MemGPT-Style Paging

Problem

A fixed context window forces a hard choice between losing state and stuffing irrelevant content. Naive truncation drops whatever happens to be at the boundary, which may be exactly the information the next turn needs. Stuffing the window with potentially-relevant content from the past inflates cost and dilutes the model's attention on the actually-relevant pieces. Neither option scales; both degrade quality. The team needs a paging discipline — the way an operating system pages between main memory and disk — where the model itself can decide what to load in and what to swap out as the task evolves.

Solution

Two memory tiers. Main context: system prompt, working set, recent messages. External context: recall (raw history) and archival (vector store). The model has tool calls for read_recall, write_archival, search_archival. Paging happens at the agent's discretion; the model treats main context as RAM and external as disk.

When to use

• Long-running agents need state that exceeds the model's context window.
• The model can be trusted to manage memory via tool calls (read, write, search).
• External recall and archival storage tiers are available and queryable.