OpenClaw Optimization Guide
A living guide to making an OpenClaw AI agent faster, cheaper, and more reliable in production, covering cost visibility, provider failover, fast mode, prompt-cache retention, and memory and context management.
The OpenClaw Optimization Guide is a periodically updated field guide to running OpenClaw AI agents in production. It tracks OpenClaw's own release changes and corrects earlier claims that turned out to be wrong, covering per-agent cost visibility commands, provider failover behavior and configuration, an automatic fast mode for short conversational turns, prompt-cache retention across long sessions, and self-recovering messaging channel queues.
Its central argument is that most of an agent's practical capability comes from the harness around a model, not the model's weights alone, and it works through the operational consequences of that idea: treating paid API or cloud-hosted usage as the baseline rather than assuming a chat subscription covers agent traffic, and building runbooks around the platform's actual failover and recovery behavior instead of manual restarts.
It is written as a set of guide files, MIT licensed.