Pre-release

This release is a pre-release and may not be stable for production use.

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')}")

👉 See full tutorial

✨ 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.

Build instructions

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
├── citation.py          # Citation helper
├── 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

Architecture details

🤝 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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

arcadedb_embedded-26.8.1.dev25-cp314-cp314-win_amd64.whl (67.3 MB view details)

Uploaded Aug 1, 2026 CPython 3.14Windows x86-64

arcadedb_embedded-26.8.1.dev25-cp314-cp314-manylinux_2_34_x86_64.whl (70.4 MB view details)

Uploaded Aug 1, 2026 CPython 3.14manylinux: glibc 2.34+ x86-64

arcadedb_embedded-26.8.1.dev25-cp314-cp314-manylinux_2_34_aarch64.whl (69.3 MB view details)

Uploaded Aug 1, 2026 CPython 3.14manylinux: glibc 2.34+ ARM64

arcadedb_embedded-26.8.1.dev25-cp314-cp314-macosx_11_0_arm64.whl (65.6 MB view details)

Uploaded Aug 1, 2026 CPython 3.14macOS 11.0+ ARM64

arcadedb_embedded-26.8.1.dev25-cp313-cp313-win_amd64.whl (66.9 MB view details)

Uploaded Aug 1, 2026 CPython 3.13Windows x86-64

arcadedb_embedded-26.8.1.dev25-cp313-cp313-manylinux_2_34_x86_64.whl (70.4 MB view details)

Uploaded Aug 1, 2026 CPython 3.13manylinux: glibc 2.34+ x86-64

arcadedb_embedded-26.8.1.dev25-cp313-cp313-manylinux_2_34_aarch64.whl (69.3 MB view details)

Uploaded Aug 1, 2026 CPython 3.13manylinux: glibc 2.34+ ARM64

arcadedb_embedded-26.8.1.dev25-cp313-cp313-macosx_11_0_arm64.whl (65.6 MB view details)

Uploaded Aug 1, 2026 CPython 3.13macOS 11.0+ ARM64

arcadedb_embedded-26.8.1.dev25-cp312-cp312-win_amd64.whl (66.9 MB view details)

Uploaded Aug 1, 2026 CPython 3.12Windows x86-64

arcadedb_embedded-26.8.1.dev25-cp312-cp312-manylinux_2_34_x86_64.whl (70.4 MB view details)

Uploaded Aug 1, 2026 CPython 3.12manylinux: glibc 2.34+ x86-64

arcadedb_embedded-26.8.1.dev25-cp312-cp312-manylinux_2_34_aarch64.whl (69.3 MB view details)

Uploaded Aug 1, 2026 CPython 3.12manylinux: glibc 2.34+ ARM64

arcadedb_embedded-26.8.1.dev25-cp312-cp312-macosx_11_0_arm64.whl (65.6 MB view details)

Uploaded Aug 1, 2026 CPython 3.12macOS 11.0+ ARM64

arcadedb_embedded-26.8.1.dev25-cp311-cp311-win_amd64.whl (66.9 MB view details)

Uploaded Aug 1, 2026 CPython 3.11Windows x86-64

arcadedb_embedded-26.8.1.dev25-cp311-cp311-manylinux_2_34_x86_64.whl (70.4 MB view details)

Uploaded Aug 1, 2026 CPython 3.11manylinux: glibc 2.34+ x86-64

arcadedb_embedded-26.8.1.dev25-cp311-cp311-manylinux_2_34_aarch64.whl (69.3 MB view details)

Uploaded Aug 1, 2026 CPython 3.11manylinux: glibc 2.34+ ARM64

arcadedb_embedded-26.8.1.dev25-cp311-cp311-macosx_11_0_arm64.whl (65.6 MB view details)

Uploaded Aug 1, 2026 CPython 3.11macOS 11.0+ ARM64

arcadedb_embedded-26.8.1.dev25-cp310-cp310-win_amd64.whl (66.9 MB view details)

Uploaded Aug 1, 2026 CPython 3.10Windows x86-64

arcadedb_embedded-26.8.1.dev25-cp310-cp310-manylinux_2_34_x86_64.whl (70.4 MB view details)

Uploaded Aug 1, 2026 CPython 3.10manylinux: glibc 2.34+ x86-64

arcadedb_embedded-26.8.1.dev25-cp310-cp310-manylinux_2_34_aarch64.whl (69.3 MB view details)

Uploaded Aug 1, 2026 CPython 3.10manylinux: glibc 2.34+ ARM64

arcadedb_embedded-26.8.1.dev25-cp310-cp310-macosx_11_0_arm64.whl (65.6 MB view details)

Uploaded Aug 1, 2026 CPython 3.10macOS 11.0+ ARM64

File details

Details for the file arcadedb_embedded-26.8.1.dev25-cp314-cp314-win_amd64.whl.

File metadata

Download URL: arcadedb_embedded-26.8.1.dev25-cp314-cp314-win_amd64.whl
Upload date: Aug 1, 2026
Size: 67.3 MB
Tags: CPython 3.14, Windows x86-64
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for arcadedb_embedded-26.8.1.dev25-cp314-cp314-win_amd64.whl
Algorithm	Hash digest
SHA256	`28fdd2b00e07f389bee35d52481c2f136ec58e74b3acee9940c1c7a65f40d309`
MD5	`cc7dace4f64894612ed79135761a0a3e`
BLAKE2b-256	`c7b3da17602b336112b06236e42015004b385db2a112a5163f14662ff1ac33a0`

Algorithm	Hash digest
SHA256	`cf8bf9bc40c70de40f93bcd77ff2ec892a03321f913cd0315f99b85d68a330cc`
MD5	`2cd9abd1813a105b218ab73b311f5d09`
BLAKE2b-256	`b9ff2b364f69c030b3fe2135651e31677c4fc2e3e443e79b7388282faec1a74f`

Algorithm	Hash digest
SHA256	`d13e62e9c4ed259e8d1d4c2c7f238570e7f9f1084f16d14fb9e75ddcd1d9b057`
MD5	`94b5e2c8079257b11cd1233da50fcf5e`
BLAKE2b-256	`e4ea809aa83dda32496a1e05dc49d42adc4844a2841fbacabe457b15460518d7`

Algorithm	Hash digest
SHA256	`d4bfbc685cdfe05598df546c56913157f69195f7e928c418870792ecbd820c9a`
MD5	`e514785ab7413a11e9117050ced3b9e3`
BLAKE2b-256	`65ed43ddc1d742b923cb11639a3794f1c28856089372d1de4e84cd107b4c27b4`

Algorithm	Hash digest
SHA256	`cbe51e4d30a09f3b5a11d66a799b8e47dbfc66f29358da07bd2bf3ac02b13cf1`
MD5	`700b6104ff510554d51cfc1fd3513730`
BLAKE2b-256	`5edd43ece94c8b71f32341fcf845d7473568800ba08a224fcb0b658fb24ff881`

Algorithm	Hash digest
SHA256	`93d158e214fe3ab654009c073fb600726b782a10835470e7a227529de4ae5e4b`
MD5	`d362659f974bd44550d39727ba1ecca5`
BLAKE2b-256	`fae620998c2102756e0a8af6fb9c2d24232cfc42484e1bec3e40394677feca81`

Algorithm	Hash digest
SHA256	`bc4e0587c95be29a2c6dfd22b62ee515e2ab29af8b9f7b588f608568960617f0`
MD5	`62c9a67cebe18eee977b7acaee0d29ca`
BLAKE2b-256	`bb9bcaedc1f33653e66fe47bb6d9c744ea6085bdaa44c54a38d1eee3950cad55`

Algorithm	Hash digest
SHA256	`03ac45144fcda5516f8a013b9f203ee0ec4e4f9d16748ac62be39ae45a5b5608`
MD5	`8e3e84d9e72d65dab4ad70ed40897c5f`
BLAKE2b-256	`907dba9c47f38c755e03f98a70eb7d435b304323e692018a4bdc91b07bd101bc`

Algorithm	Hash digest
SHA256	`aeb8adc257096789de439d2fa46ce70c5e8f6b8aa3c667592368d808e1beaa5f`
MD5	`8b52a9d9c35e50fca10b2c23e9a2fd58`
BLAKE2b-256	`c4a2c1156346ea4c4d9f030410272fa5b5b0ac25bd2fe4ebb8ba3b97c909277f`

Algorithm	Hash digest
SHA256	`c3a84f5bc3712e19c25520978a2feae8489c9ddaa973913bb407034e81576786`
MD5	`0f83d9ee42ba5d8a54f27ecab02765fb`
BLAKE2b-256	`dea44725eb25ebf0cf0910b1ea945c095572808a3bd82386df6ffafc2921182c`

Algorithm	Hash digest
SHA256	`839057c9c8fbd371c2567d39c69498f548ccfe7316944d3e68c4602b945d41cd`
MD5	`35156d61b1c41cc7808d5e6b81065c33`
BLAKE2b-256	`10262777ea5bec864c2beaab6c8a30b87b733b64ad4420ec65e7e1a15d3ff318`

Algorithm	Hash digest
SHA256	`2ceef0e66e0ba35cebb1234d9fe53802b4b0ed513541bef0edab2db3f525739c`
MD5	`f2a775ff854716062ddcc91ebbd4e56d`
BLAKE2b-256	`91b91f8ca97ab012555de1cb48a7335580d88ac687ccadf9aba22f2efd6de1e2`

Algorithm	Hash digest
SHA256	`92ff368ec80805b67da5cfe63a87ca3661f32e2987bb8dca5d5af6b6a895db06`
MD5	`4cbffd5a9b8494e562798381e696ecd7`
BLAKE2b-256	`f066c64953026b16a7bad7b89dfbd8e588c34f63dc3ba7092d450607a81549a0`

Algorithm	Hash digest
SHA256	`29218df416d108e706ccb5dbaffe96ef88f8f35ea8885a4aa295bacf95f4df54`
MD5	`371692d7d556d14e65e0dd7bfcaf0c99`
BLAKE2b-256	`a9999e75f0f5f8a5ce565b8065e22f038156b73dbf9314af6ce5705e2ac642bf`

Algorithm	Hash digest
SHA256	`7c40692bbebd1e1f2e99730410d9f385782360cb2f156fe949591ca88460e9bc`
MD5	`558b67f8f580fc55095657501a4ca02c`
BLAKE2b-256	`3f47297576acd08637f9a1f7b392bfebfa976feee3b00f175d5cb276e249f784`

Algorithm	Hash digest
SHA256	`3fe361c2d8d5b0fa49cba78d1223d0c9bf34bbab3577202d1570805c5f523eed`
MD5	`2d4f30f2d88fae25830d415694c62f31`
BLAKE2b-256	`0b96b092851c7dc0e087d00c43b0cff831fd716808b6172e5da5f99c707165a6`

Algorithm	Hash digest
SHA256	`d0dc352fdfb998e1e7da3f095e9538a289b796e46b51304823278135115c0cef`
MD5	`ea2a7b2dbd31ab2f51e68571020524b5`
BLAKE2b-256	`f7676f4c0658963d8779d820594277fe4ca9667b595a1b99be91c521438537ff`

Algorithm	Hash digest
SHA256	`76fbb40019d7f4be471e1a8fccc58cdb0f6a0b97e34bce4e26e4c8bea3ee7810`
MD5	`30b853b1dc0a8a874a441c60c4efc3cb`
BLAKE2b-256	`15e3682f56fc70dbbf29a77f34707ce2e0299bb14e4c2e8433e2753fd174b3c0`

Algorithm	Hash digest
SHA256	`38354d1b46ab3f795eaaf57d23109697b20141dfcd48638d14c83c64be2b5133`
MD5	`7a56498f792d8a1522f389cce16a1796`
BLAKE2b-256	`978a8f27c9d1f1d98ba4e059aa15e59b4bc9031aee6136ca5e60bc482de1939d`

Algorithm	Hash digest
SHA256	`12d55ffc327f4efc448235844d0f3f702848387cd7979f99aec422ebb741cc91`
MD5	`cc107c24a301a8a116bfc7226d61f163`
BLAKE2b-256	`b68bf29d01836a007e33f6b660c7ac0190efb18f248652c81bdc4906dab00af5`

arcadedb-embedded 26.8.1.dev25

ArcadeDB Python Bindings

📚 Documentation

🚀 Quick Start

Installation

5-Minute Example

✨ Features

📦 What's Inside

🧪 Testing

🔧 Building from Source (Advanced)

Development

📋 Package Structure

🤝 Contributing

📄 License

🔗 Links

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance