Loading content
Agentic GraphRAG delegates the selection and coordination of retrieval tools to an AI agent. Depending on the question, the agent can use vector search, Text-to-Cypher, or other graph queries, evaluate intermediate results, and adapt the search path. The pattern suits heterogeneous and multi-step questions but increases the need for constraints, observabilit…
Use this profile to understand the building block briefly, place it in the model, and open related building blocks.
Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Agentic GraphRAG is a retrieval pattern in which an AI agent selects the next search tool, evaluates intermediate results, and assembles the answer step by step.
The pattern emerges from GraphRAG systems that cannot rely on a single fixed retriever and instead need to switch among multiple retrieval actions. Neo4j's GraphRAG documentation describes interchangeable retrievers such as VectorRetriever, Text2CypherRetriever, HybridRetriever, and ToolsRetriever, plus custom implementations. This lets an agent coordinate the right access path, intermediate checks, and course correction for each question.
Think of Agentic GraphRAG as a control room with feedback. The agent reads the question, chooses a tool, inspects what comes back, and decides whether another retrieval step is needed. Vector search, graph queries, and Text-to-Cypher are different routes through the same control loop. The answer is produced only after enough reliable context has been gathered.
The agent decides which retrieval step should happen next.
Multiple search methods are combined in a controlled sequence.
Semantic similarity finds text or document passages that fit the question.
Natural language is translated into graph queries so relationships can be used directly.
The graph provides linked facts and relationship context for multi-step questions.
This pattern is useful for heterogeneous, multi-step questions when it is not obvious whether text search, graph queries, or both are the better source. It also helps when answer quality depends on intermediate selection and context enrichment. The trade-offs are more latency, higher cost, and a stronger need for limits, observability, and error control; without clear rules, the agent can wander unnecessarily.
Where this building block is located in the topic model.
No structure path available.
Explore how this building block connects to concepts, methods, technologies, and tools.
These sources establish the term and its professional meaning.
All direct connections of the current building block in a compact text view.
This classification shows where the building block typically matters, how demanding it is, and what kind of impact it has in the model.
The level within the organization (enterprise, domain, team) at which the AssetBlock is applied.