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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!
Discussion (6 Comments)Read Original on HackerNews
I assume that this is "$ spent on search + $ spent on LLM" < budget, but how do you handle the LLM spending more than you would expect on a request? Or is this handled by max_tokens and some form of pricing table? (and if so, how does caching play a role?)