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🤖 AI Task Manager Agent

A smart Task Manager Agent built using Python and Streamlit, designed to manage tasks interactively while simulating basic AI-driven behavior.

Unlike a traditional to-do list, this project focuses on creating an agent-like experience, where the system can understand user input and perform actions such as adding, viewing, and managing tasks dynamically.

✨ Features

1. Add Tasks

Users can add tasks through input, and the system stores them dynamically.

2. View Tasks

Displays all tasks along with their current status (completed / pending).

3. Agent-like Interaction

The system is designed to behave like a simple task management agent, capable of interpreting user intent and executing relevant actions.

4. Session-Based Storage

Tasks are stored using Streamlit session state, allowing persistence during runtime.

🧠 Agent Concept

This project simulates an AI agent workflow, where:

  • User input is interpreted
  • The system decides the action (add/view/manage)
  • Tasks are updated accordingly

Example:

Input → "Add task to study DSA" Agent → Detects intent → Adds task

📸 Application Preview

Task Manager Agent UI

📁 Project Structure

task-manager-agent/ │ ├── app.py ├── file_handling.py ├── .env ├── screenshots/ │ └── app-preview.png └── README.md

💻 Example Code Snippet

def add_task(text=None):
    if text is None:
        if st.session_state.new.strip():
            text = st.session_state.new.strip()
        else:
            return

    if "tasks" not in st.session_state:
        st.session_state.tasks = []

    st.session_state.tasks.append({
        "task": text,
        "status": False
    })

💻 Example Usage

Adding a Task

Input: Add task to complete project

Output: Task added successfully

Viewing Tasks

  • Complete project → Pending
  • Practice DSA → Pending

⚙️ How to Run

Install dependencies:

pip install streamlit python-dotenv

Run the app:

streamlit run app.py

🛠 Technologies Used

  • Python
  • Streamlit
  • Session State
  • dotenv

⚠️ Challenges Faced

  • Managing session state correctly
  • Handling user input dynamically
  • API request limitations (rate limits)
  • Designing agent-like behavior without full AI backend

🚀 Future Improvements

  • Integrate real LLM APIs
  • Add task prioritization
  • Enable deadlines and reminders
  • Store tasks in a database (SQLite)
  • Improve UI/UX

🎯 Learning Outcomes

  • Built an interactive Streamlit app
  • Understood state management
  • Designed an agent-like workflow
  • Improved debugging and logic building

👨‍💻 Author

Sahil Sah