"LLM::Graph" uses a graph structure to manage dependencies between tasks, where each node represents a computation and edges dictate the flow. Asynchronous behavior is a default feature, with specific options available for control.
WL's LLMGraph is more developed and productized, but Raku's "LLM::Graph" is catching up.
I would like to say that "LLM::Graph" was relatively easy to program because of Raku's introspection, wrappers, asynchronous features, and pre-existing LLM functionalities packages. As a consequence the code of "LLM::Graph" is short.
Wolfram Language does not have that level introspection, but otherwise is likely a better choice mostly for its far greater scope of functionalities. (Mathematics, graphics, computable data, etc.)
In principle a corresponding Python "LLMGraph" package can be developed, for comparison purposes. Then the "better choice" question can be answered in a more informed manner. (The Raku packages "LLM::Functions" and "LLM::Prompts" have their corresponding Python packages implemented already.)
I personally don’t see what advantages Python as a language (not an ecosystem) would have here.
