Building the Next-Gen Agentic App with GraphRAG & NVIDIA cuGraph

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Imagine competing in a high-stakes hackathon where the most cutting edge of AI - Agentic Applications, GraphRAG on ArangoDB, and NVIDIA GPU-accelerated graph analytics - converge to redefine the future of GenAI-powered business solutions.

With GraphRAG emerging as the most advanced Retrieval-Augmented Generation (RAG) approach, this competition challenges you to develop an intelligent agent that can query and reason over graph data with precision and efficiency.

Why GraphRAG?

Traditional RAG systems rely solely on vector-based retrieval, which can lead to hallucinations, context fragmentation, and a lack of structured reasoning. GraphRAG addresses these shortcomings by integrating graph-based retrieval, preserving contextual relationships between entities and enabling more accurate, structured, and interpretable AI-generated responses.

By leveraging GraphRAG, participants can minimize hallucinations, improve knowledge retrieval, and enhance AI-generated insights - a fundamental breakthrough for enterprises using generative AI in business-critical applications.

Microsoft itself has been a thought leader in acknowledging GraphRAG as the next evolution of natural language GenAI approaches, as explained here and in even more detail here.

Your Mission

Build an Agentic Application that integrates GraphRAG and GPU-accelerated graph analytics to solve a real-world problem.

At the highest level, the Hackathon involves the following steps/deliverables:

1. 
   

   Choose a dataset that is relevant to something of interest to you or your organization. ArangoDB recommends a few public datasets if you don’t have your own.

   
   


2. 
   

   Convert/load the dataset into NetworkX.

   
   


3. 
   

   Persist the NetworkX data to a graph within ArangoDB.

   
   


4. 
   

   Build an Agentic App on top of the graph that processes natural language queries.

   
   

The bulk of the creative effort in the Hackathon will relate to Step 4 - Building the Agentic App. The previous 3 steps are simply a lead up to that effort.

Participants have two dataset options:

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  Bring Your Own Data (BYOD) – Use open-source datasets relevant to specific industries or a use case of interest to you or your team (e.g., social networks, cybersecurity, supply chain, healthcare, transportation, etc.). This dataset should be compatible with a graph structure (either already in a graph or convertible to a graph). This data will be loaded into NetworkX (and then persisted to ArangoDB) as one of the steps in the Hackathon. For instance, if you want to explore some publicly-available datasets, consider the following sites:

  
  




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  Stanford Large Network Dataset Collection

  
  


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  Netzschleuder: the network catalogue, repository and centrifuge

  
  


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  Network Repository

  
  




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  Use one of ArangoDB’s Provided Datasets – These are pre-configured graph datasets provided by ArangoDB. Note that these datasets can be loaded directly into ArangoDB, thereby allowing you to skip the “Data Preparation into NetworkX” stage:

  
  




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  Synthea (Medical)

  
  


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  Common Vulnerability Exposures (Cybersecurity)

  
  


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  Flights (Transportation)

  
  


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  GDELT Open Intelligence (Geopolitical)

  
  

To streamline development, ArangoDB is providing a sample Jupyter Notebook Template with pre-built placeholders to help structure each and every step of the process.

Feb. 10, 2025 - March 10, 2025

ArangoDB

Online

$29,750