Magnolia: persistent-memory agent framework for computational chemistry
Magnolia is a persistent-memory AI agent framework that integrates LLM-driven feedback with computational chemistry pipelines (docking, molecular dynamics, quantum chemistry), lab notebooks and chat logs via a three-tier memory (session → project → cross-project). The authors demonstrate Magnolia across four case studies (COMPAS aromatic evaluation, butane and modified-DNA MD, protein–peptide docking), reporting 317 tracked runs, 116 project-tier entries, four promoted cross-project rules, and improved task pass rates in controlled replays versus runs without memory.