Give Your AI Apps the Context They Lack

Generic chatbots don’t cut it anymore. Build personalised AI applications based on the data from your Enterprise knowledge base.

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Personalized AI with Context

LLMs successfully predict words. But, they don't have access to your data and can't read your mind to get context. That's why they hallucinate.

GraphRAG pipelines feed LLMs by combining search techniques and graph algorithms. You get accurate, context-rich answers that go beyond the generic LLM outputs everyone defaults to since their models were trained on the same public data.

Vectors and Graphs joined forces!

AI Apps for Enterprise need context, relevance and structure. Use Memgraph as a context engine to go beyond surface-level results of keyword matches. Vector search finds semantically similar nodes, graph traversal uncovers deep relationships, and community detection reveals hidden structures.

Ceders-Sinai

Advancing on Alzheimer’s research

Microchip

Smarter customer support

Precina Health

Personalized diabetes care

Why GraphRAG with Memgraph?

LLM Agnostic, Bring Your Own Model (BYOM)

No lock-ins. It works with any LLM—OpenAI’s GPT models, Anthropic’s Claude, Meta’s LLaMA, or DeepSeek. Use the best model for the job, swap or add more as needed, and keep your AI stack flexible.

Dig Deeper for In-Depth Context

LLMs skim the surface. Memgraph dives deep, retrieving connected, structured data instead of isolated facts. Vector search finds similar ideas, graph traversal uncovers relationships, and dynamic filtering ranks the most relevant insights—so AI generates context-rich, precise responses instead of shallow keyword matches.

Dynamic Algorithms for Changing Data

Static graphs are fine—until your data changes. Memgraph’s dynamic graph algorithms let you process updates in real-time, avoiding costly recomputations. Designed for high-speed, production-scale environments, these algorithms adapt instantly as your graph evolves.

Secure, Production-Ready, and Enterprise-Supported

AI apps can’t afford security gaps. We ensure fine-grained access control, so sensitive data doesn’t leak through LLM queries. Whether you’re dealing with compliance, enterprise security, or high-stakes AI—Memgraph is built for production with the support to match.

“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.”

David Meza, Head of Analytics Human Capital at NASA

Memgraph Tools to build GraphRAG

Memgraph as Your Context Engine

Use Memgraph as the backbone for your GraphRAG apps. It's built for scale, performance, and can handle complex queries across large knowledge graphs—whether you're working with millions of nodes or performing real-time calculations.

Vector Search

Vector search is a new feature that combines the strengths of similarity search with relevance-based graph search, enabling efficient and precise information retrieval within graph-based datasets.

Integrated Algorithms

Memgraph's powerful algorithms—Community Detection (Louvain, Leiden), PageRank, and Graph Traversals—ensure your AI isn't just making things up. It responds based on real, structured relationships embedded within your data.

GraphChat

Query your graph with plain English. GraphChat is your direct line to your graph database inside Memgraph Lab. Forget about writing complex queries—just ask. GraphChat translates your natural language question into a Cypher query, runs it on Memgraph, and provides you with the best possible answer in human language.

This two-phase generative AI app gives you answers grounded in the context of your knowledge graph.

LangChain and LlamaIndex Integrations

Memgraph integrates with Langchain and LlamaIndex, allowing for multi-hop retrieval to answer complex questions by connecting data from different sources. You can easily integrate Memgraph with your existing LLM workflows to power advanced knowledge extraction.

Upcoming Features

Reasoning Graph: AI That Picks the Right Tools for the Job

Memgraph isn't just storing data—it's thinking with it. Reasoning Graph removes the guesswork by automating search and retrieval.

It analyzes query intent and dataset structure, dynamically choosing the best method—vector search, graph traversal, or multi-hop reasoning—so AI gets the right answers without developer intervention.

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