Tavily and Jev: search and context decisions for research agents
Tavily supplies real-time web evidence while Jev uses structured outputs to decide when to search, which results matter and whether there is enough context, reducing LLM inference overhead.
Tavily + @typesafeai's Jev is where real-time web intelligence meets low-latency decision-making. A research agent constantly decides when to search, which results matter, and whether it has enough context to continue. For an agent using Tavily + Jev, Tavily supplies fresh web evidence while Jev powers those decision points with structured outputs that plug directly into the workflow. Together, developers can offload repeated routing and context-selection decisions from general-purpose LLMs, keeping larger models focused on reasoning and generation. It's a practical path to more efficient agentic workflows, with less inference overhead between finding information and using it. Check out the demo, and when you can, give it a try yourself. If you don't have a Tavily account yet, you can grab a free one at http://app.tavily.com/