Vector databases provide raw storage primitives for embeddings.
| Feature | Rice | Vector Databases |
|---|---|---|
| Abstraction | Cognitive memory types with built-in APIs | Raw vectors requiring custom logic |
| Context | Decay, attention, and relevance scoring | k-NN similarity search |
| Execution | Integrated procedural skills | None |
| Learning | Trace-based episodic memory | Manual embedding management |
Rice provides cognitive memory types (Working, Episodic, Procedural, Semantic) with purpose-built APIs. Vector databases provide raw vector storage, leaving you to build all the memory logic yourself.
Rice includes decay, attention scoring, and relevance-based retrieval. Vector databases offer k-NN similarity search but no context management algorithms.
Rice includes Procedural Memory for running compiled skills server-side. Vector databases are purely storage with no execution capabilities.
Rice's Episodic Memory stores complete interaction traces (Input, Action, Outcome, Reasoning) for learning from experience. Vector databases store raw embeddings, requiring you to manage the structure manually.
Rice is for building agents where you want memory management handled for you. Vector databases are for building custom RAG pipelines where you need full control over every aspect.