Total Recall at What Cost? Benchmarking the Serving Cost of Agentic Memory Systems
Long-running conversational agents increasingly rely on a memory system to avoid resending the whole conversation each turn, yet how much that costs to serve has received little systematic benchmarking. We compare three memory systems (Mem0, Hindsight, and Mastra Observational Memory) against two reference strategies -- a fixed-size rolling window and resubmitting the full transcript -- across two backbones and conversations of up to 400 turns, pairing every cost measurement with answer accuracy on 665 LoCoMo questions. First, a memory system's serving cost cannot be predicted from conversation length and message size alone: a regression that tracks the two reference strategies closely mi...
| Authors | Natchanon Pollertlam, Witchayut Kornsuwannawit |
|---|---|
| Primary category | cs.CL |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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