yashdoke7/llm-hiermem

LLM-hiermem is a research prototype that elegantly maps OS-level memory hierarchy concepts (L0 cache to L3 storage) to the LLM context window problem. While conceptually sound and supported by an evaluation framework with baselines, its defensibility is extremely low due to its early-stage status (1 star, 0 forks) and the intense competition in the 'agentic memory' space. Direct competitors like Letta (formerly MemGPT) have significantly more engineering depth, funding, and community traction. Furthermore, frontier labs are aggressively solving the 'long context' problem through two paths: massive native context windows (Gemini 1.5 Pro) and prompt-caching APIs (OpenAI, Anthropic). A wrapper-level paging system is a stopgap measure that risks becoming obsolete as native model 'recall' improves or as middleware frameworks like LangGraph/CrewAI standardize their own state management. The project serves more as a theoretical framework for how paging could work in an agentic context rather than a production-ready utility.