Data as of Aug 25, 2026 · Based on 695 AI responses · See how Parse measures this
AI Knowledge Graph Platforms
Parse
https://parse.gl
| # | Brand | What AI says | Mention rate |
|---|---|---|---|
| 1 | The industry standard graph database for LLM-driven knowledge graph storage and GraphRAG. | 83% | |
| 2 | Dominant framework for connecting LLMs to knowledge graph traversal and extraction. | 64% | |
| 3 | High-performance graph database optimized for real-time analytics and LLM-ready graph construction. | 56% | |
| 4 | Primary provider of LLM intelligence for entity extraction and natural language reasoning. | 43% | |
| 5 | Critical framework for document ingestion and structured knowledge graph property indexing. | 42% | |
| 6 | 27% | ||
| 7 | 27% | ||
| 8 | 26% | ||
| 9 | 26% | ||
| 10 | 23% | ||
| 11 | 15% | ||
| 12 | 14% | ||
| 13 | 14% | ||
| 14 | 11% | ||
| 15 | 11% | ||
| 16 | 10% | ||
| 17 | 9% | ||
| 18 | Specialized framework for global reasoning and community-based document summarization over graph structures. | 7% | |
| 19 | 6% | ||
| 20 | 6% | ||
| 21 | 6% | ||
| 22 | 6% | ||
| 23 | 5% | ||
| 24 | 5% | ||
| 25 | 5% |
Who wins on each AI
ChatGPT favors specialized development tools like Tigergraph and Stardog, while Google AI Overviews places higher emphasis on emerging open-source RAG tools like LightRAG and Microsoft's ecosystem.
Sources AI cited
neo4j.com is the page AI reaches for most here, cited in 75% of analyzed answers.
“standalone graph database” → “AI-native GraphRAG platform”
Declined as workflows shifted toward LLM-only pipelines since 2026.
Rose from absent at launch to #3 since 2026.
Increased from 6% to 39% mention rate between late 2025 and 2026.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| 82% | 80% | ||
| 64% | 75% | ||
| 58% | 48% | ||
| 49% | 43% | ||
| 38% | 34% |
The two models disagree most about Neo4j LLM Knowledge Graph Builder (ChatGPT #20, Google #7) and ArangoDB (ChatGPT #10, Google #22).
LangChain leads the AI knowledge graph space by providing the foundational orchestration for LLM-driven entity extraction. Neo4j follows closely behind in a contested landscape as the standard storage and reasoning backend for graph-augmented AI.
Across 695 AI responses, Neo4j is mentioned most, named in 83% of them, followed by LangChain (64%) and Memgraph (56%).
Parse measures each brand's mention rate — the share of answers naming it — across 695 AI responses to this market's buyer questions. Answers are collected daily and the ranking is published weekly.
Brands enter the ranking when AI answers mention them. Parse collects answers daily and publishes the re-measured set weekly, so new brands appear as AI starts recommending them.
AI answers have shifted from proposing niche NLP libraries like spaCy toward integrated LLM-backed construction tools. Platforms like
Neo4j LLM Knowledge Graph Builder have become the consistent top-cited solutions across both ChatGPT and Google AI Overviews throughout 2026.
AI answers have shifted from proposing niche NLP libraries like spaCy toward integrated LLM-backed construction tools. Platforms like
Neo4j LLM Knowledge Graph Builder have become the consistent top-cited solutions across both ChatGPT and Google AI Overviews throughout 2026.
Responses consistently define the optimal stack as a graph database like Neo4j paired with an orchestration layer like
or . Across 2026, the framing evolved toward GraphRAG architectures integrating both vector and graph retrieval.
Responses consistently define the optimal stack as a graph database like Neo4j paired with an orchestration layer like
LangChain or
LlamaIndex. Across 2026, the framing evolved toward GraphRAG architectures integrating both vector and graph retrieval.