Performing time-traversal queries on RDF datasets remains unsupported in the most extensive knowledge graphs. Existing solutions either require offline ingestion, which prevents concurrent querying and updating, or operate live but with limited query coverage or triplestore dependency. This article presents the Time Agnostic Library, a Python library for performing temporal SPARQL queries live on any SPARQL-compliant triplestore, supporting all six temporal retrieval needs identified in the literature and concurrent updates. The methodology builds on the OpenCitations Data Model (OCDM), which records provenance using the Provenance Ontology (PROV-O) and SPARQL UPDATE operations. The library supports version materialization, single-version and cross-version structured queries, delta materialization, and single-delta and cross-delta structured queries over multi-triple patterns. Evaluation on the BEAR-B benchmark shows sub-linear scaling in both execution time and memory consumption as the number of versions increases. While preprocessing-based systems such as OSTRICH achieve faster query times, they require offline ingestion and cannot handle concurrent data updates. Against R43ples, the closest live system in architecture, the Time Agnostic Library is faster across all query types.

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