Demotion Beats Deletion: Graded Forgetting for Capacity-Bounded Agent Memory

Persistent LLM agents accumulate memory faster than they can afford to read it, and every deployed system resolves this by deleting. Anthropic’s Memory Stores cap a store at 10,000 entries and instruct operators to remove stale ones; Mem0 prunes through LLM-issued DELETE; ChatGPT’s background consolidation rewrites in place. Deletion is an irreversible decision taken without knowing which memory a future query will need. We ask whether it is necessary. We present graded forgetting: traces are demoted through discrete retention stages and compressed to progressively lossier fidelity tiers, but never removed, so a capacity budget is met by shedding detail rather than items. We introduce recall-after-eviction, a protocol that isolates the capability at stake by conditioning on questions whose supporting evidence a capacity bound has destroyed. On an 833-day, 1,115-memory timeline held to 35% of its full size, deletion answers 2.5% of questions about the evicted past while graded forgetting answers 60.0% - 80% of the uncapped ceiling, from the same byte budget. Against Mem0, the strongest published baseline and one with no capacity policy of its own, the ordering inverts precisely when the budget binds: Mem0 leads without a bound and falls to 26.7% under one, against 66.7% for graded forgetting. The contribution is a capability under capacity pressure rather than an accuracy gain, and we argue this is the axis on which agent memory should be evaluated.