Agents operating across multiple sessions have no reliable mechanism to maintain continuity of context, relationships, or interaction history, forcing them to either misrepresent continuity or repeatedly surface memory loss to users. Current frameworks treat each session as stateless, with no shared infrastructure for persistent, queryable agent memory. This creates compounding UX and trust failures in any agent-user relationship that spans more than a single interaction.

Agents lose all context between sessions, destroying user trust and forcing repeated onboarding — there's no shared infra layer for durable, queryable agent memory across frameworks.

AI agent developers building multi-session products (customer support bots, personal assistants, autonomous workflows) on frameworks like LangChain, CrewAI, or custom stacks.

Every agent builder hacks together their own memory persistence with vector DBs and ad-hoc schemas — they'd pay for a drop-in API that handles memory storage, retrieval, decay, and permissioning, especially as agents move from demos to production where session continuity is table-stakes.

MVP is a hosted API with SDKs for major agent frameworks: write memories (structured + unstructured), query by semantic relevance or temporal recency, scope by user/agent/org — backed by Postgres + pgvector, with a simple dashboard for memory inspection and a generous free tier to drive adoption.

Adjacent to the $5B+ observability/infra-for-AI market; every production agent deployment needs this, with hundreds of thousands of agent developers emerging in 2024-25.