A real memory, and the receipts.

Your memory, visible.

Every conversation leaves traces: names, places, moments, plans. LeapMemory weaves them into a living map, thousands of tiny memories linking to each other like the synapses of a brain. That map is the memory graph, and it is yours to open any time.

Each dot is something from your life: your best friend, your dog, the city you grew up in. The graph is not a picture of the memory. It is the memory. When you ask about your dog, LeapMemory walks the lines and brings back the vet visit, the park, the friend who gave him to you. Connected things surface together, the way your own memory works. And the graph is open: you can see exactly what your AI knows, check it, and delete what you want gone.

The same graph is what your product gets through the API. Every user gets an isolated graph of their own, built automatically from their conversations, no schema to design and no pipeline to maintain. When your app asks a question, LeapMemory walks that user's graph and returns a small, relevant slice instead of the whole history. That walk is why recall stays sharp on a tiny token budget. The numbers are in the benchmark below.

Measured, not estimated.

Here is the test, in plain terms. We took one real working session: 59 messages of dense technical conversation. We wrote 10 questions about it, each with a known correct answer. Then the same AI model answered every question twice. Round one: we pasted the entire old conversation in front of the question. Round two: we gave it one small LeapMemory recall instead.

The 81% is the difference in how much text the model had to read. Pasting the history meant sending about 25,000 tokens with every single question. A recall averaged under 5,000. Same questions, same correct answers, 81% fewer tokens to pay for.

We scored both rounds against the known answers. When the model read a LeapMemory recall, it answered 10 of 10 correctly. When the model read the full old conversation pasted as text, it answered 7 of 10. The three missed answers were in that pasted conversation. The model had them in front of it and could not find them.

Only questions that both rounds answered correctly count toward the 81%. And the token counts come from the provider's bill, not our math.

Both rounds ran on the same model, Claude Sonnet 4.6, with identical instructions.

Walls, not filters.

Almost every memory product stores all of its users in one big database. Your facts and a stranger's facts sit in the same tables, and a user_id column plus a query filter is the only thing keeping them apart. Miss that filter once, in one line of code, and one user sees another user's memory. This class of bug is so common it has a name: cross-tenant data leakage.

LeapMemory does not put you in a shared pool. Your memory lives in its own databases, created for you when your memory is created, holding your facts and nothing else. There is no filter to get wrong, because there is nothing to filter. A bug in our query code could not show your memories to someone else, because the other person's queries never touch your databases. And when you delete your memory, we do not mark rows as hidden. We drop the databases themselves. Gone is gone.

Every tenant you create through the API gets physically separate databases, provisioned automatically in seconds. Not a namespace, not a schema prefix, not row-level security. Separate databases. Cross-tenant leakage becomes architecturally impossible instead of one WHERE clause away. Deletion is a database drop, so when a compliance review asks how you erase a user, the answer is structural, not a promise about cleanup jobs. If you sell to regulated industries, this is the sentence you need: your customer's data never shared storage with anyone else's.

LeapMemory runs on TenantsDB because we take user data and privacy seriously, and we wanted infrastructure that takes it just as seriously. Whether you use LeapMemory personally or build on the API, every memory lives in its own separate databases, physically apart from everyone else's, from the first fact saved to the moment it is deleted. Privacy here is not a setting you enable or a promise we make. It is the architecture itself. Your data cannot leak into someone else's memory, and theirs cannot leak into yours, because there is no shared place for that to happen. And it makes GDPR compliance the default, not an afterthought: erasing a memory erases its databases, completely and provably.

Save in one language, recall in another.

Most memory tools match words. If you save a fact in Spanish and ask in English, the words do not match, so the memory looks empty. LeapMemory does not store your sentences as sentences. It extracts what the sentence means, who, what, where, and stores that. Meaning survives translation even when words do not.

Tell it "la bicicleta naranja está aparcada en el muelle" and later ask "where is my bike?". The fact comes back: by the pier. Not because the words matched, they never did, but because both sentences point at the same thing. This works across 19 languages, verified with the same test in every one of them, and you can mix them freely: save in German, ask in English, follow up in Spanish. It is one memory, not one memory per language.

This is not translation bolted on top. LeapMemory works on meaning directly, so a fact saved casually in one language is found precisely in another, with nothing to configure and no language setting to pick. Write the way you write, in whatever language the moment calls for, and the memory keeps up. Every language below passed the same recall test, one by one, and the models underneath understand many more. For developers, this means one API serves users worldwide with zero per-language work on your side.

Never forgets, never rewrites.

Most memory products quietly lose your past. To save space, they compress old conversations into summaries, and a summary is a decision about what to keep made without asking you. Details go missing. Words get paraphrased. Three years later, what remains is a memory of a memory.

LeapMemory stores your exact words the moment they arrive, and they stay exactly as you said them. Nothing is summarized away, nothing expires, nothing gets rewritten to save space. A conversation from three years ago comes back word for word, as retrievable as one from this morning.

Raw turns are the source of truth. Everything else, the graph, the search index, the embeddings, is derived from them and can be rebuilt from them. No destructive summarization anywhere in the pipeline means no silent data loss to debug and no user asking why the memory changed its mind. Storage is cheap. Your users' trust is not.

This permanence is the whole point of a memory. A memory that fades is just a longer context window, and the value compounds only if nothing is lost along the way. The friend you mentioned once in passing, the decision you made two jobs ago, the way you phrased something that mattered: these are not equally useful every day, but the day one of them matters, it is there, intact, in your words. Your memory grows richer the longer it lives, and it lives as long as you want it to.

Every AI, one brain.

Every AI app you use today keeps its own memory. What you told one assistant, another has never heard. Switch tools and you start over, introducing yourself from zero, because the memory belongs to the app, not to you.

LeapMemory turns that around: the memory is yours, and the assistants connect to it. Set it up once over MCP, the open standard AI tools use to reach outside themselves, and every assistant that supports it shares the same brain. Works with Claude today. Tell it about your week, and your next conversation somewhere else already knows. Change tools whenever you like. Your memory does not care which app is asking.

One memory API behind every surface you ship. Your web app, your mobile app, your agent, your MCP integration all read and write the same user memory, so the experience stays continuous no matter where the user shows up. And because memory lives with you rather than inside a model vendor, swapping or mixing LLMs never resets what your product knows about its users.

Nothing hidden.

Most memory products are a black box. Facts go in, answers come out, and you are asked to trust what happens in between. LeapMemory ships with a memory browser instead: a full window into the memory, open any time.

Search any name, place, or thing your memory holds and watch the graph center on it. Read every fact, with a confidence score and the exact sentence it came from. Scroll the raw conversation history, word for word. And when something should not be there, delete it: removing a memory removes it everywhere, the words, the searchable pieces, and every fact that came only from it, in one action. What your AI knows about you is never a secret from you, and never out of your hands.

The same browser exists for every tenant you create: open any tenant from the dashboard and inspect its turns, facts, and graph while you build. Deletion is in the API too, one call removes a turn and everything derived from it, so user-facing "delete this memory" features and erasure requests are a single endpoint, not a data-cleanup project.