Universal memory systems like Mem0 and Zep are falling short of their promises in real-world applications. Benchmarks showed that these systems are not only costlier—up to 77 times more than traditional long-context methods—but also less accurate, with performance dropping to around 50% precision. Testing with MemBench revealed that Zep consumed an astonishing 1.17 million tokens for just 4,000 conversational cases, resulting in an average latency of 224 seconds and a total cost of approximately $152.6. This is a far cry from the efficiency touted in marketing claims. The fundamental issue lies in how these systems handle data. Both Mem0 and Zep rely heavily on LLMs for fact extraction and processing, which introduces significant latency and potential inaccuracies. Mem0 runs multiple inference tasks for every user interaction, while Zep triggers a cascade of updates through its knowledge graph, compounding latency and costs. The reliance on non-deterministic LLMs for data interpretation leads to corrupted data before it even reaches the database. This means that even if retrieval costs are low, the overall cost per conversation skyrockets due to the inefficiencies in processing. The experiment's findings reveal a critical misunderstanding in applying these memory systems. Semantic memory tools like Zep and Mem0 excel at tracking user preferences but fail as working memory solutions that need precision and reliability. They are fundamentally mismatched for tasks requiring exact state tracking, such as error logs or variable names. The conclusion is clear: these systems need to be treated as separate entities, each designed for different types of memory, rather than a one-size-fits-all approach.