Memory-augmented Large Language Models frequently fail to maintain long-term context due to opaque evaluation mechanisms that obscure the root causes of failure. To address this, we introduce MemEval, a stage-wise attribution framework that approximates where failures arise across extraction, update, retrieval, and utilization. On LoCoMo, our automated meta-evaluator achieves 81.62% stage-level consistency with human judgment. Our analysis reveals three critical findings: (1) Extraction is the universal bottleneck, causing information loss at the entry point; (2) Retrieval and utilization failures become prominent in complex reasoning and temporal tasks, even when relevant memories are stored or retrieved; and (3) Scaling base model capacity does not guarantee more reliable memory behavior, as higher-capacity backbones may introduce early-stage misalignment that propagates downstream. MemEval provides a practical diagnostic aid for trend-level, stage-aware analysis of memory systems. Code is available at: https://anonymous.4open.science/r/MemEval-8D41.