WeaveMem: Typed Experience Graphs for Applicability Reasoning in LLM Agents

Persistent language agents rely on evidence distributed across interaction histories that extend far beyond their context windows to answer queries. Existing memory systems retrieve records through semantic similarity or reconstruct context using associative structures. However, similarity retrieval overlooks indirectly useful evidence, while associative reconstruction may recover connected memories without establishing whether their relations are relevant to the query. We formulate this missing capability as applicability reasoning: determining whether and how retrieved memories jointly support a query through typed and directional relations that form a compatible and complete evidence chain. To operationalize this capability, we introduce WeaveMem, a long-term agent memory system built around a Typed Experience Graph and Applicability Tracing. The Typed Experience Graph grounds typed and directional relations in atomic memories and projects them onto compact experiences with provenance. Applicability Tracing constructs query-specific evidence chains, selects compatible and sufficient traces, and recovers their atomic evidence for answer generation. Across evaluations averaged over five independent runs on LoCoMo, LongMemEval-S, PersonaMem-32K, and BEAM-1M, WeaveMem achieves the highest overall score on each benchmark among the evaluated memory systems. Controlled evaluations on relation-dependent queries further show that WeaveMem more accurately identifies how retrieved memories relate to one another and jointly constitute complete, valid evidence chains.