Built an open-source fact-checker for AI agents, it won't let a claim through unless it can actually back it up

The thing that kept bugging me: ask an AI "how sure are you?" and it says 95% almost every time, whether it's right or not. Fine for a chat. Not fine when an agent is about to act on that info — write it into a report, save it to a knowledge base, make a decision. So we built the thing that checks first. You give it a claim, it goes and fetches real sources right now (never pulls from the model's memory), and comes back with two separate numbers: a truth score (is it actually true) and a confidence score (how much you should trust that answer — drops when sources are thin or disagree with each other). Plus the sources and a plain-language "why." The part that ended up being the most useful for us: it's not limited to checking against the public web. It can also verify a document's claims against the sources it cites — which matters for internal stuff (policies, metric definitions, schema docs) that the web has no opinion on. We built a demo that plants four errors into a fake finance-policy bundle and it catches all four while leaving the untouched control file alone. Fully open source, MIT licensed, works standalone in a browser, as an MCP tool for your agent, or over a whole knowledge base. Two free API keys and you're running it locally in a couple minutes. Would genuinely love feedback/issues if you try it — link's in the first comment (sub rule). If it's useful to you, a star on the repo helps more than you'd think.