The OKF-go provides a high-performance suite of utilities to validate bundles, harvest metadata from databases and APIs, assemble context for Large Language Models (LLMs), and run an MCP Server.
View on GitHub ↗The Open Knowledge Format (OKF) is a vendor-neutral, lightweight specification for structuring organizational knowledge (documentation, runbooks, metrics, database schemas, and API definitions) into machine-readable, human-friendly Markdown files.
By representing knowledge as a directory of Markdown files with structured YAML frontmatter, OKF bridges the gap between structured metadata repositories and unstructured text documentation, serving as an ideal substrate for AI agents, Retrieval-Augmented Generation (RAG) pipelines, and the Model Context Protocol (MCP).
The Open Knowledge Format (OKF) is an open, vendor-neutral specification developed by Google Cloud for representing structured metadata as plain Markdown files with YAML frontmatter. For the full format specification, conventions, schema details, and reference examples, see the official Google Cloud OKF Specification.
The OKF-go provides a high-performance suite of utilities to validate bundles, harvest metadata from databases and APIs, assemble context for Large Language Models (LLMs), and run an MCP Server.
Conformance Engine & Linter (okf lint): Validates knowledge bundles for YAML frontmatter correctness, required attributes, and broken internal/external markdown links. Metadata Harvesters (okf harvest): Automatically extracts and converts schemas from databases (PostgreSQL, MySQL, Cloud Spanner, BigQuery), OpenAPI specs, Protobuf files, Git repositories, and web pages into OKF concept documents. Context Assembler (okf assemble): Performs graph-based Breadth-First Search (BFS) starting from a core concept,
From the project README.
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