Large language model (LLM) agents increasingly depend on external memory to support long-horizon interaction. Yet many current memory systems still rely on largely fixed default rules once their pipelines are configured. They often apply the same extraction, retrieval, and evidence-organization choices across inputs. This is restrictive. Different memory needs require different choices. We propose AdaMemPIP, an adaptive memory orchestration framework for LLM agents. AdaMemPIP uses an LLM to route both memory storage and memory usage. For storage, it selects extractors for each new batch of dialogue records and writes the extracted items to matched memory libraries. For usage, it selects retrieval methods, target libraries, and organization strategies for each query, then executes the resulting pipeline. The framework is also extensible: new extractors, retrievers, stores, and organizers can be added without changing the overall control logic. This makes AdaMemPIP a flexible framework to long-term agent memory.

Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating forgetting. However, repeatedly replaying these samples may cause the overfitting problem. We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations. To address this issue, we propose a novel continual extraction model for analogous relations. Specifically, we design memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem. We also introduce integrated training and focal knowledge distillation to enhance the performance on analogous relations. Experimental results show the superiority of our model and demonstrate its effectiveness in distinguishing analogous relations and overcoming overfitting.