Stream computing is an emerging computational model for performing complex operations on and across multi-source, high volume data ?ows. Given that the deployment of the model has only started, the pool of mature applications employing this model is fairly small, and therefore the availability of workloads for various types of applications is scarce. Thus, there is a need for synthetic generation of large-scale workloads for evaluation of stream computing applications at scale. This paper presents a framework for producing synthetic workloads for stream computing systems. Our framework extends known random graph generation concepts with stream computing spe-cific features, providing researchers with realistic input stream graphs and allowing them to focus on system development, optimization and analysis. Serving the goal of covering a disparity of potential applications, the presented framework exhibits high user-controlled configurability. The produced workloads could be used to drive simulations for performance evaluation and for proof-of-concept prototyping of processing, networking and operating system hardware and software.