Why AI Agents like OpenClaw Burn Through Tokens and How to Cut Costs

This post details how AI agents, specifically OpenClaw, can burn through tokens due to inefficient search and memory management. It proposes solutions by introducing hybrid search (combining BM25 and vector search) and persistent, editable memory. For hybrid search, it highlights index1 (single-machine, SQLite-based), QMD (single-machine, LLM re-ranking), and Milvus (distributed, team/enterprise scale) as options. For memory, it discusses the need for inspectable and controllable storage beyond framework-dependent solutions.