ArcadeDB MCP Server

Multi Model DBMS Built for Extreme Performance

                         

             

ArcadeDB is a Multi-Model DBMS created by Luca Garulli, the same founder of OrientDB, after SAP's acquisition. Written from scratch with a brand-new engine made of Alien Technology, ArcadeDB is able to crunch millions of records per second on common hardware with minimal resource usage. ArcadeDB reuses OrientDB's SQL engine (heavily modified) and some utility classes. It's written in LLJ: Low Level Java - still Java21+ but only using low level APIs to leverage advanced mechanical sympathy techniques and reduce Garbage Collector pressure. Highly optimized for extreme performance, it runs from a Raspberry Pi to multiple servers on the cloud.

ArcadeDB is fully transactional DBMS with support for ACID transactions, structured and unstructured data, native graph engine (no joins but links between records), full-text indexing, geospatial querying, and advanced security.

ArcadeDB supports the following models:

• Graph Database (compatible with Neo4j Cypher, Apache Tinkerpop Gremlin and OrientDB SQL)
• Document Database (compatible with the MongoDB driver + MongoDB queries and OrientDB SQL)
• Key/Value (compatible with the Redis driver)
• Search Engine
• Time Series (with InfluxDB Line Protocol, Prometheus remote_write/read, and PromQL support)
• Vector Embedding
• Geospatial

ArcadeDB understands multiple languages:

• SQL (from OrientDB SQL)
• Neo4j Cypher (Open Cypher)
• Apache Gremlin (Apache Tinkerpop v3.7.x)
• GraphQL Language
• MongoDB Query Language

ArcadeDB key capabilities:

• 70+ Built-in Graph Algorithms — Pathfinding, centrality, community detection, link prediction, graph embeddings, and more — all available out of the box
• Parallel Query Execution — SQL queries leverage multiple CPU cores for faster execution on large datasets
• Materialized Views — Pre-computed query results stored and automatically maintained
• MCP Server — Built-in Model Context Protocol server for AI assistant and LLM integration
• AI Assistant — Integrated AI assistant in Studio (Beta) for query help and database management
• Geospatial Indexing — Native spatial queries and proximity searches with geo.* SQL functions
• TimeSeries — Columnar storage with Gorilla/Delta-of-Delta compression, InfluxDB/Prometheus ingestion, PromQL queries, Grafana integration
• Hash Indexes — Extendible hashing for faster exact-match lookups alongside LSM-Tree indexes

ArcadeDB can be used as:

• Embedded from any language on top of the Java Virtual Machine
• Embedded from Python via bindings: arcadedb-embedded-python
• Remotely by using HTTP/JSON
• Remotely by using a Postgres driver (ArcadeDB implements Postgres Wire protocol)
• Remotely by using a Redis driver (only a subset of the operations are implemented)
• Remotely by using a MongoDB driver (only a subset of the operations are implemented)
• By AI assistants via the built-in MCP Server (Model Context Protocol)

For more information, see the documentation.

Use Cases

Explore real-world examples in the arcadedb-usecases repository — self-contained projects with Docker Compose, SQL schemas, and runnable demos covering:

• Recommendation Engine — graph traversal + vector similarity + time-series
• Knowledge Graphs — co-authorship and citation networks with full-text search
• Graph RAG — retrieval-augmented generation with LangChain4j and Neo4j Bolt
• Fraud Detection — graph, vector, and time-series signals with Cypher
• Real-time Analytics — IoT and service monitoring with time-series
• Social Network Analytics — materialized view dashboards with polyglot queries
• Supply Chain — multi-tier visibility with PostgreSQL protocol and JavaScript

Getting started in 5 minutes

Start ArcadeDB Server with Docker:

docker run --rm -p 2480:2480 \
           -e ARCADEDB_SETTINGS="-Darcadedb.server.rootPassword=playwithdata -Darcadedb.server.defaultDatabases=Imported[root]{import:https://github.com/ArcadeData/arcadedb-datasets/raw/main/orientdb/OpenBeer.gz}" \
           arcadedata/arcadedb:latest

Pass database settings in ARCADEDB_SETTINGS and any extra JVM flags in JAVA_OPTS: Docker replaces an environment variable rather than appending to it, so keeping the two apart leaves the image's own garbage collector and heap sizing (ARCADEDB_OPTS_GC and ARCADEDB_OPTS_MEMORY) intact. The heap is sized as a percentage of the container memory limit, so docker run -m 512m and a multi-GB production container both work without further tuning. The image runs on Java 25, where server.sh also enables compact object headers (-XX:+UseCompactObjectHeaders), after checking that the JVM accepts the flag (a bare distribution on Java 25 or later does the same); set ARCADEDB_OPTS_HEADERS to override it, or to an empty value to opt out.

Now open your browser on http://localhost:2480 and play with ArcadeDB Studio and the imported OpenBeer database to find your favorite beer.

ArcadeDB is cloud-ready with Docker and Kubernetes support.

You can also download the latest release, unpack it on your local hard drive and start the server with bin/server.sh or bin/server.bat for Windows.

Releases

There are four variants of (about monthly) releases:

• full - this is the complete package including all modules
• minimal - this package excludes the gremlin, redisw, mongodbw, graphql modules
• headless - this package excludes the gremlin, redisw, mongodbw, graphql, studio modules
• base - core engine, server, and network only — excludes all optional modules (console, gremlin, studio, redisw, mongodbw, postgresw, grpcw, graphql, metrics)

The nightly builds of the repository head can be found here.

You can also build a custom distribution with only the modules you need using the Custom Package Builder:

curl -fsSL https://github.com/ArcadeData/arcadedb/releases/download/26.3.1/arcadedb-builder.sh | \
  bash -s -- --version=26.3.1 --modules=gremlin,studio

Available optional modules: console, gremlin, studio, redisw, mongodbw, postgresw, grpcw, graphql, metrics. The builder supports interactive mode, Docker image generation, and offline builds from local Maven repositories.

An experimental GraalVM native-image build (fast startup, low RAM, no JVM required) is also available - see docs/native-image.md for prerequisites, the build/target matrix, and Docker usage.

Java Versions

Starting from ArcadeDB 24.4.1 code is compatible with Java 21.

Java 21 packages are available on Maven central and docker images on Docker Hub.

We also support Java 17 on a separate branch java17 for those who cannot upgrade to Java 21 yet through GitHub packages.

To use Java 17 inside your project, add the repository to your pom.xml and reference dependencies as follows:

<repositories>
    <repository>
        <name>github</name>
        <id>github</id>
        <url>https://maven.pkg.github.com/ArcadeData/arcadedb</url>
    </repository>
</repositories>
<dependencies>
<dependency>
    <groupId>com.arcadedb</groupId>
    <artifactId>arcadedb-engine</artifactId>
    <version>26.3.1-java17</version>
</dependency>
</dependencies>

Docker images are available on ghcr.io too:

docker pull ghcr.io/arcadedata/arcadedb:26.3.1-java17

Embedding Gremlin alongside the engine

Always use the shaded classifier for gremlin when embedding it, whether alongside arcadedb-engine or on its own. Its ANTLR runtime is relocated into a private package, so it never collides with the engine's ANTLR 4.13.2 on a shared classpath.

The plain arcadedb-gremlin jar resolves ANTLR to the engine's 4.13.2 (pulled transitively via arcadedb-engine), which the engine's SQL/Cypher parsers require. TinkerPop's Gremlin string-query parser ships a precompiled ANTLR 4.9.1 parser that only deserializes against the relocated runtime inside the shaded jar, so the plain jar alone will not run Gremlin string queries - use the shaded classifier.

<dependency>
    <groupId>com.arcadedb</groupId>
    <artifactId>arcadedb-engine</artifactId>
    <version>26.8.1</version>
</dependency>
<dependency>
    <groupId>com.arcadedb</groupId>
    <artifactId>arcadedb-gremlin</artifactId>
    <version>26.8.1</version>
    <classifier>shaded</classifier>
</dependency>

Building and Testing

Build the entire project (skipping tests):

mvn clean install -DskipTests

Build the Docker image (skipping tests):

mvn clean install -DskipTests -Pdocker

Running Unit Tests:

Run the full unit test suite:

mvn test

Some tests are tagged to indicate their cost:

• slow - functional tests that take noticeably long (large batches, multi-second elapsed time, big payloads)
• benchmark - microbenchmarks not intended for regular CI runs; excluded by default (see below)

benchmark-tagged tests are excluded by default, so a plain mvn test already skips them. To also skip slow tests and run only the fast ones:

mvn test -DexcludedGroups="slow,benchmark"

To run only a specific tag (e.g. benchmark tests in isolation), clear the default exclusion or it cancels out the selection and nothing runs:

mvn test -Dgroups="benchmark" -DexcludedGroups=

Running Integration Tests:

Run all the integration tests (requires Docker):

mvn verify -Pintegration

Run integration tests excluding the end-to-end, load, and HA tests:

mvn verify -Pintegration -pl !e2e,!load-tests,!e2e-ha

Running End-to-End Tests:

All end-to-end tests (requires Docker):

mvn verify -Pintegration -pl e2e,load-tests,e2e-ha

Test Suites at a Glance

The codebase is covered by several complementary test suites, each with a distinct scope. The "CI" column says when each one runs; the scheduled ones have a status badge at the top of this page.

Suite	How it runs	CI	Scope
Unit tests	mvn test (*Test)	every PR	Fast, in-process tests of a single component in isolation: engine internals (storage, pages, WAL, indexes, serialization), query parsing and execution (SQL, Cypher, Gremlin, GraphQL), schema, graph traversals, and security. The bulk of coverage; no external services required. CI splits them into lanes by JUnit tag: slow (long functional tests) and vector (LSM vector-index rebuilds) run in their own lanes, benchmark is excluded by default.
Integration tests	mvn verify -Pintegration (*IT)	every PR	Tests spanning multiple components or a running server within the same JVM/module: HTTP/REST API, wire protocols (Postgres, MongoDB, Redis, Bolt, gRPC), and cross-module behavior. Some require Docker.
HA integration (ha-raft)	mvn verify -Pintegration -pl ha-raft	daily	Raft high-availability clusters of several servers started inside one JVM: replication, leader election, schema and security propagation, snapshot install, and catch-up of lagging followers. Moved out of the PR pipeline because of its length.
End-to-end (e2e)	mvn verify -Pintegration -pl e2e	every PR	Black-box tests against a real ArcadeDB server in a Docker container (Testcontainers), exercising it the way external clients do: JDBC/Postgres queries, the remote Java API, server-side JavaScript functions, and the Bolt and gRPC drivers.
Load tests (load-tests)	mvn verify -Pintegration -pl load-tests	daily	Throughput and stability under sustained concurrent workloads against single-server and three-node clusters in containers, including high-volume document and time-series ingestion. Verifies no data loss or corruption under contention.
HA end-to-end (e2e-ha)	mvn verify -Pintegration -pl e2e-ha	daily	Resilience and correctness of the Raft cluster under scripted failures: leader failover, rolling restarts, split-brain, network partitions/delay/packet loss, replication convergence, and cluster-wide operations (backup/restore, import, drop database, user management). Uses Testcontainers and fault injection (Toxiproxy).
HA chaos (HaChaosIT)	mvn verify -Pintegration -pl e2e-ha -Dit.test=HaChaosIT -Dfailsafe.excludedGroups=	weekly	Long randomized run (60 minutes by default) against a containerized cluster: concurrent writers while a seeded scheduler injects faults (kill, stop, rolling restart, pause, isolate, split, latency, packet loss, and a follower frozen for more than 60 s that must recover without reformatting its Raft storage), and every acknowledged write is checked against all replicas after each step. Each run prints its seed so a failure can be replayed with -Dchaos.seed=...; tune with -Dchaos.nodes, -Dchaos.duration, -Dchaos.faults, -Dchaos.writers. Tagged chaos, so the plain e2e-ha run skips it.
Benchmarks	mvn verify -Dgroups=benchmark -DexcludedGroups=	weekly	Microbenchmarks and comparison runs (@Tag("benchmark")). They measure rather than assert, and are excluded from every other run.
Python client (e2e-python)	cd e2e-python && pytest tests/	every PR	Verifies the Postgres wire protocol against real Python clients (psycopg2, asyncpg) and the SQLAlchemy ORM, and the Bolt protocol against the official neo4j driver, running against a server in a Docker container (Testcontainers).
JavaScript client (e2e-js)	cd e2e-js && npm install && npm test	every PR	Verifies Node.js client compatibility over the Bolt (neo4j-driver) and Postgres (pg) protocols, running against a server in a Docker container (Jest + Testcontainers).
C# client (e2e-csharp)	cd e2e-csharp/ArcadeDB.E2ETests && dotnet test	every PR	Verifies the Postgres wire protocol (Npgsql) and the Bolt protocol (Neo4j.Driver, plain and TLS) from .NET, running against a server in a Docker container (xUnit + Testcontainers).
Go client (e2e-go)	cd e2e-go && go test ./...	every PR	Bolt conformance with the official neo4j-go-driver, plain and TLS, against a server in a Docker container. Implements the shared scenario spec in bolt/conformance/spec.yaml, like the other client suites.
Bolt driver matrix	bolt-nightly.yml workflow	daily	Reruns the Bolt tests of the JavaScript, Python, C# and Go suites against several released versions of each official driver, and publishes the result in COMPATIBILITY.md.
Studio (e2e-studio)	cd e2e-studio && npm install && npm test	every PR	Browser tests of the Studio web UI with Playwright: database creation, queries and charts, graph view (styling, context menu, export), table views, and vector search.

Community

Join our growing community around the world, for ideas, discussions and help regarding ArcadeDB.

• Chat live with us on Discord
• Follow us on Twitter
• or on Bluesky
• Connect with us on LinkedIn
• or on Facebook
• Questions tagged #arcadedb on Stack Overflow
• View our official Blog

Security

For security issues kindly email us at support@arcadedb.com instead of posting a public issue on GitHub.

License and Attribution

ArcadeDB is Free for any usage and licensed under the liberal Open Source Apache 2 license. We are committed to remaining Open Source Forever — see our Governance for the structural guarantees that make this more than a promise. If you need commercial support, or you need to have an issue fixed ASAP, check our pricing page.

For third-party attributions and copyright notices, see:

• NOTICE - Required legal attributions
• ATTRIBUTIONS.md - Detailed third-party acknowledgments
• LICENSE - Full license text
• GOVERNANCE.md - License guarantee and project governance

Thanks To

for providing YourKit Profiler to our committers.

Contributing

We would love for you to get involved with ArcadeDB project. If you wish to help, you can learn more about how you can contribute to this project in the contribution guide.

Have fun with data!

The ArcadeDB Team

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