Building Agentic GraphRAG Systems: Ontology-First Design for Agent Memory
Published on 05.05.2026
GraphRAG Is a Data Modeling Problem, Not a Retrieval Algorithm
TLDR: GraphRAG's real complexity lies in defining a proper ontology before touching any retrieval logic. Without schema discipline, LLM-extracted entities become unusable noise fast. This article walks through a production-grade architecture for agentic GraphRAG that connects to agents via an MCP server.
Building Agentic GraphRAG Systems
RDF vs. Property Graphs, and the Three Extraction Modes
TLDR: Property graphs beat RDF for GraphRAG and agent use cases. Extraction falls into three modes: structured (schema-guided LLM), semi-structured (metadata parsing), and unstructured (free-form LLM, useful only for schema discovery). Mixing them up introduces noise at production scale.
TLDR: The two core data modeling choices for a knowledge graph are an append-only log with a materialized view, or a single mutable collection. The append-only approach gives you versioning and temporal audit trails at the cost of RAM and operational complexity. The single mutable collection is the materialized view without the log.
TLDR: GraphRAG retrieval is a two-stage process: first, merge text and semantic search results using Reciprocal Rank Fusion to find entry points, then walk 2-3 hops across typed edges to expand the result set. The graph traversal step is the actual differentiator from standard RAG.
TLDR: GraphRAG becomes agentic when the agent reads from and writes to the knowledge graph autonomously through an MCP server. The search_memory tools bring only relevant graph slices into context. The write_memory tools run the full data and memory pipelines on demand, enabling continual learning from conversations.