Memgraph’s GraphRAG: Your Shortcut to Personalized GenAI Apps

Look, everyone knows the drill—knowledge workers burn through 20-30% of their day just looking for information. That’s hours wasted on mundane searches instead of solving actual problems. But what if you could immediately stop digging and start getting real answers from your data?

That’s where GraphRAG comes in. Memgraph's solution fuses the power of graph databases, LLMs, and Retrieval-Augmented Generation (RAG) to pull together the correct information quickly. Much less fact-checking, with minimal hallucinations. Just real-world, context-driven answers that matter to you.

With Memgraph’s in-memory graph database at the core, GraphRAG integrates knowledge graphs, advanced algorithms, and LLMs to create more intelligent AI applications that don’t just regurgitate data—they understand it.

What’s GraphRAG?

GraphRAG combines the power of knowledge graphs, LLMs, and dynamic algorithms to enhance RAG systems. Instead of just scraping the surface, GraphRAG dives deep into your knowledge graph to extract accurate, contextually relevant insights, minimizing hallucinations and delivering answers grounded in your proprietary data.

Memgraph sits at the heart of this triangle, acting as the context engine for your GenAI applications. With Memgraph, developers can build personalized chatbots, agents, and AI systems faster and easier.

Why Should You Care?

• LLMs are great at generating responses, but their context windows and attention spans limit them. They need your domain-specific data to give relevant, accurate answers, and they can’t just process massive datasets by themselves.
• GraphRAG offers a way to feed LLMs with precisely the right data—your data. This combination eliminates the guesswork and provides relevant results every time.
• Memgraph powers this experience by integrating graphs, which excel at modeling complex relationships between entities. In a world where context is key, graphs are your superpower.

Memgraph Features for Building GraphRAG-Powered AI Apps

With Memgraph 3.0, developers can build AI apps, chatbots, and agents.

Refer to GraphRAG with Memgraph for detailed insights into how it all works.

1. Vector Search

Memgraph 3.0 introduces vector search, enabling similarity and relevance-based graph search in a unified system. This feature is perfect for pinpointing the most relevant nodes in your knowledge graph.

2. Advanced Algorithms at Your Disposal

Memgraph comes pre-loaded with powerful algorithms that ensure your AI is responding with real, structured relationships. This includes:

• Community Detection (Louvain, Leiden, and LabelRankT for dynamic updates)
• Centrality Algorithms (PageRank, Katz, and more)
• Graph Traversals to expand search across complex datasets

These tools let your AI capture the hidden patterns in your data, ensuring higher accuracy and deeper insights than simple keyword-based systems.

3. GraphChat: Natural Language Meets Graph Power

GraphChat in Memgraph Lab lets you query your graph database in plain English—no Cypher expertise is required. It translates your questions into graph queries, retrieves precise answers from your knowledge graph, and cuts through the noise to deliver insights that matter.

For GraphRAG workflows, GraphChat bridges natural language and graph intelligence. It integrates LLMs to add context-rich responses, enabling real-time exploration of data. With DeepSeek support, you can now connect advanced models directly to GraphChat, pushing the boundaries of what’s possible with your knowledge graph.

Read more about GraphChat in docs.

4. Built-In Integrations with LangChain and LlamaIndex

LangChain and LlamaIndex integrations make it easier to create end-to-end AI workflows with Memgraph. You can perform multi-hop retrieval to gather data from different sources, making your AI capable of answering even the most complex, multi-faceted questions.

Read more about Memgraph integrations in docs.

Check out release notes and release announcement to see which other major changes we’ve pushed out with Memgraph 3.0.

Why Memgraph Is Key for GraphRAG

• Easy setup. Get started quickly with minimal hassle.
• Fast response times. Handle real-time data and queries effortlessly.
• Flexible adaptability. Works seamlessly with various data models, integrations, and workflows.
• Scalable to your data needs. From small projects to massive datasets, Memgraph scales with you.

Now, let’s see how our users are using Memgraph to build GraphRAG.

Real-World Success Stories

Cedars-Sinai: Advancing Alzheimer’s Research

Cedars-Sinai built a GraphRAG system on top of their Alzheimer’s Disease Knowledge Base (AlzKB). It enables precise, context-rich querying, supporting groundbreaking research with multi-hop reasoning and accurate predictions.

Learn more about this story: Using Memgraph for Knowledge-Driven AutoML in Alzheimer’s Research at Cedars-Sinai.

Microchip: Smarter Customer Support

Microchip uses Memgraph to create a GraphRAG-powered assistant, delivering efficient, context-aware customer support at scale.

Learn more about this story: How Microchip Uses Memgraph’s Knowledge Graphs to Optimize LLM Chatbots.

Precina Health: Personalized Diabetes Care

Precina Health uses Memgraph for real-time patient data retrieval, enabling personalized diabetes treatment plans for underserved communities.

Learn more about this story: How Precina Health Uses Memgraph and GraphRAG to Revolutionize Type 2 Diabetes Care with Real-Time Insights.

🚀 Even NASA has integrated Memgraph into their AI workflows. Use case breakdown coming soon, stay tuned!

David Meza, Head of Analytics Human Capital at NASA:

At NASA, we are integrating Memgraph in our Human Capital Intelligent Query System to efficiently manage our human capital knowledge graph, enabling faster retrieval of relevant information for employees. Its graph-based approach allows us to keep track of real-time updates, ensuring accurate connections between various policy documents and data sources. By incorporating Memgraph into our RAG process, we enhance our system’s responsiveness and better address NASA’s knowledge extraction without requiring extensive manual data coordination.

Ready to Build Smarter AI Apps?

It’s time to move past generic AI and create applications that truly understand data and knowledge in your organization.

Explore the future of GenAI development today: Get Started with Memgraph.