Constant-Cost Persistent Semantic State Memory Engine for LLM Agents

Why your agent’s input footprint doesn’t have to grow with conversation length and what changes when it stops. If you’ve shipped anything with an LLM in the loop, you know the shape of the bill. Turn five hundred is something you start architecting around. The conversation history grows, the prompt grows, every call ships the entire past back into the model, and the cost curve is - almost insultingly - linear in the thing you actually want of: useful interaction. The standard answers are familiar. Truncate. Summarize. Stuff the recent N turns and pretend turn 1 didn’t matter.