Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

Large language models (LLMs) enable personal assistant agents that work with users over long-term interactions. These assistants need memory that retains information across conversations and updates it when that information changes. Existing memory systems typically extract and summarize conversations into retrievable memory records. However, this process can fragment related information across records, causing incomplete retrieval and conflicting updates, while repeated summarization progressively discards details. We propose Infini Memory, a long-term memory system inspired by organizing notes around topics while preserving links to their source conversations. It organizes extracted entries into topic documents that serve as persistent units of revision. When new information arrives, the topic-document pipeline revises each selected document using its complete body and the incoming entries. A separate Source-Evidence Layer archives dialogue before extraction and preserves links from entries to their sources as documents evolve. At query time, the system retrieves relevant topic documents and follows source links to recover missing details. Experiments on MemoryAgentBench show that Infini Memory reaches an overall score of 64.7, 19.2 points above the strongest evaluated baseline. On the original LongMemEval-S benchmark, it achieves 96.4% accuracy across six question types.