Open Knowledge Format (OKF): Google's Standard for AI Agent Memory
Google Cloud launched Open Knowledge Format (OKF) v0.1—a vendor-neutral spec for AI agent knowledge as markdown + YAML frontmatter. How it works, vs CLAUDE.md, Karpathy LLM wiki, and sample bundles on GitHub.
On June 12, 2026, Google Cloud published the Open Knowledge Format (OKF) v0.1—an open specification that turns the emergent "LLM wiki" pattern into a portable standard for AI agent knowledge.
OKF is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need.
No new runtime. No required SDK. Just markdown files with YAML frontmatter in a directory—shippable in git, readable on GitHub, consumable by any agent.
BigQuery enrichment agent + static HTML visualizer
Samples
GA4 e-commerce, Stack Overflow, Bitcoin datasets
The Problem OKF Solves
Foundation models improve faster than most organizations can assemble context. When an agent needs to answer "How do we compute weekly active users from our event stream?", the answer fragments across:
Surface
Example content
Metadata catalogs
Table schemas (vendor-specific APIs)
Wikis / Notion
Runbooks, metric definitions
Code
Docstrings, notebook comments
People's heads
Join paths, deprecation notices
Every agent builder re-solves the same context assembly problem. Every catalog vendor reinvents schemas. Knowledge stays locked to the surface that created it.
OKF's bet: the missing piece is a format, not another platform.
What an OKF Bundle Looks Like
An OKF bundle is a directory of concept documents. File path = concept identity:
---
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`.
Markdown links turn the directory into a graph richer than filesystem hierarchy alone.
Reserved files
File
Purpose
index.md
Progressive disclosure as agents navigate
log.md
Chronological change history
The full v0.1 spec—including conformance criteria—fits on one page.
Three Design Principles
1. Minimally opinionated
Only type is required. What types exist, what other fields to use, what body sections to include—producer's choice. OKF defines interoperability, not content models.
2. Producer/consumer independence
Producer
Consumer
Human-authored wiki
AI agent
BigQuery export pipeline
HTML visualizer
LLM-generated bundle
Search index
Same format, swappable tooling at each end.
3. Format, not platform
No proprietary account or SDK required to read or write. Value scales with adoption breadth, not vendor lock-in.
OKF does not replace CLAUDE.md—it can contain the structured knowledge CLAUDE.md points at. Example: CLAUDE.md says "read /okf/sales/metrics/weekly_active_users.md before analytics tasks."
Compare to MCP for live tool access; OKF for curated static knowledge.
What Google Shipped
Reference producer: BigQuery enrichment agent
Walks a BigQuery dataset, drafts OKF concept docs for every table/view, then runs a second LLM pass to enrich with citations, schemas, and join paths from authoritative documentation.
Reference consumer: Static HTML visualizer
Turns any OKF bundle into an interactive graph view—single self-contained HTML file, no backend, no data leaves the page.
Google updated Knowledge Catalog to ingest OKF bundles and serve them to Google Cloud agents—enterprise path for teams already on GCP.
The Karpathy Connection
Andrej Karpathy's LLM wiki gist (5,000+ stars) argues:
LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass.
Humans abandon wikis when bookkeeping fails; agents excel at it. The pattern keeps reappearing—Obsidian + coding agents, AGENTS.md repos, index.md/log.md artifacts.
OKF's contribution: agree on what fields every document carries and what filenames mean, so your wiki and my wiki and a catalog export cooperate without translation.
Three-layer mapping (Karpathy → OKF)
Karpathy layer
OKF equivalent
Notes
Raw sources (immutable)
External datasets, docs, APIs
OKF bundles are the compiled layer; producers ingest from raw
Both reserve index.md (catalog) and log.md (change history). See the full LLM Wiki guide for ingest/query/lint operations and the implementation ecosystem (AutoSci, memwiki, secure-llm-wiki, synthadoc, and others).
Browse sample bundles — GA4, Stack Overflow, Bitcoin in repo
Try the visualizer — open a bundle in the HTML tool
Write a producer — export from your DB, docs site, or wiki
Write a consumer — agent that reads OKF before tasks; search index; viewer
Contribute — file issues, PRs; v0.1 designed for backward-compatible growth
For Claude Code users: OKF bundles mount like any markdown corpus—agents with file tools traverse index.md hierarchies naturally.
Open Questions (v0.1 Is a Starting Point)
Google explicitly calls v0.1 a starting point, not a finished standard:
Contradiction handling — Two OKF docs disagree; no merge semantics yet
Faceted search — Some practitioners want richer tagging than minimal spec
Live vs static — OKF is file-based; stale docs are a process problem
Name collision — Unrelated "OKF" supply-chain spec exists (OKF-SCIS); Google's OKF is data/analytics/agent knowledge
Community feedback will shape v0.2.
Who Should Care
Audience
Why
Data teams
Export catalog knowledge agents can actually read
Agent builders
Stop bespoke wiki parsers per project
Platform vendors
Produce/consume without lock-in
Enterprise AI
Version-controlled knowledge in git next to code
explainx.ai readers
Complements MCP tools + CLAUDE.md memory patterns
Summary
Open Knowledge Format (OKF) v0.1 is Google's bid to make agent knowledge portable: markdown files, YAML frontmatter, markdown links as graphs, one required type field, no mandatory SDK.
It formalizes what Karpathy, Obsidian users, and CLAUDE.md authors already discovered—LLMs work better with curated, linked, maintainable markdown libraries than with repeated document search.
The spec is the contribution. BigQuery agent, visualizer, and sample bundles lower the cost of trying it. Whether OKF becomes lingua franca depends on producers outside Google adopting it.
