# OmniTaskAgent
A powerful multi-model task management system that can connect to various task management systems and help users choose and use the task management solution that best suits their needs.
## Features
- **Task Management System**: Create, list, update and delete tasks, support status tracking and dependency management
- **Task Decomposition and Analysis**: Break down complex tasks into subtasks, support complexity assessment and PRD automatic parsing
- **Python Native Implementation**: Built entirely in Python, seamlessly integrated with the Python ecosystem
- **Multi-Model Support**: Compatible with multiple models like OpenAI, Claude, etc., not limited to specific API providers
- **Editor Integration**: Integrate with editors like Cursor through MCP protocol for smooth development experience
- **Intelligent Workflow**: Implement intelligent task management process based on LangGraph's ReAct pattern
- **Multi-System Integration**: Can connect to various professional task management systems like mcp-shrimp-task-manager and claude-task-master
- **Cross-Scenario Application**: Suitable for general development projects, vertical domain projects, and other task systems
## Installation
```bash
# Install using uv (recommended)
uv pip install -e .
# Or install using pip
pip install -e .
# Install Node.js dependencies (for MCP server)
npm install
```
## Configuration
Create a `.env` file in the project root directory for configuration:
```ini
# Required: API keys (configure at least one)
OPENAI_API_KEY=your_openai_api_key_here
# Or
ANTHROPIC_API_KEY=your_anthropic_api_key_here
# Optional: Model configuration
LLM_MODEL=gpt-4o # Default model
TEMPERATURE=0.2 # Creativity parameter
MAX_TOKENS=4000 # Maximum tokens
```
## Usage
### Command Line Interface (Recommended)
The simplest way to use is through the built-in command line interface:
```bash
# Start interactive command line interface
python -m omni_task_agent.cli
```
Common command examples:
- `Create task: Optimize website performance Reduce page load time by 50%`
- `List all tasks`
- `Update task 1 status to completed`
- `Decompose task 2`
- `Analyze project complexity`
### Using in LangGraph Studio
LangGraph Studio is a development environment specifically designed for LLM applications, used for visualizing, interacting with, and debugging complex agent applications.
First, ensure langgraph-cli is installed (requires version 0.1.55 or higher):
```bash
# Install langgraph-cli (requires Python 3.11+)
pip install -U "langgraph-cli[inmem]"
```
Then start the development server in the project root directory (containing langgraph.json):
```bash
# Start local development server
langgraph dev
```
This will automatically open a browser and connect to the cloud-hosted Studio interface, where you can:
1. Visualize your agent graph structure
2. Test and run agents through the UI interface
3. Modify agent state and debug
4. Add breakpoints for step-by-step agent execution
5. Implement human-machine collaboration processes
When modifying code during development, Studio will update automatically without needing to restart the service, facilitating rapid iteration and debugging.
For advanced features like breakpoint debugging:
```bash
# Enable debug port
langgraph dev --debug-port 5678
```
### Editor Integration (MCP Service)
1. Run the MCP server:
```bash
# Start STDIO-based MCP service
python run_mcp.py
```
2. Configure MCP settings in your editor (like Cursor, VSCode, etc.):
```json
{
"mcpServers": {
"task-master-agent": {
"type": "stdio",
"command": "/path/to/python",
"args": ["/path/to/run_mcp.py"],
"env": {
"OPENAI_API_KEY": "your-key-here"
}
}
}
}
```
## Project Structure
```
omnitaskagent/
├── omni_task_agent/ # Main code package
│ ├── agent.py # LangGraph agent definition
│ ├── config.py # Configuration management
│ └── cli.py # Command line interface
├── examples/ # Example code
│ └── basic_usage.py # Basic usage example
├── tests/ # Test cases
├── run_mcp.py # MCP service entry
├── adapters.py # MCP adapters
├── langgraph.json # LangGraph API configuration
├── package.json # Node.js dependencies
└── pyproject.toml # Python dependencies
```
## Reference Projects
- [mcp-shrimp-task-manager](https://github.com/cjo4m06/mcp-shrimp-task-manager.git) - Task management system implemented in JavaScript
- [AutoMCP](https://github.com/NapthaAI/automcp.git) - Tool for creating MCP services
- [LangGraph](https://github.com/langchain-ai/langgraph/tree/main/libs/prebuilt) - Agent building framework
- [langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters.git) - LangChain MCP adapters
## License
MIT
