Which integrations are available for this server?

Uses LangChain MCP adapters to integrate the task management agent with the broader LangChain ecosystem for enhanced agent capabilities. Uses LangGraph's ReAct pattern and agent building framework to implement intelligent task management workflows with visualization, debugging, and step-by-step execution capabilities. Enables multi-model task management system to use OpenAI models like GPT-4 for intelligent task decomposition, complexity analysis, and automated workflow management.

How do I use OmniTaskAgent?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@OmniTaskAgent create a task to refactor the authentication module and break it into subtasks" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

# 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:

# 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:

# 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):

# 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

Editor Integration (MCP Service)

Run the MCP server:

# 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" } } } }

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