The Graph Engine for AI Context

Complement vector search with structured, connected context and traceable multi-hop reasoning across enterprise data in milliseconds.

Connected context across your data.

Documents & Data Sources

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Memgraph

AI Workloads

1. 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.
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2. AI Memory

LLMs are stateless. Vector-based memory retrieves what sounds similar, not what's structurally relevant. Memgraph stores and connects three types of long-term memory: semantic, episodic, and procedural, as a unified graph that any AI system can query in real time.
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3. Agentic AI

The core question for any agent is: what should I do next? A reasoning graph makes the answer explicit, an action space where graph algorithms find the highest-scoring path from current state to goal. Memgraph is the agent’s real-time reasoning engine.
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Auditability as structure.

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.

Sub-ms Traversals
1,000+ tx/sec reads & writes
100 GB–4 TB Graph sizes
ACID Compliant with persistence

One engine. Two workloads.

The same in-memory architecture that powers AI context also drives real-time graph analytics.

Use Cases

Fraud detection
Eliminate chargeback fees and unrecoverable fraud in real-time. Enhance your fraud system by mining relationships between entities.
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Network analysis
Get a 360° view of activity. Risk is multi-faceted, stop looking at entities in isolation.
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Data lineage
Ensure the reliability of your data and prevent its misuse.
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Knowledge graph
Unify siloed data into a queryable graph of entities and relationships that any team can traverse.
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Identity & access
Build IAM systems that track complex permissions and check access rules in milliseconds at scale.
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Supply chain
Model multi-tier supplier networks. Simulate disruption and reroute in real time.
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Migration Support

Everything you need to migrate from Neo4j, in one place.
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Trusted in Production

What Teams Are Building with Memgraph

NASA
"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."
Cedars-Sinai
"Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen."
Capitec Bank
"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."

Getting Started for Developers

Start in minutes

Fully functional Community Edition, free forever. Cypher query language, Python client libraries, LangChain and LlamaIndex integrations. Migrating from Neo4j? Familiar interfaces and protocols.

Installation

Install Memgraph on Linux/MacOS

curl -sSf "https://install.memgraph.com" | sh

Install Memgraph on Windows

iwr https://windows.memgraph.com | iex

Insurance, Legal, Manufacturing - How Previsant Connects AI Agents and Context Graphs Across Domains with Memgraph
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Get Stronger Multi-tenancy, Enriched GraphRAG, and Stability for Production Workloads with Memgraph 3.10
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Introducing MemGQL: One Query Language Across All Your Data Sources
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How to Connect AI Agents with Context Graphs Across Domains
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Building a Kubernetes Graph Engine for Agents
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How MemGQL Lets You Query Distributed Data Without ETL
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How Capitec uses graph databases to protect its clients
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