The problem
A conversation is only as good as what it remembers.
A model call on its own starts fresh every turn. The first turn establishes who the user is and what matters; by the next, that context is gone unless something holds it. The behavior teams actually want — an assistant that remembers a preference, a history, a decision — depends on memory that lives outside any single call.
Memory is what turns a series of prompts into a product that knows its user.
How it works
Working, episodic, and long-term — retrieved together.
Memory exposes three tiers: working memory held in the active context with a short lifetime, episodic memory of what has happened, and durable long-term memory. Retrieval is hybrid — vector similarity, graph traversal, and temporal context are combined, so a lookup can find what's semantically close, what's related, and what's recent at once.
It's multi-tenant and continuous across sessions: an application recalls the right context for the right user, and an agent resumes where it left off rather than relearning the basics each time. Our Services team reaches for it on every build, so a product ships with a memory rather than bolting one on later.
- Three tiers: working memory in the active context, episodic memory of what happened, and durable long-term memory
- Hybrid retrieval — vector similarity, graph traversal, and temporal context, combined
- Continuity across sessions, so an agent picks up where it left off
- Multi-tenant by design, with per-tenant isolation
- One API our Services team builds on, so memory is there from the first feature
Give your product a memory.
Memory is one of the components our Services team builds AI-native products on. Tell us what you're building.