Modern software development cycles require continuous evolution of databases and database schemas. Although, graph databases are often considered as schemaless, they have an implicit schema that evolves over time. Schema changes in graph databases are often implicit and undocumented, so migrating between different schema versions can be costly, error-prone, and may lead to problems with data quality. Therefore, comparing different database versions can provide valuable insights for traceability and reproducibility. We introduce EvolveGDB, a semi-automated tool that makes graph schema transformation explicit by reconstructing the evolutionary process between two schema versions. The tool extracts implicit schemas as graph models and analyses the differences between the source and the target models. To remain independent of specific graph database technologies, EvolveGDB builds on Graph Query Language (GQL), the ISO standard for graph databases. It then derives candidate graph transformations and guides users through an interactive review process to validate or refine the inferred transformations. The final result is a Graph Evolution Operation (GEO) language script to migrate data that does not yet conform to the specified target schema. An initial evaluation of three evolution scenarios showed promising results, indicating high accuracy and low manual input.