Python "SystemError" from a C Extension or Interpreter Bug in CI

A SystemError signals an internal failure in the interpreter or a C extension - a C function returned an inconsistent result. It rarely means your pure-Python code is wrong; it usually points at a broken or mismatched native module.

What this error means

Code raises SystemError, often "returned a result with an error set" or "null argument to internal routine", from inside a compiled module (numpy, lxml, a custom C extension). It can be triggered by an ABI mismatch after a partial upgrade.

Python traceback

SystemError: <built-in function ...> returned a result with an error set
# or
SystemError: PY_SSIZE_T_CLEAN macro must be defined for '#' formats
  File ".../mypkg/_speedups.pyx", line 88, in mypkg._speedups.parse

Common causes

C-extension or ABI mismatch

A compiled extension built against a different Python/NumPy ABI behaves inconsistently at the C level, raising SystemError. Common after upgrading one half of a coupled pair.

A genuine bug in a native module

A C/Cython extension that mishandles the error state (returns NULL without setting an exception, or vice versa) surfaces as SystemError in the caller.

How to fix it

Reinstall the native module cleanly

Force a fresh, ABI-matched build/wheel of the offending extension and its coupled dependencies.

Terminal

pip install --force-reinstall --no-cache-dir <pkg>
# for the numpy-coupled stack, reinstall together
pip install --force-reinstall numpy <pkg>

Pin a known-good version

Identify the compiled module in the traceback (the .so/.pyx frame).
Pin that package - and anything it shares an ABI with - to a release combination known to work.
Rebuild in a clean environment so no stale .so is reused.

How to prevent it

Keep ABI-coupled packages (NumPy and its consumers) pinned together.
Rebuild native extensions from clean environments in CI.
Report reproducible SystemErrors upstream - they’re usually native-module bugs.