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The features below aren’t included in the default GQLAlchemy installation. To use them, make sure to install GQLAlchemy with the docker extra.

How to manage Memgraph Docker instances in Python

Through this guide, you will learn how to start, stop, connect to and monitor Memgraph instances with GQLAlchemy.


You can also use this feature with Neo4j:

db = Neo4j(host="localhost", port="7687", username="neo4j", password="test")

First, perform all the necessary imports:

from gqlalchemy.instance_runner import (

Start the Memgraph instance

The following code will create a Memgraph instance, start it and return a connection object:

memgraph_instance = MemgraphInstanceDocker(
    docker_image=DockerImage.MEMGRAPH, docker_image_tag="latest", host="", port=7687
memgraph = memgraph_instance.start_and_connect(restart=False)

We used the default values for the arguments:

  • docker_image=DockerImage.MEMGRAPH: This will start the memgraph/memgraph Docker image.
  • docker_image_tag="latest": We use the latest tag to start the most recent version of Memgraph.
  • host="": This is the wildcard address which indicates that the instance should accept connections from all interfaces.
  • port=7687: This is the default port Memgraph listens to.
  • restart=False: If the instance is already running, it won't be stopped and started again.

After we have created the connection, we can start querying the database:

memgraph.execute_and_fetch("RETURN 'Memgraph is running' AS result"))[0]["result"]

Pass configuration flags

You can pass configuration flags using a dictionary:

config={"--log-level": "TRACE"}
memgraph_instance = MemgraphInstanceDocker(config=config)

Stop the Memgraph instance

To stop a Memgraph instance, call the stop() method:


Check if a Memgraph instance is running

To check if a Memgraph instance is running, call the is_running() method:


Where to next?

Hopefully, this guide has taught you how to manage Memgraph Docker instances. If you have any more questions, join our community and ping us on Discord.