Googles specification (and tooling) for the LLM wiki Google has released the Open Knowledge Format (OKF), a vendor-neutral specification for representing knowledge as plain markdown files with YAML frontmatter, along with tooling including a reference agent and visualizer. The format aims to enable human and AI collaboration on knowledge artifacts using standard software engineering workflows like version control and pull requests. This repository is primarily about the Open Knowledge Format OKF .OKF is a universal, vendor-neutral formatfor representing knowledge as plain markdown files with YAML frontmatter. It isnot tied to any particular agent, framework, model provider, or serving system. The goal is simple: Anyone can produceOKF — humans authoring by hand, agents built on any framework Google ADK, LangChain, custom , export pipelines from existing catalogs Dataplex, Unity Catalog, Collibra, … , or scripts walking a database.Anyone can serve and consumeOKF — a static file server, a knowledge-management UI Obsidian, Notion, MkDocs , an LLM loading files into context, a search index, or a graph viewer like the one bundled in this repo.The agent below is a proof of conceptdemonstratingoneway to produce OKF bundles automatically. The format itself is the contribution; this agent and the visualizer exist to make the format tangible at both ends — production and consumption. See OKF in practice— three ready-to-browse bundles produced by this agent, checked into : bundles/ — GA4 e-commerce dataset bundles/ga4/ viz.html — Stack Overflow public dataset bundles/stackoverflow/ viz.html — Bitcoin blocks/transactions bundles/crypto bitcoin/ viz.html OKF represents catalog knowledge as plain markdown files with YAML frontmatter, organized in a directory hierarchy. That choice unlocks a few properties that are hard to get from a service-owned metadata store: Human- and agent-readable. No SDK or query language stands between a reader and the content. An engineer can cat a concept; an LLM can ingest it verbatim into context. Version-controllable out of the box. Bundles live in git. Pull requests, line-by-line diffs, blame, and review workflows just work — knowledge curation becomes a normal software-engineering activity. Portable and lock-in free. A bundle is a directory. Ship it as a tarball, host it in any repo, mount it from any filesystem, or sync it to any system that speaks files. No proprietary API stands between you and your metadata. Mixes structured and unstructured data deliberately. Use frontmatter for the few fields you want to query, filter, or index on type , resource , tags , timestamp ; use the markdown body for the prose, schemas, and example queries that LLMs and humans actually read. Minimally opinionated, freely extensible. A small set of required keys ensures interoperability, but bundles can carry arbitrary extra frontmatter keys and arbitrary body sections without breaking consumers. Composes with existing tooling. Many knowledge tools — Notion, Obsidian, MkDocs, Hugo, Jekyll — already speak markdown plus YAML frontmatter, so bundles can be browsed, edited, or rendered without custom UI. Progressive disclosure built in. Auto-generated index.md files let an agent or human navigate the hierarchy one level at a time instead of loading the entire bundle into context. Graph-shaped, not just tree-shaped. Concepts link to each other via normal markdown links, expressing relationships richer than the parent/child implied by the directory layout. The net effect is that reference agents, consumption agents, and humans collaborate on the same artifacts in the same way they already collaborate on source code. python3.13 -m venv .venv .venv/bin/pip install --index-url https://pypi.org/simple/ -e . dev - BigQuery: gcloud auth application-default login plus a project for billing gcloud config set project