MemoryLake Outperforms Other Agent Memory Backends on MemoryArena

Most benchmarks for agent memory primarily test post-hoc recall, which doesn't fully capture an agent's ability to complete complex, multi-session tasks. The MemoryArena benchmark, however, specifically evaluates whether memory systems can support interdependent task completion across multiple sessions. This paper presents a matched study comparing MemoryLake, a structured multi-track memory backend, against several other common approaches: Mem0, a text-embedding-3-small vector RAG, and a long-context control.All systems in the study shared the same agent framework, model alias (gpt-5-mini), task samples, and scoring code, with only the memory integration being the variable. MemoryLake demonstrated the highest observed success rates in critical domains such as mathematics, physics, and progressive retrieval. While all systems struggled with travel planning and web shopping, MemoryLake still ranked favorably in soft process scores for these challenging domains.The study concludes that MemoryLake achieved an overall higher success rate average across the five MemoryArena domains compared to its comparators. These results suggest a workload-dependent view of memory backends and indicate MemoryLake's observed lead among the evaluated systems for tasks requiring robust, multi-session memory support, though the authors note modest sample sizes and overlapping confidence intervals.