| | ---
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| | title: MangoMAS — Multi-Agent Cognitive Architecture
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| | colorFrom: yellow
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| | colorTo: red
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| | sdk: gradio
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| | sdk_version: "6.5.1"
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| | app_file: app.py
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| | pinned: true
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| | license: mit
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| | tags:
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| | - mixture-of-experts
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| | - mcts
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| | - multi-agent
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| | - cognitive-architecture
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| | - neural-routing
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| | - pytorch
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| | - reinforcement-learning
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| | ---
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| |
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| | # MangoMAS — Multi-Agent Cognitive Architecture
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| |
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| | An interactive demo of a production-grade multi-agent orchestration platform featuring:
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| |
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| | - **10 Cognitive Cells** — Biologically-inspired processing units (Reasoning, Memory, Ethics, Causal, Empathy, Curiosity, FigLiteral, R2P, Telemetry, Aggregator)
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| | - **MCTS Planning** — Monte Carlo Tree Search with policy/value neural networks for task decomposition
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| | - **MoE Router** — 7M parameter Mixture-of-Experts neural routing gate with 16 expert towers
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| | - **Agent Orchestration** — Multi-agent task execution with learned routing and weighted aggregation
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| |
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| | ## Architecture
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| |
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| | ```
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| | Request → Feature Extractor (64-dim) → RouterNet (MLP) → Expert Selection
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| | ↓
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| | [Agent 1, Agent 2, ..., Agent N]
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| | ↓
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| | [Cognitive Cell Layer]
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| | ↓
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| | Aggregator → Response
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| | ```
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| |
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| | ## Technical Blog Posts
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| |
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| | - [Building a Neural MoE Router from Scratch](https://huggingface.co/blog/ianshank/moe-router-from-scratch)
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| | - [MCTS for Multi-Agent Task Planning](https://huggingface.co/blog/ianshank/mcts-multi-agent-planning)
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| | - [Cognitive Cell Architecture Design](https://huggingface.co/blog/ianshank/cognitive-cell-architecture)
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| |
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| | ## Author
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| |
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| | Built by [Ian Cruickshank](https://huggingface.co/ianshank) — MangoMAS Engineering
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| |
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