MiroFish – Open-Source AI Prediction Engine using Swarm Intelligence (Multi-Agent Simulation)
Summary
MiroFish is an open-source prediction engine that uses multi-agent swarm intelligence to simulate real-world outcomes. It builds a parallel digital world populated by thousands of AI agents — each with distinct personalities, long-term memory, and behavioral logic — then injects real-world data (breaking news, policy drafts, financial signals) and runs hundreds of simulations to forecast how scenarios might unfold. Users can interact from a 'god perspective,' tweaking variables and interrogating individual agents or a dedicated ReportAgent. Built on GraphRAG, Zep Cloud memory, and any OpenAI SDK-compatible LLM, it's containerized and runs locally. Live demo: https://666ghj.github.io/mirofish-demo/
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Signal Graph
Second Order
Open-source multi-agent simulation engines like MiroFish collapse the cost of scenario planning from institutional-scale consulting engagements to a local Docker container. If even rough-fidelity swarm simulations become standard tooling, the competitive advantage shifts from having access to forecasting infrastructure to knowing which variables to inject and which simulation outputs to trust — a judgment layer most organizations haven't built. Decision-makers who adopt these tools without developing evaluation rigor risk anchoring strategy to whichever simulation narrative feels most coherent, not most valid.
Third Order
As swarm-based prediction tools proliferate and improve, they create a new epistemic surface: decisions increasingly justified by simulated futures rather than historical analysis or expert judgment. Over a 3–5 year horizon, this produces a bifurcation — organizations that treat simulations as hypothesis generators paired with human scrutiny will compound insight, while those that treat simulation outputs as forecasts will compound confidence without accuracy. The deeper structural risk is that cheap, plausible-looking prediction becomes a vector for confirmation bias at institutional scale, where the sandbox validates whatever assumptions were baked into the agent population from the start.