Memgraph

Founded by Dominik Tomicevic in 2016

Query connected data and power AI reasoning with an in-memory graph database

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Memgraph to store, query, and analyze connected graph data directly in memory. It provides fast multi-hop traversals and real-time execution using the Cypher query language. You can build retrieval-augmented generation pipelines, manage long-term agent memory, and run complex graph algorithms. Teams also use it for real-time fraud scoring, identity and access management, and supply chain tracking. It integrates directly with common AI orchestration frameworks and Python libraries, making it easy to turn relational or unstructured records into structured knowledge graphs.

What Memgraph does

Execute Cypher queries against in-memory graph structuresTraverse multi-hop relationships across interconnected entitiesRun graph algorithms on production data using PythonConnect text embeddings with structured knowledge graphsImport relational data from CSV, SQL, or XLSX into graphsInspect reasoning paths taken by autonomous agentsScore real-time transaction activity to identify anomaliesIn-memory graph database engine for sub-millisecond traversal speedsFull compatibility with the open Cypher query languageACID compliant transactions backed by disk persistenceDirect integrations with LangChain and LlamaIndex for AI contextMulti-hop relationship tracking for GraphRAG architecturesInspectable decision traces and graph scoring for autonomous agentsPython client libraries and NetworkX support for graph algorithms

Pricing breakdown

Pricing model: Free tier

Tutorials & Demos

Frequently asked

LangChain

LlamaIndex Python

Python

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