Visualize and interact with your graph data to better understand your results and extract insights. Memgraph Lab can also visualize geospatial data by automatically detecting nodes that have numerical lat and lng properties.
Profile and optimize your Cypher queries by analyzing diagnostic information and execution plan details. This way, you can detect errors in your queries and construct more efficient execution plans.
Get a better understanding of your models by creating visual representations of schema objects. Learn about the structure of your network and find out which properties are included in your nodes and relationships.
Memgraph Lab lets you implement and manage custom procedures called query modules. You can extend the Cypher query language with procedures written in Python, C/C++, and Rust.
Create and manage transformation modules that enable you to stream real-time data directly into Memgraph. Connect to sources such as Apache Kafka, Apache Pulsar, and Redpanda.
Memgraph Lab was created as part of Memgraph — an open-source platform for graph computation on streaming data that includes a suite of ecosystem tools like MemgraphDB, a graph database, MAGE, a graph algorithm library, and more.
Visualize and explore graphs by querying your data in MemgraphDB.
Write Cypher queries with the help of code completion, quick info, and member lists.
Learn how your data is connected and what the graph model looks like.
Memgraph Lab lets you write and manage custom procedures implemented in Python.
Inspect logs directly from Memgraph Lab instead of searching for log files on servers.
Design your own layout with split-screen views.
Create streams and transformation modules for analyzing real-time data.
Import and export graph data in the form of Cypher queries.
Load datasets directly from Memgraph Lab in order to get started quickly.
Style your graphs by using the Graph Style Script language.
Customize the look of your graph data by using the Graph Style Script language. It allows you to style labels, colors, and images of nodes and edges.