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A minimal guide to rendering your current knowledge graph to an interactive HTML file with one call. Before you start:
  • Complete Quickstart to understand basic operations
  • Have some data processed with cognify (knowledge graph exists)

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What Graph Visualization Shows

  • Nodes (entities, types, chunks, summaries) with color coding
  • Edges with labels and weights; tooltips show extra edge properties
  • Interactive features: drag nodes, zoom/pan, hover edges for details

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Code in Action

import asyncio
import cognee
import os
from cognee.api.v1.visualize.visualize import visualize_graph


async def main():
    await cognee.add(["Alice knows Bob.", "NLP is a subfield of CS."])
    await cognee.cognify()

    visualize_graph_path = os.path.join(
        os.path.dirname(__file__), ".artifacts", "graph_visualization.html"
    )
    await visualize_graph(visualize_graph_path)


if __name__ == "__main__":
    asyncio.run(main())
This simple example uses basic text data for demonstration. In practice, you can visualize complex knowledge graphs with thousands of nodes and relationships.

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What Just Happened

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Step 1: Create Your Knowledge Graph

await cognee.add(["Alice knows Bob.", "NLP is a subfield of CS."])
await cognee.cognify()
First, create your knowledge graph using the standard add → cognify workflow. The visualization works on existing graphs.

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Step 2: Generate Visualization

    visualize_graph_path = os.path.join(
        os.path.dirname(__file__), ".artifacts", "graph_visualization.html"
    )
    await visualize_graph(visualize_graph_path)
This creates an interactive HTML file with your knowledge graph. You can specify a custom path or use the default location.

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Quick Options

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Default Location

from cognee.api.v1.visualize.visualize import visualize_graph

# Writes HTML to your home directory by default
await visualize_graph()

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Custom Path

from cognee.api.v1.visualize.visualize import visualize_graph

# Writes to the provided file path (created/overwritten)
await visualize_graph("./my_graph.html")

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Tips

  • Large graphs: Rendering a very big graph can be slow. Consider building subsets (e.g., smaller datasets) before visualizing
  • Edge weights: If present, control line thickness; multiple weights are summarized and shown in tooltips
  • Static HTML: Files are static HTML; you can open them in any modern browser or share them as artifacts

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Additional examples

Additional examples about Graph visualization are available on our github.

Code Graph

Learn about code graph visualization

Core Concepts

Understand knowledge graph fundamentals

Custom Data Models

Learn about custom data models