Refine Cycle Plugin Makes OpenClaw Agents Learn From Repeating Mistakes

Refine Cycle is an OpenClaw plugin that targets a specific failure mode: the agent fixing a mistake inside one conversation and then repeating it in the next. It looks across recent sessions, identifies mistakes that keep coming back, and writes one short lesson the agent sees from then on.

How it works

The loop is three steps, not a training run:

• Scan — looks across the agent's recent sessions for recurring mistakes.
• Write — distills each recurring error into one short lesson that is injected into the agent's context from then on.
• Verify — later checks whether the same mistake came back.

The plugin is cross-session by design. The failure it addresses is conversational amnesia: the same wrong date format, the same missing flag, the same command that never works on your machine. An agent can put a mistake right in one conversation and make it again in the next. Refine Cycle remembers across conversations so you stop explaining the same thing twice.

Measured results

The author reports a large jump on repeating mistakes:

• OpenClaw on its own: 8% of repeating mistakes handled correctly.
• With the plugin: 91%.

Where it comes from

Refine Cycle adapts the /refine concept from Prime Intellect's Prime Agent (Continual Harness) to the OpenClaw plugin system. If you have looked at the Prime Agent continual-harness approach, this is the same idea ported to ClawHub packaging and install conventions.

Install

Plugin page: https://clawhub.ai/bergschloss/plugins/refine-cycle
Source: https://github.com/Bergschloss/Refine-Cycle-for-OpenClaw

Who it's for

Anyone running an OpenClaw agent across long-lived sessions where the same class of error keeps resurfacing — wrong formats, missing flags, machine-specific commands — and who is tired of re-stating the correction in every new conversation.