Should I Use OKF or a Vector Database? (They Solve Different Problems)

Your AI agent needs to know things.

You have docs, runbooks, schemas, and tribal knowledge scattered across Notion, Confluence, READMEs, and people's heads. You need to make that knowledge machine-readable.

So you reach for a vector database.

That might be the right call. Or it might be overkill.

The real question is not "which is better." It is "which problem am I solving."

What OKF Actually Is

OKF stands for Open Knowledge Format.

It is a specification from Google Cloud (v0.1, Apache-2.0) for representing knowledge as plain Markdown files with YAML frontmatter.

One file equals one concept. A table. An endpoint. A metric. A runbook. A decision.

Each file has a small header:

---
type: BigQuery Table
title: Orders
description: One row per completed customer order.
resource: bigquery://acme/sales/orders
tags: [sales, orders]
timestamp: 2026-05-28
---

Then a Markdown body with the actual knowledge. Links between files form a graph:

Part of the [sales dataset](/datasets/sales.md). Feeds [gross revenue](/metrics/gross-revenue.md).

That is it. No SDK. No database. No runtime. No API.

Just files you can cat, git diff, and open in any editor.

What a Vector Database Actually Is

A vector database stores high-dimensional numerical representations (embeddings) of your data.

You chunk your documents into pieces, run them through an embedding model, and store the resulting vectors. When a query comes in, the database finds the most similar vectors using algorithms like HNSW or product quantization.

It is purpose-built infrastructure for similarity search.

Popular options include Pinecone, Weaviate, Qdrant, and the vector capabilities inside Elasticsearch and PostgreSQL (pgvector).

The Real Divide

The debate is not about syntax. It is about two different layers of the knowledge problem:

OKF is a format. It defines how knowledge is structured, linked, and versioned. It is the source of truth.

A vector database is retrieval infrastructure. It indexes embeddings and returns approximate nearest neighbors. It is the query engine.

They are not competing tools. They operate at different levels.

Why People Reach for Vector Databases

Vector databases solve a real problem: fast semantic search over large corpora.

When you have thousands of documents and need to find the most relevant chunks for a user query, vector search is the right tool. It understands meaning, not just keywords. A search for "affordable laptops for students" can surface "budget notebooks for college."

This is powerful for:

• Retrieval augmented generation (RAG)
• Semantic search across documentation
• Recommendation engines
• Image and multimodal search

If your knowledge base is massive and your primary need is "find me the most relevant chunk," a vector database is the right choice.

Why People Reach for OKF

OKF solves a different problem: knowledge structure and portability.

When your knowledge is scattered across proprietary tools, buried in monolithic docs, or rotting in wikis nobody updates, OKF gives you a format that:

• Keeps relationships explicit (links between concepts)
• Supports progressive disclosure (read only what you need)
• Works with git (diffs, reviews, branches, PRs)
• Has zero vendor lock-in
• Is readable by both humans and agents natively

An agent reading an OKF bundle starts at index.md, follows relevant links, and reads 3 small files instead of dumping 40,000 tokens of a monolithic doc into context.

That is cheaper. That is faster. That produces fewer hallucinations.

The Comparison Nobody Makes Clear

	Vector DB / RAG	OKF Bundle
Agent-readable	Yes (lossy chunks)	Yes (native Markdown)
Keeps relationships	No (chunks lose them)	Yes (explicit graph)
Progressive disclosure	Top-k only	Index then links
Git diff / review	No	Yes
Infrastructure required	Embeddings + DB + pipeline	None
Vendor lock-in	Medium	None
Human-readable	No	Yes
Handles scale	Millions of documents	Hundreds to thousands of concepts

When to Choose a Vector Database

Choose a vector database when:

• You have a massive, unstructured corpus (millions of documents)
• Your primary need is semantic similarity search
• You are building a RAG pipeline for an LLM
• You need real-time retrieval over constantly changing data
• You already have embedding infrastructure in place

Vector databases are retrieval engines. They are excellent at what they do.

When to Choose OKF

Choose OKF when:

• Your knowledge is structured and cross-cutting (schemas, APIs, runbooks, metrics)
• You want knowledge versioned alongside code
• You need relationships between concepts preserved explicitly
• You want agents to traverse knowledge efficiently without loading everything
• You care about portability and zero lock-in
• Your team already uses git workflows

OKF is a knowledge format. It is excellent at what it does.

The Sweet Spot: Use Both

Here is the part that makes the debate feel outdated.

OKF does not replace RAG. It feeds it.

A curated OKF bundle is a structured, versioned corpus you can embed into a vector database. You keep an inspectable source of truth and let retrieval sit on top.

The workflow:

1. Curate knowledge as an OKF bundle (versioned in git, reviewed in PRs)
2. Embed the bundle into a vector database for fast semantic retrieval
3. Agents use vector search to find relevant concepts, then follow OKF links for context

You get both: fast retrieval and structured relationships. The vector database finds the needle. OKF shows you the thread it is attached to.

The Mental Model

You cannot appreciate OKF if you have never maintained a 3,000-line README that nobody reads. You cannot appreciate vector databases if you have never needed to search across thousands of documents for semantic meaning.

Both solve real problems. Both create real problems when used in the wrong context.

Ask one question:

Am I organizing knowledge, or am I searching through it?

If organizing, OKF. If searching, vector database. If both, use both.

Final Point

The OKF versus vector database debate is not a competition. It is a layering question.

OKF is how you structure and version knowledge. A vector database is how you retrieve it at scale.

One is a format. The other is infrastructure.

Most teams do not need to pick one forever. They need to pick the right one for the problem they are solving today. And often, the right answer is both.

The tool is not the problem. The problem is using the wrong tool and then blaming it for the mismatch.