Multi-agent AI enables evidence-based cell annotation in single-cell transcriptomics

Abstract Cell type annotation remains a critical bottleneck, with current methods often inaccurate and requiring extensive manual validation, particularly in disease contexts. While large language models (LLMs) show promise, they can be unreliable due to hallucinations. We developed CyteType, a multi-agent framework that generates competing hypotheses grounded in full expression data and study context, validates against external databases, and iteratively self-evaluates. Comprehensive benchmarking demonstrates that CyteType substantially outperforms reference-based and LLM-based methods, with self-generated confidence scores reliably identifying trustworthy annotations. CyteType transforms cell type annotation from label assignment into evidence-grounded biological discovery. Python (AnnData compatible): https://github.com/NygenAnalytics/CyteType R (Seurat compatible): https://github.com/NygenAnalytics/CyteTypeR Competing Interest Statement G.A., Y.S., S.B., P.D., G.K., and G.M.D. are employees of, hold equity in, or consult for Nygen Analytics AB, which develops commercial solutions based on this work. A.A. declares no competing interests. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.