Content addressable memories (CAMs) are special-purpose in-memory computing units that support parallel searches directly in memory. There is growing interest in CAMs for data-intensive applications such as machine learning, data mining, and bioinformatics, which has led to a rapidly growing CAM design space. CAM cells can be implemented exclusively by CMOS or with various non-volatile memory (NVM) devices. In addition to traditional binary and ternary CAMs (BCAMs and TCAMs), analog CAM (ACAM) and multi-bit CAM (MCAM) designs have recently been introduced, which could further improve density, and also support unique in-memory distance functions. Furthermore, aside from the widely-used exact match function, CAM-based approximate match functions, such as threshold match and best match, have been proposed to further extend the utility of CAMs to new application spaces. As the CAM design space is large, evaluating different CAM design options for a given application is both crucial and challenging. This paper presents EvaCAM, a circuit-level modeling and evaluation tool for CAMs. EvaCAM supports TCAM, ACAM, and MCAM designs implemented in either CMOS or NVMs, for both exact and approximate match functions. It also allows for the exploration of different CAM designs under various optimization targets. EvaCAM has been validated against measured data from fabricated chips and detailed SPICE simulations. A comprehensive design space exploration for CAMs is provided to illustrate the impact of various design decisions and to demonstrate the use cases of EvaCAM.