OMNISIM — Simulate Any Reality

OMNISIM is an AI multi-agent swarm simulation and prediction engine that lets you describe any real-world scenario in plain text and returns the most likely outcomes ranked by probability. Aimed at analysts and researchers who want to model complex systems, OMNISIM auto-detects the domain from your input — biology, finance, politics, or epidemiology — then generates up to 500 cognitively distinct agents and runs a full swarm simulation to surface the mathematically optimal outcome. The workflow has four stages: Seed (drop a plain-text description of a scenario), Extract (a Temporal GraphRAG pipeline pulls entities, relationships, and domain dynamics), Simulate (hundreds of heterogeneous agents reason, debate, and update beliefs in parallel with belief propagation), and Filter (mathematical outcome filtration ranks predictions by expected value and probability mass). Agents are modeled with MBTI-style traits, life experiences, social networks, and behavioral logic rules, and the engine supports full-mesh, clustered, and hierarchical swarm topologies plus individual, group-cluster, and temporal memory layers. Documented use cases include simulating how 500 traders react to a Fed rate hike, modeling how a pathogen spreads through a population, predicting election outcomes via voter belief propagation, and forecasting outbreak trajectories with heterogeneous compliance. Because it is domain-agnostic and driven by free-text seeds, OMNISIM is positioned as a general-purpose "simulate any reality" engine rather than a single-industry forecasting tool.

Analysts and researchers use OMNISIM to simulate complex scenarios and forecast the most probable outcomes across domains like finance, biology, politics, and epidemiology.