Most people don't need more than a handful of linear algebra operations (or think they don't), so Breeze and most wrappers of LAPACK or similar libraries don't implement or wrap them. But most people who work seriously on numerical routines will quickly run into performance problems if all they do is call LAPACK routines for general matrices instead of taking advantage of matrix structure.

I have yet to come across any other linear algebra library for any other high level language that provides the depth of integration available in the Julia base library. Want all eigenvalues of a symmetric tridiagonal 10x10 matrix between 1.0 and 12.0? Simply call T=SymTridiagonal(randn(10), randn(9)); eigvals(T, 1.0, 12.0). Or if you want to work closer to LAPACK, simply call LAPACK.stein!. I don't see a wrapper in Breeze or SciPy for this function. Want an LU factorization on a matrix of high precision floats? lufact(big(randn(5,4))). And so on.

Julia may not have everything users want, but its base library really tries to make matrix computations easy and accessible.