spaCy installation fails on Python 3.13? Here’s the fix: use Python 3.12 and avoid pip build errors

Getting ModuleNotFoundError or Meson/compiler logs when pip install spacy on Python 3.13? See why it fails, how to switch to Python 3.12 to install spaCy fast.

Installing spaCy on a fresh machine and immediately hitting ModuleNotFoundError can be confusing, especially when pip install spacy keeps failing with long build logs. If your Python is 3.13, that’s exactly the trap: spaCy doesn’t currently support Python 3.13, so the installation falls back to building low-level dependencies from source and crashes with compiler-related errors.

Minimal reproducer

The failure appears even with the smallest possible script that only imports the library.

import spacy as nlp_mod

What’s actually going on

As of May 17, 2025, spaCy does not seem to support Python 3.13. On that interpreter, pip tries to satisfy spaCy’s dependency chain by pulling source distributions. In the output you can see a tarball fetched for numpy (for example, numpy-2.0.2.tar.gz) and a Meson-based build kicking in. The build then dies with messages like Unknown compiler(s) and a missing Visual Studio toolchain, which is why you see subprocess-exited-with-error and metadata-generation-failed. The underlying issue is the Python version, not pip itself.

The situation and status are tracked here: Spacy installation on python 3.13 fails.

The fix

Switch to Python 3.12 and install spaCy there. You can do this by creating an environment with Python 3.12 using tools you already use in your workflow, such as uv or conda (including micromamba). After activating that 3.12 environment, installing spaCy proceeds as expected, and the minimal script above imports cleanly.

If you’re exploring installation behavior in other contexts, pip has a --prefer-binary flag, which asks pip to pick prebuilt wheels when available instead of compiling locally. It won’t change spaCy’s Python 3.13 compatibility, but it can cut down on unnecessary builds when wheels exist.

Working code after the environment change

The application code doesn’t need changes; what matters is running it under a supported interpreter version.

import spacy as nlp_mod

Why this matters

Version compatibility between libraries and the interpreter is a first-order concern in Python packaging. When a package doesn’t support your Python version, pip often falls back to building dependencies from source, which on Windows requires a native toolchain and typically leads to noisy, misleading errors. Recognizing that the top-level issue is version support saves time and prevents chasing compiler setups you don’t actually need.

Conclusion

If pip install spacy fails on Python 3.13 with build logs mentioning Meson, compilers, or metadata-generation-failed, don’t fight the toolchain. Use Python 3.12 for spaCy as of May 17, 2025, via uv or conda/micromamba, and the import will work as intended. Keep an eye on the linked GitHub issue for updates on Python 3.13 support. When troubleshooting similar problems in the future, first verify the package’s supported Python versions, then pick or pin the interpreter accordingly; it’s the fastest path to a clean install.