22a3ca7af5
translate every llm-facing string-literal in
backend/app/services/report_agent.py — the four tool-description
constants, the plan/section/chat system+user prompts, the react loop
templates, the inline messages re-injected during the section and chat
loops, the _execute_tool error returns, and the plan_outline default and
fallback outline content. preserve every {interpolation} token, the
literal final answer: trigger and <tool_call> xml tag, the four primary
tool names, and all three get_language_instruction() postfix call sites.
also switch the unused-tools join separator from "、" to ", " so it
renders naturally inside the now-english react templates.
removes the chinese language bias that the english postfix alone could
not overcome — under accept-language: en the report agent now produces
english-flavoured analytical reports and chat replies; under
accept-language: zh the postfix continues to steer the model into
chinese with no semantic delta. logger calls (#6) and docstrings or
comments (#7) are deliberately untouched.
Closes #5
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|---|---|---|
| .claude | ||
| .github/workflows | ||
| .kiro | ||
| .ticket | ||
| backend | ||
| frontend | ||
| locales | ||
| static/image | ||
| .dockerignore | ||
| .env.example | ||
| .gitignore | ||
| CLAUDE.md | ||
| Dockerfile | ||
| LICENSE | ||
| README-EN.md | ||
| README-ZH.md | ||
| README.md | ||
| docker-compose.yml | ||
| package-lock.json | ||
| package.json | ||
README.md
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.
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
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🎬 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
- 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
🚀 Quick Start
Option 1: Docker Deployment (Recommended)
The Docker setup brings up the full stack — frontend, backend, and Neo4j — with a single command.
# 1. Copy and configure environment variables
cp .env.example .env
# Edit .env and fill in LLM_API_KEY (and Neo4j password if you want to override the default)
# 2. Pull images and start
docker compose up -d
Reads .env from the project root, exposes ports 3000 (frontend) and 5001 (backend).
A faster mirror address is provided as a comment inside
docker-compose.yml; uncomment to use it.
Option 2: Source Code Deployment
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 |
| Neo4j | 5.x Community | Local knowledge graph database | neo4j --version |
Install Neo4j (choose one):
# macOS
brew install neo4j
# Linux (Debian/Ubuntu)
# See official docs: https://neo4j.com/docs/operations-manual/current/installation/linux/
# Windows / All platforms
# Download Neo4j Desktop: https://neo4j.com/download/
# Set password before first start, then launch
neo4j-admin dbms set-initial-password your_neo4j_password
neo4j start
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
# Knowledge Graph — local Neo4j + Graphiti (free, no rate limits)
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_neo4j_password
# Embedding model (uncomment if using a non-OpenAI provider, e.g. Gemini)
# EMBEDDING_MODEL=gemini-embedding-001
Optional — Accelerated LLM Configuration:
If you have a faster/cheaper provider, you can route auxiliary calls through it. Omit these entirely if not used (do not leave placeholder values).
LLM_BOOST_API_KEY=your_api_key_here
LLM_BOOST_BASE_URL=your_base_url_here
LLM_BOOST_MODEL_NAME=your_model_name_here
Note: MiroFish has migrated from Zep Cloud to local Graphiti + Neo4j. No third-party knowledge-graph account is required — completely free with no rate limits.
2. Install Dependencies
# 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
📬 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!