PolyMemDB: A Polyglot Database System for AI Memory Management

2026-08-26

AI Summary

为解决AI长期记忆管理中数据碎片化和事实一致性问题，PolyMemDB采用多语言存储架构和概率推理引擎来管理和整合多种类型的数据。

Abstract

With the widespread adoption of personal intelligent agents, users generate massive, heterogeneous data during long-term interactions. Leveraging this data as long-term memory helps reduce token overhead and deliver personalized experiences. However, existing memory systems face two primary limitations: they rely on single-storage paradigms that fragment multi-dimensional data, and they lack fine-grained data provenance to resolve long-term factual conflicts, thereby worsening LLM hallucinations. In this demonstration, we introduce PolyMemDB, a novel system tailored for managing agent memory. PolyMemDB has a polyglot storage architecture designed to track and manage various memory types, including graph, vector, probability and spatial-temporal data. To ensure factual consistency and reduce hallucinations, it features a probabilistic inference engine that integrates temporal decay with semiring aggregation, resolving long-term factual conflicts, providing detailed data provenance, and enabling users to trace reasoning chains transparently.

Problem

• memory management
• heterogeneous data
• data provenance
• factual conflicts
• LLM hallucinations

Innovation

• Polyglot Storage Architecture
• Probabilistic Inference Engine
• Temporal Decay
• Semiring Aggregation