OKF: The Open Standard for LLM Wikis · AI Automation Society

OKF: The Open Standard for LLM Wikis

OKF — Open Knowledge Format is an open standard for organizing LLM Wikis: personal, team, or project knowledge bases designed to be read, maintained, and updated by AI agents.

Direct Summary

The idea is simple: instead of dumping loose documents into a RAG system, you create a structured wiki in Markdown, with organized pages, links between concepts, entities, summaries, and metadata.

With this structure, the AI agent does more than search documents. It reads, understands, extracts important information, and integrates that knowledge into a living system that can evolve over time.

The problem is that everyone builds their wiki differently. One person uses one folder structure, another uses a different one. Field names change. Metadata changes. Links between pages change. This makes it harder to share knowledge bases across agents, teams, and systems.

OKF solves this by creating a simple standard for organizing these wikis.

It mainly defines two things:

How files and folders should be organized.
Which metadata fields should appear at the top of each document.

The most important field is type, which identifies the kind of content: concept, video, entity, note, decision, resource, project, and so on.

Other fields may include title, tags, relationships with other content, links, related videos, and connections between concepts.

The Big Advantage

OKF allows different agents to understand knowledge bases created by other people or teams.

This opens the door to:

a more organized personal second brain;
shareable knowledge bases;
team wikis;
knowledge packages from content creators;
agents that navigate concepts more accurately;
better integration with Markdown, Obsidian, Notion, GitHub, and coding agents;
reuse of knowledge across different projects.

n practice, OKF tries to do for knowledge bases what standards like MCP do for tools: create a common way for systems to communicate.

The agent no longer depends on a random structure. Instead, it can find information inside a predictable organization.

How It Works in Practice

An OKF knowledge base can have a main index, folders by content type, and Markdown files with metadata at the top.

The agent starts by reading the index, understands which areas exist, identifies the relevant files, and only goes deeper where needed.

This creates progressive navigation: first the agent sees the overall map, then it enters the related concepts, documents, or entities.

That reduces mess, improves search, and helps the agent answer more accurately.

Example Use Case

Imagine a knowledge base with videos, lessons, documents, decisions, projects, and concepts.

Instead of leaving everything scattered, OKF organizes each part into standardized files and folders.

Then you can ask:

“What is the main idea about agents in this set of content?”

The agent reads the index, finds the related concepts, opens the right documents, and answers based on the structured knowledge.

Criticism of OKF

The most common criticism is that OKF may seem too simple.

It does not try to solve everything. It mainly standardizes organization, indexes, document types, and metadata.

But that simplicity is also its strength.

The goal is not to create a complex system. The goal is to create a minimal layer so different wikis can be read, shared, and maintained by AI agents.

In One Sentence

OKF turns an AI-powered second brain into a standardized, shareable, and agent-friendly knowledge base, instead of letting everyone build isolated and incompatible wikis.

https://okf.md/spec/

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OKF: The Open Standard for LLM Wikis