SPARQL is essential for querying Knowledge Graphs (KGs), but much information exists in external sources rather than within KGs. To address this, we propose SparqLLM, a retrieval-augmented query processing approach that leverages user-defined functions (UDFs) and named graphs to augment SPARQL queries with diverse external sources, including search engines, large language models (LLMs), and vector search. By doing so, SparqLLM significantly enhances SPARQL’s capabilities, enabling a single query to access multiple heterogeneous sources while ensuring query provenance and explainability. This demonstration highlights the potential of SparqLLM to enrich query results with comprehensive, up-to-date information and showcases its application in a real-world use case.