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Team Task Manager

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A production-ready full-stack project and task management system with React, Express, MongoDB, and a LangGraph-orchestrated Python AI Swarm service.

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About

A production-ready full-stack project and task management system with React, Express, MongoDB, and a LangGraph-orchestrated Python AI Swarm service.

README

Node.js npm Python FastAPI Express MongoDB License

A production-ready, full-stack project and task management application integrated with a specialized multi-agent AI Swarm microservice. The platform features a responsive glassmorphic React frontend, a secure Express API server, and a LangGraph-orchestrated Python service for project health auditing, workload risk detection, and smart task recommendations.


🗺️ System Architecture

The application is built as a three-tier system communicating over secure cross-origin APIs:

graph TD
    Client[React Client <br> Port 5173] <-->|HTTP / JSON / JWT| Server[Express API Server <br> Port 5000]
    Server <-->|MongoDB Driver| DB[(MongoDB <br> Port 27017)]
    Server <-->|HTTP / JSON / JWT| Swarm[FastAPI AI Swarm <br> Port 8000]
    Swarm <-->|Motor / PyMongo| DB
    Swarm -->|Ollama Client| LocalLLM[Ollama Local LLM <br> Port 11434]
    Swarm -->|OpenRouter API| CloudLLM[OpenRouter AI <br> Cloud]
    
    subgraph AI Swarm Service
        Swarm
        LocalLLM
        CloudLLM
    end

📸 Visual Preview

🔐 Bento Promo & Onboarding

The split-screen authorization screen features a custom Bento Promo Panel mapping system network structures and AI service layers, complete with a platform-wide contact footer:

Try With this id

User id- [email protected] & password:- abc123456

⚠️ DEMO ACCOUNT NOTICE ⚠️This demo account is shared publicly. Please do not flood the database with spam, create unnecessary new accounts, or abuse the system.

Bento Promo Panel

📊 Real-time Dashboard & AI Insights

Monitor project completion progress, health metrics, and run deep-dive task workload audits using the LangGraph swarm analyzer:

Dashboard and AI Insights

📁 Workspace & Project Auditing

Manage projects, allocate team access, and audit overall workspace status:

Project Audits

📋 Interactive Kanban Task Board

Assign, review, and filter tasks. Track team performance and instantly detect overdue items:

Kanban Task Board

📄 Document Classification & Compliance Risk Mapping

Upload organizational contracts and memos to execute compliance audits and OPA policy-based verification:

Intelligent Document Audit


✨ Features & Benefits

💻 Core Platform

  • Role-Based Access Control (RBAC): Secure access levels featuring admin and member roles. The initial registered user is promoted automatically to admin.
  • Kanban Task Board: Intuitive drag-and-drop workflow status updating (To DoIn ProgressDone).
  • Real-time Analytics: High-performance dashboard detailing project task metrics, completed percentages, workload statistics, and overdue tasks.
  • Document Audit System: Secure contract management allowing admins to upload and classify documents (SEC filings, legal contracts, internal memos).

🧠 AI Swarm Engine

  • Multi-Agent Orchestration: Powered by LangGraph to execute complex workflow graphs (Retrieve ➔ Analyze ➔ Synthesize ➔ Finalize).
  • Model Context Protocol (MCP): Utilizes custom MCP servers for data retrieval:
    • Task Retriever Server: Gathers real-time task statuses and user workloads directly.
    • Project Auditor Server: Audits project metrics, checks due dates, and detects overload risks.
    • Report Synthesizer Server: Compiles natural language project health summaries.
  • Flexible LLM Backends: Integrates with local LLMs (via Ollama) or cloud LLMs (via OpenRouter like DeepSeek-R1 or Claude-3.5-Sonnet).

🔒 Policy-Based Security

  • OPA (Open Policy Agent): Security policies defined via Rego (ai-swarm/security/policies.rego) run at the agent level to guard tool execution, ensuring members can only request authorized tools (e.g. limiting contract analysis exclusively to admins).

🎨 Premium UI/UX Experience

  • Glassmorphic Theme: Dark slate layouts (bg-gradient-to-br from-slate-900 via-slate-800 to-slate-900) combined with backdrop-blur sidebars and headers.
  • Bento Promo Panel: Beautiful, responsive onboarding graphics detailing platform capabilities during Login/Signup.
  • Contrast-Optimized: High-visibility glowing teal brand styling (text-teal-400) ensuring perfect accessibility.

📁 Repository Structure

team-task-manager/
├── package.json          # Root scripts for concurrent development
├── docs/                 # Detailed system documentation and guides
│   ├── DEV_TRACKING.md   # Setup notes, troubleshooting, and dev history
│   ├── TESTING_REPORT.md # End-to-end integration and verification logs
│   └── AI_INTEGRATION_LEARNING_PATH.md # Complete multi-agent building path
│
├── client/               # React Frontend (Vite, Tailwind, Lucide React)
│   ├── src/
│   │   ├── components/   # Bento PromoPanel, Layout, and routing guards
│   │   ├── pages/        # Kanban board, Dashboard, Contracts, Auth
│   │   └── api/          # Axios configurations with JWT auth interceptors
│   └── package.json
│
├── server/               # Express API Server (Node.js, MongoDB, JWT)
│   ├── src/
│   │   ├── controllers/  # Auth, projects, contracts, and tasks endpoints
│   │   ├── models/       # Mongoose Schemas (User, Project, Task, Contract)
│   │   └── routes/       # Express routes including /api/ai proxy endpoints
│   └── package.json
│
└── ai-swarm/             # Python AI Swarm Microservice (FastAPI, LangGraph)
    ├── api/main.py       # FastAPI application and query router
    ├── orchestration/    # LangGraph agent definitions and state machines
    ├── mcp_servers/      # TaskRetriever, ProjectAuditor, ReportSynthesizer
    ├── security/         # OPA integration and policies.rego policies
    └── run.py            # Service runner and prerequisite validator

🚀 Getting Started

📋 Prerequisites

Component Requirement Check Command
Node.js >= 18.0.0 node -v
npm >= 9.0.0 npm -v
Python >= 3.8.0 python --version
MongoDB >= 6.0 mongod --version
Ollama (Optional) Local Service ollama --version

🔧 Installation & Setup

  1. Clone the Repository

    git clone https://github.com/anan5093/team-task-manager.git
    cd team-task-manager
    
  2. Install Node.js Dependencies

    # Install root tools (concurrently)
    npm install
    
    # Install Express Server dependencies
    npm install --prefix server
    
    # Install React Client dependencies
    npm install --prefix client
    
  3. Install AI Swarm Dependencies Ensure you have a Python virtual environment activated:

    cd ai-swarm
    python -m venv .venv
    
    # Windows:
    .venv\Scripts\activate
    # Unix/macOS:
    source .venv/bin/activate
    
    pip install -r requirements.txt
    cd ..
    

⚙️ Environment Configuration

Create the following files in their respective folders:

1. Express Backend Setup (server/.env)

PORT=5000
NODE_ENV=development
MONGO_URI=mongodb://127.0.0.1:27017/team-task-manager
JWT_SECRET=generate-a-long-secure-random-key-here
JWT_EXPIRES_IN=7d
CLIENT_URL=http://localhost:5173
SWARM_API_URL=http://localhost:8000/api/swarm
SWARM_ENABLED=true
AI_SERVICE_SECRET=your-shared-agentic-secret

2. React Client Setup (client/.env)

VITE_API_URL=http://localhost:5000/api

3. AI Swarm Service Setup (ai-swarm/.env)

# LLM Provider Configuration
OLLAMA_BASE_URL=http://localhost:11434
MODEL=tinyllama

# For Cloud LLM override (Optional)
USE_OPENROUTER=false
OPENROUTER_API_KEY=your-openrouter-key-here
OPENROUTER_MODEL=deepseek/deepseek-r1:free

# Service Bindings
EXPRESS_API_URL=http://localhost:5000/api
FASTAPI_PORT=8000
MONGO_URI=mongodb://127.0.0.1:27017/team-task-manager
JWT_SECRET=generate-a-long-secure-random-key-here
AI_SERVICE_SECRET=your-shared-agentic-secret

🏃 Running the Application

For the application to run successfully, ensure MongoDB (and Ollama if running locally) is running in the background.

Step 1: Start Database & LLM Engine

# Start MongoDB (Default port: 27017)
mongod

# Start Ollama (Default port: 11434)
ollama serve

Step 2: Start the Web App Services

From the root directory, run:

npm run dev

This concurrently boots the React Frontend and the Express Backend.

Step 3: Start the Python AI Swarm Service

Open a new terminal window, activate your virtual environment, and run:

cd ai-swarm
python run.py

This runs validation checks and starts the FastAPI service.

Service Network Map

Service Port Endpoint / URL
React Client 5173 http://localhost:5173/
Express Server 5000 http://localhost:5000/health
FastAPI Swarm 8000 http://localhost:8000/health

🛠️ Usage Workflows

  1. User Registration: Sign up via the login screen. The first account created will be given the admin role automatically.
  2. Setup Projects & Teams: Admins can navigate to Projects, create a new workspace, and invite registered members.
  3. Task Allocation: Create tasks inside the Task Board, assigning specific users, descriptions, and due dates.
  4. Kanban Operations: Team members move tasks between Todo, In-Progress, and Done states. Overdue tasks are highlighted automatically in red.
  5. AI Dashboard Insights: Navigate to a project dashboard and click Get AI Insights to execute the multi-agent swarm analysis. The service analyzes workloads, formats risks, and returns recommendations.
  6. AI Document Audit: Upload a contract (Admin only) and click Analyze Contract to trigger the compliance risk evaluation.

📚 Documentation & Help

  • Detailed Development Logs: Review DEV_TRACKING.md for step-by-step setup guides, troubleshooting steps, and session logs.
  • AI Architecture Walkthrough: Refer to AI_INTEGRATION_LEARNING_PATH.md for details on LangGraph configuration, MCP state models, and security structures.
  • Testing Reports: See TESTING_REPORT.md for end-to-end trace validation and performance analysis.

For bugs, inquiries, or support, please open an issue in the GitHub Issues tab or use the Discussions panel.


👥 Maintenance & Contributing

This project is currently maintained by @anan5093.

Contributions are highly encouraged! Please review our Contribution Guidelines to get started. For details on code style standards, linting, and development tracking, please read the Developer Tracking Docs.


📄 License

This project is licensed under the MIT License. See the LICENSE file for more information.

from github.com/anan5093/team-task-manager

Installing Team Task Manager

This server has no published package — it is built from source. Open the repository and follow its README.

▸ github.com/anan5093/team-task-manager

FAQ

Is Team Task Manager MCP free?

Yes, Team Task Manager MCP is free — one-click install via Unyly at no cost.

Does Team Task Manager need an API key?

No, Team Task Manager runs without API keys or environment variables.

Is Team Task Manager hosted or self-hosted?

Self-hosted: the server runs locally on your machine via the install command above.

How do I install Team Task Manager in Claude Desktop, Claude Code or Cursor?

Open Team Task Manager on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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