Webhound is an advanced research engine built specifically to solve the open-ended nature of agent-driven information gathering. Because research traditionally lacks a natural finish line—where an agent might spend ten minutes or ten hours on the same prompt while both answers still look finished—Webhound introduces a deterministic, budget-driven approach to AI investigations.
Users simply provide a question alongside a specific dollar budget. Webhound then actively follows leads, cross-references weak claims, and digs deeper until its assigned budget is completely consumed. Once finished, it delivers a comprehensive, cited report or a sourced dataset, complete with the original sources and working documents behind the findings.
Designed with flexibility in mind, Webhound can be run independently by users or integrated directly into existing AI workflows. Developers can call it from their own agents via the API or Model Context Protocol (MCP), making it a powerful infrastructure layer for applications requiring verified, deep-dive research.
Webhound is a research engine for AI agents that investigates questions based on a user-defined dollar budget, following leads and checking claims until the budget is spent.
You control the depth of the research by assigning a specific dollar budget to your question. Webhound continues to follow leads and check weak claims until that budget is consumed.
It returns a cited report or a sourced dataset, along with all the underlying sources and working documents used during the research process.
Yes, you can run Webhound yourself or call it directly from your agent using MCP (Model Context Protocol) or its API.
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Co-founder @ webhound.
