Researchers working on low-resource natural language processing (NLP) often spend an enormous amount of time on engineering training pipelines rather than focusing on critical tasks such as data collection, preprocessing, and linguistic analysis. We address this bottleneck by introducing LowResNLTK, a lightweight open-source Python framework that separates data processing from model training. LowResNLTK abstracts complex engineering barriers, enabling researchers to train state-of-the-art transformer models using high-level APIs with minimal boilerplate code. The framework supports common NLP tasks natively, such as part-of-speech tagging, sentence classification, and sequence-to-sequence modeling, while maintaining full model performance transparency. We evaluated it in a multilingual setting for various tasks, including classification, part-of-speech tagging, named entity recognition, and machine translation. Empirical validation consistently shows that LowResNLTK maintains performance comparable to standard implementations for these languages, while reducing development overhead.