Self-Improving Agent
Self-improving agent framework. Captures learnings, errors, and corrections to enable continuous improvement.
Details
Self-improving agent framework with a high star rating. It captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) the agent needs to learn from interactions, (2) you want to track and apply corrections, or (3) you need auto-enhancement of performance over time.
When to use
Use when the agent needs to learn from interactions, track and apply corrections, or auto-enhance over time. Complements Capability Evolver for teams that want explicit error→correction loops.
Learning you can audit
Self-Improving Agent is a framework for making your assistant better in a way you can actually inspect. It captures learnings, errors, and corrections as they happen, then applies them so the same mistake does not recur. Nearly 16,000 downloads and a high star rating reflect a real appetite for this: people do not just want an assistant that improves, they want to see what it learned.
That auditability is what distinguishes it from Capability Evolver, ClawHub's other flagship learning skill. Evolver improves behaviour through machine learning you mostly observe from outside; Self-Improving Agent keeps an explicit ledger of error and correction. One is a black box that gets better; the other shows its homework.
When this is the right choice
Reach for it when the agent needs to learn from interactions, when you want corrections tracked and applied rather than repeated, or when performance should improve automatically over time. It is particularly valuable in settings where you need to explain the assistant's behaviour to someone else — a team, a client, a compliance review — because the record of what was corrected and why actually exists.
Installing Self-Improving Agent
Run the command below from your OpenClaw directory and restart the assistant. Then — and this is the step that matters — actually correct it when it errs. The framework can only capture the feedback you give. A few deliberate corrections in the first week seed the loop far faster than passive use.
clawhub install self-improving-agentRun both learning loops
For teams that want explicit error-to-correction loops alongside broader ML-driven improvement, this skill and Capability Evolver complement each other well, and plenty of mature setups run both. Designing that learning stack — what to track, what to automate, how to review what the assistant has learned — is exactly the kind of configuration work we handle in client engagements.