Open Source Graph Database
A relational schema can store relationships perfectly well; the trouble starts when the real question is how things connect across many hops, and every answer turns into a stack of joins that gets slower as the graph deepens. A graph database treats edges as first-class data with native traversal, so walking a network of connections stays cheap no matter how far you follow it. Choosing an open source engine here means the storage layout and query planner are open to inspect and tune rather than sealed inside a proprietary product, and the graph you build, along with its IDs, labels, and properties, stays in a system you can export from and run on hardware you own.
Dgraph
Horizontally scalable distributed graph database with GraphQL, ACID transactions, and linearizable reads
Cognee
Open-source AI memory platform for agents with self-hosted knowledge graph memory across sessions
Neo4j
Graph database with ACID transactions, Cypher queries, and managed or self-managed deployment
NebulaGraph
Distributed graph database with horizontal scalability, high availability, and OpenCypher-compatible queries
JanusGraph
Distributed graph database for large graphs, with transactional queries across multi-machine clusters
Apache AGE
PostgreSQL extension that adds graph database support with openCypher and SQL
Cartography
Python tool that maps infrastructure assets and relationships into a Neo4j graph database
Apache HugeGraph
Graph database for large-scale vertex and edge storage with OLTP, Gremlin, and Cypher access
ArcadeDB
Open-source multi-model database with SQL, graph, document, key-value, vector, and time-series support
Our picks
Different engines suit different graphs, so here is a strong option across the main styles.
Mature property-graph default: Neo4j Neo4j stores connected data as nodes and relationships and pairs that model with the Cypher query language and ACID transactions, plus tools for querying, visualizing, and analyzing the graph. The Community Edition is open source under GPLv3, and you can run a standalone server, develop locally with Neo4j Desktop, or use the managed Aura service.
Distributed graph at scale: Dgraph Dgraph is sharded across a cluster and provides distributed ACID transactions, consistent replication, and linearizable reads for graphs that will not fit one machine. It uses a GraphQL-inspired query language and returns JSON or Protocol Buffers over gRPC and HTTP, and it controls on-disk layout in Go to cut disk seeks during distributed joins and traversals.
Multi-model in one engine: ArcadeDB ArcadeDB handles graph, document, key-value, vector, and time-series data in a single ACID engine, linking records natively without joins. Queries can be written in SQL, Cypher, Gremlin, GraphQL, or MongoDB syntax, and it runs embedded or as a server. It ships 70+ built-in graph algorithms and scales from a Raspberry Pi to a cluster under Apache 2.0.
Graph inside PostgreSQL: Apache AGE Apache AGE adds a graph model to PostgreSQL as an extension, so teams that want one store for relational and graph data can run openCypher and ANSI SQL side by side. It supports multiple graphs, property indexes on vertices and edges, and hybrid SQL plus Cypher queries while keeping full PostgreSQL functionality intact.
Matching an engine to the graph you actually have
The first fork is the data model, because it shapes every query you will ever write. A property graph, where vertices and edges carry their own attributes, fits application features like recommendations, permission inheritance, or fraud rings; Neo4j, NebulaGraph, and Apache AGE all speak this style through Cypher or an OpenCypher dialect. RDF and triple models fit semantic interoperability and externally defined vocabularies instead, and the two are not interchangeable skins over one engine. If you want a single storage layer for graph work alongside document or relational data, a multi-model engine like ArcadeDB or a Postgres extension like Apache AGE keeps both in one database.
How the engine executes traversals matters as much as what it can express. Native adjacency structures make repeated edge hops cheap, while systems layered over another store behave differently under high fan-out nodes and deep paths; JanusGraph, for instance, sits on Cassandra, HBase, or Bigtable. Benchmark with your own graph shape, since a social graph, a dependency graph, and a knowledge graph stress the planner in different ways. If the data spans billions of edges across a cluster, distributed engines like Dgraph and NebulaGraph are built for horizontal scale, whereas a single-node store will hit a wall you cannot tune your way past.
Query language portability is weaker than SQL, so decide early. Gremlin, Cypher, and GraphQL-style dialects are not interchangeable, and a powerful engine with an awkward language pushes traversal logic into application code that is harder to test. Check driver quality in your main language, and for a specialized job such as mapping infrastructure security relationships, a purpose-built loader like Cartography can matter more than the raw engine underneath it. Because queries do not move easily, favor exports that preserve vertex IDs, edge IDs, labels, and direction.
Related categories
Frequently asked questions
When does a graph database beat a relational database?+
Reach for a graph database when the important question is about relationships across several hops rather than rows that share a key: dependency impact, identity resolution, recommendations, fraud rings, permission inheritance, and network topology. A relational database can store edges in join tables, but deep traversal queries get hard to read and expensive as hop depth grows. If most queries stay one join deep, a relational store is usually the simpler choice.
Should I pick a property graph or an RDF model?+
Choose a property graph when your application thinks in vertices, edges, labels, and attributes attached to those objects; it is usually easier for product features and operational graphs, and Neo4j, NebulaGraph, and Apache AGE all take this route. Choose RDF when you need triples, semantic vocabularies, reasoning, or standards-based data exchange. The decision affects query language, tooling, and migration, so settle it before loading serious data.
How much does the query language lock me in?+
More than a SQL dialect does. Gremlin, Cypher, OpenCypher, and GraphQL-style languages are not interchangeable, so business logic written as stored procedures or engine-specific traversals is the hardest thing to move later. Keep traversal logic in your application where portability matters, confirm the language expresses variable-length paths and shortest-path queries declaratively, and check that a solid driver exists for your programming language before committing.
Can I get graph features without running a separate database?+
Yes, if you already run PostgreSQL. Apache AGE is an extension that adds a graph model and openCypher queries on top of Postgres while keeping full relational functionality, so graph and relational data live in one database. ArcadeDB takes a different route, bundling graph, document, key-value, and vector models in a single multi-model engine that runs embedded or as a server, which avoids operating two separate stores.
Why are graph databases hard to scale?+
Because scaling depends on graph shape more than record count. Evenly distributed shallow relationships behave very differently from celebrity nodes or deep dependency chains, and edges that cross partition boundaries can turn cheap hops expensive. Distributed engines like Dgraph and NebulaGraph shard across a cluster for exactly this reason. Benchmark read paths and write paths separately, because bulk loading, transactional edge creation, and analytical traversals stress different parts of the system.
How do I load data from relational tables into a graph?+
Treat it as a modeling exercise, not a file conversion. Tables carry implicit relationships through foreign keys and repeated values, so you decide which rows become vertices, which join records become edges, and which columns become properties. Preserve stable IDs from the source so later incremental loads update existing nodes instead of creating duplicates. Import a representative slice first and validate a few real traversals before loading everything.
Are graph databases used for AI and knowledge graphs?+
Increasingly, yes. Knowledge graphs give retrieval and reasoning systems structured relationships rather than loose text, and Cognee builds a self-hosted knowledge graph so AI agents keep persistent memory across sessions, combining vector embeddings with graph reasoning. General engines like Neo4j and NebulaGraph are also used to store knowledge graphs directly. The fit depends on whether you need agent memory specifically or a general graph store you query yourself.