Recent advances in Large Language Models (LLMs) have stimulated increasing interest in LLM-based agents for long-horizon interactive decision-making. As a lightweight alternative to parameter-level optimization, many studies enhance LLM agents through retrievable experience memory. However, existing retrieval strategies primarily optimize for the relevance of historical memories, leaving the applicability conditions and verifiable execution effects of retrieved trajectories insufficiently captured. To mitigate this executability gap between relevance-driven retrieval and executable reuse, we propose Effectiveness-Driven Memory (EffectMem), a framework that reframes trajectory memory as executable guidance whose applicability and effects are checked during interaction. During memory construction, EffectMem structures successful trajectories into positive executable contracts and localizable failures into negative experience guards. At inference, EffectMem retrieves candidate contracts, verifies their applicability to the current state, executes aligned chunks under guard constraints, and invokes discrepancy-guided correction when observed effects deviate from expected postconditions. Experiments on ALFWorld, ScienceWorld, and WebShop demonstrate that EffectMem outperforms competitive baselines and improves the effectiveness of memory-augmented LLM agents.