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Product Overview
MiroFish is a multi-agent swarm intelligence prediction engine. Given seed material (news, policy drafts, financial signals, novel chapters, etc.) and a natural-language prediction question, it builds a knowledge graph, populates a parallel "digital sandbox" with thousands of personality-driven AI agents, runs a social simulation, and returns an analytical report plus an explorable simulated world.
The user-facing experience is a guided 5-step workflow: Graph Build → Environment Setup → Simulation → Report → Interaction. Long-running steps (LLM ontology extraction, graph build, profile generation, simulation, report) execute as background tasks the UI polls for progress.
Core Capabilities
- Knowledge graph construction — Files (PDF, text) are parsed, an LLM
extracts ontology, and Graphiti writes nodes/edges into Neo4j scoped per
project (
group_id). - Persona-driven agent generation — Entities pulled from the graph become OASIS agent profiles with traits, memory, and behavior priors.
- Dual-platform social simulation — CAMEL-OASIS runs Twitter and Reddit agents in parallel rounds with a configurable action set.
- ReACT-loop report agent — A reasoning agent answers the prediction
question using graph tools (
SearchResult,InsightForge,Panorama,Interview). - Post-simulation interaction — Users can chat with any simulated agent or the report agent to probe results.
Target Use Cases
- Macro decision rehearsal — Stress-test policies, PR strategies, or market moves against a synthetic public before committing.
- Public-opinion / political forecasting — Project how an event or narrative may diffuse across social platforms.
- Narrative and creative simulation — Explore alternate endings, what-if scenarios, or fiction continuations (e.g. Dream of the Red Chamber lost-ending demo).
- Operator-led research — Internal analysts upload reports and inspect the resulting graph + simulation rather than running ad-hoc surveys.
Value Proposition
MiroFish converts a static document into a dynamic, interrogable digital society. Where traditional forecasting summarizes data, MiroFish lets decision-makers watch the future play out — observing emergent collective behavior, intervening from a "god view," and reading both an analytical report and the underlying agent interactions that produced it.
The pipeline is deliberately provider-agnostic at the LLM layer (any OpenAI-SDK-compatible endpoint works) and self-hosted at the graph layer (Neo4j + Graphiti, no third-party graph service required), so the same system can run from a developer laptop to a managed deployment without vendor lock-in.
Focus on patterns and purpose, not exhaustive feature lists