RAGFoundry is a library designed to enhance the ability of large language models (LLMs) to utilize external information by fine-tuning models on specially created RAG-augmented datasets. The library facilitates efficient model training using Parameter-Efficient Fine-Tuning (PEFT), allowing users to easily measure performance improvements with RAG-specific metrics. It features a modular design, enabling workflow customization through configuration files.