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MOTO is an AI Autonomous Deep Research Harness created by Intrafere Research Group designed for mathematical research and academic paper generation. This system can run for days autonomously, supporting multiple simultaneous AI models working in parallel from LM Studio (local) or OpenRouter API (cloud). We believe that MOTO may show very early signs of artificial super intelligence (ASI). While it is very important to note some papers show astounding creative solution-potential, others may be entirely hallucinatory/fictitious – it is unclear if this is a side effect of creative thinking. All MOTO papers should be viewed with extreme scrutiny.

GitHub Repository

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Thank you for your interest in MOTO. We’re excited to share this powerful autonomous research tool with the community!

What’s Included

The open source release includes:

• Complete codebase – Full Python backend and React frontend
• One-click launcher – Easy setup with launch.bat for Windows
• Comprehensive documentation – Installation guides, quickstart, and design specifications
• Multi-model support – Use LM Studio (local), OpenRouter (cloud), or both
• Production-grade RAG – 4-stage retrieval with ChromaDB and Nomic embeddings
• Three-tier architecture – Aggregator, Compiler, and Autonomous Research modes

Quick Start

Getting started with MOTO is simple:

1. Download – Clone or download from GitHub
2. Install – Follow the INSTALL.md guide (Python 3.8+, Node.js required)
3. Launch – Run launch.bat (Windows) or startup.sh (Linux/Mac)
4. Configure – Set up LM Studio local server or add OpenRouter API key
5. Research – Enter your prompt and start autonomous deep research

Learn More

Before you download, explore these resources:

• About MOTO – Learn how the autonomous research harness works
• About AI Orchestrators – Understand the technology behind MOTO
• Getting Started Guide – Detailed setup and usage instructions
• Use Cases & Examples – See what MOTO can do
• Community & Contributors – Join the MOTO community