## [Knowledgeable Agents with FalkorDB Graph RAG](https://docs.ag2.ai/0.14.0/docs/blog/2024/12/06/FalkorDB-Structured/)

AG2 v0.5—Structured Output & GraphRAG: Transform LLM Responses and Knowledge - YouTube

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**TL;DR:** \* We introduce a new ability for AG2 agents, Graph RAG with FalkorDB, providing the power of knowledge graphs \* Structured outputs, using OpenAI models, provide strict adherence to data models to improve reliability and agentic flows \* Nested chats are now available with a Swarm

### [FalkorDB Graph RAG](https://docs.ag2.ai/0.14.0/docs/blog/2024/12/06/FalkorDB-Structured/#falkordb-graph-rag)

Typically, RAG uses vector databases, which store information as embeddings, mathematical representations of data points. When a query is received, it's also converted into an embedding, and the vector database retrieves the most similar embeddings based on distance metrics.

Graph-based RAG, on the other hand, leverages graph databases, which represent knowledge as a network of interconnected entities and relationships. When a query is received, Graph RAG traverses the graph to find relevant information based on the query's structure and semantics.