GraphRAG in Production: Neo4j, Microsoft, and Graphiti Implementations Compared

Why GraphRAG Took Over Specific Use Cases

Vector RAG retrieves chunks. The chunks may or may not capture the entities and relationships a question actually depends on. GraphRAG builds a knowledge graph of the corpus and retrieves subgraphs — entities plus their connections — instead of raw chunks.

For multi-hop questions ("which products did the customers who churned in March also buy?") and questions about relationships, GraphRAG outperforms vector RAG by 10-30 percentage points on standard benchmarks. By 2026 three production implementations dominate.

The Three Implementations

flowchart TB
    Neo[Neo4j-based<br/>LangChain + LlamaIndex] --> StableProd[Strength: maturity, ecosystem]
    MS[Microsoft GraphRAG<br/>Open-source] --> Comm[Strength: community-detection, summarization]
    Graphiti[Graphiti<br/>Zep] --> Temp[Strength: temporal awareness]

Neo4j + LangChain / LlamaIndex

The most common path in 2026. Neo4j as the graph store, LangChain or LlamaIndex as the orchestration layer. Mature, well-documented, deep partner ecosystem (vector indexes inside Neo4j, Cypher generation from LLMs, etc.).

• Strengths: production-grade graph DB, vibrant ecosystem, vector indexes inside the same store
• Weaknesses: you build the ingestion pipeline yourself
• Best for: enterprises that want full control over the schema and ingestion logic

Microsoft GraphRAG

Microsoft Research's open-source pipeline. Distinctive feature: it runs hierarchical community detection on the graph and generates per-community summaries. Queries that span the corpus get answered against community summaries; precise queries get answered against subgraphs.

• Strengths: best-in-class for whole-corpus questions, community summaries enable broad questions vector RAG fails on
• Weaknesses: heavier index pipeline; expensive at LLM-scale graph construction
• Best for: research / analysis use cases where the corpus has natural communities (news archives, research papers)

Graphiti

Zep's open-source temporal-aware GraphRAG. Tracks bi-temporal facts — what was true, when it was true, when we learned about it — making it especially good for memory and conversational systems.

• Strengths: native temporal awareness, designed for agent memory, real-time updates
• Weaknesses: smaller ecosystem; opinionated schema
• Best for: agent memory systems, customer-history applications, anything where "when was this true" matters

How They Build the Graph

sequenceDiagram
    participant Doc as Source Document
    participant LLM as Extractor LLM
    participant Graph as Graph Store
    participant Comm as Community Detector
    Doc->>LLM: extract entities + relationships
    LLM-->>Graph: upsert entities + edges
    Graph->>Comm: cluster (Microsoft GraphRAG)
    Comm->>Graph: write community summaries

All three use an LLM to extract entities and relationships from raw text. They differ in:

• Schema strictness (Neo4j is whatever you define; Microsoft is freer; Graphiti is opinionated)
• Whether they run community detection (Microsoft yes, others no by default)
• Whether they track time (Graphiti yes, others can but you build it)

Query Patterns

Local Query (Subgraph Retrieval)

A precise question retrieves the subgraph around the mentioned entities and feeds it to the LLM. All three handle this well.

Global Query (Community Summary Retrieval)

A broad question ("what are the main themes in this corpus?") retrieves community summaries, not subgraphs. Microsoft GraphRAG is purpose-built for this; the others can be adapted with extra work.

Temporal Query

"What did the customer prefer six months ago?" Graphiti handles this natively; Neo4j and Microsoft GraphRAG require explicit time properties on edges.

Cost Reality

GraphRAG is more expensive at indexing time than vector RAG — you make a lot of LLM calls to extract entities and relationships. Typical cost is 5-20x vector RAG indexing cost.

At query time it is comparable or cheaper than vector RAG, because you retrieve focused subgraphs instead of large chunk lists.

For corpora that change rarely, the indexing premium pays back. For high-velocity corpora, the index cost can dominate.

When to Use Which

flowchart TD
    Q1{Need to answer<br/>whole-corpus questions?} -->|Yes| MSc[Microsoft GraphRAG]
    Q1 -->|No| Q2{Need temporal<br/>fact tracking?}
    Q2 -->|Yes| Gc[Graphiti]
    Q2 -->|No| Neo4jc[Neo4j-based]

For most enterprises in 2026, Neo4j-based is the right default. Microsoft GraphRAG when broad-corpus reasoning is core. Graphiti when memory or temporal context dominates.

The Hybrid That Wins

The pattern in production: vector RAG for chunk-level recall, GraphRAG for entity-level reasoning, fused at query time. Neither alone wins; the combination outperforms either by 5-15 percent on most enterprise benchmarks.

Sources

• Microsoft GraphRAG paper — https://arxiv.org/abs/2404.16130
• Microsoft GraphRAG repository — https://github.com/microsoft/graphrag
• Neo4j GraphRAG documentation — https://neo4j.com/labs/genai-ecosystem
• Graphiti temporal graph — https://github.com/getzep/graphiti
• "GraphRAG vs Vector RAG benchmarks 2025" — https://blog.langchain.dev