Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models

Jul 14, 2026

A new independent testbed evaluates long-term memory (LTM) frameworks for LLM-based agents in distributed cloud-edge environments. The study compares mem0, Graphiti, cognee, RAG, and full-context baselines on the LoCoMo benchmark, finding that mem0, RAG, and full-context achieve 77–81% accuracy, while Graphiti and cognee reach only 55–56%. RAG matches the top accuracy at 8.4 times lower total cost of ownership than mem0, and both are the only non-dominated backends on the Pareto frontier.

Why it matters: This work provides the first independent, reproducible comparison of LTM frameworks for multi-agent systems, showing that retrieval completeness, not context volume, drives accuracy and that RAG offers the best cost-performance trade-off.

Full story at: arXiv Information Retrieval ↗