Ross ROSS = Recommend OSS · open-source software intelligence for agents

MiroShark/MiroShark

Simulate anything, for $1 & less than 10 min - Universal Swarm Intelligence Engine observed · 2026-08-28

github.com/MiroShark/MiroShark · homepage · Python · AGPL-3.0 (copyleft) observed · 2026-08-28

Health v2 · maintenance only

59/100

  • Activity 99
  • Release rhythm 35
  • Longevity 11

Flags: no_releases young no_readme

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 166
  • days_rel: n/a
  • days_push: 9
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1439 stars · 299 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

service · maturity active

agent-framework simulation machine-learning llm-inference rag data-visualization analytics artificial-intelligence simulation large-language-models analytics fintech python cloud self-hosted swarm-intelligence multi-agent-simulation prediction-market knowledge-graph neo4j counterfactual-analysis persona-simulation social-simulation x402-payments scenario-forecasting ai-agents web-server

4 sources

Member repositories

RepositoryRoleHealth v2
MiroShark/MiroSharkmain59

For agents

markdown · JSON · MCP: product_card(name="MiroShark/MiroShark")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem