LightMem: Lightweight and Efficient Memory-Augmented Generation

Despite their remarkable capabilities, Large Language Models (LLMs) struggle
to effectively leverage historical interaction information in dynamic and
complex environments. Memory systems enable LLMs to move beyond stateless
interactions by introducing persistent information storage, retrieval, and
utilization mechanisms. However, existing memory systems often introduce
substantial time and computational overhead. To this end, we introduce a new
memory system called LightMem, which strikes a balance between the performance
and efficiency of memory systems. Inspired by the Atkinson-Shiffrin model of
human memory, LightMem organizes memory into three complementary stages. First,
cognition-inspired sensory memory rapidly filters irrelevant information
through lightweight compression and groups information according to their
topics. Next, topic-aware short-term memory consolidates these topic-based
groups, organizing and summarizing content for more structured access. Finally,
long-term memory with sleep-time update employs an offline procedure that
decouples consolidation from online inference. Experiments on LongMemEval with
GPT and Qwen backbones show that LightMem outperforms strong baselines in
accuracy (up to 10.9% gains) while reducing token usage by up to 117x, API
calls by up to 159x, and runtime by over 12x. The code is available at
https://github.com/zjunlp/LightMem.