# MemGPT-Style Paging

> Treat the context window as RAM and external storage as disk — the model issues tool calls to page memory in and out at its own discretion.

- **Category**: Agentic AI
- **Subcategory**: Memory
- **Canonical URL**: https://designpattern.fyi/patterns/memgpt_paging/

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## Description
**Intent**: Treat the LLM context window as RAM and external storage as disk, with the model issuing tool calls to page memory in and out.
**Context**: A long-running agent's conversation or document state grows past the model's context window. The team needs to keep the agent useful over interactions spanning thousands of turns, or over documents larger than any provider window.
**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.



## Use Cases
- 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.






## Trade-offs


### Advantages

- Conversation continuity far beyond the context window limit.

- Inspectable memory tiers — archival is queryable independently for debugging.




### Considerations & Drawbacks

- Tool definitions themselves consume context budget — you pay for the RAM/disk metaphor.

- Page-fault tool calls add latency on every memory access that misses main context.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/memgpt-style-paging/)