The memory layer for AI agents.

Modern models are brilliant and forgetful. A raw LLM starts every session from zero — so turn 1,000 costs as much as turn 1, and an agent never truly gets better at your work. Khwan exists to close that gap.

Khwan is a pure memory layer. It never runs your model. The only loop is prepare → your model → record: Khwan builds the context before your call — memory, a written identity, and a coherence gate — and learns from the answer after. You keep your model, your provider, and your keys.

The result compounds. Synthesis distills what worked into standing lessons, so prompts get shorter and sharper instead of re-stuffing context every call. The longer an agent runs, the less it costs and the more consistent it becomes.

Khwan is early — a founder-led effort building in the open with the teams using it. If that's the stage you like to build alongside, we'd love to hear from you.