Project Info
Inspiration
Fashion inspiration is everywhere, but turning a saved outfit into something you can actually buy is slow and fragmented. Gatebreaking removes that bottleneck.
What it does
Upload an outfit image, and Gatebreaking identifies each clothing item, searches live shopping listings, and ranks the closest purchasable matches.
How we built it
We built a multi-agent pipeline using Fetch.ai uAgents and Agentverse. Claude analyzes outfit images, SerpAPI searches Google Shopping, and specialized search and ranking agents return the best matches through an orchestrator.
Challenges we ran into
Our biggest challenges were handling inconsistent model-generated JSON, search latency, coordinating asynchronous agents, and separating ASI:One user addresses from inter-agent Chat Protocol addresses.
Accomplishments we're proud of
We created an end-to-end system that turns visual inspiration into ranked, shoppable product links through a coordinated multi-agent workflow.
What we learned
Reliable agent systems need more than strong models. Clear message contracts, request tracking, address handling, observability, and graceful failure handling are equally important.
What's next
Next, we want to enable automatic payments through Agentverse, allowing agents to complete purchases directly with merchant stores.
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Technology
- PythonUnchecked
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AI coding agents
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Codebase size
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