# Open Knowledge Format (OKF) bundle

> Source: <https://specification.website/spec/agent-readiness/okf-bundle/>
> Published: 2026-06-19 00:00:00+00:00

# Open Knowledge Format (OKF) bundle

Publish your whole knowledge base as an Open Knowledge Format bundle — a tree of Markdown concept files with typed front matter — so an agent can ingest the entire corpus in one fetch instead of scraping page by page.

## What it is

The Open Knowledge Format (OKF) is a convention for packaging a body of knowledge as a tree of Markdown files an agent can consume directly. Each file is a **concept**: a YAML front-matter block followed by Markdown prose. The only hard requirement is a non-empty `type`

field; the format leans on a small set of recommended fields (`title`

, `description`

, `resource`

, `tags`

, `timestamp`

) and lets producers add their own keys, which consumers must preserve rather than reject.

A bundle is otherwise plain files and folders. `index.md`

files give progressive disclosure — a reader can list a directory without parsing every concept. A root `log.md`

records change history newest-first. A `references/`

directory mirrors external standards as first-class concepts so a check can cite them with a bundle-relative link. There is no manifest, no schema server, and no runtime: a bundle is the directory itself.

## Why it matters

**One ingest, not N scrapes.** An agent that wants your whole corpus gets it in a single download instead of crawling every HTML page and stripping navigation.**Typed, predictable structure.** Every concept declares what it is. Front matter carries the metadata an agent would otherwise have to infer.**Graceful for consumers.** Conformance is deliberately loose — unknown types, extra keys, and missing optional fields must not cause rejection — so a bundle stays usable as it grows.

OKF deliberately leaves **serving and discovery out of scope**. A bundle on its own is undiscoverable; pair it with a discovery surface — an [AI Catalog entry](/spec/agent-readiness/agentic-resource-discovery/) and an [ llms.txt](/spec/agent-readiness/llms-txt/) pointer — so agents can find it.

## How to implement

Generate the bundle from your existing source of truth; do not hand-maintain a second copy. For each item, emit `<path>.md`

with a `type`

and the recommended fields, reusing the same Markdown body you already serve. Emit an `index.md`

per directory (no front matter, except the bundle root, which may carry `okf_version`

). Add a `log.md`

from your change history. Mirror each cited standard once under `references/`

and link checks to it. Offer the tree browsably and, optionally, as a single archive for “take everything” consumers. Then advertise it.

This site ships it: the bundle is generated from the same content collection as every other surface and served browsable at [ /okf/](/okf/index.md), with the whole tree packaged as

[. Each check carries its](/okf.tar.gz)

`/okf.tar.gz`

`status`

as an RFC 2119 `conformance`

keyword and links to the standard it derives from; every cited source is mirrored under [. The bundle is advertised in our](/okf/references/index.md)

`/okf/references/`

[AI Catalog](/spec/agent-readiness/agentic-resource-discovery/)and in

[. Its](/llms.txt)

`/llms.txt`

`mediaType`

is interim and unregistered (`application/okf-bundle+gzip`

) pending a blessed OKF media type.## Common mistakes

- Hand-authoring the bundle so it drifts from the source. Generate it.
- Putting front matter in
`index.md`

files — only the bundle-root`index.md`

may carry it. - Shipping the bundle with no way to find it. OKF solves packaging, not discovery; advertise it separately.
- Claiming a registered media type the artefact does not have. Until OKF has one, declare an honest interim type.

## Verification

- Every non-index, non-log
`.md`

parses as YAML front matter with a non-empty`type`

. `index.md`

files carry no front matter (except the root’s`okf_version`

).- Concept count matches your source corpus; the tree round-trips cleanly from the archive.
- The bundle is reachable from at least one discovery surface (AI Catalog entry,
`llms.txt`

, or a`Link`

header).

## Related topics

## Sources & further reading

-
[Open Knowledge Format (OKF) specification](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md)— Google -
[Open Knowledge Format — reference implementation and samples](https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf)— Google -
[Agentic Resource Discovery (ARD) specification](https://agenticresourcediscovery.org/)— ARD Project (Linux Foundation)