HAM — Hierarchical Agent Memory – Agentic infrastructure that reduces token usage

Summary: HAM uses scoped memory files to reduce context size by 50%, lowering token usage, cost, and carbon footprint for AI agents.

What it does

It organizes memory hierarchically to limit context bloat, minimizing tokens sent per request and optimizing resource use.

Who it's for

Developers building AI coding agents needing efficient token management to control operational costs.

Why it matters

It prevents excessive token use that inflates costs and resource consumption in AI applications at scale.