Task Management AI Agent

This is a full-stack task management application that features an AI agent to help you manage your tasks. You can create, update, list, and delete tasks by chatting with the AI agent in a real-time interface.

This project is built with a modern tech stack, featuring a Python backend and a Next.js frontend.

Tech Stack

• Backend: Python, FastAPI, LangGraph, SQLAlchemy, PostgreSQL
• Frontend: Next.js, TypeScript, TailwindCSS, Socket.IO
• AI: Google Gemini

Prerequisites

Before you begin, ensure you have the following installed on your local machine:

• Python (3.9 or later)
• Node.js (18.x or later)
• PostgreSQL

Getting Started

Follow these steps to get the project up and running on your local machine.

1. Clone the Repository

git clone https://github.com/vignesh1683/agentic-task-management.git
cd agentic-task-management

2. Set Up the Backend

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up the database:

   • Log in to your PostgreSQL server and create a new database.

     CREATE DATABASE taskmanager;

5. Configure the environment variables:

   • Create a .env file in the backend directory by copying the example file:

     cp .env.example .env

   • Open the .env file and update the following variables with your own credentials:

     DATABASE_URL=postgresql+asyncpg://user:password@localhost:5432/taskmanager
     GEMINI_API_KEY=your_gemini_api_key
     

3. Set Up the Frontend

1. Navigate to the frontend directory:

   cd ../client

2. Install the required dependencies:

   npm install

4. Run the Application

1. Start the backend server:

   • In the backend directory, run the following command:

     uvicorn app.main:app --reload

   • The backend will be running at http://localhost:8000.

2. Start the frontend server:

   • In the client directory, run the following command:

     npm run dev

   • The frontend will be running at http://localhost:3000.

Project Structure

.
├── backend
│   ├── app
│   │   ├── agent         # AI agent logic with LangGraph
│   │   ├── database      # Database connection and models
│   │   ├── websocket     # WebSocket connection manager
│   │   └── main.py       # FastAPI application entrypoint
│   ├── .env.example    # Example environment variables
│   └── requirements.txt  # Python dependencies
├── client
│   ├── src
│   │   ├── app           # Next.js application pages
│   │   ├── components    # React components
│   │   ├── lib           # Helper functions and hooks
│   │   └── types         # TypeScript type definitions
│   ├── package.json      # Node.js dependencies
│   └── next.config.ts    # Next.js configuration
└── README.md

Available Scripts

In the client directory, you can run the following scripts:

Script	Description
dev	Runs the app in development mode.
build	Builds the app for production.
start	Starts the production server.
lint	Runs the linter to check for code quality.

API Endpoints

The backend exposes the following endpoints:

• GET /health

  • Returns a status of "healthy" to indicate that the server is running.

• WS /ws/chat

  • The main WebSocket endpoint for real-time communication between the client and the AI agent.
  • Client to Server: Send a JSON message with a message key containing the user's input.

    {
      "message": "Create a new task to buy groceries"
    }

  • Server to Client: The server sends various message types, including agent_response, task_update, and error.

Contributing

Contributions are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.