This comprehensive guide provides everything you need to use RepoMaster effectively, from basic configuration to advanced usage patterns.
- 🚀 Getting Started
- 🧠 Intelligent Task Processing Engine
- 💻 Usage Modes
- 🔧 Advanced Usage
- 📝 Use Cases
- 📖 Running Tests
- Python 3.11+
- Git
- Internet connection for repository cloning
git clone https://github.com/QuantaAlpha/RepoMaster.git
cd RepoMaster
pip install -r requirements.txtCopy the example configuration file and customize it with your API keys:
cp configs/env.example configs/.env
# Edit the configuration file with your API keys
nano configs/.env # or use your preferred editorConfiguration Template (from configs/env.example):
# Set the default API provider (openai, claude, deepseek, azure_openai)
DEFAULT_API_PROVIDER=openai
# OpenAI Configuration
OPENAI_API_KEY=your_openai_key
OPENAI_MODEL=gpt-5
# Claude Configuration
ANTHROPIC_API_KEY=your_claude_key
ANTHROPIC_MODEL=claude-4-sonnet
# DeepSeek Configuration
DEEPSEEK_API_KEY=your_deepseek_key
DEEPSEEK_MODEL=deepseek-v3
# Google Gemini Configuration
GEMINI_API_KEY=your_gemini_key
GEMINI_MODEL=gemini-2.5-pro
# Web Search APIs (Required for deep search functionality)
SERPER_API_KEY=your_serper_key # For Google search results - Get API key at: https://serper.dev/login
JINA_API_KEY=your_jina_key # For web content extraction - Get API key at: https://jina.ai/💡 Note: The configs/env.example file contains the complete configuration template with all available options and detailed comments.
Simply describe your task in natural language. RepoMaster automatically finds the right GitHub tools and makes them work together to solve your task.
python launcher.py --mode backend --backend-mode unifiedRepoMaster features a sophisticated Multi-Agent System where specialized AI agents work in harmony to deliver optimal solutions. Our intelligent dispatcher automatically routes tasks to the most suitable agent combination:
| 🔍 Deep Search Agent | 💻 Programming Assistant Agent | 🏗️ Repository Exploration Agent |
|---|---|---|
| Advanced Search & Web Analysis | Code Generation & Programming | Repository Understanding & Task Execution |
| • Advanced web research & data retrieval | • Intelligent code generation | • Autonomous code exploration |
| • Information synthesis & analysis | • Algorithm implementation | • Complex task orchestration |
| • Query optimization | • Debug & code optimization | • Multi-repo coordination |
👤 User Task Input
↓
🧠 AI Intelligent Dispatcher
↓
🔀 Task Analysis & Agent Selection
↓
┌─────────────────┬─────────────────┬─────────────────┐
│🔍 Deep Search & │💻 Programming │🏗️ Repository │
│ Web Research │ Assistant │ Exploration │
│ │ │ │
│ • Web search │ • Code generation│ • Repo analysis │
│ • Data synthesis │ • Algorithm impl │ • Task execution │
│ • Context build │ • Debug support │ • Multi-repo ops │
└─────────────────┴─────────────────┴─────────────────┘
↓
🎯 Intelligent Result Orchestration
↓
✅ Perfect Solution Delivered
✨ Key Innovation: No manual agent selection required - our AI dispatcher intelligently combines agents based on task complexity and requirements, ensuring optimal performance for every request.
The primary way to use RepoMaster - one command, all GitHub resources at your service:
python launcher.py --mode backend --backend-mode unifiedWhy Unified Multi-Agent Interface?
- 🧠 AI-Powered Task Analysis: Automatically understands your intent
- 🤝 Intelligent Agent Collaboration: Seamlessly coordinates multiple agents as needed
- 🎯 Context-Aware Routing: Dynamically selects optimal agent combinations
- ⚡ Zero Configuration: No manual agent selection required
Launch the interactive web interface for visual multi-agent interaction:
python launcher.py --mode frontend
# Access: http://localhost:8501
# Configure file upload size limit (default: 200MB)
python launcher.py --mode frontend --max-upload-size 500 # Set to 500MBMulti-Agent Dashboard Features:
- 🌐 Interactive multi-agent chat interface
- 📁 File upload and management across agents (configurable size limit)
- 👥 Multi-user session support
- 📊 Real-time agent collaboration visualization
For developers who want direct access to individual agents:
Individual Agent Interfaces (Click to expand)
# Direct access to Deep Search Agent
python launcher.py --mode backend --backend-mode deepsearch
# Direct access to Programming Assistant Agent
python launcher.py --mode backend --backend-mode general_assistant
# Direct access to Repository Exploration Agent
python launcher.py --mode backend --backend-mode repository_agent💡 Note: These direct agent interfaces are primarily for development, testing, and specialized workflows. For optimal performance and seamless agent collaboration, the unified multi-agent interface is recommended for production use.
# Frontend
bash run.sh frontend
# Backend modes
bash run.sh backend unified
bash run.sh backend deepsearch
bash run.sh backend general_assistant
bash run.sh backend repository_agentRepoMaster supports various configuration options to customize your experience:
# Frontend configuration options
python launcher.py --mode frontend \
--streamlit-port 8502 \ # Custom port (default: 8501)
--streamlit-host 0.0.0.0 \ # Custom host (default: localhost)
--max-upload-size 1000 \ # File upload limit in MB (default: 200)
--log-level DEBUG # Logging level (default: INFO)
# Backend configuration options
python launcher.py --mode backend --backend-mode unified \
--api-type openai \ # API provider (default: basic)
--temperature 0.1 \ # Model temperature (default: 0.1)
--work-dir /custom/path \ # Working directory (default: coding)
--timeout 300 \ # Request timeout in seconds (default: 120)
--max-tokens 8000 # Maximum token count (default: 4000)File Upload Size Configuration:
- Default limit: 200MB
- Range: 1MB - 2000MB (2GB)
- Affects web interface file uploads
- Example:
--max-upload-size 500sets limit to 500MB
from core.agent_scheduler import RepoMasterAgent
# Simple task execution
task = "Transform this portrait into Van Gogh style using content.jpg and style.jpg"
result = repo_master.solve_task_with_repo(task)For detailed programming examples, see our Documentation.
"Train an image classifier on CIFAR-10 dataset using transfer learning"
- Automatically finds relevant ML repositories and frameworks
- Sets up complete training pipeline with best practices
- Handles data loading, model configuration, and training execution
"Extract tables from PDF reports and convert to structured CSV format"
- Discovers PDF processing libraries and tools
- Implements extraction pipeline with error handling
- Outputs clean, structured data in the desired format
"Create a REST API for user authentication with JWT tokens"
- Searches for authentication frameworks and security libraries
- Generates production-ready API with proper security practices
- Includes documentation and testing examples
"Detect and count objects in surveillance video footage"
- Finds state-of-the-art object detection models
- Implements video processing pipeline with optimization
- Provides detailed analysis results and visualizations
# Run configuration tests
python test_config.py
# Run full test suite
pytest tests/
# Run specific benchmark
python -m core.git_task --config configs/gittaskbench.yamlgit clone https://github.com/QuantaAlpha/RepoMaster.git
cd RepoMaster
pip install -e ".[dev]"
pre-commit install- 🐛 Bug fixes
- ✨ New feature development
- 📚 Documentation improvements
- 🧪 Test case additions
- 🔧 Tools and utilities
- 📧 Email: quantaalpha.ai@gmail.com
- 🐛 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
Last updated: December 2024