🤖 MathModelAgent 📐

An agent designed for mathematical modeling
Automatically complete mathematical modeling and generate a ready-to-submit paper.

简体中文 | English

🌟 Vision

Turn 3 days of competition into 1 hour
Automatically generate an award-level modeling paper

✨ Features

• 🔍 Automatic problem analysis, mathematical modeling, code writing, error correction, and paper writing
• 💻 Code Interpreter
  • Local Interpreter: Based on Jupyter, code saved as notebook for easy editing
  • Cloud Code Interpreter: E2B and daytona
• 📝 Generate a well-formatted paper
• 🤝 Multi-agents: modeling expert, coding expert, paper expert, etc.
• 🔄 Multi-LLMs: Different models for each agent
• 🤖 Support for all models: litellm
• 💰 Low cost: workflow agentless, no dependency on agent framework
• 🧩 Custom templates: prompt inject for setting requirements for each subtask separately

🚀 Future Plans

• Add and complete webui, cli
• Comprehensive tutorials and documentation
• Provide web service
• English support (MCM/ICM)
• LaTeX template integration
• Vision model integration
• Proper citation implementation
• More test cases
• Docker deployment
• Human in loop: User interaction (model selection, @agent rewriting, etc.)
• Feedback: evaluate the result and modify
• Cloud integration for code interpreter (e.g., e2b providers)
• Multi-language: R, Matlab
• Drawing: napki, draw.io, plantuml, svg, mermaid.js
• Add benchmark
• Web search tool
• RAG knowledge base
• A2A hand off: Code expert reflects on errors multiple times, hands off to smarter model agent

Video Demo

For case references, check the demo folder. If you have good cases, please submit a PR to this directory

📖 Usage Guide

Three deployment options are available, choose the one that suits you best:

1. Docker
2. Local deployment
3. Automated script deployment

If you want to run the CLI version, switch to the master branch. It's easier to deploy, but will not be updated in the future.

🐳 Option 1: Docker Deployment (Recommended: Simplest)

1. Configure Environment Variables

cp backend/.env.dev.example backend/.env.dev
cp frontend/.env.example frontend/.env.development

Fill in the configuration in:

• backend/.env.dev
• frontend/.env.development

2. Start Services

docker-compose up -d

3. Access

You can now access:

• Frontend interface: http://localhost:5173
• Backend API: http://localhost:8000

💻 Option 2: Local Deployment

Make sure Python, Nodejs, and Redis are installed on your computer

1. Configure Environment Variables

Copy /backend/.env.dev.example to /backend/.env.dev (remove the .example suffix)

Configure Environment Variables

It is recommended to use models with strong capabilities and large parameter counts.

Copy /frontend/.env.example to /frontend/.env.development (remove the .example suffix)

2. Install Dependencies

Clone the project

git clone https://github.com/jihe520/MathModelAgent.git

Start backend

Start Redis

cd backend
pip install uv # Recommended: use uv to manage python projects
uv sync # Install dependencies
# Start backend
# Activate Python virtual environment
source .venv/bin/activate # MacOS or Linux
venv\Scripts\activate.bat # Windows
# Run this command for MacOS or Linux
ENV=DEV uvicorn app.main:app --host 0.0.0.0 --port 8000 --ws-ping-interval 60 --ws-ping-timeout 120 --reload
# Run this command for Windows
set ENV=DEV ; uvicorn app.main:app --host 0.0.0.0 --port 8000 --ws-ping-interval 60 --ws-ping-timeout 120

Start frontend

cd frontend
npm install -g pnpm
pnpm i # Make sure pnpm is installed
pnpm run dev

Tutorial

Results and outputs are generated in the backend/project/work_dir/xxx/* directory:

• notebook.ipynb: code generated during execution
• res.md: final results in markdown format

🚀 Option 3: Automated Script Deployment (Community Contribution)

Need an automatic deployment script? mmaAutoSetupRun

Need to customize prompt templates? Prompt Inject: prompt

🤝 Contribution & Development

DeepWiki

• The project is in experimental development stage (updated when I have time), with frequent changes and some bugs being fixed.
• Everyone is welcome to participate and make the project better.
• PRs and issues are very welcome.
• For requirements, refer to Future Plans.

After cloning the project, install the Todo Tree plugin to view all todo locations in the code.

.cursor/* contains overall architecture, rules, and mcp for easier development.

📄 License

Free for personal use. For commercial use, please contact me (the author).

License

🙏 Reference

Thanks to the following projects:

• OpenCodeInterpreter
• TaskWeaver
• Code-Interpreter
• Latex
• Agent Laboratory

Others

💖 Sponsor

Buy Me a Coffee

Thanks to sponsors: danmo-tyc

👥 GROUP

For questions, join the group

QQ Group: 699970403