I tried using wolfram alpha as a tool for an llm research agent, and I couldn't find any tasks it could solve with it, that it couldn't solve with just Google and Python.

The obvious use case here is deep mathematical research, where the LLM can focus its reasoning on higher level concepts.

For example, if it can reduce parts of the problem to some choices of polinomials, its useful to just "know" instantly which choice has real solutions, instead of polluting its context window with python syntax, Google results etc.

the tasks where wolfram actually outperforms python+google are symbolic: exact algebraic simplification, closed-form integrals, formal power series, equation solving over specific domains. for numeric work you're right that python wins. but for cases where you need a guarantee that x^2-1 = (x+1)(x-1) and not a floating-point approximation of it, wolfram is in a different category. the question is whether LLMs are running into those cases often enough to justify the overhead.

Well sure, in theory any mathematical problem can be solved with any Turing complete programming language. I think the idea here is that for certain problem domains Mathematica might be more efficient or easier for humans to understand than Python.