  This paper describes a novel Python package, named causalgraph, for modeling
and saving causal graphs embedded in knowledge graphs. The package has been
designed to provide an interface between causal disciplines such as causal
discovery and causal inference. With this package, users can create and save
causal graphs and export the generated graphs for use in other graph-based
packages. The main advantage of the proposed package is its ability to
facilitate the linking of additional information and metadata to causal
structures. In addition, the package offers a variety of functions for graph
modeling and plotting, such as editing, adding, and deleting nodes and edges.
It is also compatible with widely used graph data science libraries such as
NetworkX and Tigramite and incorporates a specially developed causalgraph
ontology in the background. This paper provides an overview of the package's
main features, functionality, and usage examples, enabling the reader to use
the package effectively in practice.
