Modern healthcare and service industries face significant challenges in managing appointments efficiently. Traditional booking systems are often manual, time-consuming, and prone to errors or double-bookings. Patients and clients expect seamless, real-time interactions—preferably through natural language and voice—while providers need intelligent scheduling that adapts to their availability and priorities.

Medora AI addresses these needs by providing:

• Frictionless, voice-first appointment booking for patients and clients.
• Intelligent, multi-agent orchestration that optimizes scheduling, resolves conflicts, and adapts to urgency.
• Automated notifications and reminders to reduce no-shows and improve communication.
• A scalable, extensible platform ready for future AI advancements.

• Conversational Booking: Users can book, cancel, or check appointments using natural language—via chat or voice.
• Voice Interaction: Speak to the system and hear responses back, making the experience accessible and hands-free. (Now powered by streamlit-audiorec for reliable browser-based recording.)
• Multi-Agent Intelligence: Three specialized AI agents (User Bot, Doctor Bot, Scheduler Bot) collaborate to understand user intent, recommend doctors, and manage scheduling.
• Hybrid LLM/Intent Architecture: Routine actions (like booking/canceling) are handled by the orchestrator for reliability; all other queries are answered directly by a powerful LLM (default: llama3-8b-8192 on Groq).
• Smart Scheduling: The system checks doctor/provider availability, validates time slots, and prevents double-booking.
• Conflict Resolution: If a requested slot is unavailable, the system suggests alternatives in real time.
• Priority Handling: Urgent and emergency requests are prioritized using a Model Context Protocol (MCP).
• Email Notifications: Users receive booking confirmations, cancellations, and reminders automatically.
• Dashboard & Manual Controls: Staff can view, add, or cancel appointments directly from the dashboard.
• Doctor Directory: Users can browse available doctors, their specialties, and schedules.

• Python 3.8+
• Streamlit: For the interactive web UI.
• LangChain & LangGraph: For multi-agent orchestration and conversational AI.
• Groq LLM (llama3-8b-8192): For advanced language understanding and generation.
• gTTS, pygame, SpeechRecognition: For text-to-speech and speech-to-text capabilities.
• streamlit-audiorec: For browser-based audio recording.
• smtplib, email: For sending email notifications and reminders.
• dotenv, yaml: For configuration management.
• Logging: For robust monitoring and debugging.
• pytz, numpy, sounddevice, vosk: For time zone handling and audio processing.

• Manual Scheduling Hassles: Eliminates the need for phone calls or manual entry by enabling conversational, automated booking.
• Double-Booking & Conflicts: Prevents scheduling errors by checking provider availability and resolving conflicts dynamically.
• Missed Appointments: Reduces no-shows with automated reminders and confirmations.
• Accessibility: Makes appointment management easier for users with disabilities or those who prefer voice interaction.
• Urgency Management: Ensures that urgent and emergency cases are prioritized appropriately.
• Scalability: Supports multiple providers and can be extended to other industries (consultants, salons, etc.).
• Staff Efficiency: Frees up administrative staff from repetitive scheduling tasks.

While Medora AI already leverages a robust multi-agent system, integrating an Agent Development Kit (ADK) in the future will unlock even greater capabilities:

• Continuous Learning: Agents can adapt to user preferences, learn from historical booking patterns, and personalize recommendations.
• Custom Agent Creation: Easily add new agents for specialized tasks (e.g., insurance verification, follow-up scheduling).
• Plug-and-Play Upgrades: Swap or upgrade agent logic without rewriting the core system.
• Advanced Analytics: Use ADK to analyze appointment trends, provider utilization, and user satisfaction.
• Cross-Domain Expansion: Extend the system to other domains (legal, education, wellness) by developing new agent modules.

• Python 3.8 or higher
• Streamlit
• Required Python packages (see requirements.txt)

git clone https://github.com/extremecoder-rgb/medoraAI.git
cd medora-ai
pip install -r requirements.txt
streamlit run app.py

• Book an Appointment:
  Start a conversation with "I would like to book an appointment" or use the voice input (mic button next to chat input).
• Check Availability:
  Ask "Show me available doctors" or "What are the next available appointments?"
• Manage Appointments:
  View, cancel, or add appointments via the dashboard or chat.
• Receive Notifications:
  Get email confirmations and reminders automatically.

• Make sure your browser has microphone access enabled.
• If you see "Voice processing failed", try speaking clearly and for at least 2 seconds.
• Ensure you have a stable internet connection (Google Speech Recognition API is used).
• If you see a detailed error, check the stack trace for audio format or permission issues.

• If you see errors like model_not_found or 404, update your .env or settings.yaml to use a supported model (e.g., llama3-8b-8192).
• Make sure your GROQ_API_KEY is correct and you have access to the selected model.
• Restart the app after changing model settings.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

• Built with Streamlit
• Powered by Groq LLM (llama3-8b-8192)
• Voice processing by PyAudio, gTTS, SpeechRecognition, and streamlit-audiorec
• Multi-agent architecture inspired by LangChain

For support, email hsuswiowkskow@gmail.com or open an issue in the repository.

Made with ❤️ by Subhranil Mondal