VEKTOR Persistent Memory

Example Use Cases

• 1

  Maintain long-term, structured memory beyond ephemeral context windows.

• 2

  Understand temporal sequences, causality, and entity relationships in their interactions.

• 3

  That require a local-first, cost-effective memory solution without relying on cloud services.

Description

VEKTOR replaces traditional flat vector databases with a multi-layered memory architecture that captures semantic, temporal, causal, and entity relationships. It enables AI agents to maintain long-term memory, develop personas, and perform complex reasoning by preserving and organizing interactions over time. The system runs locally using SQLite, supports multiple LLMs, and eliminates embedding API costs by using local transformers.