Building Efficient Search Agents with JEV + You.com Knowledge
A deep dive on reranking real-time web data before synthesis
We recently launched Knowledge in the You.com Web Search API, bringing structured, real-time data from authoritative sources across domains like stocks, sports, and more.
For many queries, Knowledge can answer the question directly. Passing the full set of retrieved web results to a synthesis model in these cases can add unnecessary context and token consumption.
In this workshop, we’ll walk through how we use Jev through OpenRouter as a reranking layer between retrieval and synthesis to select only the most relevant results before they’re passed downstream.
On the Vertical RTK benchmark, this approach reduced returned tokens by 3x while maintaining 84% accuracy.
What we’ll cover
How Knowledge fits into the You.com Web Search API
Why retrieved context can become redundant for synthesis
Where reranking fits into a search/agent pipeline
Using Jev through OpenRouter as a lightweight reranking layer
The architecture behind the implementation
The code and implementation details
Benchmark results and the tradeoffs we observed
This is a hands-on technical walkthrough for developers building search, RAG, and agentic systems where token consumption, latency, and retrieval quality matter.
Bring your questions — we’ll leave time for a live Q&A.