Install MAGE graph algorithm library

Use MAGE with an instance installed within a Docker container, from a prebuilt package on Ubuntu or CentOS, or built from source.

Docker

Install Memgraph with Docker using memgraph-platform or memgraph-mage images which include the MAGE library so no additional installation is required to run the graph algorithms on your data.

You can download a specific version of MAGE

For example, if you want to download version 3.2, you should run the following command:

docker run -p 7687:7687 --name memgraph memgraph/memgraph-mage:3.2

The following tags are available on Docker Hub:

x.y - production MAGE image
x.y-relwithdebinfo - contains debugging symbols and gdb
x.y-malloc - Memgraph compiled with mallocinstead of jemalloc (x86_64 only)
x.y-relwithdebinfo-cuda - Memgraph built with CUDA support* - available since version 3.6.1.

*To run GPU-accelerated algorithms, you need to launch the container with the --gpus all flag. This requires the installation of NVIDIA Container Toolkit. See the NVIDIA Container Toolkit documentation for more details.

For versions prior to 3.2, MAGE image tags included both MAGE and Memgraph versions, e.g.

docker run -p 7687:7687 --name memgraph memgraph/memgraph-mage:3.1.1-memgraph-3.1.1

A no-ml image (e.g. 3.1.1-memgraph-3.1.1-no-ml) was also provided, but this has now been discontinued as of 3.2 onwards.

Install from a package

On Ubuntu 24.04 (DEB x86_64 and aarch64) and CentOS 9 / CentOS 10 (RPM, x86_64 only), MAGE is available as a prebuilt memgraph-mage package, so you don’t have to build it from source.

Install Memgraph

Install the Memgraph package first — the memgraph-mage package depends on a matching memgraph package of the same version. Follow the Ubuntu or CentOS installation guide.

Download the MAGE package

Download the memgraph-mage package that matches your Memgraph version and distro from the direct download links. For example:

# Ubuntu 24.04
wget https://download.memgraph.com/memgraph-mage/v3.12.0/ubuntu-24.04/memgraph-mage_3.12.0-1_amd64.deb
# CentOS 9
wget https://download.memgraph.com/memgraph-mage/v3.12.0/centos-9/memgraph-mage-3.12.0-1.centos-9.x86_64.rpm

CUDA and cuGraph variants are also available for Ubuntu — see the download links page.

Install MAGE

Install the package with your distribution’s package manager so its dependencies are resolved:

# Ubuntu (DEB)
sudo apt install ./memgraph-mage_3.12.0-1_amd64.deb
# CentOS (RPM)
sudo dnf install ./memgraph-mage-3.12.0-1.centos-9.x86_64.rpm

During installation the package downloads and installs the Python dependencies the MAGE query modules need, so the machine needs network access.

Restart Memgraph

Restart Memgraph so the newly installed modules are loaded:

sudo systemctl restart memgraph

Build from source (Linux)

Follow the steps if you want to use the MAGE library with installed Linux based Memgraph package.

Make sure the instance is not running

Algorithms and query modules will be loaded into a Memgraph instance on startup once you install MAGE, so make sure your instances are not running.

Install dependencies

To build from source, you will need:

Python3
Make
CMake
Clang
UUID
Rust and Cargo

Set up the machine

Run the following commands:

sudo apt-get update && sudo apt-get install -y \
    libcurl4 \
    libpython3.12 \
    libssl-dev \
    openssl \
    build-essential \
    cmake \
    curl \
    g++ \
    python3 \
    python3-pip \
    python3-setuptools \
    python3-dev \
    clang \
    git \
    pkg-config \
    uuid-dev \
    xmlsec1 \
    ninja-build \
    --no-install-recommends

Download the MAGE source code

Clone the Memgraph source code from GitHub:

git clone https://github.com/memgraph/memgraph.git && cd memgraph/

Set up the toolchain

Download and install the Memgraph Toolchain:

curl -L https://s3-eu-west-1.amazonaws.com/deps.memgraph.io/toolchain-v7/toolchain-v7-binaries-ubuntu-24.04-amd64.tar.gz -o toolchain.tar.gz
sudo tar xzvfm toolchain.tar.gz -C /opt

Install runtime dependencies for the toolchain:

sudo ./environment/os/install_deps.sh install TOOLCHAIN_RUN_DEPS

Install Rust and Python dependencies

Run the following command to install Rust and Python dependencies:

cd mage
curl https://sh.rustup.rs -sSf | sh -s -- -y 
export PATH="/root/.cargo/bin:${PATH}" 
python3 -m pip install -r python/requirements.txt 
python3 -m pip install -r ../src/auth/reference_modules/requirements.txt
python3 -m pip install torch-sparse torch-cluster torch-spline-conv torch-geometric torch-scatter -f https://data.pyg.org/whl/torch-2.6.0+cpu.html
python3 -m pip install dgl -f https://data.dgl.ai/wheels/torch-2.6/repo.html

To install the dependencies for GPU-accelerated algorithms, you need to use the GPU-specific requirements file:

python3 -m pip install -r python/requirements-gpu.txt

Run the `setup` script

Run the following command:

source /opt/toolchain-v7/activate
python3 setup build
sudo cp -r dist/* /usr/lib/memgraph/query_modules

If you don’t need all of the algorithms you can build only some of them based on the laguauge.

To build C++ based algorithms run:

python3 setup build --lang cpp

To build Python based algorithms run:

python3 setup build --lang python

The script will generate a dist directory with all the needed files.

It will also copy the contents of the newly created dist directory to /usr/lib/memgraph/query_modules. Memgraph loads algorithms and modules from this directory.

If something isn’t installed properly, the setup script will stop the installation process. If you have any questions, contact us on Discord.

Start a Memgraph instance

Algorithms and query modules will be loaded into a Memgraph instance on startup

If your instance was already running you will need to execute the following query to load them:

CALL mg.load_all();

If your changes are not loaded, make sure to restart the instance by running systemctl stop memgraph and systemctl start memgraph.

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