Background:The complex nature of biological data has driven the development of specialized software tools. Scientificworkflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aidreproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complexworkflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machinelearning.Findings:SciPipe is a workflow programming library implemented in the programming language Go, for managingcomplex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular withworkflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamicscheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to supportagile development of workflows based on a library of self-contained, reusable components. It supports running subsets ofworkflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit tracefor every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. Theutility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline.Conclusions:SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machinelearning, through a flexible application programming interface suitable for scientists used to programming or scripting.