Agentic GraphRAG: AI's Logical Edge
Stephen Chin from Neo4j demonstrates how knowledge graphs solve the fundamental problem of LLM hallucinations in agentic systems. Learn Neo4j's MCP tools (Cypher query generation, agent memory), GraphRAG architecture patterns combining vector search with graph traversal, and a real enterprise case study achieving 85% employee adoption.
You're using the LLM for what it's good at, which is language translation. So the user can enter whatever convoluted question they want, which would never translate to a beautiful Cypher query.Stephen Chin, Neo4j (09:42)
GraphRAG vs baseline RAG
Cypher, Memory, Cloud APIs
Enterprise case study
The Problem: Agentic Systems That Hallucinate
Gartner predicts agentic systems are heading toward "doom and utter failure" due to hallucinations. Stephen demonstrates this with a concrete example: asking OpenAI's reasoning API about classroom capacity results in biased reasoning (assigning all girls to cooking class) and incorrect calculations anchored on irrelevant articles about the "non-attacking kings problem."
"We have a problem with a lot of agentic systems where they're not meeting use cases as was you know Gartner's prediction of doom and utter failure. And they have a lot of hallucinations in them."
Gartner's prediction on agentic systems failure rate
Watch (01:18)"The LLM is good at extrapolating information like doing language tasks, figuring out things and it gives the impression of intelligence where there's no real intelligence. There's no real kind of human reasoning behind it."
LLM limitations and false impression of intelligence
Watch (02:42)"The fact the LLM has inserted biases, it's done incorrect reasoning along the way. This means you're going to get the wrong business results and it's very hard to figure this out."
Business impact of LLM hallucinations
Watch (03:00)"And so we overascribe things it can do and there's a bunch of things it can't do well. Now those are things that knowledge graphs are actually really good at."
Knowledge graphs complement LLM weaknesses
Watch (03:24)Business Impact of Hallucinations
In drug discovery, supply chain optimization, or financial analysis, hallucinations don't just produce wrong answers—they produce catastrophically wrong business decisions. The fundamental issue is that LLMs give the impression of intelligence without real reasoning. Knowledge graphs provide the structured reasoning and factual accuracy that LLMs lack.
MCP + Knowledge Graphs: Modular Agentic Architecture
Monolithic agentic architectures are hard to maintain, secure, and scale. The Model Context Protocol (MCP) solves this by separating client and server, enabling modular tool integration. Neo4j has built three MCP servers that plug into any agentic framework.
"The challenge with this is that it's a very monolithic architecture. It's hard to maintain. It's hard to swap out the tools and it also kind of puts you in a situation where you can't secure the system."
Problems with monolithic agentic architectures
Watch (04:33)"So a good way to solve this is using MCP. You kind of use MCP as your tools where your agents are talking to them."
MCP as solution to monolithic architecture
Watch (04:38)"All those memory vendors that just spoke—Zep, Cognney, MemGPT—they all run on top of Neo4j. They use Neo4j as the core graph database."
Neo4j as foundation for memory vendors
Watch (05:40)Cypher Query Tool
Generates Cypher queries from natural language prompts. LLM translates user questions into graph queries automatically.
Memory Module
Graph-based agent memory. Stores and retrieves conversational context, semantic relationships, and episodic memories.
Cloud API Tool
Provision Neo4j databases and configure graph infrastructure programmatically from your agents.
Framework Agnostic: Neo4j integrates with LangChain, LangGraph, LlamaIndex, Haystack, and others. Major memory vendors (Zep, Cognney, MemGPT) all use Neo4j as their core graph database.
GraphRAG: When Vector Search Meets Knowledge Graphs
The key insight: similarity is not relevance. Vector search is excellent at language translation but doesn't understand semantic relationships. GraphRAG combines both: vector embeddings translate queries into concepts, then graph traversal pulls related nodes and relationships for true context.
"When you use GraphRAG, you get some advantages in terms of the results coming back and typically a lower rate of hallucinations."
GraphRAG benefits over baseline RAG
Watch (05:26)"So similarity is not relevance. It doesn't mean it actually understands the problem."
Key insight: vector similarity ≠ semantic relevance
Watch (06:54)"Because all you want to do is make sure the LM gets things higher up in the buffer for context windows. Even if LLMs now have larger context windows basically what the evals show is they ignore most of it and they look at the stuff at the top."
LLM context window behavior - focus on top content
Watch (09:13)The GraphRAG Pattern
- User asks question (e.g., "What is emphysema?")
- LLM performs vector search to find similar concepts (translates language to vector space)
- Vector embeddings point to graph nodes - embeddings stored as properties on nodes
- Graph traversal pulls related context - diagnoses, conditions, treatments, relationships
- Enriched context passed to LLM for accurate, grounded response
Context Window Reality
Even with large context windows, LLMs ignore most content and focus on the top. Pre-filtering and post-filtering strategies are critical to ensure relevant information appears at the top of the context buffer.
Community Detection
Use community grouping algorithms (Louvain, Label Propagation) to identify clusters of related nodes. Combine with cosine similarity to determine relevance. Graph data science algorithms enhance retrieval beyond pure vector similarity.
Architecture Pattern: Use LLMs for What They're Good At
The most powerful GraphRAG pattern leverages LLMs for language translation while delegating reasoning and knowledge retrieval to graphs. Users can ask convoluted questions that would never translate to clean Cypher queries—the LLM handles the translation, the graph handles the semantics.
"Basically what you're doing is you're using the LM for what it's good at, which is language translation. So the user can enter whatever convoluted question they want, which would never translate to a beautiful Cypher query."
LLM for language translation, graphs for semantic understanding
Watch (09:42)"And then you can go to the graph and you can say well these embeddings all point to this node in the graph. So it's probably about emphysema. Now I want to pull back the nodes which are either like you could use cosine similarity or you could use community grouping algorithms."
Vector-to-graph traversal pattern for context retrieval
Watch (09:55)GraphRAG vs. Baseline RAG: Healthcare Example
Direct LLM
Generic responses, potential hallucinations, no grounding in medical knowledge
Baseline RAG (Vector Only)
Better results but incomplete. Vector similarity ≠ relevance. Missing relationships between diagnoses and conditions
GraphRAG (Vector + Graph)
Accurate, comprehensive. Pulls emphysema node + related diagnoses + conditions + treatments. Lower hallucination rate
Enterprise Case Study: 85% Employee Adoption
CLM, a Neo4j customer, replaced all their SaaS systems with a GraphRAG implementation for their enterprise wiki, HR systems, and internal documentation. The results demonstrate real-world viability of GraphRAG at scale.
"They replaced all of their SaaS systems with a GraphRAG project. They're one of our customers. They took an enterprise wiki's HR systems internal documentation. 250k employee questions after the first year. 2,000 daily queries processed. 85% employee adoption."
CLA enterprise case study results
Watch (09:30)Questions in First Year
Daily Queries
Employee Adoption
Why This Matters
85% employee adoption is extraordinarily high for enterprise knowledge systems. This proves GraphRAG isn't just theoretical—it delivers real value in production. The combination of accurate retrieval (graph) and natural language understanding (LLM) creates a system employees actually trust and use.
Key Takeaways for AI Engineers
LLMs Hallucinate, Graphs Don't
The Problem
- •Gartner predicts agentic systems are doomed due to hallucinations
- •LLMs give impression of intelligence without real reasoning
- •Business impact: wrong decisions in critical domains (drug discovery, supply chain)
- •Knowledge graphs provide structured reasoning and factual accuracy
MCP Enables Modular Agentic Architecture
Solution
- •Monolithic architectures are hard to maintain, secure, and scale
- •MCP separates client and server for plug-and-play tool integration
- •Neo4j MCP tools: Cypher query generation, memory module, cloud APIs
- •Framework agnostic: LangChain, LangGraph, LlamaIndex all supported
Similarity ≠ Relevance
GraphRAG Insight
- •Vector search is great at language translation, not semantic understanding
- •GraphRAG combines: vector embeddings → graph nodes → related context
- •Result: Lower hallucination rate, more accurate responses
- •Example: Healthcare query pulls emphysema + diagnoses + conditions + treatments
Use LLMs for Language, Graphs for Reasoning
Architecture Pattern
- •LLMs translate user questions into vector embeddings
- •Graph traversal pulls semantically related nodes and relationships
- •Users can ask convoluted questions—no clean Cypher needed
- •Pre/post-filtering critical: LLMs focus on top of context window
GraphRAG Works in Production
Proof
- •CLA enterprise case study: 250K questions, 2K daily queries
- •85% employee adoption—extraordinarily high for enterprise systems
- •Replaced all SaaS systems with GraphRAG for wiki, HR, docs
- •Neo4j underlies major memory vendors: Zep, Cognney, MemGPT
Getting Started Resources
Learning
- •Neo4j Certified Developer program: free exam, t-shirt, LinkedIn badge
- •Nodes 2024 conference: 24 hours of free content across 3 time zones
- •Stephen Chin writing GraphRAG book with O'Reilly (pre-release coming soon)
- •Graph Academy courses on building chatbots with LLMs and GraphRAG
Source Video
Stephen Chin
Developer Relations Team Lead • Neo4j
Author, upcoming GraphRAG book with O'Reilly
Agentic GraphRAG: AI's Logical Edge
Research Note: All quotes in this report are timestamped and link to exact moments in the video for validation. This analysis covers LLM hallucination problems, Neo4j's MCP tool ecosystem, GraphRAG architecture patterns (vector + graph), enterprise implementation with 85% adoption, and practical getting started resources.
Key Concepts: GraphRAG, Neo4j, MCP (Model Context Protocol), knowledge graphs, LLM hallucinations, vector search, agentic systems, Cypher query language, agent memory, enterprise AI, LangChain, LangGraph, community detection algorithms