We consider problems related to functional expressibility [1] (and its variations) in a logical calculus L. Basic problems are: a) given a formula F, a system  of formulas and a set of rules R for obtaining formulas from  to find out weather F can be obtained from  by means of R, and in the case of positive answer to get out the precise way how to get it (they say F is expressible by rules R via  in L); b) to find out if any formula of L can be obtained from  by means of R (i.e.  is complete by rules R in L); c) to find out if  is almost complete relative to rules R in L, i.e.  in incomplete, but for any formula G, which is not expressible by rules R via  in L the system  [ fGg is already complete. The above problems are related to complex routine calculations and are instances of search problems. The idea is to use search techniques developed in Artificial Intelligence, and especially those so called nature inspired. Related to above described problems we consider that most promised one is the technique called Genetic Programming [2]. There are different frameworks in different programming languages that provide some tools that implement concepts from Genetic Programming. For Java language we consider frameworks described in [3, 4]. For Python language we consider frameworks mentioned in [5, 6, 7]. We analyze the ease of use, the ease of prototyping programming solutions to the problems of expressibility in logical calculi using a set of defined tools. Other goal is to connect them to Jade agents [8]. Note: Research realized by partial support of the grant no: 20.80009.5007.22.