Chowdhury, N.; George, A.; Purohit, S.; Contolesi, A.; Bredeweg, E. L.; Czajka, J.; Stratton, K. G.; Gao, Y.; Stephenson, M.; Elmore, J. R.; Scott, A.; Leach, D. T.; Jerger, A.; Lemmon, T.; Piehowski, P.; Tate, K.; Fulcher, J. M.; Beliaev, A.; Burnum-Johnson, K.; Rigor, P.; Bardhan, J.

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular phenotypes and guiding microbial strain engineering, yet broad adoption remains challenging due to the computational expertise required. To overcome that, we present ChatGEM, an agentic platform that enables interactive GEM simulation through natural language. Built on the multi-agent ADEPT framework, ChatGEM integrates COBRApy within a retrieval-augmented generation (RAG) architecture that coordinates code generation and execution through specialized agents. Benchmarking across three tasks of increasing complexity showed that RAG-enabled code generation improved the mean overall performance score from 2.63 to 4.20 while reducing the execution time significantly starting from routine to complex tasks. Application of ChatGEM using an enzyme-constrained GEM (ecGEM) for four engineered Pseudomonas putida KT2440 strains identified the constitutive strain as the optimal chassis for succinate overproduction using a succinate leakage index - a prediction observed experimentally. Therefore, ChatGEM democratizes metabolic modeling by enabling researchers without computational expertise to perform sophisticated GEM-based analyses through natural language, and, hence, accelerating scientific discovery.