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查看完整 README ↗Three answers in, one culturally coherent evening out — powered by the Qloo taste graph.
Most agents answer well. Taste Concierge knows what people like: it maps a 3-question interview to Qloo's taste-graph entities (250M+), then chains insights across dining, film, music and retail to compose an evening that fits that person — not the charts.
An LLM alone recommends popular things. Taste Concierge recommends that person's things:
/search./v2/insights call is seeded by
your entities — and the playlist is additionally seeded by the chosen
film, so the music follows the night's centerpiece. That's cultural
coherence, not four independent top-10s.Remove Qloo and the product collapses: without the taste graph there is no entity affinity, no cross-domain coherence, and no grounding — just generic popular answers.
# 1. Get a hackathon API key: see the Devpost overview page / docs.qloo.com
# 2. Configure and start
cp .env.example .env # put your key in QLOO_API_KEY
npx vercel dev # local dev server
Deploy:
npx vercel --prod # set QLOO_API_KEY in the Vercel dashboard
index.html the interview UI + plan cards (zero-framework, fast)
api/plan.ts serverless endpoint: the agent loop
src/lib/qloo.ts Qloo client (X-Api-Key, GET-only, hackathon base URL)
src/lib/agent.ts the pipeline: mapAnswersToEntities → composeEvening
→ recomposeWithRejection (the negative-feedback loop)
The agent pipeline is deliberately small and inspectable — every Qloo call and every affinity decision can be traced in the code.
Built for the Qloo Agentic Hackathon. License: MIT.