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.

Composable

Swap ranking functions or traversal strategies without modifying surrounding code.

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.

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.

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

Multi-hop graph queries at the speed your pipeline needs, no bottleneck between the 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 evolves.

  • 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 to a single provider.

Integrations

Build with your existing stack.

LangChain Knowledge graph construction from unstructured data and natural language querying via MemgraphQAChain. LlamaIndex Create knowledge graphs from unstructured data and query with natural language via Memgraph graph store. LightRAG Fast retrieval-augmented generation combining graph databases with LLMs for creating and querying knowledge graphs. Memgraph MCP Server Connect any MCP-compatible client, Claude, VS Code, custom agents, directly to Memgraph for Cypher queries and graph analysis. Memgraph 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.

GraphRAG 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 schema
Performance benchmarks
Tailored atomic GraphRAG queries
Stack integrations
Handoff documentation

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 Meza
NASA

“Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
Jason H. Moore
Cedars-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 Schmidt
Capitec Bank

AI Workloads

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.