Planet Drupal

Zero to new Drupal site in 133 seconds John Locke Sat, 08/01/2026 - 15:00

Gabor asked in Slack about how people contributing to Drupal manage multiple different Drupal versions, and different contributed module branches, when using AI agents:

Dev Corner

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You have a supplier spreadsheet with a category column that reads Animals > Dogs > Food, and you want Drupal to end up with a real taxonomy tree: Animals, with a child Dogs, with a child Food, each level created only if it does not exist yet, and every product referencing the leaf of its own path.

Neither Drupal core nor Feeds does that on its own. Feeds Tamper Term Hierarchy does: it reads the path out of the column, creates the taxonomy terms that do not already exist, and respects the hierarchy between them. It has been on drupal.org since 2021 and reached its first stable release, 1.0.0, this week.

The interesting part is not the splitting. Any parser can split a string on a delimiter. The interesting part is deciding, for each segment, whether the term you are looking at already exists, and that turns out to be a question about identity that most import tooling gets wrong.

Name is not identity

Take a small catalogue:

sku,name,category
1001,Rope leash,Animals > Dogs > Accessories
1002,Dry food 3kg,Animals > Dogs > Food
1003,Dry food 1kg,Animals > Cats > Food

The tree you want out of it:

Animals
├── Dogs
│   ├── Accessories
│   └── Food
└── Cats
    └── Food

There are two terms called Food in that tree. One is the dog food category, the other is the cat food category, and they have to stay separate. If they collapse into one, every dog product and every cat product point at the same category, your faceted search stops making sense, and the client finds out before you do.

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Drupal’s automation tools solve different problems. The harder question is how they can exchange work without weakening their execution models, permissions, or auditability.

Author: Will Huggins

In 2025, the Drupal AI Initiative launched with a clear vision: to establish Drupal as the premier open-source AI platform for digital experiences.

One year later, the market momentum is clear. What began as a highly focused working group has grown into a powerful ecosystem supported by 32 global partner organisations, over 50 active contributors, and over $1.5 million in committed funding. Most importantly, with the core AI technology now clocking up over 18,000 installs, organisations are actively building their next-generation marketing engines on Drupal.

For digital teams, AI presents a host of opportunities. The power to increase speed of production on one hand, while maintaining quality, consistency and governance on the other. Drupal is addressing this head-on by creating two dedicated product workstreams: Inside AI and Outside AI.

This blog post outlines what this means for your digital roadmap and how Drupal can help your digital marketing operations win in the age of AI.

"Inside AI" vs. "Outside AI"

As AI has evolved from chat boxes into autonomous, multi-step agents, digital leaders need a platform that does two things simultaneously: empowers human creators inside the browser and securely integrates with external marketing systems.

To accelerate our product roadmap, we have divided our day-to-day development into two specialised, business-focused tracks:

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Reusable Drupal components become awkward when each placement needs a different presentation. ERFO adds controlled variation without duplicating or altering the referenced content.

The Drupal AI module provides everything needed for a RAG chatbot: AI Search embeds your content into a vector database, AI Assistant API wraps an LLM with a grounded search prompt, and AI Chatbot puts a Deep Chat block on the page. We cover setup, provider selection, module stability, troubleshooting tools, and the data flow considerations that government sites need to get right.

Custom Formatters is old. I started it in 2009, my first year building seriously for Drupal, and shipped it through Drupal 6 and 7. Then in late 2016, not long after the first Drupal 8 alpha, I stepped away (work, life, the usual reasons) and for the best part of a decade it wasn't mine to ship.

It didn't die, though, and that's almost entirely down to one person. Andrii Podanenko (podarok), backed by ITCare and the Open Y distribution, carried it through the Drupal 8 beta, the Drupal 10 port, and the start of the Drupal 11 line, essentially single-handedly, for years. Huge thanks to Andrii for looking after it all that time. I've picked the 4.1.x line back up alongside him, and 4.1.0 is the first release to come out of that.

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Today we are talking about Drupito, its Business model, and Marketplaces with guest Ashraf Abed. We'll also cover Generate (Social Media) Image as our module of the week.

For show notes visit: https://www.talkingDrupal.com/563

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Some WordPress sites reach a point where the content model no longer fits the organization's needs. Custom content types, custom entities, and custom fields become necessary to represent internal data precisely, and Drupal supports all three, shaping that data with full flexibility.

Other sites reach that point because of AI. Drupal has built a strong AI ecosystem over recent years, enabling integrations of every needed type: content creation, accessibility checks, RAGs, among others. These are the two situations behind most WordPress-to-Drupal migrations: an architecture the site has outgrown, or an AI integration WordPress cannot support natively.

The challenge of migrating WordPress content to Drupal

The core challenge in a WordPress-to-Drupal migration is migrating the content itself. WordPress sites may host thousands of pieces of content that need to be moved to the Drupal site.

A full migration project runs through several phases: redesigning the site if needed, building the new architecture, and creating a Drupal node for every WordPress post or page. Creating the content is where the real work concentrates, since every image, every user, and every translation has to make it across, on top of adapting content to a different format defined by the new architecture.

A manual content migration is not viable due to its high costs and risks. Handled by hand, this work is prone to human error. A single error is easy to fix, but a…

An AI model does not decide what data it can access, which tools it can use, or whether it can act without approval. People make those decisions at different points. Upstream, a model developer trains and tests the model and decides whether and how to release it. Downstream, a developer builds the model into a system, connects that system to data and tools, and decides whether a person must review its proposed actions before they take effect.

Those choices determine whether harm is possible at all. So when harm occurs, responsibility should fall on those who controlled the relevant choices. That responsibility may be shared: model developers control training and release, product builders control permissions and deployment, and users control deliberate misuse.

Responsibility should follow meaningful control.

That principle is missing from much of the debate over open-weight AI models, which often treats the decision to release a model as the only one that counts.

Axios recently reported that United States officials had considered measures that could restrict American companies from using Chinese open-weight models. Open-weight models make their trained parameters available for others to download, modify, and run on their own infrastructure, without going through the company that built them.

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It’s not new and it’s not sexy, but Schema.org is getting a lot more attention these days. The reason is AI.

You’ve probably heard that Schema improves your AI search results — apply it to your site and voilà, better results. But what is it? How does it help with AI? And, more controversially, does it actually help at all?

If you’re reading this article online, you’ve already experienced the impact of one of history’s most influential inventions. Chances are you’ve also visited several websites today to check the news, compare products, or fill out an online form. 

This is a guest post from the incredible team at 1xINTERNET, a Top-Tier Drupal contributor and digital agency headquartered in Frankfurt, Germany.

When the Drupal Association announced that 1xINTERNET had become one of the world's Top-Tier Drupal Contributors, it was a proud moment for the company. Reaching the highest level of contribution recognition places 1xINTERNET among a select group of organisations helping shape the future of one of the world's leading open-source content management systems.

Yet, ask anyone inside the company about the achievement, and you'll hear the same response: becoming a Top-Tier Contributor was never the ultimate goal.

Instead, it is the natural outcome of more than a decade of believing that if you build your business on open source, you should help build open source itself.

For over thirteen years, 1xINTERNET has invested in the Drupal ecosystem, not only by delivering digital platforms for clients, but by contributing code, maintaining projects, sponsoring community events, supporting governance, leading strategic initiatives and encouraging employees to actively participate in the community.

Today, the company sponsors more than 500 hours of Drupal contribution every month, actively supports more than 85 Drupal projects, has sponsored over 50 Drupal events, and has contributed to hundreds of issues across the Drupal ecosystem. Those numbers tell one story. The people behind them tell another.

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Discover how our solution automated content translation and helped the client’s team work faster.

Take Away Marlene Wanberg, Frontend Developer at Tag1 and a Drupal builder since 2007, built a suite of fifteen AI agent skills that runs a full automated accessibility audit and narrows thousands of raw scanner findings down to a short worklist of confirmed fixes.

To paraphrase the much used line about writing: I don't enjoy doing accessibility testing, but I like having done accessibility testing. When a site becomes more usable for everyone, I feel good. Users feel good. Clients feel good. (Regulators feel good.) The process of getting there can be very tedious.

Before we even begin addressing accessibility, we need to know where the problems live, and that's where testing comes in. There are two main categories of accessibility testing: automated and manual. Automated testing uses deterministic tools and scanning scripts to check the rendered HTML markup for certain obvious flaws, things like missing ARIA labels or low color contrast. Manual testing checks what those tools can't. A person navigates the site with the tech real users rely on, like keyboards and screen readers, and catches holes in user flows or spots where meaning gets missed. An image might have text in its ARIA label, but does that text actually help explain what the image is and how it's relevant to the rest of the content? Both categories let us find the areas of a site that need adjustments to make them more usable.

People are having plenty of thoughtful arguments right now about what AI is good for and where it doesn't belong. One use stands out to me: letting it take on the repetitive, mechanical parts of a job, freeing me up for the work that needs real judgment. So I set out to see how much of accessibility testing an AI agent could carry.

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I've been working on DDEV for years now, and there's something I don't say often enough:

You make this so worthwhile.

Stas and I love to get up in the morning to hear what you have to say, learn from your experiences, share our path together.

We feel so thankful to be creating something useful in collaboration with you.

All of us have had jobs before where some boss was making random decisions on product features that we knew might be irrelevant in weeks or months. It's a frustrating feeling, and that lack of control is so terrible. With DDEV and your guidance, we always know that you're keeping us on track about real needs for real features. It's fantastic.

Real Feedback About Real Problems

Your questions in Discord or Slack and the issues you file aren't "noise". They're signal. When something breaks (or is awkward) in your workflow, you tell us, often with enough detail that we can reproduce it immediately. When something is confusing, you ask questions that reveal where our assumptions were wrong.

That feedback shapes DDEV in ways that internal testing never could. We don't use DDEV on every possible OS, with every PHP framework, in every hosting environment. You do. And you tell us what you find.

We Love Your Questions and Comments.

AI has been replacing human interaction in support situations, and in many cases doing a decent job. It usually does a good job with questions about DDEV.

But getting answers to questions is not the only purpose of support. It's also a great way to communicate problems and ambiguities to project maintainers.

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In April 2025, I published Claude Code meets Drupal, my first public experiment with an AI coding agent. I have been experimenting with coding agents ever since, often by building tools to solve problems in my own work.

Last week, I joined the Drupal AI Learners Club to discuss several experiments I had already published. Angie Byron started the club and runs it with co-organizer Amber Himes Matz. It gives people in the Drupal community a place to show how they are using AI and talk honestly about what works and what does not.

I spent an hour walking through the experiments, starting with Drupal Digests, a tool that uses AI to summarize key developments across Drupal Core, Drupal CMS, Drupal Canvas, and the Drupal AI initiative.

Drupal Digests led to another experiment: AI-generated Rector rules. When a Drupal Core change deprecates an API, Drupal Digests analyzes the issue and code changes and generates a rule that can automate the corresponding upgrade in other Drupal projects.

I also showed an API catalog that helps AI agents discover my website's search API.

These are only some of my AI experiments. Most begin as tools I build for myself, and many never go any further. When one seems useful beyond my own work, I publish it so others can try it and improve it.

Once it is public, we can see whether people use it and want to help improve it. If they do, it may eventually become a community project. If not, that is useful to know too.

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Several of the e-commerce stores we build and run ship with MRW, one of the main carriers in Spain and Portugal. Until recently, shipping an order meant leaving the store: open MRW's customer portal, retype the customer's address, print the label, then copy the shipment number back into Drupal so the customer gets a tracking link. Multiply by every order, every day, and add returns, which meant doing the same dance with the addresses swapped.

We replaced that with a Drupal module. It now runs the daily expedition of the first of them, and we are releasing it to the community: Commerce MRW is available on drupal.org, with a 1.1.0 release.

What it does

Commerce MRW integrates Drupal Commerce with MRW through SAGEC, the carrier's SOAP webservice for creating and managing shipments (envíos). The whole expedition cycle happens on the shipment admin pages the team already uses:

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Learn how Drupal's service container and dependency injection work, why they matter for testable code, and what Drupal 11 changes for your service classes.

Beta 3 release of Drupal AI Context Beta 3 of Drupal AI Context , also known as Context Control Center (CCC), is now available. CCC helps Drupal sites provide governed, reusable context for AI workflows and agents, from brand voice and editorial standards to organisational knowledge and governance rules. This gives AI systems access to more structured and relevant information while allowing teams to manage that information centrally. Following the beta 2 release , beta 3 expands how context is authored, imported, selected and extended. It also introduces redesigned administration pages, more granular agent controls and broader integration options.