Graphs are used in many different applications as they are an intuitive way for representing complex relationships between entities, as for example in social, communication, financial or geographical networks. Graphs in these domains can be very large, potentially spanning multiple millions and even billions of nodes and edges. In order to get analytical insights out of these structures, scalable implementations of graph algorithms are necessary. Rust is the ideal language for implementing such algorithms, due to its well-known aspects, such as "fearless concurrency" and memory safety as well as its great out-of-the-box performance and its expressive type system. In our presentation, we will talk about the "graph" project, a collection of open source crates that we are working on. The project includes an in-memory graph representation, APIs for building in-memory graphs from various data sources, and a small collection of high-performance graph algorithms. In addition to these building blocks, we started developing a Python wrapper called graph-mate for a NetworkX-like experience and an Apache Arrow endpoint for integrating the project in distributed applications. The presentation will include a project overview, a walk through the Rust API, and a demo for using the project via Python.