How Builders at Hack for Humanity Used FalkorDB to Solve Real Community Problems

Hack for Humanity brought more than 20,000 participants across up to 130 communities in 52 countries, including 199 people at a San Francisco event hosted by Entrepreneurs First, where teams built projects addressing civic transparency, parking, tourism, public reporting, health information, wildlife encounters, and language preservation. Participants used FalkorDB graph databases not only for storage but to support AI agents with fast, isolated, relationship-based data retrieval and evidence-backed responses. Highlighted projects included Board of SuperVision, which transformed 66 San Francisco government meetings into a graph of votes, legislation, speakers, and video citations; Can I park here?, which used geospatial curb and street-sweeping data to identify legal nearby parking and send reminders; and LOCAL GACHA, which recommended less-crowded Tokyo destinations while explaining shared attributes behind each suggestion. Across projects, teams commonly used AI models such as Gemini to extract typed facts from raw inputs, stored those facts as nodes and edges through repeatable imports, queried them through read-only Cypher connections with safeguards, and returned source-linked data to users or models to improve transparency and reliability.

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