Anasazi is an extensible and interoperable framework for large-scale eigenvalue algorithms. The motivation for this framework is to provide a generic interface to a collection of algorithms for solving large-scale eigenvalue problems. Anasazi is interoperable because both the matrix and vectors (defining the eigenspace) are considered to be opaque objects---only knowledge of the matrix and vectors via elementary operations is necessary. An implementation of Anasazi is accomplished via the use of interfaces. Current interfaces available include Epetra and so any libraries that understand Epetra matrices and vectors (such as AztecOO) may also be used in conjunction with Anasazi. One of the goals of Anasazi is to allow the user the flexibility to specify the data representation for the matrix and vectors and so leverage any existing software investment. The algorithms that are currently available through Anasazi are block Krylov-Schur, block Davidson, and locally-optimal block preconditioned conjugate gradient (LOBPCG) method.