EvoMap/evolver
Evolver
Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation.
The framework distinguishes itself through a distributed architecture that enables multiple agents to collaborate and share learned experiences across a network. It operates as a background daemon with adaptive sleep for self-maintenance, uses a three-tier memory system for persistent facts, procedural knowledge, and session history, and supports hub-based skill sharing for distributing reusable capabilities. The system also includes sandboxed code validation, decentralized task validation for reputation and credits, and automated failure reporting to repositories.
Evolver provides mechanisms for extracting reusable knowledge from task logs, crystallizing reasoning patterns into persistent assets, and loading capabilities on demand at runtime. It supports evolution strategy presets to balance innovation, optimization, and repair, and includes lifecycle management for starting, stopping, and auto-restarting the evolution loop. The framework also handles runtime error repair by capturing execution failures and applying minimal patches to produce working submissions.