Dual-Cycle Reasoner

Provides dual-cycle metacognitive reasoning framework that detects when autonomous agents get stuck in repetitive behaviors through statistical anomaly detection and semantic analysis, then automatically diagnoses failure causes and generates recovery strategies using case-based learning.

This MCP server implements a dual-cycle metacognitive reasoning framework for autonomous agents, built by CyqleLabs using TypeScript with advanced loop detection and recovery mechanisms. The implementation combines statistical anomaly detection, semantic analysis using Hugging Face transformers, and case-based reasoning to identify when agents get stuck in repetitive behaviors like endless scrolling or clicking patterns, then automatically diagnoses failure causes and generates recovery strategies. Built with three core detection strategies (action pattern analysis, state invariance tracking, and progress stagnation detection), belief revision capabilities, and a case-based learning system that adapts from past experiences, it serves developers building autonomous web agents, teams needing robust loop prevention for browser automation tasks, and researchers working on self-monitoring AI systems that can break out of unproductive behavioral cycles.