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title: Google’s OKF: The Missing Layer Between RAG and AI Agents
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# Google’s OKF: The Missing Layer Between RAG and AI Agents

**[Medium](https://daily.dev/sources/medium_js)** · 2 min read · 2 upvotes · 0 comments

## Summary

Google's Open Knowledge Format (OKF) is a structured, human-readable knowledge organization layer designed to sit between traditional RAG pipelines and AI agents. Rather than treating all documents as a flat collection of embeddings, OKF creates a hierarchical map of concepts, relationships, and metadata. This allows AI agents to first understand where relevant knowledge lives before invoking semantic search, making RAG more targeted and efficient. The approach is analogous to a table of contents for AI systems and could extend to codebases, APIs, and architecture documentation for coding agents.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@kamalmeet/googles-okf-the-missing-layer-between-rag-and-ai-agents-4ba474edcc51>

## Questions this post answers

### What is Google's Open Knowledge Format (OKF) and how does it relate to RAG?

Google's Open Knowledge Format is a structured, human-readable directory format that organizes knowledge into concepts, relationships, metadata, and hierarchy, acting like a table of contents for AI. It does not replace retrieval-augmented generation but sits in front of it, letting an agent first identify which section of a knowledge base is relevant before running semantic search over embeddings.

_Explore how daily.dev surfaces practical takes on structuring knowledge for RAG and agent pipelines._

### How would an AI agent use a knowledge map like OKF differently than plain RAG when searching across many documents?

With plain RAG, a question searches embeddings across an entire document collection, such as ten physics books, regardless of topic. With a structured map like OKF, the agent first determines that a query about Newton's Laws belongs to Mechanics, narrows to the relevant books and chapters, and only then runs retrieval on that smaller, targeted subset.

_Developers weighing retrieval architectures can follow evolving RAG and agent design patterns on daily.dev._

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- [Architecture as Code: How Google’s OKF Makes Coding Agents Follow Your Architecture](https://daily.dev/posts/architecture-as-code-how-google-s-okf-makes-coding-agents-follow-your-architecture-cc2o3991u) · Medium · 2 upvotes · 0 comments
- [How the Open Knowledge Format can improve data sharing](https://daily.dev/posts/how-the-open-knowledge-format-can-improve-data-sharing-qqswihmau) · Google Cloud · 15 upvotes · 0 comments
- [Using OKF to provide long term context to your agent](https://daily.dev/posts/using-okf-to-provide-long-term-context-to-your-agent-9rqtpfok6) · iO tech\_hub · 10 upvotes · 0 comments
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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag)

[View this post on daily.dev](https://daily.dev/posts/google-s-okf-the-missing-layer-between-rag-and-ai-agents-ifmrkg5ll)

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