Graphmem: Durable, Multi-agent Graph Memory for LLM Agents

A growing family of systems gives LLM agents long-term memory by letting an LLM curate a structured note graph over time, tagging, linking, and revising entries as experience arrives, rather than using a static vector index (Xuet al., 2025; Park et al., 2023; Gutiérrez et al., 2024; Rasmussen et al., 2025). These designs report large gains in small, validation-scale evaluations. Two questions remain open: whether this curation can run durably as first-class, auditable structure, and whether its benefits hold at real scale under statistical scrutiny. This study examines both questions with graphmem, a durable, concurrency- safe graph memory architecture built on RustFS (durable storage) and FalkorDB (a graph database with a native vector index), instantiating A-Mem’s (Xu et al., 2025) Zettelkasten-style evolution loop while making every link a real, traversable edge that removes an id-hallucination failure mode present in A-Mem’s own reference implementation. Using graphmem’s reproducible harness, this paper reports a full-scale, statistically tested evaluation of this evolution paradigm across three long-conversation QA benchmarks (LoCoMo (Maharana et al., 2024), LongMemEval (Wu et al., 2025), BEAM (Tavakoli et al., 2026)). Memory beats no memory on nearly every well-powered comparison, but evolution’s edge over a simpler retrieval-only ablation reaches significance on only one of three benchmarks, and a category-level claim about its cross-session value does not survive scaling up or a controlled reasoning-mode toggle. Running A-Mem’s own code directly, not only its published numbers, yields the same pattern: its simplest configuration’s apparent edge over graphmem is also not statistically significant. A separate, LLM-cost-free storage comparison isolates the architecture’s own contribution: FalkorDB’s throughput increases under concurrent load, where A-Mem’s in-process store’s throughput decreases under the identical load pattern.