Doing research with agents is fun until they blow way past budget, jumble the sources, and don't even give you the best possible answer, just sound confident. And if you want to run some research task on local data - you have no idea where your data ends up after the prompt consumes it. So I built this tool: a deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data. 1. Never spend more than you budgeted (measured overshoot is 0%). 2. Every claim carries a source 3. Data stays local (give a CSV, it'll analyze it without the data ever leaving your machine) Works with most LLMs, including coding agents, subscriptions, local models, etc. It's free and open source, would appreciate all feedback! Comments URL: https://news.ycombinator.com/item?id=49303046 Points: 8 # Comments: 0
A deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data.
Ask a question. mole decomposes it, searches, reads sources, extracts claims, checks each claim against the text it came from, looks for contradictions between them, and writes an answer with citations. Every model call is reserved against a budget before it happens and settled after, so the ceiling you set is the ceiling it hits.
It runs as a single static binary on your machine, uses your own API keys, and speaks MCP so a coding agent can drive it — either by handing mole a question and collecting the answer, or, in toolkit mode, by doing the reasoning with its own model while mole supplies the parts that are not model calls.
Three things mole does that a chat interface with web search does not.
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