-
-## ⚡ 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](https://666ghj.github.io/mirofish-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` |
-
-#### 1. Configure Environment Variables
-
-```bash
-# Copy the example configuration file
-cp .env.example .env
-
-# Edit the .env file and fill in the required API keys
-```
-
-**Required Environment Variables:**
-
-```env
-# 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
-
-# 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
-
-```bash
-# One-click installation of all dependencies (root + frontend + backend)
-npm run setup:all
-```
-
-Or install step by step:
-
-```bash
-# Install Node dependencies (root + frontend)
-npm run setup
-
-# Install Python dependencies (backend, auto-creates virtual environment)
-npm run setup:backend
-```
-
-#### 3. Start Services
-
-```bash
-# 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:**
-
-```bash
-npm run backend # Start backend only
-npm run frontend # Start frontend only
-```
-
-### Option 2: Docker Deployment
-
-```bash
-# 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)](https://github.com/camel-ai/oasis)**, We sincerely thank the CAMEL-AI team for their open-source contributions!
-
-## 📈 Project Statistics
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/README-ZH.md b/README-ZH.md
new file mode 100644
index 0000000000..13fbcb4d81
--- /dev/null
+++ b/README-ZH.md
@@ -0,0 +1,203 @@
+
+
+
+
+MiroFish团队长期招募全职/实习，如果你对多Agent应用感兴趣，欢迎投递简历至：**mirofish@shanda.com**
+
+## 📄 致谢
+
+**MiroFish 得到了盛大集团的战略支持和孵化！**
+
+MiroFish 的仿真引擎由 **[OASIS](https://github.com/camel-ai/oasis)** 驱动，我们衷心感谢 CAMEL-AI 团队的开源贡献！
+
+## 📈 项目统计
+
+
+
+
+
+
+
+
diff --git a/README.md b/README.md
index a47976c4b9..de082935a7 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
A Simple and Universal Swarm Intelligence Engine, Predicting Anything
-
+
[](https://github.com/666ghj/MiroFish/stargazers)
[](https://github.com/666ghj/MiroFish/watchers)
@@ -16,183 +16,183 @@
[](https://hub.docker.com/)
[](https://deepwiki.com/666ghj/MiroFish)
-[](https://discord.com/channels/1469200078932545606/1469201282077163739)
+[](http://discord.gg/ePf5aPaHnA)
[](https://x.com/mirofish_ai)
[](https://www.instagram.com/mirofish_ai/)
-[English](./README-EN.md) | [中文文档](./README.md)
+[English](./README.md) | [中文文档](./README-ZH.md)
-## ⚡ 项目概述
+## ⚡ Overview
-**MiroFish** 是一款基于多智能体技术的新一代 AI 预测引擎。通过提取现实世界的种子信息（如突发新闻、政策草案、金融信号），自动构建出高保真的平行数字世界。在此空间内，成千上万个具备独立人格、长期记忆与行为逻辑的智能体进行自由交互与社会演化。你可透过「上帝视角」动态注入变量，精准推演未来走向——**让未来在数字沙盘中预演，助决策在百战模拟后胜出**。
+**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**.
-> 你只需：上传种子材料（数据分析报告或者有趣的小说故事），并用自然语言描述预测需求
-> MiroFish 将返回：一份详尽的预测报告，以及一个可深度交互的高保真数字世界
+> 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
-### 我们的愿景
+### Our Vision
-MiroFish 致力于打造映射现实的群体智能镜像，通过捕捉个体互动引发的群体涌现，突破传统预测的局限：
+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:
-- **于宏观**：我们是决策者的预演实验室，让政策与公关在零风险中试错
-- **于微观**：我们是个人用户的创意沙盘，无论是推演小说结局还是探索脑洞，皆可有趣、好玩、触手可及
+- **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
-从严肃预测到趣味仿真，我们让每一个如果都能看见结果，让预测万物成为可能。
+From serious predictions to playful simulations, we let every "what if" see its outcome, making it possible to predict anything.
-## 🌐 在线体验
+## 🌐 Live Demo
-欢迎访问在线 Demo 演示环境，体验我们为你准备的一次关于热点舆情事件的推演预测：[mirofish-live-demo](https://666ghj.github.io/mirofish-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](https://666ghj.github.io/mirofish-demo/)
-## 📸 系统截图
+## 📸 Screenshots
-点击图片查看使用微舆BettaFish生成的《武大舆情报告》进行预测的完整演示视频
+Click the image to watch the complete demo video for prediction using BettaFish-generated "Wuhan University Public Opinion Report"
-### 2. 《红楼梦》失传结局推演预测
+### 2. Dream of the Red Chamber Lost Ending Simulation
-点击图片查看基于《红楼梦》前80回数十万字，MiroFish深度预测失传结局
+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"
-> **金融方向推演预测**、**时政要闻推演预测**等示例陆续更新中...
+> **Financial Prediction**, **Political News Prediction** and more examples coming soon...
-## 🔄 工作流程
+## 🔄 Workflow
-1. **图谱构建**：现实种子提取 & 个体与群体记忆注入 & GraphRAG构建
-2. **环境搭建**：实体关系抽取 & 人设生成 & 环境配置Agent注入仿真参数
-3. **开始模拟**：双平台并行模拟 & 自动解析预测需求 & 动态更新时序记忆
-4. **报告生成**：ReportAgent拥有丰富的工具集与模拟后环境进行深度交互
-5. **深度互动**：与模拟世界中的任意一位进行对话 & 与ReportAgent进行对话
+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
-## 🚀 快速开始
+## 🚀 Quick Start
-### 一、源码部署（推荐）
+### Option 1: Source Code Deployment (Recommended)
-#### 前置要求
+#### Prerequisites
-| 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` |
+| 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` |
-#### 1. 配置环境变量
+#### 1. Configure Environment Variables
```bash
-# 复制示例配置文件
+# Copy the example configuration file
cp .env.example .env
-# 编辑 .env 文件，填入必要的 API 密钥
+# Edit the .env file and fill in the required API keys
```
-**必需的环境变量：**
+**Required Environment Variables:**
```env
-# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API）
-# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/
-# 注意消耗较大，可先进行小于40轮的模拟尝试
+# 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
-# Zep Cloud 配置
-# 每月免费额度即可支撑简单使用：https://app.getzep.com/
+# Zep Cloud Configuration
+# Free monthly quota is sufficient for simple usage: https://app.getzep.com/
ZEP_API_KEY=your_zep_api_key
```
-#### 2. 安装依赖
+#### 2. Install Dependencies
```bash
-# 一键安装所有依赖（根目录 + 前端 + 后端）
+# One-click installation of all dependencies (root + frontend + backend)
npm run setup:all
```
-或者分步安装：
+Or install step by step:
```bash
-# 安装 Node 依赖（根目录 + 前端）
+# Install Node dependencies (root + frontend)
npm run setup
-# 安装 Python 依赖（后端，自动创建虚拟环境）
+# Install Python dependencies (backend, auto-creates virtual environment)
npm run setup:backend
```
-#### 3. 启动服务
+#### 3. Start Services
```bash
-# 同时启动前后端（在项目根目录执行）
+# Start both frontend and backend (run from project root)
npm run dev
```
-**服务地址：**
-- 前端：`http://localhost:3000`
-- 后端 API：`http://localhost:5001`
+**Service URLs:**
+- Frontend: `http://localhost:3000`
+- Backend API: `http://localhost:5001`
-**单独启动：**
+**Start Individually:**
```bash
-npm run backend # 仅启动后端
-npm run frontend # 仅启动前端
+npm run backend # Start backend only
+npm run frontend # Start frontend only
```
-### 二、Docker 部署
+### Option 2: Docker Deployment
```bash
-# 1. 配置环境变量（同源码部署）
+# 1. Configure environment variables (same as source deployment)
cp .env.example .env
-# 2. 拉取镜像并启动
+# 2. Pull image and start
docker compose up -d
```
-默认会读取根目录下的 `.env`，并映射端口 `3000（前端）/5001（后端）`
+Reads `.env` from root directory by default, maps ports `3000 (frontend) / 5001 (backend)`
-> 在 `docker-compose.yml` 中已通过注释提供加速镜像地址，可按需替换
+> Mirror address for faster pulling is provided as comments in `docker-compose.yml`, replace if needed.
-## 📬 更多交流
+## 📬 Join the Conversation
-
-
-
-[](https://github.com/666ghj/MiroFish/stargazers)
-[](https://github.com/666ghj/MiroFish/watchers)
-[](https://github.com/666ghj/MiroFish/network)
-[](https://hub.docker.com/)
-[](https://deepwiki.com/666ghj/MiroFish)
-
-[](https://discord.com/channels/1469200078932545606/1469201282077163739)
-[](https://x.com/mirofish_ai)
-[](https://www.instagram.com/mirofish_ai/)
-
-[English](./README-EN.md) | [中文文档](./README.md)
-
-
-