Webhound

A research engine for your agent

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The budget dial is a smart framing. Research has no natural finish line, and looking finished is exactly how a shallow answer slips through.

The thing I would want in the output is a fetch date on every source, so a report from last month can be re-checked instead of trusted forever. Does the dataset mode keep a timestamp per row?

makes sense, flagging the lack of evidence as a limitation instead of just going quiet is the right default. thanks for the answer

The budget-based depth control is an interesting way to make research effort explicit. How does Webhound decide when a claim needs further verification, and can users inspect why the agent stopped researching a particular lead?

Moe — the disagreement-surfacing answer to Gal is solid. My research problem's different though: federal contract award data and past-performance records aren't scattered across the open web, they're stuck behind a clunky government portal with no public API or real search. Does Webhound handle sources like that, or is it built for open-web research?

The dollar budget is a clever constraint, but the stronger idea might be making research depth explicit. Most tools hide the stopping decision behind a confident-looking paragraph. Here, at least, I can decide whether a question deserves five dollars or five minutes. I like that “not enough evidence” can be a valid output.

Budget as the stopping primitive answers how much, and there's a second question sitting under it: how much does the same $5 vary? Two runs on one question at one budget follow different leads, and an agent that follows leads is path-dependent by construction — whichever source it happens to open early reshapes everything after it. The report is a function of the budget and of which door it went through first.

That lands hardest exactly where Clemente was pointing, on the MCP path. A human feels a thin answer and re-runs it. An agent takes the first report as ground truth. I do eval work on my own app's generated output, and the number that changed how I ship wasn't the average score — it was the spread across identical inputs. The mean looked healthy for weeks while the bottom of the distribution was quietly unusable.

Have you measured that spread on a fixed question and budget? And does the per-claim confidence score reflect run-to-run stability, or only the evidence inside the single run that produced it?

Depth over speed is a refreshing pitch when everything else is racing to answer in two seconds. Exposing budget as the control on research quality is smarter than hiding it behind a vague quality slider. When it builds a dataset rather than a report, how does it handle two sources that contradict each other, does the row keep both values or does the agent pick one?

This looks useful, I lose hours copying company details off websites into a spreadsheet by hand. Having the choice between a clean dataset and a fully cited report covers pretty much every research job that lands on my desk. If I ran the same query again next month, would it give me a fresh dataset I could diff against the old one to see what changed?

I'd love to see how Webhound handles edge cases, like sources with paywalls or outdated information. How do the research agents adapt to these challenges?

I see that this is positioned as the "research engine" behind your AI agent, but it wasn't clear to me how this is different than getting the frontier model to do deep research and continually prompting it to continue researching after the default stopping point.

probe real submit pathBudget as the stopping rule makes sense, especially when another agent is waiting on the answer. The trust layer I’d want is a short handoff note: what was checked, what was intentionally skipped, and which unresolved claims could change the decision if someone spends another hour.

Budget as the stopping rule makes sense, especially when another agent is waiting on the answer. The trust layer I’d want is a short handoff note: what was checked, what was intentionally skipped, and which unresolved claims could change the decision if someone spends another hour.

Interesting! That framing of budget-as-stopping-primitive is definitely worth attention. Most research tools stop when the prose sounds finished rather than when the work is actually done. Curious whether you guys plan the search tree up front and rank leads by expected payoff? Is it greedy step by step and just stops when the meter hits zero?

the budget-as-stopping-primitive idea makes sense to me. curious about the opposite failure mode though - what happens when a topic just doesn't have much written about it, like an internal process at a small company or something too new to have coverage. does it recognize early that more budget won't surface anything and stop, or does it keep spending trying to find sources that don't exist? that seems like the case where the dial wouldn't actually save you money.

Congrats on the launch, Moe.
Webhound is tackling a really interesting problem with AI research the stopping problem is definitely something that gets overlooked. I especially like the idea of using a dollar budget as a research primitive instead of letting agents run indefinitely. I will defiently try this out

Moe, blunt feedback on the core idea: the dial only works if I can calibrate it, and I cannot. I do not know whether my question is a one dollar question or a fifty dollar one, and by your own answer to Freya the fourth or fifth lead is often where the apparent dead end breaks. That means people systematically underfund exactly the questions that deserved more work.

So the output I would want is not only the report, it is what another five dollars would likely buy: how many leads are still open, how many claims are still marked uncertain, which of them the run considered load bearing. That turns the budget from a guess into a decision I can make again with information.

It matters more on the MCP path, because there the caller is an agent and nobody is sitting there to feel that the answer arrived thin. Does the run return that unfinished state as structured data, or does it only live inside the report?

How well does it handle complex prompts with multiple research objectives in a single request?