EvoMemKG: An Evolvable Memory Agent for Multi-hop Knowledge Graph Reasoning

Integrating knowledge graphs (KGs) with large language models (LLMs) enhances factual accuracy and interpretability in question answering (QA). However, existing agent-based methods rely on static memory mechanisms, which fail to address the state-space explosion in multi-hop reasoning and lack self-evolution capabilities. To overcome these limitations, we propose EvoMemKG, an agent framework with an evolvable memory mechanism for multi-hop KG reasoning. EvoMemKG introduces a dual-layer memory: (1) a working memory that manages and compresses intermediate states using a relation-based compression algorithm, effectively reducing context usage; and (2) an experience memory that distills historical reasoning paths into reusable, generalized strategies, enabling cross-task knowledge transfer and self-evolution. We further design a double-loop workflow that orchestrates the LLM, the dual-layer memory, and KG environment to enable end-to-end autonomous reasoning. Extensive evaluations on three KGQA datasets across two KGs demonstrate that EvoMemKG achieves state-of-the-art performance without requiring additional training or specialized tools, with improvements of up to 20% over strong baselines on complex multihop queries.