Webhound

Webhound runs autonomous, multi-step research loops on questions that top search results cannot adequately answer. You give it a research task and a token budget; the agent plans its own sequence of searches, follows threads across sources, and keeps working until the budget is exhausted or the task is resolved. Outputs include sourced findings — each claim tied to a traceable origin — making the results usable in contexts where unsourced summaries would be rejected.

The budget-controlled depth model is the differentiating mechanic. Instead of a fixed number of search rounds or a subscription tier that dictates what the agent attempts, you control spend per task. A shallow market-sizing check costs less than a full regulatory-exposure sweep of a target company. That pay-as-you-go structure, the vendor states, comes with no subscription requirement — you load credit and spend it as tasks warrant.

Webhound fits agent pipelines that need research as a callable step: competitive intelligence runs, changelog and pricing-page monitoring, lit reviews for benchmarking, and company diligence with flagged regulatory exposure. API and MCP access, as described on the product page, mean you can trigger research tasks programmatically and pipe structured results downstream without a human intermediary. Where it breaks: teams that require data to stay inside their own infrastructure have no self-hosted path, and the sparse public documentation makes it hard to audit how the agent handles ambiguous or contradictory source material before you commit a task budget to finding out.