Hi LivelyLion31
Yes, the reason we designed Trains with an automagic integration is exactly that reason, so users do not need to learn another package and that with almost no effort you get most of the benefits.
Regrading the TB files, from experience most users will use the TB files short after they executed the experiment, usually for debugging and in depth capabilities (like network debugger profile etc), metric view is something that is much easier to do on a centralized server (like on the Trains-Server).
So we could not find good uses cases for constantly storing the TB protobuf files on the backend (they are extremely large!).
That said you can always upload the TB protobuf as an artifact at the end of the experiment:
Task.current_task().upload_artifact('tensorboard', '/tmp/my.tensorboard_file/pb')

If you guys feel spinning a TB serving all the tensorboard is something you will use. You can quickly write a code that will do just that, and launch it with trains-agent. There is a nice example of using trains-agent as a way to spin a jupyter notebook that can server as a good reference:
https://github.com/allegroai/trains/blob/master/examples/execute_jupyter_notebook_server.py