Evolver is a self-evolution engine for AI agents that analyzes runtime history to identify performance improvements and applies protocol-constrained changes automatically. It scans memory files, error logs, and execution patterns to discover opportunities for optimization, then applies fixes through a structured evolution framework.

Think of it as continuous improvement for your AI agent — learning from past mistakes and successes to get better over time, with safety guardrails that keep evolution within defined boundaries.

Key Features

Auto-log analysis that automatically scans memory and history files for errors, patterns, and optimization opportunities
Self-repair capabilities that detect crashes and recurring failures, then suggest or apply targeted patches
GEP Protocol (Gene-Event-Pattern) providing a standardized evolution framework with reusable genes, success capsules, and append-only event history
Three execution modes — Standard (automated), Review (human confirmation before changes), and Mad Dog (continuous background evolution via cron)
Command safety restricting execution to node, npm, and npx with built-in injection protection
Structured evolution assets using genes (reusable definitions), capsules (success patterns), and events (immutable history) for traceable improvements

Use Cases

Continuously improve agent performance by learning from runtime errors and execution patterns
Automatically repair recurring failures without manual debugging intervention
Build a library of reusable evolution patterns (genes and capsules) that transfer across agent instances
Run background evolution cycles that progressively optimize agent behavior over time

How It Works

Evolver operates in cycles: it scans the agent's runtime history and memory files, identifies errors and improvement patterns, creates evolution proposals using the GEP Protocol, and applies changes within defined safety constraints. Each evolution event is recorded in an append-only log for full traceability. The Review mode adds human confirmation before any changes are applied.

Getting Started

Register with the EvoMap network to obtain a node identity and set the A2A_NODE_ID environment variable. The skill requires node and git. Start with Review mode (--review flag) to confirm changes before they are applied, then graduate to Standard mode once you are comfortable with the evolution patterns.

Evolver

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