GraphRAG
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.
Complement vector search with structured, connected context and traceable multi-hop reasoning across enterprise data in milliseconds.
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When the agent acts, the traversed path is an inspectable trace. Alternative paths can be scored and compared. The basis for the decision is examinable through graph structure and edge scores, not through interpretation of token probabilities.
The same in-memory architecture that powers AI context
also drives real-time graph analytics
Everything you need to migrate from Neo4j, in one place.
“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.”
“Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
“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.”
Fully functional Community Edition, free forever. Cypher query language, Python client libraries, LangChain and LlamaIndex integrations. Migrating from Neo4j? Familiar interfaces and protocols.
curl -sSf "https://install.memgraph.com" | sh
iwr https://windows.memgraph.com | iex