LearnLib has been designed with a particular emphasis on scalability and high performance. When using comparable algorithms, it outperforms LibAlf, an automata learning library written in C++. The concrete speed-up depends on the algorithm, the alphabet size, and the model size. Furthermore, it is highly non-linear (see below). As a result, on “big” models the new LearnLib can be several orders of magnitude faster than the aforementioned other libraries.

Apart from these performance gains attained by clever engineering, the open-source LearnLib features novel algorithms (such as the TTT algorithm), which may significantly reduce the number of membership queries during learning.

Let us now take a brief look at how LearnLib’s algorithms perform against their counterparts from LibAlf. Also, due to other specifics of the libraries, different experimental setups appear fair. More information on this can be found here (LibAlf).


When looking at the classic L* algorithm for learning DFAs, the version provided with LearnLib has a non-constant speedup over LibAlf’s version (left). The same applies to the Kearns/Vazirani algorithm with binary search, which in general appears to be the fastest algorithm shipped with LibAlf (middle). For moderately sized systems (500 states, 10 input symbols), every algorithm in LearnLib is several orders of magnitude faster than the equivalent LibAlf version (right).

Performance comparison between LearnLib (blue) and LibAlf (red). Left: performance characteristics of L* versions (alphabet size = 10). Middle: performance characteristics of Kearns/Vazirani with binary search versions (alphabet size = 10). Right: performance of comparable learning algorithms (automaton size = 500, alphabet size = 10). Click on an image to enlarge.

A more detailed comparison between the open-source LearnLib and LibAlf can be found here.