Self-Evolving Multi-Agent Swarms: Autonomous Quality Audit, Repair, and Verification Loops for Production AI Agent Systems

We present LocalKin, a self-evolving multi-agent swarm architecture capable of autonomously auditing, repairing, and verifying its own constituent agents without human intervention. The system runs 78 specialized agents on a single consumer machine (16GB Mac Mini) with a total memory footprint of 960MB - approximately 12.5MB per agent - compared to 200MB or more per agent in Python-based frameworks such as AutoGen and CrewAI. The core contribution is a fully autonomous improvement loop consisting of four stages: quality audit, feedback synthesis, targeted repair, and verification. Over a continuous 5-day autonomous deployment, the system completed more than 30 improvement cycles, autonomously modified 68 agent configuration files, and discovered, evaluated, and integrated techniques from 6 research papers found on arXiv and HuggingFace - all with zero human intervention.

Note (2026-05-09): This version bundles English + 中文 in a single PDF (English first, then Chinese), generated directly from the canonical Markdown source files.