Fraud investigations, biomedical research and infrastructure monitoring share a problem: the answer sits several relationships away from the question, in data that keeps changing. Memgraph builds the infrastructure for that problem, from the database engine to the workspace where analysts follow the evidence.
Memgraph is a high-performance, in-memory graph database written in C++ for transactional and analytical workloads. It runs sub-millisecond multi-hop traversals while the graph is being written to, with ACID transactions, persistence through snapshots and write-ahead logging, and full compatibility with Neo4j’s Cypher. Vector search, streaming ingestion and the MAGE graph algorithm library run in the same database. Enterprise deployments add high availability with automatic failover, role- and label-based access control, multi-tenancy, and backup and restore.
For AI applications, Memgraph supplies the structure that vector search misses. Vector search finds what’s similar; graph traversal finds what’s connected. GraphRAG pipelines retrieve relationships and evidence that span documents. AI memory systems store facts, past interactions and procedures as connected entities. Agents plan by traversing a reasoning graph of states and scored actions, and every decision leaves a path you can inspect. Atomic GraphRAG runs search, relevance expansion, ranking and prompt assembly as one Cypher query, so retrieval runs inside the database and needs more than 10x less pipeline code than orchestrating separate services. Memgraph works with LangChain, LlamaIndex, Mem0, Cognee and the Model Context Protocol.
Most connected data doesn’t live in a graph database, so Memgraph Zero queries it where it already lives. Its first component, MemGQL, is a federated query engine that accepts ISO-standard GQL, translates each query into the native language of each backend, and returns unified results from PostgreSQL, ClickHouse, DuckDB, Iceberg and graph databases. A mapping file turns tables into nodes and relationships, with no ETL pipeline and no copy of the source data. Agents get one endpoint and one schema for discovering and querying the organization’s data.
Memgraph Lore gives that graph to the people who have the questions. It is a graph intelligence workspace where fraud analysts, compliance officers and researchers ask questions in plain language, explore the graph the agent renders, and see the query behind every answer so they can check it, re-run it and defend it. Investigations persist across sessions and become reports and live dashboards for colleagues who never open Lore. Lore runs on the user’s own machine against their own model, so no data passes through Memgraph.
NASA connects people, skills and projects across the agency in a knowledge graph for expert discovery and workforce planning. Cedars-Sinai links genes, drugs and clinical pathways in an Alzheimer’s knowledge graph of more than 230,000 entities to support drug repurposing research. Capitec Bank uses Memgraph for fraud detection and analysis. Teams in cybersecurity, financial services, retail and network operations run real-time graph analytics on the same engine.