AI Notes Maker - Multi-Agent Note Taking System

A modular, multi-agent system for generating comprehensive notes on any topic using AI. The system uses specialized agents to plan research, retrieve relevant content, and synthesize high-quality notes.

🚀 Features

• Multi-Agent Architecture: Specialized agents for planning, retrieval, and synthesis
• Document Intelligence: Automatically processes PDF, TXT, MD, and DOCX files
• Configurable Output: Customizable depth, audience, and format
• Interactive & Batch Modes: CLI interface for both interactive and automated usage
• Comprehensive Logging: Built-in recording and monitoring capabilities
• Modular Design: Clean, maintainable codebase with proper separation of concerns

Architecture

The system consists of three main agents:

1. Agent 1 (Planner): Strategic planning and orchestration
2. Agent 2 (Synthesizer): Content synthesis and note writing
3. Agent 3 (Retriever): Information retrieval using vector search

Installation

# Clone the repository
git clone https://github.com/yash3056/notesmaker.git
cd notesmaker
# Install dependencies
pip install -r requirements.txt

Quick Start

1. Place your documents in the documents/ folder
2. Run the system:

fromsrc.mainimportcreate_notesnotes=awaitcreate_notes(
topic="your topic here",
requirements={
"depth": "comprehensive",
"include_examples": True,
"format": "structured",
"audience": "students"
}
)

📁 Project Structure

notesmaker/
├── config/ # Configuration management
│ ├── __init__.py
│ └── settings.py # Dataclass-based configuration
├── src/ # Main source code
│ ├── agents/ # Specialized AI agents
│ │ ├── __init__.py
│ │ ├── base_agent.py # Abstract base agent
│ │ ├── planner.py # Research planning agent
│ │ ├── retriever.py # Content retrieval agent
│ │ └── synthesizer.py # Note synthesis agent
│ ├── core/ # Core system components
│ │ ├── __init__.py
│ │ ├── data_structures.py # Core data models
│ │ ├── document_loader.py # Document processing
│ │ ├── llm_wrapper.py # LLM abstraction layer
│ │ └── system.py # Main system orchestration
│ └── utils/ # Utility functions
│ ├── __init__.py
│ ├── helpers.py # Helper functions
│ └── recorder.py # Logging and monitoring
├── tests/ # Test suite
│ ├── test_agents/
│ ├── test_core/
│ └── test_utils/
├── examples/ # Usage examples
│ ├── basic_usage.py
│ └── advanced_usage.py
├── backup_original/ # Original monolithic files
├── documents/ # Source documents directory
├── main.py # Main entry point
├── setup.py # Package installation
├── requirements.txt # Dependencies
└── README.md # This file

🛠️ Installation

1. Clone and Install Dependencies

git clone https://github.com/yash3056/notesmaker.git
cd notesmaker
pip install -r requirements.txt

2. Environment Setup

Copy the example environment file and configure your settings:

cp .env.example .env

Edit .env with your configuration:

• Set your preferred LLM model
• Configure API keys if using external services
• Adjust system parameters

3. Install as Package (Optional)

pip install -e .

📖 Usage

Interactive Mode

python main.py --interactive

The system will prompt you for:

• Topic for note generation
• Depth level (basic/intermediate/comprehensive)
• Target audience (students/professionals/general)
• Whether to include examples

Batch Mode

python main.py --topic "Machine Learning" --depth comprehensive --audience students

Command Line Options

python main.py [options]
Options:
--topic TOPIC Topic for note generation
--depth LEVEL Depth: basic, intermediate, comprehensive
--audience AUDIENCE Target: students, professionals, general
--examples / --no-examples Include examples (default: yes)
--interactive, -i Run in interactive mode
--model MODEL LLM model to use

📚 Adding Documents

Place your source documents in the documents/ directory. Supported formats:

• Text files: .txt, .md
• PDF files: .pdf (requires pypdf)
• Word documents: .docx (requires python-docx)

The system will automatically:

1. Scan the documents directory
2. Load and process all supported files
3. Build a searchable index
4. Use relevant content during note generation

🧩 Architecture

Core Components

• MultiAgentSystem: Main orchestrator that coordinates all agents
• DocumentLoader: Handles loading and processing of various document formats
• LLMWrapper: Provides abstraction layer for different language models
• Data Structures: Type-safe models for messages, plans, and configurations

Agent System

1. PlannerAgent: Analyzes the topic and creates a structured research plan
2. RetrieverAgent: Searches documents and retrieves relevant content
3. SynthesizerAgent: Combines research into comprehensive, well-formatted notes

🔧 Development

Running Tests

pytest tests/

Adding New Agents

1. Create new agent file in src/agents/
2. Inherit from BaseAgent
3. Implement required methods
4. Add to src/agents/__init__.py
5. Register in the MultiAgentSystem

🔄 Migration from Monolithic Version

This project was refactored from two monolithic scripts (aI_agent_2.py and record.py) into a clean, modular architecture. The original files are preserved in backup_original/ for reference.

Key Improvements

• Modular Design: Separated concerns into logical modules
• Type Safety: Added comprehensive type hints and Pydantic models
• Better Testing: Proper test structure with pytest
• Configuration Management: Centralized, dataclass-based configuration
• Package Structure: Proper Python package with setup.py
• Enhanced Documentation: Comprehensive README and code documentation