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Yes, this is a big disadvantage. But have you tried Mamba that aims at implementing Anaconda more efficiently? It works really well in most cases.
https://mamba.readthedocs.io/
The herbie project using egraphs to explore different ways of rewriting floating point expressions. https://herbie.uwplse.org/ One can also write custom rulesets in egglog (a new egraph rewriting system / language / datalog) https://egraphs-good.github.io/egglog/?example=herbie
The approach is not yet anywhere near being able to touch all the domains sympy can handle. Destructive term rewriting tends to be a bit more forgiving to unsoundness in the rules and still returning roughly meaningful results. EGraph rewriting (and other automated reasoning systems) tend to just return junk as soon as you aren't careful about your semantics. Associativity and commutativity are ubiquitous in CAS applications and encoding these concepts in general purpose terms is rather unsatisfying. The post above emphasizes specialty methods for polynomials, which it would be desirable to find a clean way to integrate into egraph techniques. Variable binding (which is treated in a rather mangled form in CAS systems) is seemingly important for treating summation, differentiation, and integration correctly. The status of doing variable binding efficiently and correctly in egraphs is also unclear imo.
The herbie project using egraphs to explore different ways of rewriting floating point expressions. https://herbie.uwplse.org/ One can also write custom rulesets in egglog (a new egraph rewriting system / language / datalog) https://egraphs-good.github.io/egglog/?example=herbie
The approach is not yet anywhere near being able to touch all the domains sympy can handle. Destructive term rewriting tends to be a bit more forgiving to unsoundness in the rules and still returning roughly meaningful results. EGraph rewriting (and other automated reasoning systems) tend to just return junk as soon as you aren't careful about your semantics. Associativity and commutativity are ubiquitous in CAS applications and encoding these concepts in general purpose terms is rather unsatisfying. The post above emphasizes specialty methods for polynomials, which it would be desirable to find a clean way to integrate into egraph techniques. Variable binding (which is treated in a rather mangled form in CAS systems) is seemingly important for treating summation, differentiation, and integration correctly. The status of doing variable binding efficiently and correctly in egraphs is also unclear imo.
You might enjoy ruler https://github.com/uwplse/ruler
It would be very interesting for SMT and CAS to converge a bit more. SMT in expressiveness and domains and CAS in rigor.
The modality of their usage is different. CAS tends to return some expressions of interest, which it is hard to get SMT to do. Either you get "unsat" or a particular model from an SMT solver, not a simplified expression (ok, z3 has a simplify command, which is pretty cool).
SMT today is not obviously expressive enough to handle most of the domains and questions that come up in CAS systems.
Most SMT solvers do not intrinsically handle transcendental functions or any notions of calculus, abstract algebra, etc.
CAS systems are largely interested in problems of equational reasoning, whereas SMT's bread and butter is gluing together "trivialities" like linear inequalities and congruence closure with SAT search.
Sounds like a great project idea to make a toy demo of this direction you'd like to see. Maybe comparable to https://github.com/norvig/paip-lisp/blob/main/docs/chapter15... and https://github.com/norvig/paip-lisp/blob/main/docs/chapter8.... which are a few hundred lines of Lisp each, but do enough to be interesting.