Memori is a LLM-agnostic layer that transforms agent execution and conversation history into structured, persistent state. Memori captures tool calls, decisions, traces, and user context as durable, queryable state that agents can rely on across sessions, models, and workflows. Memori is benchmarked on LoCoMo, an independent benchmark developed by SNAP Research. Memori delivers industry-leading results, achieving 82% accuracy using only 1,294 tokens / 5% of full-context cost, enabling teams to reduce inference spend by up to 95%.