Hymoex

Overview

Hymoex (Hybrid Modular Coordinated Experts) is an architectural paradigm for multi-agent systems, designed to be applied with any execution framework (LangGraph, CrewAI, Pydantic AI, AutoGen, and more).

Hymoex solves four critical problems:

1. Architecture Decision Paralysis — clear modality selection based on expert count
2. Expert Coordination Breakdown — MoE gating with 96.7% selection accuracy
3. Brittle Scalability — progressive migration preserving 100% of existing agents
4. Framework Fragmentation — patterns validated across 7 major frameworks

Three Modalities

Modality	Experts	Use Case
M1 — One-Line MoE	k ≤ 2	Simple parallel tasks
M2 — One-Line Supervisor	3–5	Coordinated workflows
M3 — MoE MultiLine	5+	Enterprise-scale teams

Migration is progressive — all existing agent definitions are preserved when moving from M1 → M2 → M3.

Abstract

Hymoex introduces an architectural paradigm addressing four root causes of multi-agent coordination failure. The paper presents a seven-role taxonomy grounded in Clean Architecture, three deployment modalities (M1/M2/M3) with progressive migration, and a novel MoE gating adaptation achieving 96.7% expert selection accuracy.

Benchmarks using Gemini Flash demonstrate 45% token reduction at 3 agents and 93% at 10 agents vs. flat architectures. Framework examples are provided for LangGraph, CrewAI, AutoGen, Pydantic AI, OpenAI Swarm, Vercel AI SDK, and Mastra.