The Power of Knowledge Graphs in Your Favorite LLM Workflows

Improve the quality of your LLM responses using our MCP server in your favorite AI client or workflow.



 
Watch an Introduction Installation Instructions

InfraNodus MCP: Supercharge Your LLM Workflows

Talk to your knowledge graph with natural language and get insights and answers to your questions. Use advanced graph science to detect main topics and gaps in your own knowledge or public discourse.

Retrieve Relevant Information

Let your LLM retrieve relevant information from your knowledge graph without complex vector storage setup.

Optimize Your Knowledge

Graph representation shows your LLM the main topics and gaps, so you can optimize it for better results.

Automated Workflows

Our tools help you perform complex SEO / LLMO optimizations in 1 click, detect gaps in a collection of texts, and more.



Multilingual

English, German, French, Portugese, Spanish, etc.

Interoperable

Connect to n8n, Claude, Cursor, Dify, Crew AI, and more.

Private by default

Has a fully private mode where no logs are stored. EU servers.

Quality documentation

Detailed tutorials on our support portal.



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How InfraNodus MCP Server Can Help You

 

All texts in InfraNodus are represented as knowledge graphs. This means your LLM is always aware of the general context and hidden relations and gaps that they would normally miss.

Topic modeling and text mining using text network analysis

You can easily integrate InfraNodus MCP server in your favorite LLM automations for augmented data retrieval. Every graph is like a brain that you can connect to get the best responses possible.

Get the relevant excerpts using the graph

Find the content gaps: find the topical clusters and ideas that are not yet linked, use them to optimize your knowledge base or to feed them to LLM agents to improve the quality of their responses.

Structural holes in knowledge graphs generate new ideas

Our MCP has tools that can generate research questions based on the gaps identified in the knowledge graph. Feed these questions to AI agents to conduct better research.

Use OpenAI's GPT to generate research questions and innovative ideas for your discourse




Import Content from Multiple Sources


You can use the main web version of InfraNodus to import content from multiple sources and services. Then plug this knowledge into your favorite LLM workflow via the MCP server.

 

Plain Text

Copy and paste any text or send via API

PDFs & Markdown

Multiple text files import: txt, pdf, md.

CSVs & Spreadsheets

Import by columns, advanced filtering

Google & SEO

Search results, Scholar, related search queries


YouTube

Transcripts, comments, search results

Websites

Scrape whole websites, URLs, and sitemaps

Amazon

Product search results and customer reviews

News & RSS

RSS feeds, latest news, XML files





Use It for Writing, Research, SEO / LLM Optimization, and More

Here are some use cases that you can leverage the power of InfraNodus MCP server for.

Use Case: Optimize your content for search engines and LLMs



Problem: Your content is not picked up by search engines and LLMs.

If you ask ChatGPT to optimize your content for SEO, you'll get generic AI-generated text that will be punished by search engines and push your content down.


 
Solution: Use the InfraNodus MCP server SEO tool to optimize your content

Our SEO tool will analyze the content directly from your favorite LLM, identify the current supply and demand for the main topics and entities, find the gaps within, then suggest a detailed content plan to bridge these gaps while gaining topical authority in the domain.


SEO Case Study    Try It Now

Use Case: Teach your LLMs to reason using the knowledge graphs



Problem: Standard LLM responses do not take relations into account

Most LLM worksflows treat text as separate chunks of information and miss out the relations within the text.


 
Solution: Use InfraNodus GraphRAG to add relational context to your LLM responses based on the "expert" ontology graphs

InfraNodus will convert user prompts into graphs and overlay them on top of the knowledge base graph. It will then follow the graph's edges to retrieve important relational context and add it to the LLM context window to improve the response quality.

Building Expert Ontology Case Study    Try It Now

Use Case: Brainstorm and research with our MCP server



Problem: AI-generated ideas are too generic

Standard AI tools will focus on the most likely prediction. That's how they work. So you get low quality ideas..


 
Solution: InfraNodus focuses on the gaps and periphery in your knowledge to help find new unorthodox connections.

InfraNodus will represent your texts as a knowledge graph, analyze its structure, and find how it can developed further based on the gaps and the peripheral edges of your content.

MCP Server for Research Case Study    Try It Now



 

Use Case: Add creative thinking tools to your AI agents



Problem: Standard LLM agents are not good in detecting content gaps

As a result, they provide generic responses and miss out on important research and marketing opportunities.


 
Solution: Integrate InfraNodus content gap detection tools into your AI agent workflows

InfraNodus tools can be used by your AI agents to detect content gaps and steer your model towards bridging those gaps to generat new ideas. InfraNodus can also generate research questions that can be used as prompts in your AI workflows.


Crew AI Content Gap Case Study    Try It Out


 
 
Knowledge Graph Examples

These are the examples of the knowledge graphs that can be used to augment your LLM workflows.


 
 
 
 

Start Using InfraNodus

Sign up for an account now, so you can start using InfraNodus to augment your LLM workflows.

How Does InfraNodus Work?


InfraNodus visualizes any text as a knowledge graph. Drawing an analogy from social sciences, words are represented as nodes, while their co-occurrences create the connections between them. This network representation enables the use of advanced graph theory algorithms to identify clusters of related ideas, highlight the most influential concepts and hidden intermediaries, and reveal gaps within the discourse. This approach can be used to enhance your perspective on any discourse and explore nuances of meaning that might otherwise remain hidden.

Additionally, AI-driven algorithms are employed to generate insights and new ideas for any discourse based on the topical clusters and gaps discovered.

To learn more about the algorithms used in InfraNodus and for citations, please, refer to this peer-reviewed paper:

Paranyushkin, D (2019). InfraNodus: Generating Insight Using Text Network Analysis, Proceedings of WWW'19 The Web Conference, www.infranodus.com (ACM library, PDF).


 

Ecological Thinking and Cognitive Variability

Gregory Bateson coined a beautiful term: "ecology of the mind". What is a mind that is ecological? It has the ability to have an overview, but it can also zoom in on any idea. It embraces diversity, but it can also obsess over one thing when needed. It can discover the obvious, but it can also reveal the hidden and ponder the gaps that have yet to be bridged. Focused, yet adaptive. Rational, yet poetic.

InfraNodus is a tool that is developed to help you think this way. It is made to promote ecological dynamics and diversity on a cognitive level. Too often, people get stuck in certain ideas or thought patterns. InfraNodus can be used as a "mind antivirus" against obsessive loops — biased ideas, mundane patterns, totalitarian thinking, propaganda, and narrow-mindedness — proposing a pan~archic form of thinking that spans a range of states and modalities.

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Discover the power of ecological thinking and cognitive variability using knowledge graphs and AI-driven insights.

Pricing Options

Your support is what makes it possible for us to develop this tool. All quotas are per each text. There is no limitation on the number of texts you can process every month. Prices do not include VAT for EU private customers.

Lock in current prices for life and save up to 35% (€100-€200) on our annual plans.

Monthly Annual Save up to 35%

 

 
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InfraNodus vs. Other MCP Servers

Other MCP servers for content analysis and SEO are limited by their inability to understand the underlying structure and relationships in your data. InfraNodus provides a unique approach to text analysis that reveals the hidden patterns and insights in your data.

other MCPs

  • Standard RAG workflows that treat your text as disjointed data chunks
  • Generic, predictable responses
  • Cannot deal with general queries
  • Black-box approach hides reasoning
  • No graph, no deeper understanding of context
  • Lack holistic approach

InfraNodus MCP

  • GraphRAG that understands relations in your data and provides rich context
  • Innovative responses that focus on content gaps
  • Has a holistic view of your data
  • Interactive graph shows you what's happening
  • GraphRAG finds gaps in your knowledge
  • Covers you from ideation to writing to promotion

 

 

Testimonials from Our Users

InfraNodus API is used by researchers and consultants to augment their LLM workflows. Here are some of the testimonials from our users:

Learn How to Build AI Workflows Powered with Knowledge Graphs

Our support portal contains a wealth of information on using knowledge graphs with LLMs, GraphRAG, and practical applications using popular tools like Dify.AI, Crew AI, etc.

You are welcome to contact us at any time via our Discord community or by submitting a support ticket. We are always happy to help you with any questions you might have and recommend the most effective ways to use InfraNodus for your research, writing, marketing, or any other purposes.

1. How to Build a Knowledge Graph

You can use the AI to generate a knowledge graph for any topic or import any text, PDF document, web page, or Google search results. More on Generating a knowledge graph with AI

2. How to Read and Interpret Network Graphs

Studies have shown that diagrams and especially network representation of data makes you think more of connections in your data. You can identify the main elements as well as the relations between them and — most importantly — discover the gaps in your knowledge using the graphs. More on Reading the Graphs

3. Find the Most Relevant Parts in Text

Network graphs are great for non-linear reading. Look at the graph, find the part you like, click on the nodes, and you'll see the excerpt of your original data that contains this exact combination. More on Nonlinear Reading

4. Discover the Content Gaps in Your Knowledge

You can use the knowledge graph to reveal the main topics in any discourse. Some topics are not very well connected: these are the content gaps that can be used to generate new ideas and insights. More on Discovering Content Gaps