简洁通用的群体智能引擎，预测万物
A Simple and Universal Swarm Intelligence Engine, Predicting Anything

English | 中文文档

⚡ Overview

MiroFish is a next-generation AI prediction engine powered by multi-agent technology. By extracting seed information from the real world (such as breaking news, policy drafts, or financial signals), it automatically constructs a high-fidelity parallel digital world. Within this space, thousands of intelligent agents with independent personalities, long-term memory, and behavioral logic freely interact and undergo social evolution. You can inject variables dynamically from a "God's-eye view" to precisely deduce future trajectories — rehearse the future in a digital sandbox, and win decisions after countless simulations.

You only need to: Upload seed materials (data analysis reports or interesting novel stories) and describe your prediction requirements in natural language
MiroFish will return: A detailed prediction report and a deeply interactive high-fidelity digital world

Our Vision

MiroFish is dedicated to creating a swarm intelligence mirror that maps reality. By capturing the collective emergence triggered by individual interactions, we break through the limitations of traditional prediction:

• At the Macro Level: We are a rehearsal laboratory for decision-makers, allowing policies and public relations to be tested at zero risk
• At the Micro Level: We are a creative sandbox for individual users — whether deducing novel endings or exploring imaginative scenarios, everything can be fun, playful, and accessible

From serious predictions to playful simulations, we let every "what if" see its outcome, making it possible to predict anything.

🌐 Live Demo

Welcome to visit our online demo environment and experience a prediction simulation on trending public opinion events we've prepared for you: mirofish-live-demo

📸 Screenshots

🎬 Demo Videos

1. Wuhan University Public Opinion Simulation + MiroFish Project Introduction

Click the image to watch the complete demo video for prediction using BettaFish-generated "Wuhan University Public Opinion Report"

2. Dream of the Red Chamber Lost Ending Simulation

Click the image to watch MiroFish's deep prediction of the lost ending based on hundreds of thousands of words from the first 80 chapters of "Dream of the Red Chamber"

Financial Prediction, Political News Prediction and more examples coming soon...

🔄 Workflow

1. Graph Building: Seed extraction & Individual/collective memory injection & GraphRAG construction
2. Environment Setup: Entity relationship extraction & Persona generation & Agent configuration injection
3. Simulation: Dual-platform parallel simulation & Auto-parse prediction requirements & Dynamic temporal memory updates
4. Report Generation: ReportAgent with rich toolset for deep interaction with post-simulation environment
5. Deep Interaction: Chat with any agent in the simulated world & Interact with ReportAgent

🚀 Quick Start

Option 1: Source Code Deployment (Recommended)

Prerequisites

Tool	Version	Description	Check Installation
Node.js	18+	Frontend runtime, includes npm	node -v
Python	≥3.11, ≤3.12	Backend runtime	python --version
uv	Latest	Python package manager	uv --version

<<<<<<< HEAD

工具	版本要求	说明	安装检查
Node.js	18+	前端运行环境，包含 npm	node -v
Python	≥3.11, ≤3.12	后端运行环境	python --version
uv	最新版	Python 包管理器	uv --version
Neo4j	5.x Community	本地知识图谱数据库	neo4j --version

安装 Neo4j（选择适合你的方式）：

# macOS
brew install neo4j

# Linux (Debian/Ubuntu)
# 参考官方文档：https://neo4j.com/docs/operations-manual/current/installation/linux/

# Windows / 所有平台
# 下载 Desktop 版本：https://neo4j.com/download/

# 首次启动前设置密码，然后启动服务
neo4j-admin dbms set-initial-password your_neo4j_password
neo4j start

abhiyadav2345/feat/graphiti-neo4j-migration

1. Configure Environment Variables

# Copy the example configuration file
cp .env.example .env

# Edit the .env file and fill in the required API keys

Required Environment Variables:

# LLM API Configuration (supports any LLM API with OpenAI SDK format)
# Recommended: Alibaba Qwen-plus model via Bailian Platform: https://bailian.console.aliyun.com/
# High consumption, try simulations with fewer than 40 rounds first
LLM_API_KEY=your_api_key
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
LLM_MODEL_NAME=qwen-plus

<<<<<<< HEAD
# Zep Cloud Configuration
# Free monthly quota is sufficient for simple usage: https://app.getzep.com/
ZEP_API_KEY=your_zep_api_key

2. Install Dependencies

=======

知识图谱配置（本地 Neo4j + Graphiti，免费无限制）

安装 Neo4j Community Edition：https://neo4j.com/download/

macOS 用户：brew install neo4j && neo4j start

NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=your_neo4j_password

Embedding 模型（使用 Gemini API 时取消注释）

EMBEDDING_MODEL=gemini-embedding-001

> **注意：** MiroFish 已从 Zep Cloud 迁移至本地 **Graphiti + Neo4j**，无需注册任何第三方服务，完全免费且无速率限制。

#### 2. 安装依赖
>>>>>>> abhiyadav2345/feat/graphiti-neo4j-migration

```bash
# One-click installation of all dependencies (root + frontend + backend)
npm run setup:all

Or install step by step:

# Install Node dependencies (root + frontend)
npm run setup

# Install Python dependencies (backend, auto-creates virtual environment)
npm run setup:backend

3. Start Services

# Start both frontend and backend (run from project root)
npm run dev

Service URLs:

• Frontend: http://localhost:3000
• Backend API: http://localhost:5001

Start Individually:

npm run backend   # Start backend only
npm run frontend  # Start frontend only

Option 2: Docker Deployment

# 1. Configure environment variables (same as source deployment)
cp .env.example .env

# 2. Pull image and start
docker compose up -d

Reads .env from root directory by default, maps ports 3000 (frontend) / 5001 (backend)

Mirror address for faster pulling is provided as comments in docker-compose.yml, replace if needed.

📬 Join the Conversation

 

The MiroFish team is recruiting full-time/internship positions. If you're interested in multi-agent simulation and LLM applications, feel free to send your resume to: mirofish@shanda.com

📄 Acknowledgments

MiroFish has received strategic support and incubation from Shanda Group!

MiroFish's simulation engine is powered by OASIS (Open Agent Social Interaction Simulations), We sincerely thank the CAMEL-AI team for their open-source contributions!

📈 Project Statistics