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.

- 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

# Install using uv (recommended) uv pip install -e . # Or install using pip pip install -e . # Install Node.js dependencies (for MCP server) npm install

Create a.envfile in the project root directory for configuration:

# 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

The simplest way to use is through the built-in command line interface:

# Start interactive command line interface python -m omni_task_agent.cli

- 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

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):

# 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):

# Start local development server langgraph dev

This will automatically open a browser and connect to the cloud-hosted Studio interface, where you can:
- Visualize your agent graph structure
- Test and run agents through the UI interface
- Modify agent state and debug
- Add breakpoints for step-by-step agent execution
- 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:

# Enable debug port langgraph dev --debug-port 5678

# Start STDIO-based MCP service python run_mcp.py

- Configure MCP settings in your editor (like Cursor, VSCode, etc.):

{ "mcpServers": { "task-master-agent": { "type": "stdio", "command": "/path/to/python", "args": ["/path/to/run_mcp.py"], "env": { "OPENAI_API_KEY": "your-key-here" } } } }

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

- mcp-shrimp-task-manager- Task management system implemented in JavaScript
- AutoMCP- Tool for creating MCP services
- LangGraph- Agent building framework
- langchain-mcp-adapters- LangChain MCP adapters

Keep teams & agents coordinated automatically