Food webs are important tools for understanding how complex natural communities are structured and how they respond to environmental change. However their full potential has yet to be realised because of the huge amount of resources required to construct them de novo. Consequently, the current catalogue of networks that are suitable for rigorous and comparative analyses and theoretical development still suffers from a lack of standardisation and replication.Here, we present a novel R function, WebBuilder, which automates the construction of food webs from taxonomic lists, and a dataset of trophic interactions. This function works by matching species against those within a dataset of trophic interactions, and 'filling in' missing trophic interactions based on these matches. We also present a dataset of over 20,000 freshwater trophic interactions, and use this and four well-characterised freshwater food webs to test the method.The WebBuilder function facilitates the generation of food webs of comparable quality to the most detailed published food webs, but at a fraction of the research effort or cost. Furthermore, it matched and often outperformed a selection of predictive models, which are currently among the best, in terms of capturing key properties of empirical food webs. The method is simple to use, systematic and, perhaps most importantly, reproducible, which will facilitate (re-) analysis and data sharing. Although developed and tested on a sample of freshwater food webs, this method could easily be extended to cover other types of ecological interactions (such as mutualistic interactions).