Can sklearn random forest directly handle categorical features?

No, there isn't. Somebody's working on this and the patch might be merged into mainline some day, but right now there's no support for categorical variables in scikit-learn except dummy (one-hot) encoding.

You have to make the categorical variable into a series of dummy variables. Yes I know its annoying and seems unnecessary but that is how sklearn works. if you are using pandas. use pd.get_dummies, it works really well.

Most implementations of random forest (and many other machine learning algorithms) that accept categorical inputs are either just automating the encoding of categorical features for you or using a method that becomes computationally intractable for large numbers of categories.

A notable exception is H2O. H2O has a very efficient method for handling categorical data directly which often gives it an edge over tree based methods that require one-hot-encoding.

This article by Will McGinnis has a very good discussion of one-hot-encoding and alternatives.

This article by Nick Dingwall and Chris Potts has a very good discussion about categorical variables and tree based learners.