Permanent memory for AI assistants and applications. Save what was said, recall it years later, in the exact words.
Language models forget. Context windows evict, summaries blur details, and every new session starts from zero. LeapMemory gives an AI a permanent memory: you send it conversation turns as they happen, it distills them into structured memories, and it returns the relevant ones whenever asked, whether that is one minute or three years later.
It works two ways. Personally, you connect it to an AI assistant like Claude through an MCP connector, and your assistant remembers you across every chat. As a developer, you call two HTTP endpoints, and every user of your application gets their own permanent, isolated memory.
Human memory is not one system. You remember what happened (episodic memory), what things mean and how they relate (semantic memory), and you recognize who is who the moment a name comes up (recognition). LeapMemory stores every turn the same three ways, and recall uses all of them together.
This is why recall answers with three sections instead of a similarity list: a question like "where does Sam work?" is recognized (anchors_found spots Sam), answered from meaning (facts carry the relationship), and grounded in what was actually said (chunks hold the verbatim sentence). No single store could do all three.
When a turn arrives, the verbatim content is written immediately; nothing waits and nothing is lost. Extraction then runs asynchronously, typically completing within seconds: entities are recognized, relationships are mapped into a knowledge graph, and the meaning is indexed for search.
Memories never expire. There is no time limit, no summarizing away, no context window doing quiet evictions.
Most memory systems keep everyone's data in one shared pool and separate customers with filters: a tenant_id column, a row-level policy, a WHERE clause that must never be forgotten. That works until it does not; one missed filter, one injection, one bug, and someone reads someone else's memory.
LeapMemory takes the structural route. Every tenant is provisioned its own databases, across every layer a memory touches: the verbatim words, the knowledge graph, the semantic index, and the working data each live in stores that belong to that tenant alone, created when the tenant is created and dropped when it is deleted. No shared tables, no row-level policies, no tenant_id columns, no shared credentials. A query against one tenant cannot reach another, structurally: there is nothing to filter because there is nothing shared.
This is not a feature bolted onto LeapMemory; it is the platform it stands on. Tenant provisioning and isolation are enforced by TenantsDB, our database orchestration platform built for exactly one job: giving every customer their own physically separate databases. LeapMemory inherits that guarantee for every memory it stores.
This is also the deletion story: erasing a user means dropping their databases entirely, not scanning shared tables for rows and hoping nothing was missed. When a GDPR request or a compliance review asks how you erase a user, you have a structural answer, and one hard-delete call is the whole procedure.
| Section | What it is |
|---|---|
| facts | Distilled subject-verb-object statements with confidence, mention count, and the exact sentence each came from. |
| chunks | The verbatim words as they were said, ranked by relevance score. |
| anchors_found | Entities detected in your query, with type and position, that steered retrieval. |
Recall is free, always, for everyone. You pay when a memory is saved, never when it is used. It is also multilingual by structure: save in one language, recall in another, verified across 19 languages.