text: 1 
  
  School of Culture and Communication, Aarhus University, 8000 Aarhus, Denmark; 
  202204724@post.au.dk (S.W.N.); 202204836@post.au.dk (J.E.L.) 
  
  2 Department of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78457 Konstanz, Germany 
  3 VERSES Research Lab., Los Angeles, CA 90016, USA; k.friston@ucl.ac.uk 
  4 Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK 
  5 
  
  Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark; chmathys@cas.au.dk (C.M.); 
  ptw@cas.au.dk (P.T.W.) 
  
  ** ***Correspondence: cheins@ab.mpg.de 
  † 
  These authors contributed equally to this work. 
  
  **Abstract:**We introduce a new software package for the Julia programming language, 
  the library ActiveInference.jl. To make active inference agents with Partially Ob- 
  servable Markov Decision Process (POMDP) generative models available to the grow- 
  ing research community using Julia, we re-implemented the pymdp library for Python. 
  ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive 
  and behavioural modelling, as it is used in computational psychiatry, cognitive science 
  and neuroscience. This means that POMDP active inference models can now be easily 
  fit to empirically observed behaviour using sampling, as well as variational methods. In 
  this article, we show how ActiveInference.jl makes building POMDP active inference 
  models straightforward, and how it enables researchers to use them for simulation, as well 
  as fitting them to data or performing a model comparison. 