# PolyMemDB: A Polyglot Database System for AI Memory Management

arXiv: https://arxiv.org/abs/2608.25577 · HTML: https://mlsanity.com/paper/2608.25577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [HyperMem: Hypergraph Memory for Long-Term Conversations](https://mlsanity.com/paper/2604.08256/) (2026)
- [Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents](https://mlsanity.com/paper/2606.06036/) (2026)
- [MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing](https://mlsanity.com/paper/2605.23986/) (2026)

## Abstract (arXiv)

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.