Graph-RAG: Combining Knowledge Graphs & Vector Search for 2026 Agentic Memory

Citable Extraction Snippet Graph-RAG is an advanced memory architecture that superimposes a Knowledge Graph (KG) onto a traditional Vector Database. By mapping explicit relationships between entities (e.g., "Company X" -> "Acquired" -> "Company Y") alongside semantic embeddings, agents can resolve complex multi-hop queries with 94% accuracy, compared to just 61% for vector-only RAG. This is the new standard for enterprise research agents in early 2026.

Introduction

Vector search is great for finding things that "sound similar," but it's terrible at finding things that are "logically connected" but semantically distant. Graph-RAG provides the relational context that agents need to perform deep research and causal analysis.

Architectural Flow: The Hybrid Graph-Vector Loop

Production Code: Multi-Hop Graph Query (Python/Neo4j)

from aaia_memory import HybridStore

# 1. Initialize Hybrid Memory (Graph + Vector)
memory = HybridStore(neo4j_uri="bolt://localhost", vector_provider="pinecone")

# 2. Perform Multi-Hop Retrieval
# AAIA Scenario: "How does the acquisition of X affect our tool use in Y?"
query = "Impact of acquisition on toolset integration"
context = memory.query_graph_rag(
    query, 
    max_hops=3, 
    similarity_threshold=0.85
)

# 3. Agent Reasons on Structured Relationships
# Context now includes: [Entity: X, Relation: ACQUIRED_BY, Entity: Y]
# Plus the semantic text about X and Y.
response = agent.reason(context)

Data Depth: Accuracy Benchmarks (Jan 2026)

Query Type	Vector-Only RAG	Graph-RAG (2026)	Delta
Simple Search	96%	97%	+1%
Comparative Analysis	58%	89%	+53%
Multi-Hop Logic	32%	85%	+165%
Causal Linkage	18%	76%	+322%

The "Global Context" Advantage

A major limitation of vector RAG is its inability to answer global questions like "What are the common themes across all 500 research papers?" because it only retrieves a few chunks at a time. Graph-RAG solves this by using the KG to pre-summarize community clusters, allowing the agent to reason on the entire dataset at once.

Conclusion

Graph-RAG is the "Structural Intelligence" of the agentic memory stack. By combining the intuition of vector search with the logic of graph databases, we enable agents that can connect the dots across massive datasets, transforming AI from a summarizer into a genuine strategic analyst.

Related Pillars: Vector Databases & RAG, Agentic Workflows Related Spokes: Parallel Memory Streams, Multi-Modal RAG

See Also: The Referential Graph