Luna: Voice-Enabled Expense Management Agent

Luna is a voice-enabled AI assistant built with Langgraph that helps users manage their expenses through natural conversation. This project demonstrates how to create a voice interface for any Langgraph agent, combining speech-to-text and text-to-speech capabilities with a powerful agent framework.

🌟 Features

• Voice Interaction: Speak to Luna and hear responses through high-quality text-to-speech
• Expense Management: Create, query, update, and delete expenses through natural conversation
• Category Classification: Automatically categorizes expenses based on descriptions
• Database Integration: Stores expense data in a PostgreSQL database (via Supabase)
• Tool-using Agent: Built with Langgraph's agent framework for complex reasoning

🛠️ Technology Stack

Backend

• Python 3.13: Core language for the backend
• Langgraph: Agent framework for building the conversational AI
• OpenAI:
  • Whisper API for speech-to-text
  • GPT-4 Mini for the agent's reasoning
  • TTS API for text-to-speech responses
• MCP (Model Calling Protocol): For defining and using tools
• SQLAlchemy: ORM for database interactions
• Supabase: PostgreSQL database provider

Audio Processing

• sounddevice: For capturing audio from microphone
• scipy: For audio file processing

📋 Prerequisites

• Python 3.13
• OpenAI API key
• Supabase account and database
• Microphone and speakers

🚀 Getting Started

1. Clone the repository

git clone https://github.com/rosiefaulkner/langgraph-voice-agent.git
cd langgraph-voice-agent

2. Set up a virtual environment and install dependencies

(Recommended) use uv for dependency management

Setup the venv in your project directory and install all dependencies with one command.

uv sync

3. Set up environment variables

Create a .env file in the root directory with the following variables:

OPENAI_API_KEY=your_openai_api_key
SUPABASE_URI=postgresql://postgres:password@db.example.supabase.co:5432/postgres

4. Run the application

python main.py

🎤 Using Luna

1. Run the application
2. When prompted, speak your request (e.g., "Create a new expense for lunch today that cost $15")
3. Press Enter to stop recording
4. Luna will process your request, interact with the database if needed, and respond verbally
5. Continue the conversation or say "exit" or "quit" to end the session

🧩 Project Structure

langgraph-voice-agent/
├── main.py                  # Main application entry point
├── assistant_graph.py       # Langgraph agent definition
├── state.py                 # State management for the agent
├── voice_utils.py           # Audio recording and playback utilities
├── mcps/                    # Model Calling Protocol servers
│   ├── mcp_config.json      # MCP server configuration
│   └── local_servers/
│       └── db.py            # Database tools implementation
├── .env                     # Environment variables (not in repo)
├── .env.example             # Example environment variables
└── pyproject.toml           # Project dependencies

🔧 Customizing the Agent

Modifying the System Prompt

To change Luna's personality or capabilities, edit the system_prompt in assistant_graph.py:

system_prompt = """You are Luna, the company's expense manager...

Adding New Tools

1. Create a new MCP server or add tools to the existing one in mcps/local_servers/
2. Register the server in mcps/mcp_config.json
3. The tools will be automatically available to the agent

Changing Voice Settings

Modify the TTS settings in voice_utils.py:

async def play_audio(message: str):
    # ...
    async with openai_async.audio.speech.with_streaming_response.create(
        model="gpt-4o-mini-tts",
        voice="fable",  # Change the voice here
        input=cleaned_mes

... [View full README on GitHub](https://github.com/rosiefaulkner/langgraph-voice-agent#readme)