You know that feeling when every prediction model you've tried treats human behavior like a physics equation — inputs in, outputs out — and completely misses the part where one tweet triggers a cascade, where a minority opinion suddenly becomes a majority, or where two factions merge and flip the outcome? Before MiroFish, your options for social-dynamics forecasting were: run statistical regression (ignores interactions), use an ML model trained on historical data (can't generalize to novel events), or hire an analyst to write a qualitative scenario report (expensive, slow, not reproducible). Now: you upload seed material, describe your prediction question in natural language, and an engine spawns hundreds of agents who argue it out — then hands you a structured report from the emergent behavior.
You upload 'seed material' — a news article, policy document, financial report, or even fiction — and MiroFish first builds a knowledge graph from it using GraphRAG, extracting the key players, relationships, and tensions. From that graph, it automatically generates agent personas: each gets a unique personality, a stance on the topic, and long-term memory powered by Zep Cloud. Those agents then run on OASIS (an open-source simulation engine by CAMEL-AI that scales to 1 million agents) across two parallel social environments — one Twitter-like, one Reddit-like — where they post, argue, persuade, and update their memories as events unfold. When the simulation ends, a dedicated ReportAgent synthesizes the emergent behavior into a structured prediction report. The clever design insight: predictions emerge from agent behavior, not from the model's training data — so MiroFish can forecast novel events that never happened before, as long as the human dynamics are plausible.
If you work on public opinion analysis, policy forecasting, social media intelligence, or scenario planning — and you're frustrated that every existing tool either drowns in data or ignores emergent group dynamics — MiroFish gives you a framework to simulate the messy human part. It's also genuinely interesting for AI researchers studying multi-agent emergent behavior. Not production-ready for enterprise deployment yet — it's v0.1.0, built by a student, and the simulation depth scales with your LLM API budget; complex scenarios with 500+ agents can get expensive fast.
Yes — the architecture here is genuinely clever and the 18k star count in under four months from a solo student developer is a real signal. The use case for simulating public opinion around policy or PR scenarios is immediately obvious and commercially underserved. The honest caveat: you're running at v0.1.0 with 32 open issues and the simulation quality is directly proportional to how well you craft your seed material and agent setup — garbage in, garbage out applies doubly here because bad seed data produces confident-sounding but wrong agent personas.
Deep-dive insight, Easy and Pro modes, plus action playbooks — the full breakdown is one tap away.