# Knowledge Graph

> Browse 13 items tagged with Knowledge Graph.

## Items

- [FalkorDB GraphRAG](https://vectordb.ever.works/it/items/falkordb-graphrag) — A unified knowledge graph and vector database solution built on Redis that seamlessly integrates graph traversal and vector similarity search for building advanced GenAI applications with both relational reasoning and semantic search capabilities.
- [Zep](https://vectordb.ever.works/it/items/zep) — Context engineering and agent memory platform for AI agents with sub-200ms latency. Zep uses a temporal knowledge graph architecture to deliver relationship-aware context from chat history, business data, documents, and app events.
- [Temporal Knowledge Graph](https://vectordb.ever.works/it/items/temporal-knowledge-graph) — Knowledge graph architecture where facts have validity windows showing when they became true and were superseded. Core component of Zep AI's Graphiti and other agent memory systems.
- [GraphRAG](https://vectordb.ever.works/it/items/graphrag-microsoft) — Microsoft's approach to RAG that uses knowledge graphs to enhance retrieval. GraphRAG builds structured representations of documents enabling better context understanding and multi-hop reasoning for complex queries.
- [Neo4j Vector Search](https://vectordb.ever.works/it/items/neo4j-graph-vector-search) — Vector similarity search in Neo4j enabling GraphRAG by combining knowledge graphs with vector embeddings.
- [Graph RAG](https://vectordb.ever.works/it/items/graph-rag) — RAG architecture that combines knowledge graphs with vector databases, enabling multi-hop reasoning, relationship traversal, and structured knowledge representation for more accurate and explainable AI responses.
- [KRAGEN](https://vectordb.ever.works/it/items/kragen) — Knowledge Retrieval Augmented Generation ENgine that combines knowledge graphs with RAG using graph-of-thoughts prompting to solve complex biomedical problems with transparent, evidence-based reasoning.
- [GraphAcademy Knowledge Graph and GraphRAG Course](https://vectordb.ever.works/it/items/graphacademy-knowledge-graph-and-graphrag-course) — Free online courses from Neo4j GraphAcademy teaching how to build RAG systems on knowledge graphs. Covers fundamentals of combining graph databases with vector search for more accurate and explainable AI applications.
- [Neo4j GraphRAG Python](https://vectordb.ever.works/it/items/neo4j-graphrag-python) — Official Neo4j package for building graph retrieval augmented generation (GraphRAG) applications in Python. Enables developers to create knowledge graphs and implement advanced retrieval methods including graph traversals, text-to-Cypher, and vector searches.
- [Text-to-Cypher](https://vectordb.ever.works/it/items/text-to-cypher) — Natural language to Cypher query generation for Neo4j graph databases. Enables users to query knowledge graphs using plain English, critical component of GraphRAG systems for generating graph traversal queries from natural language questions.
- [Neo4j Vector Index](https://vectordb.ever.works/it/items/neo4j-vector-index) — Vector search capabilities in Neo4j graph database using HNSW indexing. Enables combining knowledge graphs with semantic similarity search for hybrid retrieval that leverages both graph relationships and vector embeddings.
- [Graphiti](https://vectordb.ever.works/it/items/graphiti) — Open-source framework for building temporally-aware knowledge graphs that power AI agent memory. Graphiti tracks when facts were true and maintains historical context, combining semantic search with graph traversal.
- [AllegroGraph](https://vectordb.ever.works/it/items/allegrograph) — A database that incorporates neuro-symbolic AI and offers a managed service (AllegroGraph Cloud) for neuro-symbolic AI knowledge graphs, indicating its relevance to advanced AI applications, likely including vector capabilities.

---

_Canonical page: https://vectordb.ever.works/it/tags/knowledge-graph_