OptGraph: Large Language Models-Enhanced Automated Optimization Modeling via Graph Retrieval-Augmented Generation

Large language models (LLMs) have emerged as a powerful tool for automated optimization modeling, but existing methods remain limited in pattern reuse, error-aware refinement, and retrieval robustness across diverse tasks. To address these limitations, we propose OptGraph, the first optimization agentic workflow that integrates graph retrieval-augmented generation (GraphRAG). Specifically, OptGraph first constructs reusable experience as a typed graph, capturing the relationships among modeling patterns, problem formalization, implementation details, and error corrections. In the inference stage, OptGraph leverages graph neighborhood information to enrich retrieved knowledge, providing structured context to improve modeling, verification, and iterative refinement. Moreover, OptGraph supports adaptive knowledge updates, enabling the distillation of execution traces and verification feedback into reusable graph knowledge without undertaking LLM parameter tuning. Experiments on benchmark datasets show that our OptGraph improves average exact accuracy by 8.9% over state-of-the-art prompt-based automated optimization modeling frameworks.