Multi-agent reasoning enables predictive design of living materials

Living materials derive function from tightly coupled cellular and material processes to deliver adaptive and therapeutic capabilities, yet their predictive design remains constrained by fragmented, cross-disciplinary knowledge and experience-driven iteration. Here we introduce LiveMat, a multi-agent reasoning framework that reconstructs living materials as a computable design space from unstructured literature. LiveMat curates and standardizes 34,738 living-material records, integrating 16,086 microorganism entries and 18,682 polymer entries into a domain-scale knowledge graph comprising tens of thousands of entities and relationships. Through constraint-driven multi-agent reasoning and expert-anchored evaluation, the system converts implicit design heuristics into explicit, auditable rules. Comparative benchmarking across leading large language models shows that limitations in living materials reasoning arise primarily from cross-domain feature integration rather than coarse-grained classification. In a prospective acute wound-healing task, LiveMat evaluates combinatorial four-component systems across six functional dimensions and identifies a top-ranked design whose in vivo performance matches state-of-the-art systems. LiveMat establishes a scalable reasoning infrastructure for cumulative, data-grounded living materials discovery.