---
title: MangoMAS — Multi-Agent Cognitive Architecture
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: "6.5.1"
app_file: app.py
pinned: true
license: mit
tags:
  - mixture-of-experts
  - mcts
  - multi-agent
  - cognitive-architecture
  - neural-routing
  - pytorch
  - reinforcement-learning
---

# MangoMAS — Multi-Agent Cognitive Architecture

An interactive demo of a production-grade multi-agent orchestration platform featuring:

- **10 Cognitive Cells** — Biologically-inspired processing units (Reasoning, Memory, Ethics, Causal, Empathy, Curiosity, FigLiteral, R2P, Telemetry, Aggregator)
- **MCTS Planning** — Monte Carlo Tree Search with policy/value neural networks for task decomposition
- **MoE Router** — 7M parameter Mixture-of-Experts neural routing gate with 16 expert towers
- **Agent Orchestration** — Multi-agent task execution with learned routing and weighted aggregation

## Architecture

```
Request → Feature Extractor (64-dim) → RouterNet (MLP) → Expert Selection
                                                              ↓
                                                    [Agent 1, Agent 2, ..., Agent N]
                                                              ↓
                                                    [Cognitive Cell Layer]
                                                              ↓
                                                    Aggregator → Response
```

## Technical Blog Posts

- [Building a Neural MoE Router from Scratch](https://huggingface.co/blog/ianshank/moe-router-from-scratch)
- [MCTS for Multi-Agent Task Planning](https://huggingface.co/blog/ianshank/mcts-multi-agent-planning)
- [Cognitive Cell Architecture Design](https://huggingface.co/blog/ianshank/cognitive-cell-architecture)

## Author

Built by [Ian Cruickshank](https://huggingface.co/ianshank) — MangoMAS Engineering