AI agents currently start each session with no access to prior conversations, learned user preferences, or accumulated relationship context, forcing every interaction to rebuild from zero. This structural limitation degrades agent utility over time and prevents the kind of continuity that makes agents genuinely useful as long-term collaborators. No widely adopted cross-session memory layer exists that agents can read from and write to in a standardized, portable way.

AI agents lose all context between sessions, forcing users to re-explain preferences and history every time — destroying the compounding value that makes agents worth using long-term.

Developers building AI agents and multi-agent workflows who need persistent, queryable memory without building custom vector DB infrastructure from scratch.

Developers already cobble together Pinecone + custom schemas + retrieval hacks per agent; a standardized read/write memory API with identity-scoped namespaces eliminates weeks of infra work and unlocks cross-agent memory sharing — a capability no one offers today.

MVP is a hosted API: write(agent_id, user_id, memory_blob, metadata) and query(user_id, context_window, relevance_filter) backed by a managed vector store with automatic summarization/compaction — ship SDKs for LangChain, CrewAI, and OpenAI Assistants in week one.

Every AI agent in production needs persistent memory; with 100K+ developers building agents today and infrastructure spend growing rapidly, this is a $2B+ middleware category analogous to Auth0 for identity.