// MemoryTax: The Bitter Lesson of Agent Memory //

Great insights for agent builders; bookmark it.

As models increase in capability, they also improve at things like memory generation.

Agent memory frameworks are everywhere right now. This study measured whether they still help once the model can search raw history itself.

The results suggest that a strong model needs less designed memory and
better tools for searching the raw record.

On one 419-turn LoCoMo conversation, Mem0 beats plain full context only with GPT-4o-mini. Building Mem0's memory costs more than an entire full-context rollout.

With Claude Opus 5.5, the Pi coding agent searching the raw history scores 99% for under half the cost of full context. Its one miss comes from a wrong answer key.

Memory also has a ceiling set by the model that wrote it. A stronger reader cannot recover what a weaker writer left out.

The author clearly defines the scope: one conversation and one run per cell. It is still a cheap experiment to repeat on your own agent before adding a memory layer.

https://arxiv.org/abs/2610.29823

Chat with Paper: https://academy.dair.ai/papers/memorytax-the-bitter-lesson-of-agent-memory-2610.29823