Large Language Models (LLMs) are transforming biomedical research, yet their integration into reproducible bioinformatics workflows remains challenging. We present {llumen}, a novel pure-R package developed by the Oncology Data Science function at Merck, designed to empower bioinformaticians to build, deploy, and manage agentic AI workflows directly within the R ecosystem.

{llumen} transcends simple chat interfaces by providing a robust framework for autonomous agents capable of complex reasoning strategies running seamlessly on diverse infrastructure, ranging from researchers' local laptops to high-performance Linux compute servers. These agents utilize a library of 30+ tools to programmatically access omics knowledge bases (e.g., PubMed, PubChem, OpenTargets), analyze large-scale biomarker and patient data via SQL and graph queries, and process multi-modal documents and histopathology images.

Key differentiators of {llumen} are its ability to facilitate hybrid model orchestration, allowing researchers to execute secure local foundation models in conjunction with commercial frontier models, as well as its focus on enterprise-grade security and data privacy, essential for pharmaceutical R&D. The framework supports fully local execution using quantized models and integrates GDPR-compliant PII guardrails, ensuring sensitive patient data remains protected. Furthermore, it offers rigorous validation features, including agent-as-judge benchmarks and reasoning traces, to ensure scientific reliability.

By bridging the gap between state-of-the-art generative AI and traditional bioinformatics infrastructure, {llumen} enables researchers to automate tasks ranging from systematic literature reviews and statistical analysis plan drafting to hypothesis generation and molecular property prediction. Here, we demonstrate {llumen}'s utility through real-world case studies in oncology target discovery and automated machine learning.

Co-authors: None