GraphRAG with Memgraph
Execute your entire GraphRAG retrieval pipeline as a single atomic database operation — not a distributed system you have to orchestrate.
Standard RAG hits a wall.
Standard RAG retrieves text chunks by similarity. GraphRAG traverses a knowledge graph to follow multi-hop relationships across entities — connecting information that similarity matching can't reach.
Atomic Pipelines.
Most GraphRAG implementations split logic across multiple systems. Atomic GraphRAG executes ingest, index, enrich, scoring, and prompt assembly as a single Cypher query — one atomic operation inside Memgraph.
Agentic GraphRAG.
Not every question maps to the same retrieval strategy. Agentic GraphRAG lets the agent select per query. Each strategy is a single Cypher query. The agent selects. Memgraph executes.
Why Memgraph.
Sub-millisecond traversals
Multi-hop graph queries at the speed your pipeline needs — no bottleneck between your knowledge graph and the LLM.
Native vector search
Similarity and structure in a single engine. No separate vector database required for hybrid retrieval.
Real-time graph updates
Knowledge graphs aren't static. Memgraph handles concurrent writes so your graph stays current as data arrives.
Traceable reasoning paths
Every answer traces back to specific entities and relationships. Audit why any result was generated.
LLM-agnostic — works with OpenAI, Anthropic, Meta, DeepSeek, or any model. No lock-in.
Build with your existing stack.
Knowledge graph construction from unstructured data and natural language querying via MemgraphQAChain.
Create knowledge graphs from unstructured data and query with natural language via Memgraph graph store.
Fast retrieval-augmented generation combining graph databases with LLMs for creating and querying knowledge graphs.
Connect any MCP-compatible client — Claude, VS Code, custom agents — directly to Memgraph for Cypher queries and graph analysis.
Connect Memgraph Lab to external MCP servers — Stripe, Elasticsearch, Slack, and others — to combine graph insights with live data from across your stack.
GraphRAG JumpStart programme.
Go from enterprise data to a working GraphRAG proof of concept — fast. A structured engagement with the Memgraph engineering team. We work with your data, your schema, and your retrieval requirements to build a production-ready GraphRAG pipeline — not a generic demo.
In production.
"Graph technology has enabled us to connect people, skills, projects, and research across the agency in ways that were previously impossible."
David Meza, Head of Analytics, Human Capital at NASA
How Cedars-Sinai Uses Memgraph for Knowledge-Driven Machine Learning in Alzheimer’s Research
Enhancing LLM Chatbot Efficiency with GraphRAG
Behind the Missions: How NASA Manages Talent with a People Knowledge Graph
Explore other AI workloads.
AI Memory
Memgraph stores the memory graph and serves precise, relationship-aware retrievals in real time.
Agentic AI
Memgraph acts as the agent's working memory and execution graph for rapid, concurrent state management.
