Implementing automatic differentiation through C++ solvers or Python wrappers is going to be exceptionally difficult. If you want a generic toolbox like torch for scientific machine learning you would want every method under the sun.

That’s the moat Julia has here. A full rewrite of the differential equation ecosystem wasn’t necessary in order to support automatic differentiation.

I do not know of another differential equations framework nearly as polished, performant, and feautere rich as the one in Julia, in any language. I actually know of no other framework that has both modern solvers and autodiff working together. The only other autodiff solvers I know of are ancient RK4-like solvers implemented in things like Tensorflow. It is hard to overstate how monumentally enabling such tools are for old-school science and they currently exist only in Julia.

while there are c++ solvers, they are missing the flexibility needed to solve many types of problems efficiently. they almost all use finite differencing, which is inaccurate and slow, and they rarely let users customize the linear solve and nonlinear solve algorithms. What the Julia diffeq suite is working towards is complete control over solver options, while picking good defaults when not customized. They let you pick forward and backward mode AD (or a combination of the two), problem specific preconditioning methods, custom root finding algorithms (including gpu based linear solvers), arbitrary precision methods for verification (or if you're just paranoid), interval arithmetic, linear uncertainty propegation, and more.