AI Knowledge Base


An AI knowledge base can function as a second brain for your personal or professional use. The basic setup is a folder of files, a semantic layer describing the concepts and their relations, and an AI agent pointed at both.

In this section we will cover the basic setup and the tools you need to create and use an AI knowledge base.

Set It Up

1
Create an Account

Sign up for a free InfraNodus account to get access to the knowledge graph tools and your API key.

Create a Free Account
2
Get InfraNodus MCP
https://mcp.infranodus.com

Add this URL as a connector via Settings > Connectors in Claude Web / Desktop and authenticate with your InfraNodus API key via OAuth.

3
Install the Skill
/plugin marketplace add infranodus/skills
/plugin install infranodus@infranodus-skills

Run these two commands in the Claude Code prompt to install the skill as a plugin (use infranodus-all@infranodus-skills to install all our skills at once). Or use npx in your terminal: 

npx skills add infranodus/skills --skill infranodus -g -a claude-code -y

npx comes bundled with Node.js. For ChatGPT and OpenClaw, see the full installation instructions.

Didn't work? You can simply download infranodus.zip from our GitHub repository and add it to your LLM client manually — see the step-by-step instructions.



Add Graph View to Your Knowledge Base

VSCode / Cursor Graph View Plugin Obsidian Graph View


Please, choose your preferred setup:

Personal Knowledge Base

You have nothing yet. Files, folders, markdown, version control, and a viewer. No programming.
AI Second Brain

You want the whole loop. Sources in, structure read, gaps surfaced, questions out.
LLM Wiki

You are working one corpus hard. Karpathy's pattern, with the graph layer wired in.
AI Reasoning Graph

You need the model to reason, not retrieve. Reasoning ontologies in plain text — types, constraints, perspectives.
AI Memory Stack

Every session starts from scratch. Notes, graphs, insights, and agent learnings in layers, so each session starts ahead of the last.
RAG vs. LLM Wiki vs. Knowledge Graph

Your AI only summarizes your vault. Choosing the semantic layer that fixes it.

 

The Three Layers: Storage, Semantic Layer, and AI Query Interface


Every working setup has the same three layers, usually built in this order.

First, you need to organize your files and documents using the Zettelkasten method, in traditional folders, or both. If you prefer not to have any hierarchy, you can also use tags and [[backlinks]] (Roam Research style).

The second layer is the semantic layer, which is a machine-readable description of the concepts and how they relate: an LLM wiki, a knowledge graph, or both. Without one, the model falls back on vector similarity. Add typed relations, constraints, and perspectives and it becomes a reasoning ontology — structure that shapes how the model thinks, not only what it finds.

The third layer is the AI query interface, which is Claude Code, Codex, or the AI in InfraNodus, pointed at the folder with that layer available as a tool.

An AI knowledge base represented as a knowledge graph of concepts, topics, and the gaps between them
A knowledge base and the graph of what is inside it — topics in colour, gaps between them.
Layer What it is Covered in
Storage Files in folders. Markdown and PDFs, a viewer — Obsidian, Cursor, Notion, or InfraNodus — and Git if you want version history. Personal knowledge base
Semantic layer A machine-readable description of the concepts and how they relate: an LLM wiki, a knowledge graph, or both. Without one, the model falls back on vector similarity. Add typed relations, constraints, and perspectives and it becomes a reasoning ontology — structure that shapes how the model thinks, not only what it finds. LLM wiki · Reasoning graph · Choosing one
Query Claude Code, Codex, or the AI in InfraNodus, pointed at the folder with that layer available as a tool. AI second brain

 

What the Graph Adds


Storage, retrieval, and a wiki all describe what a knowledge base contains. None of them describes its shape. When represented as a network, the knowledge base becomes measurable: the graph helps find relations between remote concepts, betweenness centrality finds the concepts holding the material together, community detection finds the topics that have actually formed, and pairs of topics that both exist without being connected are the content gaps — relations your material implies and never states.

A knowledge base that only accumulates converges. What makes it generative is knowing where it is not yet connected.


 

Tools


Install the InfraNodus skill, connect the MCP server to Claude, open your folder, and run /infranodus on one subfolder rather than the whole thing. You get a graph, a manifest.json your AI reads afterwards, and a report with the topics, the gaps, and the questions worth asking.


 

FAQ

What is an AI knowledge base?

Your own material — notes, papers, transcripts, datasets — stored so a large language model can read, query, and extend it. In practice: files in folders, a semantic layer describing the concepts and their relations, and an AI agent pointed at both.

How do I create a knowledge base for AI?

Put your sources in folders, convert what you can into markdown, and point Claude Code at the folder. Then add a semantic layer so the model has a structural view rather than the nearest matching paragraph. Details in the setup guide.

Is this customer support knowledge base software?

No. Help-desk knowledge bases optimize for deflecting repeat questions with the right existing answer. This is the opposite objective, and the tooling differs all the way down.

Do I need a knowledge graph, or is retrieval enough?

If your base is reference material and your questions are lookup, retrieval is enough. If you are doing research, the graph is what gives you multi-hop reasoning and gap detection — see the comparison.

Can I keep it local and open source?

Yes. Markdown files and PDFs in a folder you own, optionally under Git. The InfraNodus skills are open source and the ontology files they write are markdown.

What is a content gap?

A content gap is identified between two topical clusters that both exist in your knowledge base but are not connected to each other. Detected with community detection and betweenness centrality, so the result is reproducible rather than a model's guess.


 

Build Your AI Knowledge Base


Connect your notes, papers, and files to a knowledge graph that shows you the topics inside them, the concepts that hold them together, and the gaps between them:


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