okf-toolkit 0.1.1

CLI toolkit for working with Google's Open Knowledge Format (OKF)

okf-toolkit

CLI toolkit for working with Google's Open Knowledge Format (OKF)

okf-toolkit is a command-line tool for creating, validating, traversing, and maintaining knowledge bases stored in the Open Knowledge Format (OKF). OKF is a vendor-neutral, human-and-agent-friendly format announced by Google Cloud on June 12, 2026. It represents knowledge as a directory tree of plain-text markdown files with YAML frontmatter, making knowledge bases portable, version-controllable, and accessible to both humans and AI agents.

What is OKF?

The Open Knowledge Format is a specification for organizing structured and unstructured knowledge in a file-system-based bundle. Each unit of knowledge — a "concept" — lives in its own .md file with YAML frontmatter that provides typed metadata. Key principles:

• Simplicity: Everything is markdown. No databases, no proprietary schemas.
• Portability: A bundle is a directory tree. Copy it, commit it to Git, distribute it via any channel.
• Agent-friendly: Typed frontmatter and clear linking make it trivial for AI agents to traverse, understand, and augment knowledge bases.
• Extensibility: Producers can add any extra frontmatter keys; consumers tolerate unknown ones.
• UTF-8 throughout: No encoding guesswork.

Features

okf-toolkit provides everything you need to work with OKF bundles:

Command	Description
okf init	Scaffold a new OKF bundle directory with index.md and log.md
okf new	Interactively create a new concept with guided frontmatter prompts
okf validate	Validate bundle structure: required fields, reserved names, UTF-8, links
okf list	List all concepts with their type and description
okf show	Display a concept's full frontmatter and body content
okf index	Auto-generate or refresh index.md files from concept frontmatter
okf search	Full-text substring search across all concept bodies
okf graph	Output the link graph as Mermaid or ASCII for visualization
okf stats	Bundle statistics: concept count, type breakdown, tag cloud, link counts

Installation

Via PyPI (recommended)

pip install okf-toolkit

Via Git

git clone https://github.com/akdira/okf-toolkit.git
cd okf-toolkit
pip install -e .

Python 3.10 or newer is required. Only pyyaml is needed beyond the standard library.

Quick Start

# Initialize a new bundle
okf init my-knowledge-base

# Navigate into it
cd my-knowledge-base

# Create a concept interactively
okf new . tables/user-sessions

# Validate the bundle
okf validate .

# List all concepts
okf list .

# Search across concepts
okf search . user

# Generate index.md files from frontmatter
okf index .

# Show the link graph
okf graph .

# Get bundle statistics
okf stats .

Usage Examples

Creating a New Knowledge Base

okf init docs/knowledge-base

This creates:

docs/knowledge-base/
├── index.md    # Directory listing (auto-editable)
└── log.md      # Update history

Adding a Concept

okf new docs/knowledge-base api/checkout-endpoint

You'll be prompted for:

• type (required) — e.g., "API Endpoint", "BigQuery Table", "Playbook"
• title (optional)
• description (optional)
• resource (optional URI)
• tags (optional, comma-separated)

The tool generates a markdown file with proper YAML frontmatter and a template body.

Validating a Bundle

okf validate docs/knowledge-base

Checks every .md file for:

• Valid UTF-8 encoding
• Reserved filename violations (index.md and log.md must not have a type field)
• Required frontmatter fields (type)
• YAML parseability
• Broken markdown cross-links (reported as warnings)
• Type correctness for tags (must be a list) and timestamp (must be a string)

Exits with code 1 if any errors found, 0 otherwise.

Searching Across Concepts

okf search docs/knowledge-base revenue

Performs case-insensitive substring matching across all concept files (excluding index.md and log.md). Shows the matching file, its type, and a highlighted snippet around the match.

Generating Index Files

okf index docs/knowledge-base

Walks every directory containing concepts and generates or updates an index.md with properly formatted links, titles, types, and descriptions pulled from frontmatter. Skips directories where nothing has changed.

Visualizing the Link Graph

# Mermaid format (default) — paste into GitHub-flavored markdown
okf graph docs/knowledge-base

# ASCII format for terminal inspection
okf graph docs/knowledge-base --format ascii

Why OKF Matters for AI Agents

AI agents that work with knowledge bases face two fundamental challenges: understanding the structure of the knowledge, and knowing how to add to it. OKF addresses both:

1. Typed frontmatter gives agents semantic understanding. When an agent encounters a concept file, the type field immediately tells it what kind of thing this is — a table, a metric, a playbook, a reference. This reduces hallucination and improves retrieval accuracy.

2. Clear linking creates a traversable graph. Agents can follow markdown links between concepts just like humans do, building a graph of related knowledge.

3. The format is append-friendly. An agent that discovers new knowledge can create a new .md file with proper frontmatter using the same tools a human would.

4. Version control works out of the box. Git tracks every change to every concept. Agents can see what changed, when, and why.

5. No lock-in. Unlike a proprietary knowledge graph or database, OKF bundles are plain files. Any tool — AI or otherwise — that can read markdown and YAML can work with OKF.

Contributing

Contributions are welcome! Here's how to get started:

1. Read the AGENTS.md file in the repository root — it contains detailed instructions for AI agents and human contributors alike.
2. Open an issue to discuss your proposed change before writing code.
3. Fork the repository and create a feature branch.
4. Write tests for any new functionality. Tests live in tests/ and use Python's unittest framework.
5. Ensure existing tests pass with python3 -m unittest tests/test_okf.py -v.
6. Submit a pull request with a clear description of what and why.

Conventions:

• Python 3.10+ only, stdlib-first approach
• pyyaml is the sole external dependency — keep it that way
• New CLI commands should follow the existing pattern (see cmd_* functions in okf.py)
• Keep ANSI color helpers in the _color / green/red/etc. functions

Credits

Created and maintained by akdira.

License

Apache 2.0. See LICENSE for the full text.