Open Knowledge Format (OKF) explained
The Open Knowledge Format is an open specification from Google Cloud that represents a body of knowledge as a directory of plain markdown files. Each file carries a small block of YAML frontmatter and a free-form body. The goal is a portable way to hand an AI agent the context it needs, readable by a person and parseable by a machine, with no SDK and no catalog to lock into. Google Cloud published it in June 2026 as version 0.1.
What an OKF bundle contains
A bundle is a folder of markdown files, and the unit inside it is a concept. A concept is anything worth capturing for an agent: a table, a dataset, a metric, a runbook, an API. Every concept is one UTF-8 markdown document with two parts. A YAML frontmatter block at the top, fenced by a line of three dashes above and below, and a markdown body underneath.
The format asks for exactly one field, type. Everything else is optional, including title, description, resource, tags and a timestamp. What types exist and what fields each carries is left to whoever produces the bundle. Concepts reference each other with ordinary markdown links, so the folder becomes a graph of related knowledge rather than a flat list of files.
Structural interoperability, not yet semantic
Version 0.1 fixes a small set of things and leaves the rest open. It fixes the folder layout, the use of markdown, the YAML frontmatter, two reserved filenames and the single required field. That is structural interoperability: any tool can open a bundle and know where the pieces are.
What it does not fix is meaning. The format does not say what a metric concept must contain, or how two producers should agree on the same field names. That is semantic interoperability, and version 0.1 leaves it to producers and to conventions that have not been written yet. This is the line to keep in mind when reading the announcements around OKF. It standardizes the shape of the files, not yet what the files mean.
Where OKF fits with agent-readiness
Agent-readiness, the kind measured by independent scanners, is about whether an agent can reach and read your public site at all. OKF sits next to that, one layer in. It is a way to package the internal knowledge an agent works from once it is past the front door: the catalog, the metrics and the rules a decision depends on.
So OKF is not a replacement for an llms.txt or a markdown surface on your site. It is the same instinct, plain text an agent can read without a special client, applied to the data and context behind the site rather than the pages in front of it. For a team thinking about what an agent acts on, not only what it can see, that is the part of the picture OKF addresses.
What to do with it today
OKF is new and small, version 0.1, and the semantic half is still open. That makes it worth understanding now and worth watching, but early to build an entire knowledge catalog on. If you already serve markdown to agents and keep an llms.txt, you have the instinct OKF formalizes, and adopting it later will be a short step rather than a rebuild.
For an audit of how legibly AI agents can read your site and the data behind it, contact info@turva.dev.