# MiroShark/MiroShark

Simulate anything, for $1 & less than 10 min - Universal Swarm Intelligence Engine

Repository: https://github.com/MiroShark/MiroShark
Canonical: https://ross.abutalabs.com/products/miroshark
Homepage: https://www.miroshark.xyz
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: mirofish, swarm, swarm-intelligence, financial-forecasting, future-prediction, ai-simulation
Last push: 2026-08-24T16:00:41+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 11
- inputs: {"age_days": 166, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1439, forks 299 (observed 2026-08-28T04:04:44.132817+00:00)

## What it is
MiroShark is a universal swarm-intelligence engine that turns any document or scenario into a simulated world: it builds a Neo4j knowledge graph, grounds hundreds of AI personas in it, and runs them hour by hour across Twitter-like, Reddit-like, and prediction-market surfaces. Each run produces a citing report, trajectory charts, and interactive surfaces where you can DM agents, inject breaking news, or fork the timeline with counterfactual events.

## Use cases
- simulate how the public reacts to a press release or product launch
- forecast market and community response to a policy draft
- run historical what-if and counterfactual scenarios
- red-team a message or announcement before sending it
- model sentiment and trading behavior around a news headline
- explore emergent narratives in simulated social media and prediction markets

## When to choose
- you want crowd-level emergent behavior rather than a single LLM opinion
- you need fast, cheap (~$1/run) scenario simulations with cited, auditable reports
- you want to inject events mid-run or fork timelines to compare counterfactuals
- you need grounded personas tied to a real knowledge graph instead of generic agents

## When to avoid
- you need precise, validated numerical forecasts rather than plausible simulated behavior
- you require strict data privacy since scenarios are processed by hosted LLM services
- you want a lightweight local library without Neo4j, OpenRouter, or per-run payment dependencies
- your use case is deterministic modeling or traditional statistical forecasting

## Facets
- artifact type: service
- maturity: active
- function: agent-framework, simulation, machine-learning, llm-inference, rag, data-visualization, analytics
- domain: artificial-intelligence, simulation, large-language-models, analytics, fintech
- platform: python, cloud, self-hosted
- tags: swarm-intelligence, multi-agent-simulation, prediction-market, knowledge-graph, neo4j, counterfactual-analysis, persona-simulation, social-simulation, x402-payments, scenario-forecasting, ai-agents, web-server

## Member repositories
- MiroShark/MiroShark (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:44.132817+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:36:35.046977+00:00, confidence not recorded.
  - homepage: https://www.miroshark.xyz (fetched 2026-08-29T11:47:00.341524+00:00, sha b29169189a95)
  - site_page: https://www.miroshark.xyz/docs (fetched 2026-08-29T11:47:00.353371+00:00, sha 0b4125c3020d)
  - site_page: https://www.miroshark.xyz/about (fetched 2026-08-29T11:47:00.356192+00:00, sha 51c96d180a15)
  - site_page: https://www.miroshark.xyz/sim (fetched 2026-08-29T11:47:00.350921+00:00, sha 9bee781b6bcc)
- Data as of 2026-08-30T08:39:29.467469+00:00.