US retailers expected $850B in returns in 2025. Warehouses still inspect and route many of these items manually, slowing recovery and making wrong-item fraud harder to catch.
Reclaim is our vision for a swarm of autonomous agents that owns each return from dock scan to final disposition. It inspects the item, checks warehouse context, researches the live market, chooses the highest-recovery valid action, executes it, verifies the result, and escalates only when needed.
This project turns that vision into a working system. A new RawTree row opens a LangGraph case. A Supervisor coordinates inspection, market research, disposition, and execution. Liquid AI's LFM2.5-VL-3B runs locally as the only model. It first describes the dock photo without seeing the order, then compares it with the catalog image and returns schema-constrained JSON for identity and condition. Nimble searches live new, used, refurbished, and open-box prices, and each accepted price is stored with its source URL.
RawTree is the system of record and agent memory, holding an 8,693-product catalog, inventory ledger, event log, price cache, and decisions. Each model call gets a roughly 150-token brief rebuilt from RawTree, keeping context flat as history grows. FLUX.2 [max] generated 100 labelled return examples, from cracked screens to an apple returned as an iPhone.On 23 labelled returns, Reclaim reached 91.3% correct actions, 0 needless escalations, and recovered $1,861 more than liquidating everything.
