  We introduce a new Python 1D chemical kinetic code FRECKLL (Full and Reduced
Exoplanet Chemical Kinetics distiLLed) to evolve large chemical networks
efficiently. FRECKLL employs `distillation' in computing the reaction rates,
which minimizes the error bounds to the minimum allowed by double precision
values ($\epsilon \leq 10^{-15}$). Compared to summation of rates with
traditional algorithms like pairwise summation, distillation provides a tenfold
reduction in solver time for both full and reduced networks. Both the full and
reduced Venot2020 networks are packaged in FRECKLL as well as a TauREx 3.1
plugin for usage in forward modelling and retrievals of exoplanet atmospheres.
We present TauREx retrievals performed on a simulated HD189733 JWST spectra
using the full and reduced Venot2020 chemical networks and demonstrate the
viability of total disequilibrium chemistry retrievals and the ability for JWST
to detect disequilibrium processes.
