aaronjmars

aaronjmars / MiroShark

Public

Universal Swarm Intelligence Engine

engine mirofish prediction-engine simulation
78
5
100% credibility
Found Mar 23, 2026 at 78 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

MiroShark lets users upload documents to simulate social media reactions by generating AI agents that post, interact, and evolve opinions on platforms like Twitter and Reddit.

How It Works

1
📄 Upload your document

Share a press release, policy draft, or report to test public reaction.

2
💭 Describe the scenario

Explain what you want to simulate, like social media buzz around your news.

3
🧠 It builds understanding

The tool reads everything and maps out key people, groups, and connections automatically.

4
👥 Realistic people appear

Hundreds of lifelike AI agents with unique personalities, biases, and influence levels are created.

5
▶️ Watch the simulation unfold

Agents post, argue, like, share, and shift opinions hour by hour on fake social feeds.

6
📊 Review the smart report

Get a clear analysis of trends, influencers, sentiment shifts, and key insights.

💬 Chat with anyone

Talk directly to agents or groups to understand their views and what drove the reactions.

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Star Growth

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AI-Generated Review

What is MiroShark?

MiroShark is a Python-based universal swarm intelligence engine that lets you upload documents like press releases or policy drafts, then simulates public reactions via hundreds of AI agents mimicking social media users on Twitter and Reddit. It builds a Neo4j knowledge graph from your content, generates diverse agent personas, and runs hour-by-hour simulations of posts, replies, arguments, and opinion shifts—with pause, resume, and direct agent chats. Developers get a full web UI for graph visualization, reports, and interactions, running locally via Ollama or on any OpenAI-compatible cloud API.

Why is it gaining traction?

It stands out with seamless local-first setup via Docker Compose—no GPU needed for cloud mode—and handles massive agent swarms (100+) without custom scripting, thanks to OASIS integration for realistic social dynamics. The universal swarmer matrix shines in real-time graph memory updates, letting simulations evolve the knowledge base dynamically, plus auto-generated reports and focus-group interviews. Pause button support and agent profiling make it replayable, unlike rigid simulators.

Who should use this?

PR managers testing crisis reactions to announcements, policy analysts simulating public backlash on drafts, or traders gauging sentiment from financial news. Researchers in AI ethics or universal paperclips swarm computing will dig the agent interaction for probing emergent behaviors. Indie devs building universal github robots or media servers could adapt it for custom swarm experiments.

Verdict

Grab it for prototypes—solid docs, Docker quickstart, and AGPL license make it dev-friendly despite 78 stars and 1.0% credibility score signaling early maturity. Low test coverage means watch for edge cases in production, but it's a punchy universal swarm engine worth forking for Python AI sims.

(198 words)

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