Announcing a change and finding out how people react is usually expensive, because the mistake is already public. MiroFish takes a seed document (a news item, a policy draft, a report), spins up thousands of agents with personality and memory that interact with each other, and returns a report on how the reaction might unfold. You can also chat with any of those agents.
Who this is NOT for
If what you want is to forecast next month's sales or demand, this is not for you: its demonstrated cases are public opinion about a university event and the lost ending of a novel, and financial prediction is listed as «coming soon». To look at your own numbers, use Metabase. It is also a poor fit if you cannot pay for a usage-billed AI API without a clear cap, or if nobody on your team handles Node, Python and API keys.
It has nearly 75k stars and commits this very month, but the README shows no measurement at all of how accurate its «predictions» are. Treat it as a rehearsal of possible reactions to surface objections you had not thought of, not as an oracle to bet money on. The typical mistake is launching a big simulation first: the README itself recommends starting with fewer than 40 rounds because the API bill grows fast. Also worth knowing: the community and docs lean heavily toward China (QQ group, Alibaba model recommended).
Your first step
Before installing anything, open their online demo (https://666ghj.github.io/mirofish-demo/) and walk through the public-opinion simulation they have already built. If that kind of output would not help any real decision in your business, your trial ends there.
The MiroFish implementation guide
The project belongs to BaiFu (666ghj) and it is free: the link beside asks for nothing. The guide to set it up without getting lost is ours: step by step, common mistakes and a checklist. Leave your email and we will send it.
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