Composable
Swap ranking functions or traversal strategies without modifying surrounding code.
Atomic retrieval pipeline
One Query. Your Entire GraphRAG Pipeline.
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 expresses search, expansion, ranking, and prompt assembly as a single Cypher query, one atomic operation inside Memgraph.
Agent-compatible
A self-contained query is easier for an LLM to generate than a multi-step pipeline.
Operationally simple
One system to monitor, debug, and scale. The database is the execution layer.
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Agentic GraphRAG
User question
Relevant Context
Answer
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Agentic GraphRAG
User question
Relevant Context
Answer
Agentic GraphRAG.
Not every question needs the same retrieval strategy. Agentic GraphRAG lets the agent select per query. Each strategy is a single Cypher query. The agent selects; Memgraph executes.
01
Text2Cypher
Analytical questions
Read blog→“How many customers churned in Q1 who had open support tickets?”
02
Pivot search + relevance expansion
Local questions
Read blog→“What related factors affect this patient's treatment options?”
03
Query-focused summarisation
Global questions
Read blog→“What are the main themes across all customer feedback this quarter?”
Capabilities
Why Memgraph.
Sub-millisecond traversals. Native vector search. Real-time graph updates. Built for AI pipelines that can't afford to wait.
Sub-millisecond traversals
Native vector search
Real-time graph updates
Traceable reasoning paths
LLM-agnostic
Integrations
Build with your existing stack.
LangChain
Knowledge graph construction from unstructured data and natural language querying via MemgraphQAChain.
Read docsLlamaIndex
Create knowledge graphs from unstructured data and query with natural language via Memgraph graph store.
Read docsLightRAG
Fast retrieval-augmented generation combining graph databases with LLMs for creating and querying knowledge graphs.
Read docsMemgraph MCP Server
Connect any MCP-compatible client, Claude, VS Code, custom agents, directly to Memgraph for Cypher queries and graph analysis.
Read docsMemgraph Lab MCP Client
Connect Memgraph Lab to external MCP servers, Stripe, Elasticsearch, Slack, and others, to combine graph insights with live data from across your stack.
Read docsGraphRAG JumpStart programme.
A structured engagement with the Memgraph engineering team to build a production-ready pipeline using your data, schema, and retrieval needs.
Custom graph schemaPerformance benchmarksTailored atomic GraphRAG queriesStack integrationsHandoff documentation
Trusted in production
What teams are building with Memgraph
“Memgraph gave us a more cost-effective way to build on the graph capabilities we already knew, with a minimal learning curve for our Python and R team.”
David MezaNASA
“Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
Jason H. MooreCedars-Sinai
“Being in memory, Memgraph is fast and really performant. We score 3.5 million-plus clients daily, and the entire infrastructure runs start to end in two hours on average.”
Derick SchmidtCapitec Bank
TALENT MANAGEMENT
