RAG and Knowledge Engineering
GraphRAG — Stack
Graphrag › Stack
Technology landscape as of 2026-10-10. Knowledge cutoff May 2026.
Selection criteria
The dominant costs are the build pass and the maintenance of what it produces, so evaluate those before anything about query quality.
- Build cost per document, in model calls and in wall-clock time, extrapolated to the real corpus rather than to the pilot.
- Incremental update support. Whether adding a document updates the affected region or triggers a global recomputation, which decides whether the approach is viable on a live corpus.
- Entity resolution quality, because fragmentation and over-merging are the two defects that make traversal useless and neither is visible in a node count.
- Traversal control. Hop limits, edge-type restrictions, degree caps and deterministic expansion ordering, all of which are needed to bound a query.
- Lineage retention. Whether every node, edge and summary records the passages it came from, which is a prerequisite for any citation.
- Summarisation granularity. Whether regions are detected and summarised at several levels, and whether a query can choose the level.
- Composability with passage retrieval, including whether both can carry the same entitlement predicate.
- Query latency at depth, under concurrency, on a graph with realistic degree distribution rather than a synthetic one.
Options
| Option | Best for | Watch out for |
|---|---|---|
| Community-summary pipelines (Microsoft GraphRAG and its derivatives, LazyGraphRAG-style variants) | Corpus-wide thematic questions over a bounded, slowly changing corpus | Build cost is the headline risk; summary recomputation on update is the unsolved part |
| Graph construction and query in a property-graph database (Neo4j, Memgraph, TigerGraph, Amazon Neptune) | Traversal control, mature query languages and an operational store | Extraction and resolution are still yours; graph query performance depends heavily on degree distribution |
| RDF and SPARQL stores with inference (Apache Jena, GraphDB, Stardog, Virtuoso) | Domains with an existing ontology and a need for formal semantics | Modelling discipline is high; a model-extracted graph rarely fits a strict ontology without curation |
| Framework graph indexes (LlamaIndex property-graph and knowledge-graph indexes, LangChain graph transformers) | Prototyping a graph retriever against an existing pipeline | Defaults extract loosely and resolve naively; fine for a demo, not for a corpus |
| Vector search over node and edge descriptions, with traversal as a second step | Entering the graph from a free-text question without entity linking | Entry-point errors propagate through every hop; the traversal cannot recover a wrong seed |
| Hybrid stores combining vectors and graph in one engine | Keeping passages and structure in a single system with one predicate surface | Each half is usually weaker than a dedicated engine; verify both at your scale |
| No graph, with decomposition and deeper passage retrieval instead | Most corpora, most question populations | Genuinely corpus-wide and multi-relation questions remain unanswerable |