How I Built a Graph-Powered Study Agent Using FalkorDB and Streamlit

Description and use

Campus Brain uses FalkorDB and Streamlit to dynamically find prerequisite chains and persist student progress.

Problem

Students struggle to navigate complex curriculum prerequisites and track personalized study progress.

Outcome

Campus Brain provides fast, flexible navigation of curriculum prerequisites and persistent tracking of student progress, leveraging graph queries and an interactive frontend.

Workflow

• 1. Set up FalkorDB Cloud to store Topic nodes and PREREQUISITE_OF relationships.
• 2. Store student mastery states as MASTERED relationships in a private session graph.
• 3. Build a Streamlit UI for topic selection and progress display.
• 4. Execute Cypher queries to find prerequisite chains and update mastery states.
• 5. Persist state across sessions via FalkorDB.
• 6. Render live updates in the Streamlit app.

Evidence

During the Graph Hacks hackathon, I wanted to tackle a common problem in education: navigating complex curriculum prerequisites and tracking personalized study progress.

Source

I built Campus Brain— a graph-powered study agent designed for Data Structures.

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FalkorDB Cloud: Used as the core graph database to store educational dependency graphs (PREREQUISITE_OF relationships) and manage private student memory (MASTERED states).

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Streamlit: Used for the interactive user interface, allowing students to select topics, track their progress, and view live updates.

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For instance, finding the prerequisite chain for a target topic looks like this: cypher MATCH p = (a:Topic)-[:PREREQUISITE_OF*1..8]->(t:Topic {name: $target}) RETURN a.name, a.unit, max(length(p))

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