666ghj / MiroFish

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

⚡ 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.

概述 MiroFish 是一个由多智能体技术驱动的下一代 AI 预测引擎。通过从现实世界中提取种子信息（如突发新闻、政策草案或金融信号），它能自动构建一个高保真的平行数字世界。在这个空间内，成千上万个拥有独立人格、长期记忆和行为逻辑的智能体自由交互并进行社会演化。你可以从“上帝视角”动态注入变量，精确推演未来轨迹——在数字沙盒中预演未来，通过无数次模拟赢得决策。

You only need to:

Upload seed materials (data analysis reports or interesting novel stories)
Describe your prediction requirements in natural language

MiroFish will return:

A detailed prediction report
A deeply interactive high-fidelity digital world

你只需要：

上传种子材料（数据分析报告或有趣的文学故事）
用自然语言描述你的预测需求

MiroFish 将返回：

一份详细的预测报告
一个深度交互的高保真数字世界

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:

我们的愿景 MiroFish 致力于打造一个映射现实的群体智能镜像。通过捕捉个体交互引发的集体涌现，我们突破了传统预测的局限：

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.
宏观层面： 我们是决策者的预演实验室，让政策和公关方案可以在零风险下进行测试。
微观层面： 我们是个人用户的创意沙盒——无论是推演小说结局还是探索想象中的场景，一切都可以变得有趣、好玩且易于上手。

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

从严肃的预测到有趣的模拟，我们让每一个“如果”都能看到结果，从而实现预测万物。

🌐 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

在线演示 欢迎访问我们的在线演示环境，体验我们为您准备的舆情热点预测模拟：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”.

1. 武汉大学舆情模拟 + MiroFish 项目介绍 点击图片观看使用 BettaFish 生成的“武汉大学舆情报告”进行预测的完整演示视频。

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”.

2. 《红楼梦》失传结局模拟 点击图片观看 MiroFish 基于《红楼梦》前八十回数十万字内容，对失传结局进行的深度预测。

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

金融预测、政治新闻预测及更多案例即将推出……

🔄 Workflow

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

工作流

图谱构建： 种子提取 & 个体/集体记忆注入 & GraphRAG 构建
环境设置： 实体关系提取 & 人格生成 & 智能体配置注入
模拟： 双平台并行模拟 & 自动解析预测需求 & 动态时间记忆更新
报告生成： 带有丰富工具集的 ReportAgent，用于与模拟后环境进行深度交互
深度交互： 与模拟世界中的任何智能体聊天 & 与 ReportAgent 交互

🚀 Quick Start

Option 1: Source Code Deployment (Recommended)

选项 1：源码部署（推荐）

Prerequisites / 前置条件

Tool	Version	Description	Check
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`

1. Configure Environment Variables

# Copy the example configuration file
cp .env.example .env
# Edit the .env file and fill in the required API keys

1. 配置环境变量

# 复制示例配置文件
cp .env.example .env
# 编辑 .env 文件并填入所需的 API 密钥

2. Install Dependencies

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

2. 安装依赖

# 一键安装所有依赖（根目录 + 前端 + 后端）
npm run setup:all

3. Start Services

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

3. 启动服务

# 同时启动前端和后端（在项目根目录运行）
npm run dev

Option 2: Docker Deployment

选项 2：Docker 部署

# 1. Configure environment variables (same as source deployment)
cp .env.example .env
# 2. Pull image and start
docker compose up -d

📬 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

加入我们 MiroFish 团队正在招聘全职/实习岗位。如果您对多智能体模拟和 LLM 应用感兴趣，欢迎发送简历至：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!

致谢 MiroFish 获得了盛大集团的战略支持与孵化！MiroFish 的模拟引擎由 OASIS (Open Agent Social Interaction Simulations) 提供支持，我们衷心感谢 CAMEL-AI 团队的开源贡献！