either.
I wrote the first draft with an AI assistant and edited it myself.
