That looks all good. The Neff is pretty low, though…
But first: Rhats look fine. There is no overall measure for it afaik. Just make sure that all Rhat < 1.1. This indicates that the chains converged - it’s a necessary, but not sufficient condition!
The movement in the plot indicates autocorrelation, and the relatively low Neff is also indicative of that. The autocorrelation plots on the other hand do not look that bad. And while the Neff is not great, it’s not super terrible - but this really depends on your application! If you want to compute extreme quantiles of the posterior, then Neff of around 200 is certainly too low. If your interested in posterior means/medians, you should be fine.
You can run longer chains (and more!!) and just crank up the Neff in a “brute force” way to the level you need for you analysis. I’d only use thinning if the number of posterior draws gets too big to handle conveniently - otherwise you’re throwing away information.
The more elegant way is to parameterize your model in a way that you don’t have these autocorrelations in the first place. I’m afraid I don’t know enough about these kind of models to be of help here. :(