> For the complete documentation index, see [llms.txt](https://synaptiq-systems-1.gitbook.io/synaptiq-systems/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://synaptiq-systems-1.gitbook.io/synaptiq-systems/~/changes/GV7eJCpt5kF6deUPqR2y/knowledge-graph-integration.md).

# Knowledge Graph Integration

#### Knowledge Graph Integration

**SynaptiQ Systems** supports the integration of knowledge graphs to enable agents to store, query, and reason about structured information. This feature enhances the decision-making capabilities of agents by providing a persistent and queryable knowledge base.

**Key Features**

* **Concept Storage**: Agents can store concepts, attributes, and relationships in a graph structure.
* **Reasoning and Querying**: Agents can query the knowledge graph to retrieve relevant information or relationships.
* **Visualization**: The knowledge graph can be visualized for better understanding and debugging.

***

#### Example Workflow

**1. Add Knowledge to the Graph**

**Code Example:**

```python
pythonCopy codefrom src.utils.knowledge_graph import KnowledgeGraph

# Initialize the Knowledge Graph
knowledge_graph = KnowledgeGraph()

# Add a concept
knowledge_graph.add_concept("AI Agent", {"role": "worker", "status": "active"})

# Add a relationship between concepts
knowledge_graph.add_relationship("AI Agent", "Swarm", "belongs_to")
```

**2. Query the Knowledge Graph**

**Code Example:**

```python
pythonCopy code# Query a concept
result = knowledge_graph.query_concept("AI Agent")
print(f"Attributes of AI Agent: {result}")

# Query relationships
relationships = knowledge_graph.query_relationships("AI Agent")
print(f"Relationships of AI Agent: {relationships}")
```

**3. Visualize the Knowledge Graph**

**Code Example:**

```python
pythonCopy code# Visualize the graph
knowledge_graph.visualize_graph(output_path="knowledge_graph.png")
print("Knowledge graph saved as knowledge_graph.png")
```

***

#### Benefits of Knowledge Graphs in **SynaptiQ Systems**

* **Enhanced Reasoning**: Agents can use structured knowledge to make more informed decisions.
* **Collaboration**: Agents can share knowledge across the swarm, improving collective performance.
* **Persistent Memory**: Knowledge graphs serve as a long-term memory for agents.

***

#### Best Practices

* **Use Attributes Effectively**: Add meaningful attributes to concepts for better querying and reasoning.
* **Structure Relationships Clearly**: Define relationships that reflect real-world connections (e.g., "belongs\_to", "depends\_on").
* **Regular Updates**: Periodically update the graph to reflect the latest knowledge and task history.