Generate Mermaid diagrams of concept relationships to visually explore your Maenifold knowledge graph. This tool transforms abstract concept connections into interactive visual diagrams, enabling pattern recognition and knowledge structure understanding through graph visualization of co-occurrence relationships extracted from your memory files.
| Parameter | Type | Required | Description | Example |
|---|---|---|---|---|
| conceptName | string | Yes | Central concept for diagram generation (NOT a file!) | ”Machine Learning” |
| depth | int | No | Graph depth for relationship inclusion (default: 2) | 1 |
| maxNodes | int | No | Maximum nodes to include in diagram (default: 30) | 50 |
{
"conceptName": "Neural Networks"
}
Generates Mermaid diagram showing Neural Networks relationships with default 2-depth exploration and 30 node limit.
{
"conceptName": "GraphRAG",
"depth": 1,
"maxNodes": 15
}
Creates focused diagram showing only immediate (1-hop) GraphRAG relationships, limited to 15 most connected concepts.
{
"conceptName": "Transformer Architecture",
"depth": 2,
"maxNodes": 50
}
Generates comprehensive diagram with 2-hop relationship exploration, including up to 50 nodes for broad conceptual coverage.
{
"conceptName": "Attention Mechanism",
"depth": 1,
"maxNodes": 10
}
Creates simple diagram showing only direct Attention Mechanism relationships, limited to 10 strongest connections for clarity.
Generate visual aids for research presentations showing conceptual relationships and knowledge structure around key topics.
Create diagrams to identify sparsely connected concepts or missing relationships in your knowledge graph structure.
Visualize how concepts naturally cluster through relationship density, revealing knowledge organization patterns in your memory system.
Generate diagrams as starting points for interactive knowledge exploration, using visual connections to guide deeper investigation.
Create visual supplements for written analysis, showing relationship structures that support textual explanations and insights.
Generate concept relationship diagrams before structured thinking sessions to provide visual context for systematic exploration.
Default output format showing concepts as nodes with weighted edges representing co-occurrence strength:
graph TD
Neural_Networks -->|5| Deep_Learning
Neural_Networks -->|3| Machine_Learning
Neural_Networks -->|2| Backpropagation
When concept exists but has no relationships, shows isolated node with explanatory message:
graph TD
Isolated_Concept [No connections found]
For concepts with many connections, diagram shows strongest relationships first up to maxNodes limit, creating manageable visualizations.
Use smaller node limits to create clear, focused diagrams highlighting core concept relationships without visual complexity.
Generate broader diagrams for complete conceptual landscape visualization, showing extensive relationship networks and patterns.
Edge weights (co-occurrence counts) indicate:
Start research with Visualize to understand conceptual landscape, then use BuildContext for detailed relationship analysis.
Include visualization output in Sequential Thinking sessions as visual reference for relationship-aware analysis.
Combine visual diagrams with textual search and context building for comprehensive knowledge exploration strategies.
Generate diagrams as research artifacts demonstrating knowledge graph exploration and conceptual relationship discovery.
Cause: Concept doesn’t exist in knowledge graph or requires different spelling/capitalization
Solution: Run Sync to update graph from recent memory files, verify concept exists with SearchMemories, or check exact spelling
Cause: Knowledge graph database is empty or outdated relative to memory files
Solution: Execute Sync tool to extract [[concepts]] from memory files and build/update relationship graph
Cause: Concept exists in isolation without co-occurring with other [[concepts]] in memory files
Solution: Create memory files that mention this concept alongside related [[concepts]] to establish graph connections
Cause: Central concept has many relationships creating visually overwhelming diagrams
Solution: Reduce maxNodes parameter (try 15-20) or use depth=1 for simpler relationship visualization
Cause: Concept names with special characters or formatting incompatible with Mermaid syntax
Solution: Tool automatically sanitizes names, but complex Unicode or symbols may require concept renaming
Cause: Related concepts exist in separate memory files without co-occurrence patterns
Solution: Create bridging memory files mentioning related [[concepts]] together to establish visual connections
Cause: High maxNodes values with dense concept graphs create complex diagrams
Solution: Start with smaller limits (10-20 nodes) and increase gradually to find optimal visualization complexity
Generated Mermaid syntax renders in:
Use generated Mermaid syntax to:
.md files with Mermaid code blocks for version controlGenerate multiple diagrams around related concepts to build comprehensive visual knowledge maps showing interconnected conceptual territories.
Compare multiple concept visualizations to identify relationship patterns and knowledge graph density variations across different domains.
Create periodic visualizations of key concepts to track how relationships evolve as memory files and knowledge grow over time.
Visualize concepts that appear in multiple domains to identify knowledge bridges and interdisciplinary connection patterns.
Visualize follows Maenifold’s Ma Protocol principles:
This tool transforms your memory files’ concept relationships into visual knowledge maps, revealing the hidden structure and connection patterns in your accumulated knowledge through interactive Mermaid diagram generation.