amadad

amadad / mirofish

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Multi-agent AI prediction engine - digital sandbox for scenario simulation (fork of 666ghj/MiroFish)

18
5
89% credibility
Found Mar 18, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

MiroFish simulates social media reactions to uploaded documents by creating AI agents that interact on virtual Twitter and Reddit platforms to predict public opinion trends.

How It Works

1
🔍 Discover MiroFish

You find MiroFish, a tool that predicts how events unfold on social media by simulating AI people.

2
💻 Get it ready

Follow simple steps to run it on your own computer, like any app.

3
📤 Upload your files

Share news articles, reports, or any documents about the scenario you want to predict.

4
💭 Ask your question

Describe what you want to know, like 'How will people react to this policy over 60 days?'

5
🧠 It builds the world

MiroFish creates smart AI characters with real personalities based on your files.

6
🌐 Run the simulation

Watch thousands of AI agents post, argue, like, and spread opinions on fake social media.

📊 See your prediction

Get a clear report on how events might unfold, and chat with the AI characters.

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Star Growth

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AI-Generated Review

What is mirofish?

Mirofish is a Python-based multi-agent AI prediction engine that lets you upload documents like news articles or policy drafts, then simulates thousands of AI agents reacting on Twitter and Reddit to forecast public opinion over time. You describe the scenario in natural language, it builds a knowledge graph from your files, generates diverse agent personas, runs social media-style interactions, and delivers a report with chatty analysis. Think digital sandbox for "what if" predictions without needing your own multi-agent system from scratch.

Why is it gaining traction?

This fork stands out with full English support, local KuzuDB storage, and seamless Claude CLI integration—no API keys for basic use—making multi-agent prediction accessible beyond pay-per-token LLMs. Developers dig the end-to-end workflow: upload, prompt, simulate dual platforms, interview agents post-run, all via a Vue frontend with D3 graphs. It's a practical multi-agent GitHub playground blending OASIS sims and ReACT agents for quick scenario testing.

Who should use this?

AI product managers simulating user reactions to features, policy analysts predicting social media backlash, or researchers prototyping multi-agent systems for trajectory prediction and relational reasoning. Ideal for teams exploring controllable multi-agent motion prediction or evolvegraph-style dynamics without heavy setup.

Verdict

Grab it if you need a runnable multi-agent prediction prototype today—Docker setup is smooth, docs cover CLI providers like Claude. With 18 stars and 0.9% credibility score, it's early-stage (light tests, active fork), so expect tweaks for production; great for experiments, not enterprise yet. (198 words)

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