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

MAGE is available as a prebuilt memgraph-mage package, so you don’t have to build it from source. As of Memgraph 3.13, a single distro-agnostic DEB and RPM package is shipped per architecture. MAGE currently requires Python 3.12; it is tested on Ubuntu 24.04 and CentOS 9/10 — other distributions may work but are untested.

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:

# DEB (Debian/Ubuntu)
wget https://download.memgraph.com/memgraph-mage/v3.13.0/deb/memgraph-mage_3.13.0-1_amd64.deb
# RPM (RHEL/CentOS/Rocky/Fedora)
wget https://download.memgraph.com/memgraph-mage/v3.13.0/rpm/memgraph-mage-3.13.0_1-1.x86_64.rpm

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

Install MAGE

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

# DEB (Debian/Ubuntu)
sudo apt install ./memgraph-mage_3.13.0-1_amd64.deb
# RPM (RHEL/CentOS/Rocky/Fedora)
sudo dnf install ./memgraph-mage-3.13.0_1-1.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.

Download the Memgraph source code

MAGE is developed and built as part of the Memgraph repository. Clone the Memgraph source code from GitHub (install git first if you don’t have it — sudo apt-get install -y git):

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

Install dependencies

The repository ships scripts that install everything the toolchain and the build need — build.sh checks for both sets and stops if anything is missing:

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

Set up the toolchain

Download and install the Memgraph Toolchain:

curl -L https://s3-eu-west-1.amazonaws.com/deps.memgraph.io/toolchain-v8/toolchain-v8-binaries-x86_64.tar.gz -o toolchain.tar.gz
sudo tar xzvfm toolchain.tar.gz -C /opt

Install Rust and Python dependencies

Run the following commands from the root of the repository to install Rust and the Python packages the MAGE query modules use at runtime:

source environment/util.sh
install_rust 1.89
python3 -m pip install -r src/mage/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-2.8.0+cpu.html
python3 -m pip install dgl -f https://data.dgl.ai/wheels/torch-2.8/repo.html

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

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

Build and install MAGE

MAGE is built with the same build system as Memgraph. Run the following commands from the root of the repository:

source /opt/toolchain-v8/activate
./build.sh --mage only
sudo cmake --install build --component mage --prefix /usr

./build.sh --mage only builds just the MAGE query modules (C++, Python and Rust) without Memgraph itself — the script sets up everything else it needs (a Python virtual environment, the Conan package manager and the project’s dependencies) on first run. The built modules land in build/mage/dist.

The cmake --install command then installs the modules to /usr/lib/memgraph/query_modules, the directory Memgraph loads query modules from, together with the runtime libraries they need.

If you don’t need all of the algorithms, you can build a subset by passing specific targets:

# Only the Python modules (a copy step - fast)
./build.sh --mage only --target mage_python_modules
 
# Only the Rust modules
./build.sh --mage only --target mage_rust_modules
 
# Individual C++ modules by name
./build.sh --mage only --target map text

If something isn’t set up properly, the build will stop with an error. 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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