Highlyt

What is Highlyt?

Highlyt is a tool for turning reading highlights into a knowledge graph. It focuses on organizing and connecting the ideas found in highlights so you can see relationships across documents like books and papers.

The workflow is centered on interpreting highlight meaning (including color-coding by meaning) and then linking related highlights across sources to build a connected view of your notes.

Key Features

Color-code ideas by meaning: assign or reflect semantic meaning on highlights to keep different types of ideas visually distinct.
Link highlights across books and papers: connect related highlights from multiple sources rather than keeping them isolated per document.
Build a knowledge graph from highlights: represent highlights as nodes and relationships so connections between ideas are easier to navigate.
Connect to Claude or ChatGPT via MCP: integrate with LLMs through MCP to support connecting, transforming, or working with highlight-derived knowledge.

How to Use Highlyt

Collect highlights from your reading (books or papers) and import them into Highlyt.
Use the color-coding by meaning to label or interpret what each highlight represents.
Link related highlights across different sources to form connections in the knowledge graph.
If you want LLM-assisted workflows, connect your Claude or ChatGPT setup via MCP and use it as part of the highlight-to-graph process.

Use Cases

Research synthesis across papers: connect highlights that reference the same concept to map how different papers describe a topic.
Literature review organization: link summaries, definitions, and key claims from multiple books into a single graph of related ideas.
Building an idea map for writing: transform a set of annotated highlights into connected nodes/relationships so you can trace supporting points.
Cross-document concept tracking: find where a term or principle appears across sources by linking semantically related highlights.
Assisted knowledge structuring with an LLM: use Claude or ChatGPT (via MCP) to help interpret or structure highlights before they become part of the knowledge graph.

FAQ

Does Highlyt support connecting highlights from multiple document sources?

Yes. The page describes linking highlights across books and papers, which implies cross-source connections rather than keeping highlights within a single file.

What does “color-code ideas by meaning” mean in practice?

The product positions this as a way to categorize highlights by their meaning so that ideas of different types can be distinguished visually.

How does the integration with Claude or ChatGPT work?

The page states that Claude or ChatGPT can be connected via MCP, indicating an integration channel rather than a standalone, one-off export.

What output does Highlyt produce?

The core output is described as a knowledge graph built from highlights, with links between related highlights.

Is pricing or a specific setup workflow described?

No pricing details or step-by-step setup instructions are provided in the supplied content.

Alternatives

Note-taking and linking tools with graph or tags: alternatives that organize notes with tags, backlinks, or graph views can serve a similar organizational purpose, though they may not specifically focus on “highlights into a knowledge graph.”
Document annotation tools: tools focused on highlighting and annotation help collect the raw highlights, but may require additional steps to turn highlights into a structured knowledge graph.
AI-assisted note structuring platforms: solutions that use LLMs to summarize or structure notes can complement a highlight workflow, though they may differ in whether the organizing unit is highlights vs. pasted notes.
General knowledge graph tools: graph databases or knowledge-graph builders can achieve similar outcomes, but typically require more manual modeling and integration work compared with a highlight-first approach.