Built this internally for our coding agents but have been loving it so much that we decided to make it public.
The web UI is free to use and does not require sign up, and has every public SKILL.md on GitHub indexed, with the index refreshing every 48 hours.
Programmatic use is $20 a month for unlimited searches, and I can vouch for the fact that it's made my agents feel so much smarter and more knowledgeable. Only issue is right now you have to nudge it to use skillhound ("use skillhound first") otherwise it tends to try to figure out best practices on its own.
Hope it can be as useful to the community as it is to us, and would appreciate any feedback you have.
We're working on Webhound - budget controlled long-running deep research. You set a budget and Webhound will use that much in compute/LLM tokens to research your prompt, with built in verification cycles and optional added verification budget. Every claim is cited with evidence and a direct link to the tool calls that produced the claim
The goal is to build a deep research product for actual researchers, since we believe that it is an extremely powerful product that is still nascent but has enormous potential - which we've already seen with some early users.
The least productive teams I've been a part of are the ones where everyone is waiting for their turn to say why an idea is bad. Sometimes being "too smart" can hold you back from building something genuinely new.
Hey, appreciate the feedback. Will address all your points.
Regarding Reddit, we have our own custom handler for Reddit URLs which uses the Reddit API, which we are billed for when we exceed free limits.
For Terms of Service, you're right, that is definitely an oversight on our part. We just published both our Terms of Service and Privacy Policy on the website.
When it comes to comparing with GPT-5 and Claude, we do believe that our prompting, agent orchestration, and other core parts of the product such as parallel search results analysis and parallel agents are improvements on just GPT-5 and Claude, while also allowing it to run at much cheaper costs on significantly smaller models. Our v1 which we built months ago was essentially the same as what GPT-5 thinking with web search currently does, and we've since made the explicit choice to focus on data quality, user controllability, and cost efficiency over latency. So while yes, it might give faster results and work better for smaller datasets, both we and our users have found Webhound to work better for siloed sources and larger datasets.
Regarding account deletion, that is also a fair point. So far we've had people email us when they want their account deleted, but we will add account deletion ASAP.
Criticism like this helps us continue to hold ourselves to a high standard, so thanks for taking the time to write it up.
Could you share the session url via the feedback form if you still have access to it?
That's really strange, it sounds like Webhound for some reason deleted the schema after extraction ended, so although your data should still be tied to the session it just isn't being displayed. Definitely not the expected behavior.
Accuracy-wise we think it's almost there but probably still a few iterations away from being perfect. It's great at eliminating a lot of the collection time though.
Interestingly, we're working with B2B clients right now where we use Webhound to curate and then act as the "validation" layer ourselves. The agent lets us offer these datasets way cheaper with live updates, but still with human oversight.
Thanks for testing it! That's definitely a miss, sounds like it got confused about what you were looking for and went after board member pages instead of the actual meeting/document sites.
We're working on better query interpretation, but in the meantime you could try being more specific like "find BoardDocs or meeting document websites for each district" to guide it better. Also, you can usually figure out how it interpreted your request by looking at the entity criteria, those are all the criteria a piece of data needs to meet to make it in the set.
Fair point, most of our users have come from referrals/word of mouth so it hasn't really been an issue for us, but you're probably right that we should have more information on the landing page
Yep: NextJS frontend, NodeJS backend, Gemini 2.5 Flash LLM, Firecrawl for crawling, self-hosted SearXNG for web search, and fly.io for hosting. Beyond that everything else is built internally, we don’t use many frameworks.
Sorry about that. If you tell it to restructure the schema and search plan around MCP as model context protocol it should work. The agent can get stuck on its initial interpretation sometimes.
Yeah, we've noticed it overthinks simple tasks that could be solved with a single table scrape. The agent architecture is built for complex, multi-source problems so it overengineers straightforward queries.
Working on better task classification upfront to route simple requests more directly.
Thanks, we have noticed that it can tend to "give up" early on certain sources. Ideally the critic agent would guide it back to the correct path of continuing to go deeper, but if that doesn't work usually just adding something to the prompt or sending it a message later on telling it to go deep on these sources would work.
Good point. Our main differentiation is the shared workspace - users can step in and guide the agent mid-task, kind of like Cursor vs Claude (which can technically generate the same code that Cursor does). Firecrawl (or any crawler we may use) is only part of the process, we want to make the collaborative process for user <> agent as robust and user controllable as possible.