http://compbio.cs.huji.ac.il/FastInf/fastInf/FastInf_Homepag...
http://robotics.stanford.edu/~sgould/svl/
http://code.google.com/p/factorie/
http://people.kyb.tuebingen.mpg.de/jorism/libDAI/
http://research.microsoft.com/en-us/um/cambridge/projects/in...
BTW, factor graphs are generally more popular than Bayes Nets these days, at least in research circles. (Bayesian networks can be seen as a special case of factor graph.)
I don't think factor graphs are more popular than Bayes nets (a.k.a. directed graphical models), at least not in general machine learning (though perhaps in some particular subfield?). Directed graphs are usually most appropriate for generative models, which are very popular, especially in Bayesian approaches. In graphical models, directed, undirected, and factor graphs are all used in their appropriate contexts.
And it's not accurate to say that directed models (Bayes nets) are a special case of factor graphs: there are conditional independence structures that can be represented by Bayes nets that can't be captured exactly with factor graphs. The canonical example is
O --> O <-- O
You can use factor graphs for either directed or undirected models. See this paper:
Extending factor graphs so as to unify directed and undirected graphical models B. J. Frey Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, 257-264, Morgan Kaufmann, San Francisco, CA, August 2003.