Open Knowledge Format (OKF) is a vendor-neutral standard for publishing your content as plain markdown files with YAML frontmatter, organized so AI agents can read and cite it without scraping your HTML. Google’s Cloud Data Analytics team published it in 2026, building on Andrej Karpathy’s LLM-wiki pattern. The whole spec fits on a single page.
I run an OKF bundle on this site. It is live at /okf/ and it is generated automatically from my posts and pages. Below is what OKF actually is, why I bothered, and how to add it to WordPress, Shopify, Webflow, or Squarespace. If you only care about your own platform, skip ahead.
Key Takeaways
- OKF is a directory of markdown files with YAML frontmatter. The only required field is
type. No SDK, no API, no proprietary format. - It exists so AI agents can read your knowledge as structured files instead of scraping HTML, and it preserves the links between your pages that scraping throws away.
- WordPress has a turnkey plugin that builds the bundle for you. Shopify, Webflow, and Squarespace have no plugin yet, so you generate the bundle and host the static files.
- It will not move rankings this quarter. It is an asymmetric bet on an agent-readable web, the way schema markup was an early bet a decade ago.
- I use the same format to run my own AI second brain, where agents read cross-linked markdown bundles instead of re-reading documents.
Skip to your platform: WordPress · Shopify · Webflow · Squarespace
What Open Knowledge Format actually is
OKF is a format, not a platform. A bundle is a directory of markdown files. Each file is one concept: a product, a dataset, an article, a metric. Each file opens with YAML frontmatter for the structured metadata and then uses normal markdown for the body. Files reference each other with ordinary markdown links, so the bundle is a graph, not just a folder tree. An index.md in each directory lists what is there.
Here is the shape of a single concept file, straight from Google’s spec:
---
type: BigQuery Table
title: Orders
description: One row per completed customer order.
resource: https://console.cloud.google.com/bigquery?p=acme&d=sales&t=orders
tags: [sales, revenue]
timestamp: 2026-05-28T14:30:00Z
---
# Schema
| Column | Type | Description |
|---------------|---------|-----------------------------------|
| order_id | STRING | Globally unique order identifier. |
| customer_id | STRING | FK to [customers](/tables/customers.md). |
# Joins
Joined with [customers](/tables/customers.md) on customer_id.The only required key is type. Everything else (title, description, resource, tags, timestamp) is optional, and you can add your own keys without breaking anything that reads the bundle. A whole bundle is just nested folders of these files:
sales/
├─ index.md
├─ datasets/
│ └─ orders_db.md
├─ tables/
│ ├─ orders.md
│ └─ customers.md
└─ metrics/
└─ weekly_active_users.mdOKF is just markdown files and YAML frontmatter. That is the entire point: any platform that can host static files can serve an OKF bundle, with or without a plugin.
Why I bothered
Agents are starting to sit between your content and the person asking the question. When an agent reads a normal site, it scrapes HTML, strips the layout, and loses the structure: which page is canonical, how your pages relate, what each thing actually is. An OKF bundle hands the agent that structure directly. The frontmatter says what a thing is. The markdown links say how it connects to the rest of your knowledge. That is the part scraping cannot reconstruct.
This is the same shift I write about in LLM SEO and AEO content strategy: the work moves from being found by a search engine to being usable by an agent. OKF is the file-level version of that idea, and it pairs naturally with the AI-citation work in my answer engine optimization program. It will not show up in your analytics next month. It compounds, the way structured data did.
How to add OKF to your CMS
Because a bundle is just files, the job on every platform is the same in principle: produce the markdown files, then host them where an agent can fetch them. The difference is how much the platform does for you. WordPress has a plugin that does the whole thing. The other three do not, so you generate the bundle and host it as static files, usually on a subdomain you control.
| Platform | Native OKF support | How you ship it |
|---|---|---|
| WordPress | Yes (plugin) | Install the OKF plugin; it generates and serves the bundle |
| Shopify | No | Generate the bundle, host the files (Files / subdomain) |
| Webflow | No | Export CMS items to OKF, host on a subdomain |
| Squarespace | No | Generate the bundle, host externally, reference it |
WordPress
This is the easy one, and it is what I run. Install the OKF for WordPress plugin, point it at the post types you want included, and it generates a bundle from your published content and serves it at /okf/. Mine is live at toddmorourke.com/okf/, served as text/markdown, with every post and page turned into a concept file and an index listing them all. You maintain nothing by hand: publish a post, and it appears in the bundle.
- Install and activate the OKF for WordPress plugin.
- Choose which post types and taxonomies to include.
- Load
/okf/and confirm it returns markdown with frontmatter. - Add a line to your
llms.txtpointing at the bundle so agents can find it.
Shopify
Shopify has no OKF plugin, and it will not serve a directory of .md files from your theme. So you generate the bundle and host it. Your highest-value concepts are products and collections: one markdown file per product, with frontmatter describing it.
---
type: Product
title: Trail Runner GTX
description: Waterproof trail running shoe, Vibram outsole.
resource: https://store.com/products/trail-runner-gtx
tags: [footwear, trail]
timestamp: 2026-06-20T00:00:00Z
---
# Details
Drop: 8mm. Weight: 295g. Sizes 7 to 13.
Pairs with [merino socks](/products/merino-socks.md).- Pull your catalog and blog through the Shopify Admin API.
- Write one OKF file per product, collection, and article, with cross-links between related items.
- Host the bundle on a subdomain you control (GitHub Pages, Netlify, or Cloudflare Pages all serve static files for free), for example
okf.yourstore.com. - Reference that subdomain from your
llms.txtand footer. Regenerate on a schedule so it stays current.
Webflow
Same story as Shopify: no plugin, and Webflow will not serve raw markdown trees. The advantage is that your content already lives in CMS Collections, which the Webflow API exposes cleanly, so the export is scriptable. Map each Collection to a type (an articles collection becomes type: Article, a products collection becomes type: Product) and write one file per item.
- Read your Collections through the Webflow CMS API.
- Convert each item to an OKF file, keeping the
typealigned to its Collection and linking related items. - Host the bundle on a subdomain and point your
llms.txtat it. - Trigger a rebuild from a Webflow publish webhook so the bundle tracks your site.
Squarespace
Squarespace is the most closed of the four. You cannot serve a file directory or custom file types from Squarespace itself, so the bundle has to live somewhere else. Generate it from your content, host it externally on a subdomain, and reference it. If you are committed to Squarespace and serious about agent-readiness, this is the platform most likely to push you toward an external static host, which is fine: OKF was designed to be hosted anywhere.
- Export your pages, blog posts, and products (the Squarespace API or a content export).
- Convert each to an OKF concept file with frontmatter and cross-links.
- Host the bundle on a subdomain and link it from
llms.txt.
How I use OKF in my Claude Code brain
The bundle at /okf/ is the outward-facing half: my site, packaged for agents. The inward-facing half is how I run the actual consulting work. My workspace is a set of OKF bundles that my own agents read: one for brand and business context, one for SEO data like keyword research and audits, one for research notes pulled from newsletters and videos. The format is identical to what I publish, which is the point.
Every concept is one markdown file. Every file carries frontmatter, links to at least one related file, and gets a line in its directory’s index.md plus a dated entry in a log.md. A real one looks like this:
---
type: reference
title: URL Inventory and Internal Link Map
description: Every live URL grouped by theme, with each theme's internal-link target.
tags: [internal-links, site-structure]
timestamp: 2026-06-20T00:00:00Z
---
# Targets
Link AEO posts to [AI Search](./services/ai-search.md).
Link technical posts to [Technical SEO](./services/technical.md).The reason this works is the reason OKF works at all. Karpathy’s observation was that LLMs do not get bored, do not forget to update a cross-reference, and can touch fifteen files in one pass. The bookkeeping that makes humans abandon a personal wiki is exactly what an agent is good at. When I ask my brain a question, the agent walks the bundle and follows the links instead of re-reading raw documents every time. I wrote about the full setup in how I built an AI second brain.
Should you do this yet
Straight answer: it will not change your traffic this quarter, and skipping it causes no immediate problem. The case for doing it now is that it is cheap and the downside is capped. On WordPress it is a plugin install. Everywhere else, it is a generate-and-host job you automate once and forget. If you think agents will keep moving between your content and your buyers, an agent-readable copy of your knowledge is worth a little more every month. If you are wrong, you lose an afternoon. I made the bet on my own site, and the bundle has been running quietly at /okf/ since.
FAQs
What is the Open Knowledge Format?
OKF is a vendor-neutral standard for representing knowledge as plain markdown files with YAML frontmatter, created by Google’s Cloud Data Analytics team. A bundle is a directory of these files, one per concept, cross-linked with markdown links. The only required frontmatter field is type.
Is OKF the same as llms.txt?
No. An llms.txt is a single file that points agents to your important URLs. OKF is a structured bundle of many markdown files, one per concept, linked into a graph. They are complementary, and I run both: the llms.txt points at the OKF bundle.
Does OKF help SEO or rankings?
Not directly, and not today. OKF is a machine-readability layer for AI agents, not a Google ranking factor. The payoff is agent-driven discovery and citation, which is a slower, compounding return, similar to how schema markup paid off over years rather than weeks.
Do I still need schema markup if I publish OKF?
Yes. Schema markup describes entities inside your HTML for search engines. OKF publishes your knowledge as standalone files for agents. They cover different surfaces, and both are worth doing.
Who created OKF and when?
Google’s Cloud Data Analytics team, with tech leads Sam McVeety and Amir Hormati, published OKF v0.1 in 2026. It formalizes the LLM-wiki pattern described by Andrej Karpathy into a portable, open specification.