Multi-Layer
Memory Engine
Short · long · structured · unified memory stack
Give agents searchable, injectable, sharable long-term memory — not chat-log recall, but devices that hold long-term relationships, a stable personality, and the ability to keep growing.
Episodic + Structured memory · Vector retrieval
Three layers in concert — durable memory, stable persona, and high-quality decision-making.
Stores the content, context, and importance score of every interaction. Each memory carries an embedding vector and is retrieved by semantic similarity on demand.
User profile, core preferences, persona attributes are stored as structured fields and injected straight into the prompt context every turn.
pgvector + cosine similarity pulls Top-K most-relevant items from large memory pools. When embedding is unavailable, the system falls back automatically.
Three value pillars of the memory engine
Stronger multi-agent collaboration
Individual agents own long-term memory; swarm agents share a co-operative memory pool. Agents exchange context through a unified memory stack — low-latency, scalable, governable.
Longer-horizon task planning
Memory isn't just “remembering” — it's the foundation of planning. AI can build cross-day, cross-week, cross-month plans from long-term memory, giving task reasoning a real time dimension.
More natural role consistency
Personality + episodic memory + preference profile together hold a stable persona. The AI no longer “becomes someone else” when the conversation changes, or “contradicts yesterday”.
Broad applicability
The same architecture extends to AI homes, smart cities, healthcare and more.