Shipping huggingface_hub every week with AI, open tools, and a human in the loop

huggingface_hub is the Python client at the base of the Hugging Face ecosystem. transformers , datasets , diffusers , sentence-transformers and dozens of other libraries depend on it to talk to the Hub. Every week we don't ship a new release is a week of fixes and features stuck on main .

For a long time we released every 4 to 6 weeks. We now release every week from a single GitHub Actions workflow. We built it using open-source tools and open-weights models and kept a human in the loop at the one place where judgment matters. Nothing in this post requires a vendor contract, a closed model, or infrastructure you can't run yourself. That was a design goal from the start since we wanted a workflow other maintainers could pick up and adapt.

By the end of this post, you'll have everything you need to build your own.

The old process was partly automated, mostly manual.

Writing good notes for a new version was the heavy part, aggregating tens of PRs on different topics. Nothing technically hard but a few hours of focused attention. Add the announcements on top and a minor release was easily a half-day of work spread over several days.

So we decided to streamline the whole thing. Looking at that list, the work splits in two.

Some steps are purely mechanical and can be automated: bumping the version, committing, tagging, pushing, opening downstream test branches, opening the post-release PR. Nobody needs to think about those. They just have to happen in the right order, every time, which is what a CI workflow is good at.

The rest is different. Writing release notes, deciding what to highlight, phrasing an announcement for a human audience: that's brain work. It's the kind of judgment that kept the release manual for years. This is where AI comes in, turning a blank page into a solid first draft in seconds. It's also where we have to be careful because a draft that looks confident and is subtly wrong is worse than no draft at all.

When we decided to fix this, we set one constraint up front: every moving part had to be something any maintainer could run themselves. No closed model behind an API we couldn't swap, no proprietary release platform, no secret sauce.

The second principle: the model drafts, a human decides. Language models are good at turning thirty terse PR titles into readable release notes. They are not good at being trusted blindly. So the workflow is human-supervised: the model does the first pass, a deterministic script checks its work, and a human reviews and edits before anything ships (more on that below).