Motivation: The ability to simulate epidemics as a function of model parameters allows for the gain of insights unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like HIV may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain "truth" sets on which to test them and properly measure their performance. Results: We introduce FAVITES, a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model that produces many items relevant to an epidemic, such as contact networks, transmission networks, phylogenetic trees, and error-prone sequence datasets. FAVITES is designed to be flexible and extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various sub-models. We use FAVITES to simulate HIV datasets resembling the San Diego epidemic and show that the simulated datasets are realistic. We then use the simulated data to make inferences about the epidemic and how it is impacted by increased treatment efforts. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters. Availability and implementation: FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).