Rice represents a shift from "Memory as Storage" to "Memory as Cognition."
While the market is dominated by vector databases (storage focus) and memory layers (retrieval focus), Rice provides a complete cognitive substrate. It doesn't just store data. It manages runtime state through decay-based attention and executes deterministic skills server-side.
Solutions fall into three tiers:
| Tier | Examples | Focus |
|---|---|---|
| Cognitive Substrates | Rice | Manages state, execution, and memory in a unified layer |
| Memory Layers | Zep, Mem0 | Adds structure on top of storage to improve retrieval |
| Storage Primitives | Pinecone, Weaviate, Chroma | Raw vector storage requiring custom logic |
| Feature | Rice | Zep | Mem0 | Supermemory | Vector DBs |
|---|---|---|---|---|---|
| Memory Model | Cognitive (4-part) | Knowledge Graph | User Facts | User Profiles | Flat Storage |
| Context Algorithm | Decay & Attention | Session Window | Relevance Search | Graph + Search | k-NN Search |
| Skill Execution | WASM | No | No | No | No |
| Trace Storage | Input/Action/Outcome | Chat History | Facts only | Documents | Raw Embeddings |
| Underlying Tech | HDC | Graph + Vectors | Vectors + Graph | Postgres + Vectors | Dense Vectors |
| Best For | Autonomous Agents | Knowledge Retrieval | User Personalization | Chat with Data | DIY RAG |
Rice is designed for GenAI companies building autonomous agents that need to learn, adapt, and execute reliably.
Learn from Experience
Manage Attention
Execute Deterministically