MiroShark/MiroShark
Simulate anything, for $1 & less than 10 min - Universal Swarm Intelligence Engine 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
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
- homepage: https://www.miroshark.xyz · fetched 2026-08-29 · b29169189a95
- site_page: https://www.miroshark.xyz/docs · fetched 2026-08-29 · 0b4125c3020d
- site_page: https://www.miroshark.xyz/about · fetched 2026-08-29 · 51c96d180a15
- site_page: https://www.miroshark.xyz/sim · fetched 2026-08-29 · 9bee781b6bcc
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| MiroShark/MiroShark | main | 59 |
For agents
markdown · JSON · MCP: product_card(name="MiroShark/MiroShark")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem