Top 9 Open Source Graph Databases for 2026

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There is no single best open source graph database: Neo4j Community is the easiest general-purpose starting point, Apache AGE is best when PostgreSQL already runs your data platform, JanusGraph suits distributed property graphs, and TerminusDB suits versioned knowledge data. The right choice depends on graph model, query language, scale, license, and operational requirements.

“Open source” needs qualification in this market. A free community edition may exclude clustering, high availability, advanced security, backups, or commercial support; a source-available project may not use an OSI-approved license; and a PostgreSQL extension should not be compared with a purpose-built distributed graph engine as if they were identical products.

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Key takeaways

• Neo4j Community is the most approachable general-purpose property-graph option, but automatic high availability, horizontal scaling, fine-grained security, and several operational features are Enterprise capabilities.
• Apache AGE adds property-graph queries to PostgreSQL, making it attractive for existing PostgreSQL environments but not a separate graph database server.
• JanusGraph provides distributed property graphs and Gremlin support, but operating Cassandra, HBase, or another backend adds substantial infrastructure responsibility.
• ArcadeDB is an Apache-2.0 multi-model engine with graph, document, key-value, full-text, vector, and time-series capabilities in one product.
• Dgraph, HugeGraph, and NebulaGraph are distributed candidates, but their query languages, deployment models, operational behavior, and licensing qualifications differ significantly.
• TerminusDB is the specialist choice for versioned, collaborative, semantic, and auditable knowledge data rather than ordinary Cypher-based OLTP.

How should you interpret “open source graph database”?

An open source graph database should pass four tests: its engine or extension source is publicly available, its license permits the intended use and redistribution, the recommended edition is clearly identified, and the project has current or clearly documented maintenance status.

Those tests separate genuinely open projects from products that merely offer a free download. Licensing also affects how a database can be embedded, modified, hosted, or sold as part of a service. GPLv3, Apache 2.0, and MIT are not interchangeable, and a commercial cloud service may have terms that differ from its self-managed repository.

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The nine projects below mix several categories:

• Native property graphs: Neo4j, Dgraph, NebulaGraph, JanusGraph, and HugeGraph treat vertices, edges, labels, and properties as core graph concepts.
• Graph extension: Apache AGE adds graph functionality to PostgreSQL.
• Multi-model databases: ArcadeDB and OrientDB combine graph capabilities with document or other data models.
• Semantic and versioned graph: TerminusDB emphasizes linked data, collaboration, commits, diffs, and time travel.

A graph database is also not automatically faster than a relational database. Performance depends on indexes, cardinality, degree distribution, traversal depth, data locality, query planning, cache state, and the read/write mix. A relational database with well-designed indexes and joins can remain the better choice for tabular reporting, conventional transactions, and workloads with predictable relational access patterns.

What is the difference between operational and analytical graph workloads?

Operational graph workloads are low-latency transactions such as fraud checks, recommendations, authorization, customer-360 queries, and relationship updates. Analytical graph workloads calculate PageRank, connected components, communities, paths, embeddings, or other results over large portions of a graph. GraphRAG commonly combines entity traversal with full-text or vector retrieval.

A database that performs well on a PageRank benchmark is not automatically the right choice for a fraud check that must complete during a checkout transaction. Vendor-published measurements should be treated as leads, not universal rankings. For example, ArcadeDB publishes benchmark comparisons on its own website; reproduce those results independently before using them as a buying decision.

Which open source graph database should you choose?

1. Neo4j Community Edition: the easiest general-purpose starting point

Neo4j Community Edition is the safest default for developers who want a native property graph, the Cypher ecosystem, mature drivers, visualization, and a clear commercial upgrade path. Neo4j supports ACID transactions, Cypher, indexing, full-text indexing, and vector indexing in Community according to its current feature comparison.

Neo4j’s current edition comparison identifies Community Edition as GPLv3 and community-supported. Automatic high availability, horizontal scaling, fine-grained security, advanced manageability, change data capture, federated scale-out, hot backups, and several other enterprise operations are not included in the free Community edition.

Best fit: learning Cypher, prototypes, single-node applications, small production deployments, knowledge graphs, and teams that may eventually purchase enterprise capabilities.

Avoid when: the requirement is fully open-source high availability and horizontal scale without commercial licensing. “Free” does not mean a free production cluster with enterprise operations.

2. Apache AGE: graph queries without leaving PostgreSQL

Apache AGE is a graph-capable PostgreSQL extension, not a separate graph database server. AGE lets PostgreSQL users model nodes and edges while retaining PostgreSQL’s relational, JSON, transaction, authentication, backup, and administration ecosystem.

The Apache AGE project documents graph queries, SQL interoperability, and PostgreSQL compatibility. AGE is compelling when graph data must coexist with relational tables and the organization already operates PostgreSQL.

The trade-off is architectural. AGE inherits PostgreSQL’s storage and planning behavior rather than providing a graph-native distributed engine. Test deep traversals, high-degree vertices, bulk loading, mixed SQL/graph transactions, and extension compatibility with representative data.

Best fit: existing PostgreSQL applications, knowledge graphs that must share a transactional platform with relational data, and teams minimizing new infrastructure.

Avoid when: graph-native horizontal distribution, massive traversal workloads, or a large graph-specific tooling ecosystem is the central requirement.

3. JanusGraph: distributed property graphs with backend choice

JanusGraph is an Apache-2.0 distributed property-graph database built around Apache TinkerPop and Gremlin. JanusGraph’s graph layer uses a storage backend such as Cassandra or HBase, and backend choice affects consistency, availability, repair, compaction, caching, and cost.

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The JanusGraph documentation describes backend dependencies, Gremlin support, and vertex-centric indexes. Vertex-centric indexes are important for high-degree vertices, sometimes called supernodes, where naïvely scanning every adjacent edge can become expensive.

JanusGraph is powerful but not turnkey. Operators must understand the selected storage system, search or indexing integrations, failure recovery, schema management, and capacity planning. Berkeley DB Java Edition is non-distributed and is generally more suitable for testing or exploration.

Best fit: organizations with distributed-systems expertise and an existing Cassandra- or HBase-oriented platform.

Avoid when: a small team wants one container, simple backups, and minimal operational surface area.

4. Apache HugeGraph: a broad graph platform under Apache

Apache HugeGraph combines graph serving with loading, visualization, graph computation, and graph-AI-related components. The project supports Gremlin and OpenCypher and offers a standalone RocksDB mode as well as a distributed architecture using HugeGraph-PD and HStore.

The Apache HugeGraph repository identifies the project as Apache 2.0 and documents its standalone and distributed components. Apache HugeGraph became an Apache Top-Level Project on February 12, 2026, according to the Apache announcement.

The breadth is both strength and cost. A production deployment with serving, placement, storage, computing, dashboards, loaders, and AI integrations has more components to configure, upgrade, monitor, and recover than a single-node product. Claims about supporting billions of vertices or more than 10 billion elements should be treated as project claims, not guarantees for every query pattern or hardware configuration.

Best fit: teams evaluating graph OLTP, distributed graph computation, loaders, visualization, and graph-AI tooling together under an Apache project.

5. Dgraph: a distributed GraphQL-oriented graph database

Dgraph is a distributed graph database designed around GraphQL-inspired application access and DQL. The project describes distributed architecture, sharding, replication, ACID transactions, consistent replication, and linearizable reads as core capabilities.

The Dgraph repository identifies an Apache 2.0 license, Docker-oriented installation, and official Linux/amd64 and Linux/arm64 support. Mac and Windows should not be assumed to be officially supported production platforms.

Dgraph is a good fit when the application API and team skills already center on GraphQL. Dgraph is a less natural choice for a team whose existing queries, drivers, and migration tooling are built around Cypher or Gremlin. Buyers should also distinguish the open-source repository from the terms of any hosted or enterprise service.

6. NebulaGraph: a specialist for large distributed graphs

NebulaGraph targets large-scale distributed property graphs and low-latency traversal through its native graph architecture and nGQL query language. NebulaGraph offers self-managed and cloud-oriented deployment positioning.

The NebulaGraph product site is useful for understanding the project’s positioning, but license and edition terms should be verified against the current repository and release documentation before commercial adoption. Do not treat claims about billions of vertices, trillions of edges, or specific latency as general guarantees without the dataset, hardware, query pattern, and benchmark method.

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Best fit: specialized large distributed graph workloads evaluated by a team comfortable validating a distinct ecosystem and nGQL.

Avoid when: a small application could be served more simply by PostgreSQL plus AGE, an embedded engine, or a single-node property graph.

7. ArcadeDB: an Apache-licensed multi-model engine

ArcadeDB is the strongest candidate in this list for teams wanting graph, document, key-value, full-text, vector, and time-series capabilities in one engine. ArcadeDB supports embedded and client/server modes and advertises distributed deployment with native ACID and Raft-related capabilities.

The ArcadeDB product site lists Apache 2.0 licensing, SQL, OpenCypher, Gremlin, GraphQL, a MongoDB protocol, and a Java API. Compatibility is not binary: support for several languages does not mean identical grammar, procedures, extensions, or ecosystem parity with Neo4j.

ArcadeDB is particularly interesting for embedded applications, multi-model systems, and GraphRAG prototypes that need vector or full-text features alongside relationships. Its principal trade-off is a smaller ecosystem and adoption footprint than Neo4j. Vendor-published benchmark results should be independently reproduced rather than converted into a universal “fastest database” claim.

Best fit: teams wanting broad functionality under Apache 2.0 without an advertised community-versus-enterprise feature split.

8. OrientDB: a mature document-and-graph option

OrientDB combines document and graph models and remains relevant for existing OrientDB systems, legacy estates, and teams with a specific multi-model requirement. The project provides a graph editor, query interface, command-line console, OrientDB SQL, and traversal APIs.

The OrientDB project site presents its open-source multi-model positioning. Before selecting OrientDB for a new system, verify its current release cadence, security maintenance, support model, drivers, clustering behavior, and migration tooling. OrientDB should not automatically outrank newer projects for greenfield development.

Best fit: existing OrientDB deployments, document-plus-graph applications, and migration evaluations where compatibility has practical value.

Avoid when: the project needs the broadest current ecosystem, the newest tooling, or a clear greenfield default without validating long-term maintenance.

9. TerminusDB: versioned and collaborative knowledge data

TerminusDB is a specialist database for versioned, collaborative, structured, and semantic data. TerminusDB links JSON and JSON-LD documents into a knowledge graph and provides Git-like commits, diffs, push, pull, clone, and time-travel queries.

The TerminusDB repository identifies Apache 2.0 licensing, TerminusDB 12, Docker workflows, and WOQL, GraphQL, REST, and RDF-oriented capabilities. Enterprise capabilities include boundaries around clustering and enhanced backup and restore, so the self-managed open-source feature set should be checked against the intended deployment.

TerminusDB is not a conventional Cypher property-graph replacement. Its version-control model is valuable for data products, lineage, configuration history, collaborative knowledge bases, semantic data, and auditable changes, but may add concepts that ordinary transactional traversals do not need.

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Best fit: versioned knowledge graphs, provenance, lineage, auditable data, and collaborative structured-data workflows.

How do the query languages differ?

Query-language fit can matter more than feature-count comparisons because syntax, drivers, procedures, tooling, and team expertise determine migration cost.

Language compatibility is never automatically complete. Test functions, procedures, indexes, transaction semantics, null handling, path behavior, bulk loaders, and client-driver features with real queries.

Should you choose an embedded database or a server?

Embedded databases run inside the application process and reduce operational overhead, making them useful for local analytics, desktop software, tests, edge workloads, and single-process applications. Server databases are easier to share across services and teams and provide a clearer boundary for access control, monitoring, backups, and upgrades.

ArcadeDB supports embedded and client/server modes. TerminusDB emphasizes local and Docker-based workflows as well as collaborative server deployment. Dgraph recommends Docker and officially supports Linux/amd64 and Linux/arm64. Distributed servers additionally require quorum planning, compaction, backups, network-partition handling, monitoring, rolling upgrades, and capacity management.

Do not compare an embedded engine and a distributed cluster on “scalability” without defining the dimension. Embedded may be the best scale choice for a local analytical workload; distributed may be necessary for shared high availability and horizontal data growth.

What are the most important infrastructure differences?

JanusGraph’s Cassandra or HBase dependency can dominate total cost and operational effort. HugeGraph’s standalone RocksDB mode is simpler than its distributed HugeGraph-PD and HStore architecture. AGE inherits PostgreSQL’s administration model. Dgraph supplies a distributed graph engine rather than requiring Cassandra-style backend assembly.

How should licensing and edition boundaries affect the decision?

Put license and edition status near the top of the evaluation, not in a footnote. Ask whether the license is OSI-approved, whether copyleft obligations affect distribution, whether clustering or backups are enterprise-only, whether hosted use has separate terms, and whether support or an SLA is available.

Neo4j Community is a concrete example of the difference between free software and a fully featured production platform: Community includes important graph capabilities, but several autonomous clustering, hot-backup, multi-database, advanced-monitoring, security, and scale-out features are associated with Enterprise editions on the current comparison page.

FalkorDB should not be casually grouped with Apache, MIT, or GPL projects. Its main repository states that it uses SSPLv1, which is source-available but not an OSI-approved open-source license. Kùzu remains MIT-licensed and technically relevant, but its repository was archived on October 10, 2025, so it should not be presented as an actively maintained current choice without that caveat. ArangoDB, Memgraph, and SurrealDB also require current license verification before being called open source.

Oxigraph is a reasonable alternative when the requirement is specifically RDF and SPARQL. The Oxigraph project repository describes it as an RDF/SPARQL database and toolkit and notes that SPARQL evaluation is not yet optimized. Oxigraph is therefore better treated as a standards-focused specialist option than a general-purpose OLTP recommendation.

Which database fits each workload?

What is a practical decision tree?

1. Already operate PostgreSQL? Start with Apache AGE and compare it against a native graph using representative traversal and concurrency tests.
2. Need the simplest Cypher learning path? Start with Neo4j Community, while checking whether production needs require commercial Enterprise features.
3. Need embedded and multi-model capabilities? Evaluate ArcadeDB.
4. Need graph versioning, collaboration, provenance, or time travel? Evaluate TerminusDB.
5. Need a distributed GraphQL-oriented API? Evaluate Dgraph.
6. Need Gremlin and selectable distributed storage? Evaluate JanusGraph.
7. Need graph serving, computation, loaders, and graph-AI components together? Evaluate HugeGraph.
8. Need specialized large-scale distributed traversal? Evaluate NebulaGraph after verifying its current license, edition, and documentation.
9. Maintain an existing OrientDB system? Keep OrientDB in the comparison and test migration cost before considering replacement.

What should every proof of concept test?

Run the same representative workload against every serious candidate. A useful proof of concept should include:

• Loading a graph with the expected vertex, edge, label, property, and degree distributions.
• One-hop, two-hop, and deep traversals, including high-degree vertices or supernodes.
• Index creation before and after loading, plus index rebuild and schema-evolution behavior.
• Concurrent reads and writes, transaction rollback, read-after-write behavior, and failure semantics.
• Cold-cache and warm-cache measurements with the same hardware, storage, versions, drivers, and query plans.
• Bulk loading, incremental updates, full-text search, vector retrieval, or graph algorithms when those features matter.
• Backup, restore, recovery-point objectives, and recovery-time objectives.
• Node failure during reads and writes for distributed candidates, followed by rebalancing and repair.
• Memory, CPU, disk, network, compaction, monitoring, and operational staffing requirements.
• Client-driver behavior, authentication, authorization, migrations, upgrades, rollback, and documentation quality.

Record dataset size, query depth, read/write mix, cache state, hardware, storage, version, index configuration, cluster topology, language, and driver. Without those details, “fastest graph database” is not a reproducible conclusion.

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What are the main commercial alternatives?

Self-managed open-source software is not always the lowest-total-cost option. Cluster operations, backups, upgrades, observability, security response, and reliability staffing can cost more than a managed service.

Neo4j AuraDB is the managed route into the Neo4j ecosystem. The pricing page observed in August 2026 listed AuraDB Free at $0, Professional at $65 per GB per month with a 1 GB minimum cluster, and Business Critical at $146 per GB per month with a 2 GB minimum cluster. Prices and terms can change, so verify the current geography, billing unit, minimum size, and date before purchase. AuraDB is a poor fit when fully open-source high availability without commercial licensing is a hard requirement.

ArcadeDB enterprise support offers a paid path for support subscriptions, SLAs, priority fixes, direct developer access, custom development, and consultancy while the software remains Apache 2.0. The product page does not publish a standard support price, so treat pricing as contact-led.

TerminusDB Enterprise and DFRNT services target clustering, enhanced backup and restore, higher write performance, and studio or modeling workflows. No public price was verified; the commercial model should be treated as contact-led and evaluated only when versioned or semantic data justifies the specialist approach.

FalkorDB Cloud may interest GraphRAG and real-time graph buyers, but the self-managed FalkorDB repository states SSPLv1. FalkorDB Cloud should therefore be presented as a commercial managed option, not as equivalent to the OSI-approved open-source projects in this comparison.

For self-managed deployments, AWS, Azure, Google Cloud, DigitalOcean, and Red Hat OpenShift can provide compute, Kubernetes, PostgreSQL, storage, and monitoring infrastructure. Do not assume that a provider manages every database listed here unless a specific marketplace listing or managed product has been verified.

Frequently Asked Questions

Is Neo4j Community Edition fully open source?

Neo4j Community Edition is GPLv3 open-source software, but it does not include every production capability. Automatic high availability, horizontal scaling, fine-grained security, hot backups, advanced manageability, and other features are associated with commercial Enterprise editions in Neo4j’s current feature comparison.

Is Apache AGE a graph database?

Apache AGE is a graph-capable PostgreSQL extension, not a separate graph database server. Apache AGE is best when graph and relational data should share PostgreSQL’s transactions, storage, authentication, backups, and administration.

Which open source graph database is best for RDF and SPARQL?

Oxigraph is the most standards-focused specialist option mentioned here because it is an RDF/SPARQL database and toolkit. TerminusDB is also relevant for semantic and JSON-LD data, while property-graph systems such as Neo4j and JanusGraph are usually more natural for Cypher- or Gremlin-oriented application traversals.

Is FalkorDB open source?

FalkorDB’s main repository states that it is licensed under SSPLv1, which is source-available but not an OSI-approved open-source license. Buyers requiring an OSI-approved license should not treat FalkorDB as equivalent to Apache-2.0, MIT, or GPL projects.

Is Kùzu still an actively maintained open source graph database?

Kùzu remains MIT-licensed and usable, but its repository was archived on October 10, 2025. Kùzu should not be presented as a currently active project without clearly labeling its archived maintenance status.

The Bottom Line

Bottom line: Choose Neo4j Community for the most approachable general-purpose graph experience, Apache AGE when PostgreSQL continuity matters more than graph-native scale, JanusGraph when backend flexibility justifies operational complexity, HugeGraph when serving and graph computing must be evaluated together, Dgraph for distributed GraphQL applications, NebulaGraph for validated large-scale specialist workloads, ArcadeDB for Apache-licensed multi-model use, OrientDB mainly for existing estates, and TerminusDB for versioned, collaborative, semantic knowledge data.

The defensible choice comes from a workload-specific proof of concept—not a universal popularity or performance ranking.

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