Research has no natural finish line. An agent can spend ten minutes or ten hours on the same question, and both answers can look finished. Webhound lets you choose how much work the question deserves. Give it a question and a dollar budget. It follows leads and checks weak claims until the budget is consumed, then returns a cited report or sourced dataset with the sources and working documents behind it. Run Webhound yourself or call it from your agent through MCP or the API.
Hi Product Hunt, I’m Moe, the founder of Webhound. I’ve worked on AI research since 2023.
I started Webhound because research agents have a stopping problem.
A coding agent can stop when the tests pass. Research has no equivalent finish line. An agent can spend ten minutes, two hours, or twenty hours on the same question, and each answer can look complete. Research agents tend to stop once they have enough evidence to sound confident.
You still do not know which leads they skipped, where sources disagreed, or whether another hour would uncover the fact that changes your decision. The agent makes that stopping decision for you.
Our thesis is that budget should be a research primitive. In plain English, your prompt tells Webhound what to investigate. Your dollar budget tells it how much work to put in and caps what you can spend. At our current rate, $5 funds about 75 minutes of research.
Search finds sources for the query in front of it. Research reads those sources and follows the leads they reveal. One source can change what Webhound needs to search for next.
You get a cited report or sourced dataset, along with the sources and research notes behind it. You can inspect how Webhound reached its conclusions and where the investigation still has gaps.
Extra budget must earn its cost. We judge a larger run by the useful evidence it adds and whether that evidence improves your decision. Extra length does not count.
You can run Webhound in the app or use it behind Codex, Claude Code, Cursor, Manus, or your own software. Your agent can hand off a question, continue working, and retrieve the finished research later.
New accounts include one $5 Report or Dataset. There is no subscription.
I want blunt feedback on the core idea: does a dollar budget feel like a useful way to control how much research gets done? Do the sources and research notes help you decide what to trust?
If you have a question where missing information could cost more than the research, leave it below. We’ll run a few in public today.
About Webhound on Product Hunt
“A research engine for your agent”
Webhound launched on Product Hunt on July 27th, 2026 and earned 315 upvotes and 61 comments, earning #3 Product of the Day. Research has no natural finish line. An agent can spend ten minutes or ten hours on the same question, and both answers can look finished. Webhound lets you choose how much work the question deserves. Give it a question and a dollar budget. It follows leads and checks weak claims until the budget is consumed, then returns a cited report or sourced dataset with the sources and working documents behind it. Run Webhound yourself or call it from your agent through MCP or the API.
On the analytics side, Webhound competes within Artificial Intelligence and Search — topics that collectively have 492.6k followers on Product Hunt. The dashboard above tracks how Webhound performed against the three products that launched closest to it on the same day.
Who hunted Webhound?
Webhound was hunted by Moe Khalil. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Moe Khalil
Hi Product Hunt, I’m Moe, the founder of Webhound. I’ve worked on AI research since 2023.
I started Webhound because research agents have a stopping problem.
A coding agent can stop when the tests pass. Research has no equivalent finish line. An agent can spend ten minutes, two hours, or twenty hours on the same question, and each answer can look complete. Research agents tend to stop once they have enough evidence to sound confident.
You still do not know which leads they skipped, where sources disagreed, or whether another hour would uncover the fact that changes your decision. The agent makes that stopping decision for you.
Our thesis is that budget should be a research primitive. In plain English, your prompt tells Webhound what to investigate. Your dollar budget tells it how much work to put in and caps what you can spend. At our current rate, $5 funds about 75 minutes of research.
Search finds sources for the query in front of it. Research reads those sources and follows the leads they reveal. One source can change what Webhound needs to search for next.
You get a cited report or sourced dataset, along with the sources and research notes behind it. You can inspect how Webhound reached its conclusions and where the investigation still has gaps.
Extra budget must earn its cost. We judge a larger run by the useful evidence it adds and whether that evidence improves your decision. Extra length does not count.
You can run Webhound in the app or use it behind Codex, Claude Code, Cursor, Manus, or your own software. Your agent can hand off a question, continue working, and retrieve the finished research later.
New accounts include one $5 Report or Dataset. There is no subscription.
I want blunt feedback on the core idea: does a dollar budget feel like a useful way to control how much research gets done? Do the sources and research notes help you decide what to trust?
If you have a question where missing information could cost more than the research, leave it below. We’ll run a few in public today.