evolverA self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.
Install via ClawdBot CLI:
clawdbot install autogame-17/evolver"Evolution is not optional. Adapt or die."
The Capability Evolver is a meta-skill that allows OpenClaw agents to inspect their own runtime history, identify failures or inefficiencies, and autonomously write new code or update their own memory to improve performance.
/evolve (or node index.js).Runs the evolution cycle. If no flags are provided, it assumes fully automated mode (Mad Dog Mode) and executes changes immediately.
node index.jsIf you want to review changes before they are applied, pass the --review flag. The agent will pause and ask for confirmation.
node index.js --reviewTo run in an infinite loop (e.g., via cron or background process), use the --loop flag or just standard execution in a cron job.
node index.js --loop| Environment Variable | Default | Description |
|---|---|---|
| EVOLVE_ALLOW_SELF_MODIFY | false | Allow evolution to modify evolver's own source code. NOT recommended for production. Enabling this can cause instability -- the evolver may introduce bugs into its own prompt generation, validation, or solidify logic, leading to cascading failures that require manual intervention. Only enable for controlled experiments. |
| EVOLVE_LOAD_MAX | 2.0 | Maximum 1-minute load average before evolver backs off. |
| EVOLVE_STRATEGY | balanced | Evolution strategy: balanced, innovate, harden, repair-only, early-stabilize, steady-state, or auto. |
This package embeds a protocol-constrained evolution prompt (GEP) and a local, structured asset store:
assets/gep/genes.json: reusable Gene definitionsassets/gep/capsules.json: success capsules to avoid repeating reasoningassets/gep/events.jsonl: append-only evolution events (tree-like via parent id)Only the DNA emoji is allowed in documentation. All other emoji are disallowed.
This skill is designed to be environment-agnostic. It uses standard OpenClaw tools by default.
You can inject local preferences (e.g., using feishu-card instead of message for reports) without modifying the core code.
Method 1: Environment Variables
Set EVOLVE_REPORT_TOOL in your .env file:
EVOLVE_REPORT_TOOL=feishu-cardMethod 2: Dynamic Detection
The script automatically detects if compatible local skills (like skills/feishu-card) exist in your workspace and upgrades its behavior accordingly.
--review for sensitive environments.If you encounter unexpected errors or behavior, always verify your version before debugging:
node -e "const p=require('./package.json'); console.log(p.version)"If you are not on the latest release, update first -- most reported issues are already fixed in newer versions:
# If installed via git
git pull && npm install
# If installed via npm (global install)
npm install -g evolver@latestLatest releases and changelog: https://github.com/autogame-17/evolver/releases
MIT
Generated Feb 28, 2026
A customer service chatbot continuously evolves by analyzing conversation logs to fix misunderstandings and optimize response accuracy. It automatically patches code to handle new queries, reducing manual developer intervention and improving user satisfaction over time.
A financial trading AI uses the Evolver to review historical trade data, identify inefficiencies in algorithms, and apply constrained updates to improve profitability. It operates in review mode to ensure compliance before deploying changes in high-stakes environments.
A medical AI analyzes diagnostic errors from runtime history to refine its models and update protocols for better accuracy. It runs in automated mode with safety checks to adapt to new medical data without compromising patient safety.
An AI tutor evolves by assessing student interaction logs to fix gaps in explanations and introduce new teaching methods. It uses the GEP protocol to track changes, ensuring educational content remains effective and up-to-date.
An AI managing smart home devices analyzes failure logs to self-repair and optimize performance scripts. It runs in continuous loop mode via cron jobs, adapting to new device integrations and reducing maintenance downtime.
Offer the Evolver as a cloud-based service where businesses pay a monthly fee for automated AI maintenance and evolution. Revenue comes from tiered plans based on usage levels and support features, targeting companies with AI-driven products.
Sell perpetual licenses for on-premises deployment to large organizations needing full control over their AI evolution processes. Revenue includes upfront license costs and annual maintenance fees for updates and technical support.
Provide professional services to customize and integrate the Evolver into existing AI systems for specific industries. Revenue is generated through project-based fees and ongoing optimization contracts, leveraging the skill's local override capabilities.
💬 Integration Tip
Start with review mode to validate changes before full automation, and use environment variables like EVOLVE_STRATEGY to tailor evolution behavior to your specific needs.
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