Knowledge Graphs

Knowledge graphs represent information as a network of entities and their relationships, providing structured, queryable knowledge that complements the unstructured retrieval of vector-based RAG systems. In agent systems, knowledge graphs enable precise relationship queries ("what tools does this agent depend on?") that vector similarity search handles poorly, because they preserve the explicit connections between concepts rather than relying on semantic proximity. The trade-off is construction cost: building and maintaining an accurate knowledge graph requires structured extraction, entity resolution, and ongoing updates — significantly more effort than embedding documents into a vector database. Knowledge graphs are most valuable when your domain has complex, structured relationships that the agent needs to traverse, rather than simply finding "related" content. This concept connects to vector databases for the unstructured alternative, graph-vs-vector RAG for the architectural comparison, and retrieval-augmented generation for the broader pattern of external knowledge injection.