I build my LLM a Brain

But did you give it a heart, too? :-)

All of these systems that try to solve "the memory problem" seem to fail to justify inserting either a layer of complexity with multiple moving pieces, or an outright blackbox. What is it that makes these systems worth the cost? What is it that they do that provide significantly more value than a structured directory of markdown files, a tuned grep search, and the model you are already using to synthesize the results? If you want to kick it up a notch, abstract the mechanism into a sub-agent to avoid context pollution. I have yet to find a memory system that clearly articulates how it is worth the overhead compared to the simple solution described.

Context engineering is where the real leverage is right now. Most people focus on model selection but the retrieval and memory layer around the model makes a bigger difference in practice. What's your approach to managing context window limits — chunking with overlap, or some kind of relevance scoring before injection?