  Chemistry plays a key role in many astrophysical situations regulating the
cooling and the thermal properties of the gas, which are relevant during
gravitational collapse, the evolution of disks and the fragmentation process.
  In order to simplify the usage of chemical networks in large numerical
simulations, we present the chemistry package KROME, consisting of a Python
pre-processor which generates a subroutine for the solution of chemical
networks which can be embedded in any numerical code. For the solution of the
rate equations, we make use of the high-order solver DLSODES, which was shown
to be both accurate and efficient for sparse networks, which are typical in
astrophysical applications. KROME also provides a large set of physical
processes connected to chemistry, including photochemistry, cooling, heating,
dust treatment, and reverse kinetics.
  The package presented here already contains a network for primordial
chemistry, a small metal network appropriate for the modelling of low
metallicities environments, a detailed network for the modelling of molecular
clouds, a network for planetary atmospheres, as well as a framework for the
modelling of the dust grain population. In this paper, we present an extended
test suite ranging from one-zone and 1D-models to first applications including
cosmological simulations with ENZO and RAMSES and 3D collapse simulations with
the FLASH code. The package presented here is publicly available at
http://kromepackage.org/ and https://bitbucket.org/krome/krome_stable
