MathModelAgent

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Basic Information

MathModelAgent is an experimental end-user agent system designed to automate the full pipeline of mathematical modeling and produce a complete, submission-ready modeling paper. The project orchestrates multiple specialized agents to analyze problem statements, build mathematical models, implement and run code, diagnose and fix errors, and write formatted papers. It saves runnable artifacts such as notebook.ipynb and a result markdown file in backend/project/work_dir and supports both local and cloud code interpretation. The repository provides deployment options including a Docker compose setup and local instructions requiring Python, Node.js and Redis. The README emphasizes rapid turnaround for modeling contests and research workflows, demo cases in a demo folder, prompt templates for task customization, and an experimental status with ongoing improvements and community contributions invited.

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App Details

Features
The README lists core capabilities such as automatic problem analysis, end-to-end mathematical modeling, code generation and correction, and automated paper composition. It includes a Code Interpreter with a local Jupyter-based interpreter that saves notebooks and optional cloud backends referenced for remote execution. The system uses multi-agent roles (modeling, coding, writing) and allows assigning different LLMs per agent. It supports provider integrations noted for litellm, uses prompt inject templates for per-subtask custom prompts, and follows an agentless workflow to reduce cost. Deployment features include Docker compose, a web frontend served on port 5173 and a backend API on port 8000, and sample outputs like notebook.ipynb and res.md. The project documents templates, examples, and a demo directory and marks many enhancement items as planned.
Use Cases
MathModelAgent helps researchers and students accelerate mathematical modeling tasks by automating analysis, model construction, implementation, and paper writing so that multi-day contest workflows can be shortened significantly. It produces editable notebooks and a formatted markdown result to ease reproducibility and submission. The multi-agent split of responsibilities and per-agent model configuration help improve modularity and error isolation during iterative development. Multiple deployment options let users run the system locally or in Docker, and provided templates allow prompt-level customization for domain needs. The README warns the project is experimental, documents required dependencies and runtime ports, includes demo cases for reference, and invites community contributions to improve robustness and extend capabilities.

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