You're correct in the assumption that Nengo is ‒ among others ‒ a neuroscience research tool; primarily, because it allows you to build networks of spiking neurons. Apart from neuroscience you can also do things like mapping pre-trained deep neural networks onto spiking neurons. These networks can then be executed on neuromorphic hardware.
However, you can also run your simulations with "regular" rate-based neurons. In this case, the interface that Nengo provides is significantly more low-level than that of TensorFlow. Still, the main difference to TensorFlow is that Nengo networks are dynamic, i.e. the network is not described in terms of discrete time steps, but as a fully dynamical system (though a numerical simulator will discretise time at some point). In conjunction with neuromorphic hardware this allows things like real-time motor control with (sub-)millisecond resolution.