Graphileon Cloud has arrived!

Today we launch Graphileon Cloud (beta), allowing users to run a free Graphileon Personal Edition in a fully managed environment on AWS. It offers the same functionality as the Personal Edition for desktop and comes with two pre-installed Memgraph graph stores. Just go to, create an account and spin up an environment in under 3 minutes.


It was only a matter of time that Graphileon would move to the cloud. We see an increasing number of suppliers offering “graph database as a service”, including Memgraph, Neo4j, Redis Labs, Graphgrid, and GrapheneDB.  From now on, their customers can explore and manage their graph data by adding a connection in Graphileon. In addition, they can build applications using the low-code Graphileon approach, just by connecting instances of the 40+ Graphileon functions (nodes) by triggers (relationships). As you can see, we consider applications as graphs, because that’s what they are.

Your app is a graph

The Swiss knife for graphs

With Graphileon functions, a wide range of applications can built in just a couple of hours. The Graphileon App Library contains many examples that can be installed with a couple of clicks. Use Graphileon to explore your data, to build prototypes, or to run applications in production. It’s the one tool you need, your Swiss knife.

The Graphileon Cloud Configuration Contest (GC3)

Graphileon Cloud Configuration Contest logoWe’ve seen people build amazing things in Graphileon and every new use case is an inspiration for us to extend Graphileon’s possibilities. That’s why we launch a monthly contest in which Graphileon Cloud users can compete for prizes and show off their Graphileon skills. Read more about it here.
Winning submissions will be added to the Graphileon App Library. This way, they will serve as a source of inspiration to others and help more people leverage the possibilities of graph technology.


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Graphileon and ChatGPT having a phenomenal conversation. Part 1

Graphileon and ChatGPT had a conversation where they discussed a phenomenon, such as "obesity in children," and ChatGPT was tasked with finding cause-and-effect relationships related to that phenomenon. The response from ChatGPT was processed by Graphileon, which created a cause-and-effect graph. Graphileon then generated new prompts that could be submitted to ChatGPT again. The result was a funding proposal to address childhood obesity through community programs and education, which aimed to increase access to healthy food options, provide nutritional education, increase access to safe and convenient outdoor spaces, and create awareness campaigns. The proposal included evaluating the effectiveness of the programs by collecting data.

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Introducing Schema-Based Graph Editing and Exploration

Graphs can be created in a variety of ways, whether it’s through importing large amounts of data [Neo4j, Memgraph, Amazon Neptune] or incrementally through manual creation based on expert knowledge. Regardless of the creation method, being able to edit individual nodes and relationships, in addition to visualizing and querying the network, is essential. However, graph … Continued

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Graph(ileon): Powerful tooling to plan procurement and production in food processing

In food processing and packaging, it is essential that stock levels of fresh ingredients are sufficient, food processing machines are operational and the staff to operate them are available.  This is the field in which one of our customers, The Power Central, has been operating since 2020. Besides its own food packaging activities, The Power … Continued

Get started with Graphileon Personal Edition in the cloud or on your desktop.

The easiest way to get to know Graphileon is to spin up the Personal Edition in the Graphileon Cloud. It comes with two graph stores installed and access to the App Library with examples and apps. You can also download and install Graphileon Personal Edition to run it on your desktop. Either way, you will be able to build graphy applications an browse your graph stores in a way you never did before.