Open Source Knowledge Graph & GraphRAG Databases Compared (2026)

The open source knowledge graph and GraphRAG databases worth evaluating in 2026 are ArcadeDB, Neo4j Community Edition, Memgraph, FalkorDB, JanusGraph, TerminusDB, Cayley, and Kuzu. Below we compare each on licensing, maintenance status, query languages, and whether it can serve GraphRAG retrieval, meaning graph traversal plus vector similarity, without a second database bolted alongside it.

The short version: three of the eight index vectors in the same store as the graph, and can therefore serve GraphRAG on their own: ArcadeDB, Memgraph, and FalkorDB. They differ mainly on licence. ArcadeDB is Apache 2.0, Memgraph Community is BSL 1.1, FalkorDB is SSPLv1. Only ArcadeDB is OSI open source, and only ArcadeDB also stores documents and time series and runs embedded.

A word on where this is published. This is the ArcadeDB blog, we build ArcadeDB, and we think it is the best fit for most knowledge graph projects in 2026. You should discount our opinion accordingly. What we can do is be precise about the things that are checkable: which licence each project uses, when it last shipped a release, and what it does not do. Every status claim in this article was verified against the project’s own repository on 30 July 2026, and we say plainly where an alternative is the better choice.

Two of the eight are not really live options any more, and we say so rather than padding the list.

What Makes a Knowledge Graph Database?

A knowledge graph is not just a graph. It is a graph where the nodes represent real entities (people, documents, products, concepts), the edges carry typed meaning (authored-by, cites, depends-on), and the whole thing is used to answer questions that require following those relationships rather than filtering a table.

That imposes requirements beyond “can store nodes and edges”:

• Multi-hop traversal at usable speed. The value of a knowledge graph is in the questions that cross three or four relationships. If those queries take minutes, nobody asks them.
• Semantic retrieval. Users do not know your terminology. Someone searching for “how to handle errors in the payment service” needs to find a document titled “Exception Management in Billing Module”. That requires vector embeddings, not keyword matching.
• Exact retrieval too. Sometimes the query is a specific error code or clause number, where semantic similarity is exactly wrong and you need literal full-text matching.
• A licence you can actually build on. If you plan to embed the database in a product you distribute, a copyleft licence is a business decision, not a footnote.
• Someone still maintaining it. A knowledge graph is long-lived infrastructure. Adopting an unmaintained engine means you have adopted its unfixed bugs permanently.

That last criterion eliminates more candidates than people expect.

1. ArcadeDB

Licence: Apache 2.0 · Latest release: 26.7.3 (July 2026) · Status: actively developed

ArcadeDB is a multi-model database that stores graph, document, key-value, full-text search, vector, and time-series data in one engine, under a single ACID transaction boundary.

Why It Stands Out

For knowledge graph work specifically, the argument is that a knowledge graph needs three retrieval modes and most stacks make you run three databases to get them.

Graph, vectors, and full-text in one query. A realistic knowledge graph query does several things at once: find the entities semantically related to a question, filter to those matching an exact identifier, then traverse out to everything connected. On ArcadeDB that is one statement against one copy of the data. On most alternatives it is a graph query, a separate vector search, and an application layer merging the results, with the consistency between the two stores becoming your problem.

Native vector search. ArcadeDB indexes embeddings with JVector, using DiskANN and HNSW with SIMD acceleration, over the same records that hold the graph. There is no plugin to install and no external service to keep in sync.

Five query languages. SQL, OpenCypher 25, Apache TinkerPop Gremlin, GraphQL, and the MongoDB query language all run against the same data. The Cypher engine is native rather than a translation layer and passes 97.8% of the official TCK, which matters if you are moving existing queries across.

Apache 2.0, with no edition split. Clustering with Raft consensus, replication, embedded mode, and vector search are all in the free build. There is no Enterprise edition holding back the features you need in production, and no node or core limit.

Embedded or server. ArcadeDB runs inside your JVM, or in Python in-process via pip install arcadedb-embedded with a bundled JRE. For a knowledge graph feeding a local RAG pipeline, that removes the server entirely.

Where It’s Not the Best Fit

Honesty requires a real list here, not a token one.

It is the smallest community of the six. ArcadeDB has roughly 1,055 GitHub stars against Neo4j’s enormous ecosystem, Cayley’s 15,000, and JanusGraph’s 5,800. That translates into fewer Stack Overflow answers, fewer blog posts when you hit an edge case, and fewer engineers who already know it. If hiring people who have used your database before is a hard requirement, this is a genuine argument against us.

It is not a triplestore. If your knowledge graph is RDF, your data is in Turtle or N-Triples, and your team writes SPARQL, ArcadeDB is the wrong shape. It is a property graph engine. Use a dedicated RDF store.

It does not shard a single graph across machines. ArcadeDB replicates for availability rather than partitioning one graph across a cluster. If your graph genuinely exceeds what one machine can hold, JanusGraph over Cassandra is the more honest answer.

It runs on the JVM. The bundled-JRE Python package hides this well, but the engine is Java. If your operational policy excludes the JVM, that is decisive.

2. Neo4j Community Edition

Licence: GPLv3 · Status: actively developed

Neo4j is the reference point for property graphs, and Community Edition is a genuinely capable database. It is also the option most often adopted without reading the licence.

The Good

The ecosystem is unmatched. More documentation, more tutorials, more courses, more consultants, and more people who already know Cypher than every other option on this list combined. For a team learning graph modelling from scratch, that is worth a great deal.

Cypher is excellent. Neo4j designed the language that the rest of the industry now implements. It is genuinely pleasant for pattern matching.

Full ACID and a mature engine. Community Edition is not a crippled demo. For single-instance workloads it is a solid production database.

The Problems

GPLv3 is a business decision. Community Edition is copyleft. If you distribute software that includes or links to it, the licence reaches your application. For internal deployments this is usually irrelevant; for anything you ship to customers it is often a blocker, and the escape hatch is a commercial Enterprise licence.

No clustering. Community Edition does not support clustering, so it is limited to single-instance deployments. High availability and read scaling are Enterprise features. For a knowledge graph that becomes load-bearing infrastructure, that ceiling arrives eventually.

Graph only. Neo4j stores graphs. Documents, time series, and the rest live in other systems, which for knowledge graph work usually means running a separate store for the source documents alongside the graph of extracted entities.

When to Choose It Anyway

If your team already knows Cypher, your deployment is internal and single-instance, and you value ecosystem depth over licence flexibility, Neo4j Community Edition is a reasonable and low-risk choice. Plenty of successful knowledge graphs run on it.

3. Memgraph

Licence: BSL 1.1 (Community) · Latest release: v3.12.0 (July 2026) · Status: actively developed

Memgraph is an in-memory, Cypher-compatible graph database written in C++, and it has aimed squarely at GraphRAG and agent memory. If you have asked an AI assistant about GraphRAG recently, there is a good chance it cited Memgraph.

The Good

Built for this workload. Memgraph ships text and vector indexes in the same store as the graph, so a retrieval pipeline can run graph traversal and similarity search as one database operation rather than two systems stitched together. This is the same architectural argument we make for ArcadeDB, and it is a fair one when they make it.

In-memory speed. Holding the working set in memory gives Memgraph excellent latency on traversals, which matters when retrieval sits in the request path of an agent loop.

Cypher compatible. Existing Cypher and Neo4j-shaped tooling largely carries over.

The Problems

BSL is not open source. Memgraph Community is licensed under the Business Source License 1.1, which the Open Source Initiative does not recognise as an open-source licence. It restricts commercial use in ways a permissive licence does not, and Enterprise sits behind a separate proprietary licence. If your reason for avoiding Neo4j was licensing, read the BSL carefully before treating Memgraph as the escape.

In-memory is a cost model. RAM is the constraint. For a knowledge graph that is large and mostly cold, keeping it resident is a different budget from a disk-based engine.

Graph plus vectors, not multi-model. Documents and time series live elsewhere.

When to Choose It Anyway

If latency is your dominant constraint, your graph fits comfortably in memory, and BSL is acceptable to your legal team, Memgraph is genuinely strong at exactly this workload. It is the most direct competitor to ArcadeDB on GraphRAG and we would rather say so than pretend otherwise.

4. FalkorDB

Licence: SSPLv1 · Latest release: v4.20.1 (July 2026) · Status: actively developed

FalkorDB is the continuation of RedisGraph after Redis discontinued it, rebuilt around GraphBLAS sparse adjacency matrices and aimed explicitly at knowledge graphs for LLMs.

The Good

GraphBLAS is a genuinely good fit. Representing the graph as sparse matrices turns multi-hop traversal into linear algebra, and it makes FalkorDB fast on the pattern-matching workloads GraphRAG generates.

HNSW vector index in the same store. Like Memgraph, FalkorDB indexes embeddings alongside the graph, so hybrid retrieval does not require a second database.

Cypher, with a low-friction operational model. It runs as a Redis module, which is familiar territory for a lot of teams.

The Problems

SSPL is source-available, not open source. The Server Side Public License restricts how you may offer the software as a service and is not an OSI-approved open-source licence. For most self-hosted users this is not a practical constraint, but it is not the same freedom Apache 2.0 gives you, and it should not be described as open source.

Graph and vectors only. Same limitation as Memgraph: no document or time-series model.

Redis-module operational shape. Convenient if you already run Redis, an additional dependency if you do not.

When to Choose It Anyway

If you want fast GraphRAG retrieval, you already operate Redis, and SSPL is acceptable, FalkorDB is a well-built option with a clear focus on exactly this use case.

5. JanusGraph

Licence: Apache 2.0 · Latest release: 1.1.0 (November 2024) · Status: maintained, slow release cadence

JanusGraph is a distributed graph database under the Linux Foundation, and the direct descendant of Titan. It is the option built for graphs that genuinely do not fit on one machine.

The Good

Real horizontal scale. JanusGraph is a graph layer over a distributed storage backend, shipping support for Apache Cassandra, Apache HBase, and Oracle Berkeley DB Java Edition. If you already run Cassandra at scale, JanusGraph inherits that operational maturity and partitions a graph across it in a way most single-node engines cannot.

Genuinely open, genuinely neutral. Apache 2.0, under the Linux Foundation, with no vendor holding an Enterprise edition back. There is no commercial upsell waiting.

TinkerPop native. Gremlin is the query language, and JanusGraph is a first-class TinkerPop implementation, so the wider Gremlin tooling ecosystem works.

The Problems

You are operating at least two distributed systems. JanusGraph is not a complete database. Production means running and tuning Cassandra or HBase underneath it, plus usually Elasticsearch or Solr for indexing. The operational burden is substantially higher than any single-binary option here, and that is the dominant cost for most teams.

Slow release cadence. Version 1.0.0 arrived in October 2023 and 1.1.0 in November 2024. The repository is active, but a project shipping roughly one release a year is a different maintenance proposition from one shipping monthly.

Gremlin only, and no native vectors. There is no Cypher and no SQL. For semantic search you will pair it with a separate vector database, which for knowledge graph work means the multi-store architecture again.

When to Choose It Anyway

If your graph is genuinely too large for one machine and you already operate Cassandra or HBase, JanusGraph is the right answer and we would tell you so. That is a real scenario and no amount of single-node performance changes it.

6. TerminusDB

Licence: Apache 2.0 · Latest release: 12.0.6 (June 2026) · Status: actively developed

TerminusDB is a document graph database whose distinguishing idea is git-style version control for data: branching, merging, diffing, and time travel over the database itself.

The Good

Versioning is a first-class feature, not a pattern you implement. For a knowledge graph curated by humans, being able to branch the graph, make changes, review a diff, and merge is genuinely valuable and nothing else on this list offers it natively. Regulatory and scientific use cases where you must prove what the data said last quarter are a natural fit.

Apache 2.0 and actively shipping. Releases are current, the repository saw commits the week this was written, and the licence is permissive.

Document plus graph model. Data is modelled as documents with a schema, connected as a graph, which fits knowledge graph work better than a pure triplestore for most application developers.

The Problems

Smaller ecosystem than its age suggests. Roughly 3,370 GitHub stars and a modest contributor base. Documentation and community answers are thinner than Neo4j or JanusGraph.

No native vector search. TerminusDB does not index embeddings alongside the graph, so semantic retrieval means a separate vector store.

WOQL is a learning curve. The native query language is its own thing rather than Cypher, SQL, or Gremlin, so existing team knowledge does not transfer.

When to Choose It Anyway

If versioning and provenance are the primary requirement rather than a nice-to-have, TerminusDB is the strongest option here and the comparison is not close.

7. Cayley

Licence: Apache 2.0 · Latest tagged release: v0.7.7 (October 2019) · Status: effectively dormant

Cayley is a linked-data graph database written in Go, inspired by the graph infrastructure behind Google’s Knowledge Graph. It has 15,045 GitHub stars, which is more than anything else on this list, and it is the clearest illustration of why star counts are a poor proxy for project health.

The Good

The design is genuinely nice. A Go binary with pluggable backends, a clean HTTP API, and a small footprint. For a read-mostly linked-data store it is pleasant to work with.

Permissively licensed with no vendor. Apache 2.0, no commercial edition, no strings.

The Problems

No tagged release since October 2019. That is the single fact that matters. The repository is not archived and there is occasional commit activity, but a database that has not cut a release in nearly seven years is not something to build a knowledge graph on in 2026. Bug fixes, security patches, and dependency updates are not arriving on any schedule you can rely on.

No vector search, no modern AI integration. Cayley predates the entire embedding era and has no answer for semantic retrieval.

Query languages are its own. Gizmo, a JavaScript-based query API, alongside GraphQL and MQL variants.

Verdict

We would not start a new knowledge graph on Cayley in 2026, and we would say the same if it were our own project. Included here because its star count keeps it near the top of search results, and readers deserve to know that the number reflects 2015 enthusiasm rather than 2026 maintenance.

8. Kuzu

Licence: MIT · Latest release: v0.11.3 (October 2025) · Status: archived

Kuzu was an embedded analytical graph database with a columnar storage engine and a Cypher interface, and it was genuinely good at what it did.

What Happened

The GitHub repository was archived in October 2025 following the team’s acquisition by Apple. Development stopped. The code remains available under MIT, and community forks exist, but there is no funded maintainer and no release schedule.

Strengths, While It Lasted

Excellent analytical performance. Kuzu’s columnar engine was fast on multi-hop analytical queries, and it still appears in benchmark comparisons, including our own LDBC results, where it is marginally faster than ArcadeDB on LSQB Q2. We are not going to pretend otherwise.

Embedded and Python-first. It brought embedded graph workloads to Python properly, which is exactly the niche arcadedb-embedded now occupies.

Limitations

It is archived. Everything else is secondary. An archived database is not a foundation for new infrastructure, however good the engine was.

MIT means the code survives. If you already run Kuzu, you are not stranded, and forking is legally straightforward. But you now own a database engine, which is a larger commitment than most teams intend to make.

The Comparison at a Glance

Star counts and release data verified against each project’s GitHub repository on 30 July 2026.

Why Licensing Matters for Knowledge Graphs

Knowledge graphs have a licensing problem that other databases do not, because they tend to end up embedded in products rather than sitting behind a service boundary.

A knowledge graph that powers an internal search tool is a service you deploy, and GPLv3 is largely irrelevant. A knowledge graph that ships inside a desktop application, an on-premise product, or a customer-installed agent is distributed software, and a copyleft licence reaches your code. Teams frequently discover this after the architecture is settled.

Apache 2.0, used by ArcadeDB, JanusGraph, TerminusDB, and Cayley, permits embedding in proprietary software with no source obligation. MIT, used by Kuzu, is similarly permissive. GPLv3, used by Neo4j Community Edition, does not, and the commercial escape hatch is priced accordingly.

The second licensing question is what the free edition withholds. Neo4j reserves clustering and embedded mode for Enterprise. ArcadeDB, JanusGraph, and TerminusDB do not have a paid edition holding features back at all.

So Which One Should You Choose?

Choose ArcadeDB if you want graph traversal, vector search, and full-text retrieval in one engine under a permissive licence, and you would rather not operate three databases to build one knowledge graph. Accept that you are picking the smallest community here.

Choose Neo4j Community Edition if ecosystem depth matters more than licence flexibility, your deployment is internal and single-instance, and your team already writes Cypher.

Choose Memgraph if retrieval latency is the dominant constraint, your graph fits in memory, and your legal team is comfortable with BSL 1.1.

Choose FalkorDB if you want fast GraphRAG retrieval, already operate Redis, and SSPL is acceptable.

Choose JanusGraph if your graph genuinely does not fit on one machine and you already operate Cassandra or HBase. Do not choose it to avoid that operational burden, because it adds to it.

Choose TerminusDB if data versioning, branching, and provenance are core requirements rather than conveniences.

Do not start on Cayley in 2026, despite the star count, unless you are prepared to maintain it.

Do not start on Kuzu, because it is archived, unless you are deliberately adopting a fork and accepting ownership.

Key Takeaways

• Six of these eight are actively maintained. Cayley has not cut a release since 2019, and Kuzu was archived in October 2025.
• GitHub stars measure historical enthusiasm, not current health. Cayley has the most stars and the least maintenance.
• Licensing determines whether you can embed the database in a distributed product. Apache 2.0 and MIT permit it; GPLv3 constrains it.
• GraphRAG needs graph traversal and vector similarity together. Three of these eight do both in one store: ArcadeDB, Memgraph, and FalkorDB. The other five expect a separate vector database.
• The GraphRAG shortlist comes down to licence: ArcadeDB is Apache 2.0, Memgraph is BSL, FalkorDB is SSPL. Only ArcadeDB is OSI open source, and only ArcadeDB is multi-model and embeddable.
• ArcadeDB’s genuine weakness is community size: it has the fewest GitHub stars of the eight.

Frequently Asked Questions

What is the best open source knowledge graph database in 2026?

It depends on what you already run. ArcadeDB, Memgraph, and FalkorDB all index vectors alongside the graph and can serve GraphRAG alone; they differ on licence, with only ArcadeDB being OSI open source. JanusGraph is better at scale on Cassandra or HBase, and TerminusDB is better when versioning matters most.

Which open source database is best for GraphRAG?

GraphRAG needs graph traversal and vector similarity in the same query. ArcadeDB, Memgraph, and FalkorDB all index embeddings in the same store as the graph, so any of the three can serve GraphRAG without a separate vector database. ArcadeDB is the only one of the three under an OSI-approved open source licence.

Is Neo4j Community Edition suitable for a production knowledge graph?

It can be, with two caveats. Community Edition is GPLv3, which is a copyleft licence that affects how you can distribute software built on it, and it does not support clustering, so it is limited to single-instance deployments. Both are Enterprise Edition features.

Which open source knowledge graph databases are still actively maintained?

As of July 2026, ArcadeDB, Memgraph, FalkorDB, TerminusDB, JanusGraph, and Neo4j all ship releases. Kuzu was archived on GitHub in October 2025 after its team was acquired by Apple. Cayley’s repository is not archived but its last tagged release, v0.7.7, dates from October 2019.

Do I need a separate vector database for a knowledge graph?

Not necessarily. A knowledge graph that also serves semantic search needs both relationships and embeddings. ArcadeDB indexes vectors natively with JVector alongside the graph, so a single query can rank by similarity and traverse edges. Most alternatives require pairing the graph with a separate vector store.

What is the difference between a knowledge graph and an RDF triplestore?

A triplestore models data strictly as subject-predicate-object triples and typically queries with SPARQL. A property graph attaches arbitrary properties to nodes and edges and queries with Cypher, Gremlin, or SQL. Both can express a knowledge graph; property graphs are usually easier for application developers.

Does JanusGraph need Cassandra or HBase to run?

JanusGraph is a graph layer rather than a complete storage engine, so it runs on top of a separate backend. It ships support for Apache Cassandra, Apache HBase, and Oracle Berkeley DB Java Edition. Berkeley DB suits local development; Cassandra or HBase is expected in production.

Getting Started with ArcadeDB

If the single-engine argument is the one that lands, the fastest way to test it is to build a small knowledge graph on your own data and see whether one query really can do all three retrieval modes.

docker run --rm -p 2480:2480 -p 2424:2424 \
  -e JAVA_OPTS="-Darcadedb.server.rootPassword=playwithdata" \
  arcadedata/arcadedb:latest

Then open ArcadeDB Studio at http://localhost:2480 and start modelling. For the concepts behind entity extraction, semantic search, and temporal knowledge, see knowledge graphs on ArcadeDB. If you are building retrieval for an LLM on top of it, GraphRAG covers the hybrid retrieval patterns. If you are migrating an existing graph, the Neo4j migration guide covers Cypher and Bolt compatibility.

ArcadeDB is Apache 2.0, free in production, with no node limits and no Enterprise edition. Benchmark it against whatever you run today and keep whichever wins.