🤖 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.

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✨ 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.

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🧠 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

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📸 Application Preview

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📁 Project Structure

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

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💻 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
    })

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💻 Example Usage

Adding a Task

Input: Add task to complete project

Output: Task added successfully

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Viewing Tasks

• Complete project → Pending
• Practice DSA → Pending

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⚙️ How to Run

Install dependencies:

pip install streamlit python-dotenv

Run the app:

streamlit run app.py

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🛠 Technologies Used

• Python
• Streamlit
• Session State
• dotenv

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⚠️ Challenges Faced

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

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🚀 Future Improvements

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

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🎯 Learning Outcomes

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

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👨‍💻 Author

Sahil Sah