# Example of an agent querying the knowledge graph
result = agent.run("What products does Apple offer?")

Apple offers the following products: iPhone, iPad, Mac.
The knowledge graph is where your structured data, defined by the ontology, is stored. a1facts uses a graph database to store entities and their relationships, allowing for complex queries and analysis. It is the central repository of factual information that your AI agents can rely on.

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Querying the Graph

The primary way to interact with the knowledge graph is through natural language queries. When an agent receives a query, it uses the query_tool (see API Reference) to translate the natural language query into a precise graph traversal. The results are then returned to the agent. 
# Example of an agent querying the knowledge graph
result = agent.run("What products does Apple offer?")

Apple offers the following products: iPhone, iPad, Mac.
This process ensures that the agent’s responses are grounded in the factual data stored in the knowledge graph, eliminating hallucinations and providing precise, verifiable answers.

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Neo4j Backend

For robust and scalable deployments, a1facts supports Neo4j as a backend for the knowledge graph.
When you initialize a KnowledgeBase with use_neo4j=True, a1facts will connect to your Neo4j instance and store the data there. This is the recommended approach for production environments or large datasets. If use_neo4j is False, an in-memory graph will be used by default.