ArcadeDB Python Bindings
Native Python bindings for ArcadeDB - the multi-model database that supports Graph, Document, Key/Value, Search Engine, Time Series, and Vector models.
Status: ✅ Production Ready | Tests: 397 Passed | Platforms: 4 Supported
📚 Documentation
📖 Read the Full Documentation →
🚀 Quick Start
Installation
uv add arcadedb-embedded # or: pip install arcadedb-embedded
Requirements:
- Python 3.10–3.14 (packaged/tested on CPython 3.12) - No Java installation required!
- Supported Platforms: Prebuilt wheels for 4 platforms
- Linux: x86_64, ARM64
- macOS: Apple Silicon (ARM64)
- Windows: x86_64
5-Minute Example
import arcadedb_embedded as arcadedb
# Create database (context manager for automatic open and close)
with arcadedb.create_database("./mydb") as db:
# Create schema (DDL)
db.command("sql", "CREATE DOCUMENT TYPE Person")
db.command("sql", "CREATE PROPERTY Person.name STRING")
db.command("sql", "CREATE PROPERTY Person.age INTEGER")
# Insert data (requires transaction)
with db.transaction():
db.command("sql", "INSERT INTO Person SET name = 'Alice', age = 30")
# Query data
result = db.query("sql", "SELECT FROM Person WHERE age > 25")
for record in result:
print(f"Name: {record.get('name')}")
✨ Features
- ☕ No Java Installation Required: Bundled JRE (~63MB uncompressed)
- 🌍 4 Platforms Supported: Linux (x86_64, ARM64), macOS (ARM64), Windows (x86_64)
- 🚀 Embedded Mode: Direct database access in Python process (no network)
- 🌐 Server Mode: Optional in-process HTTP server with the Studio web UI — costs ~8MB of wheel and nothing at runtime until you call
create_server() - 📦 Self-contained: All dependencies bundled (~67MB current Linux wheel)
- 🔄 Multi-model: Graph, Document, Key/Value, Vector, Time Series
- 🔍 Multiple query languages: SQL, OpenCypher
- ⚡ High performance: Direct JVM integration via JPype
- 🔒 ACID transactions: Full transaction support
- 🎯 Vector storage: Store and query vector embeddings with HNSW (JVector) indexing
- 📥 Data import: Built-in CSV and ArcadeDB JSONL import
📦 What's Inside
The arcadedb-embedded package is platform-specific and self-contained:
Package Contents (current Linux x86_64 dev build; varies by platform and version):
- Wheel size (compressed): ~67MB
- ArcadeDB JARs (uncompressed): ~31MB across 63 JARs
- Bundled JRE (uncompressed): ~63MB (platform-specific Java 25 runtime via jlink)
- Installed package size: ~94MB
The compressed wheel size is measured from dist/*.whl, and the installed package size
is measured from the extracted site-packages/arcadedb_embedded/ directory.
Of that, the optional server stack is 12 JARs, 7.65MB uncompressed, and it adds ~8MB to the wheel (the extra ~0.8MB beyond the JARs is JRE modules that only the server needs). Server Mode breaks the cost down and explains what you pay at runtime (nothing, until you start a server).
Note: Some JARs are excluded to optimize package size (e.g., gRPC wire protocol). See scripts/jar_exclusions.txt for details.
Import: import arcadedb_embedded as arcadedb
🧪 Testing
Status: 397 passed
Tests run against the built wheel via the uv project at the repo root — no
virtualenv activation needed, and uv run works from anywhere in the repo:
# Run all tests
uv run pytest
# Run specific test file
uv run pytest tests/test_core.py -v
See testing documentation for detailed test documentation.
🔧 Building from Source (Advanced)
Linux uses Docker. macOS and Windows use a native Java 25+ JDK with jlink.
cd bindings/python/
# Install uv (one-time)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Build for your current platform (auto-detected)
./scripts/build.sh
Built wheels will be in dist/. The build script also refreshes the uv dev
environment at the repo root, so uv run pytest immediately tests the wheel
you just built.
Developer Note: See build architecture docs for comprehensive documentation of the multi-platform build architecture, including how we achieve platform-specific JRE bundling across the supported platforms on GitHub Actions.
Development
Versions are automatically extracted from the main ArcadeDB pom.xml. See versioning strategy for details on development vs release mode handling.
📋 Package Structure
arcadedb_embedded/
├── async_executor.py # Asynchronous command/query + record execution
├── core.py # Database and DatabaseFactory
├── exceptions.py # ArcadeDBError exception
├── exporter.py # Data export (JSONL, GraphML, GraphSON, CSV)
├── graph.py # Graph wrappers
├── graph_batch.py # GraphBatch high-throughput graph ingest wrapper
├── importer.py # Data import (CSV, XML, ArcadeDB JSONL)
├── __init__.py # Public API exports
├── jvm.py # JVM lifecycle management
├── _logging.py # Internal logging helpers
├── results.py # ResultSet and Result wrappers
├── schema.py # Schema management API
├── server.py # ArcadeDBServer for optional HTTP/Studio mode
├── transactions.py # TransactionContext manager
├── type_conversion.py # Python-Java type conversion utilities
└── vector.py # Vector search and HNSW (JVector) indexing
🤝 Contributing
See our contributing guidelines
📄 License
Both upstream ArcadeDB (Java) and this ArcadeDB Embedded Python project are licensed under Apache 2.0, fully open and free for everyone, including commercial use.
🔗 Links
- Documentation: https://docs.humem.ai/arcadedb/latest/
- PyPI: https://pypi.org/project/arcadedb-embedded/
- GitHub: https://github.com/humemai/arcadedb-embedded-python
- ArcadeDB Main Docs: https://docs.arcadedb.com
- Issues: https://github.com/humemai/arcadedb-embedded-python/issues
1 maintainer
Metadata
1 maintainer
Release files for arcadedb-embedded 26.9.1
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Built distributions (wheels)
Total release size: 1.4 GB
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| Tags | CPython 3.10 macOS 11.0+ ARM64 |
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