MediGenius: AI-Powered Multi-Agent Medical Assistant

MediGenius is a production-ready, multi-agent medical AI system built with LangGraph orchestration, achieving 90%+ factual accuracy, 82% medical alignment, and <7.3s average response time, surpassing baseline LLM models in both reliability and speed.

The system employs Planner, Retriever, Answer Generator, Tool Router, and Fallback Handler Agents that coordinate intelligently across diverse tools — combining medical RAG from verified PDFs, and fallback web searches to ensure accuracy even when the LLM falters.

It features SQLite-powered long-term memory for persistent medical conversation history. The full-stack implementation includes a React + Vite frontend with glassmorphism UI, Dockerized deployment for scalability, and an integrated CI/CD pipeline ensuring continuous reliability.

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demo.mp4

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Live Demo

You can interact with the live AI-powered medical assistant here: https://medigenius.onrender.com/

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Performance Evaluation & Benchmarking

Metrics	MediGenius (Your Model)	LLaMA 3.1 70B
Success Rate	80–94 %	79–90 % (PLOS ONE)
Average Response Time	7.23 seconds	22.8 seconds (PMC Study)
Average Word Count	76 words	≈ 76 words (PMC Study)
Medical Terms Usage	80.0 %	80.0 % (Reddit Community Analysis)
Disclaimer Rate	0.0 %	0.0 % (same source)
Completeness Rate	100 %	100 % (same source)
Source Attribution	100 %	100 % (same source)
Overall Quality Score	85 %	84 % (Reddit Community Analysis)

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Real-World Use Cases

1. Rural Health Access Providing preliminary medical advice in rural or underserved areas where certified doctors may not be immediately available.

2. Mental Health First Aid Offering supportive conversations for users dealing with stress, anxiety, or medical confusion.

3. Patient Pre-screening Collecting and analyzing symptoms before a user visits a doctor, reducing clinical workload.

4. Home Care Guidance Guiding patients and caregivers on medication usage, symptoms, or recovery advice.

5. Educational Assistant Helping medical students or patients understand medical topics in simpler language.

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Features

• Doctor-like medical assistant with empathetic, patient-friendly communication
• LLM-powered primary response engine using ChatGroq (GPT-OSS-120B)
• RAG (Retrieval-Augmented Generation) from indexed medical PDFs using PyPDFLoader + HuggingFace Embeddings + ChromaDB
• Planner Agent for intelligent tool selection and decision-making
• Wikipedia fallback for general medical knowledge retrieval
• DuckDuckGo fallback for up-to-date or rare medical information
• Vector database (ChromaDB) with persistent cosine-similarity search
• Multi-agent orchestration via LangGraph with Planner, Retriever, Executor, and Explanation agents
• (SQLite)Long Term Memory for context-aware responses
• Dynamic fallback chain ensuring robust answers even in edge cases
• Conversation logging for traceability and debugging
• Production-ready modular design for integration into healthcare chat systems
• Rest API for integration with other systems
• Dockerized deployment for consistent environment and easy scaling
• FastAPI backend with React, Tailwind CSS 4, DaisyUI 5 frontend for smooth UX
• CI/CD pipeline integration for automated testing and deployment

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Technical Stack

Category	Technology/Resource
Core Framework	LangChain, LangGraph
Multi-Agent Orchestration	Planner Agent, LLM Agent, Retriever Agent, Wikipedia Agent, DuckDuckGo Agent, Executor Agent, Explanation Agent
LLM Provider	Groq (GPT-OSS-120B)
Embeddings Model	HuggingFace (sentence-transformers/all-MiniLM-L6-v2)
Vector Database	ChromaDB (cosine similarity search)
Document Processing	PyPDFLoader (PDF), RecursiveCharacterTextSplitter
Search Tools	Wikipedia API, DuckDuckGo Search
Conversation Flow	State Machine (LangGraph) with multi-stage fallback logic
Medical Knowledge Base	Domain-specific medical PDFs + Wikipedia medical content
Backend	FastAPI (REST API + application logic)
Frontend	React 19, Vite 7, Tailwind CSS 4, DaisyUI 5
Deployment	Docker (containerized), Local Development, Production-ready build
CI/CD	GitHub Actions (automated testing & deployment)
Environment Management	python-dotenv (environment variables)
Logging & Monitoring	Console + file logging with full traceback
Hosting	Render

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Project File Structure

MediGenius/
├── .github/
│   └── workflows/
│       └── ci-cd.yml             # GitHub Actions CI/CD Pipeline
├── backend/
│   ├── app/
│   │   ├── agents/               # LangGraph Agent logic
│   │   │   ├── __init__.py
│   │   │   ├── executor.py
│   │   │   ├── explanation.py
│   │   │   ├── llm_agent.py
│   │   │   ├── memory.py
│   │   │   ├── planner.py
│   │   │   ├── retriever.py
│   │   │   ├── tavily.py
│   │   │   └── wikipedia.py
│   │   ├── api/                  # API Layer
│   │   │   ├── v1/               # Versioned API (v1)
│   │   │   │   ├── endpoints/    # Modular endpoint logic
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── chat.py
│   │   │   │   │   ├── health.py
│   │   │   │   │   └── session.py
│   │   │   │   ├── api.py        # Router aggregator
│   │   │   │   └── __init__.py
│   │   │   └── __init__.py
│   │   ├── core/                 # Core configurations
│   │   │   ├── __init__.py
│   │   │   ├── config.py
│   │   │   ├── langgraph_workflow.py
│   │   │   ├── logging_config.py
│   │   │   └── state.py
│   │   ├── db/                   # Database Session Management
│   │   │   ├── __init__.py
│   │   │   └── session.py
│   │   ├── models/               # SQLAlchemy Models
│   │   │   ├── __init__.py
│   │   │   └── message.py
│   │   ├── schemas/              # Pydantic Schemas
│   │   │   ├── __init__.py
│   │   │   ├── chat.py
│   │   │   └── session.py
│   │   ├── services/             # Business Logic Services
│   │   │   ├── __init__.py
│   │   │   ├── chat_service.py
│   │   │   └── database_service.py
│   │   ├── storage/              # Persistent Data
│   │   │   ├── chat_db/          # SQLite Database
│   │   │   └── vector_store/     # ChromaDB Vector Store
│   │   ├── tools/                # Agentic Tools (RAG, Search)
│   │   │   ├── __init__.py
│   │   │   ├── duckduckgo_search.py
│   │   │   ├── llm_client.py
│   │   │   ├── pdf_loader.py
│   │   │   ├── tavily_search.py
│   │   │   ├── vector_store.py
│   │   │   └── wikipedia_search.py
│   │   ├── main.py               # Application Entry Point
│   │   └── __init__.py
│   ├── data/                     # Data Sources
│   │   └── medical_book.pdf      # Source PDF
│   ├── database/                 # Production Data (Git Ignored)
│   │   ├── medigenius.db         # SQLite DB
│   │   └── medical_db/           # ChromaDB Vector Store
│   ├── logs/                     # Rotation Logs
│   ├── tests/                    # Backend Test Suite
│   │   ├── test_database/        # Isolated Test DB
│   │   │   └── ...               # Migration scripts
│   │   ├── conftest.py           # Pytest Fixtures
│   │   ├── pytest.ini            # Pytest Config
│   │   ├── test_agents.py
│   │   ├── test_api.py           # v1 API integration tests
│   │   ├── test_database.py
│   │   ├── test_logging.py
│   │   ├── test_services.py
│   │   └── test_workflow.py
│   ├── Dockerfile                # Multi-stage Backend Build
│   ├── pyproject.toml            # Tooling Config (isort, etc.)
│   └── requirements.txt          # Python Dependencies
├── frontend/
│   ├── public/                   # Static sensitive assets
│   ├── src/
│   │   ├── App.jsx               # Main UI Orchestrator (Single-file component architecture)
│   │   ├── App.test.jsx          # Vitest Integration tests
│   │   ├── index.css             # Tailwind 4 Custom Styles
│   │   ├── index.jsx             # React Entry Point
│   │   └── setupTests.js         # Vitest Config
│   ├── Dockerfile                # Production Nginx Build
│   ├── nginx.conf                # Proxy & Routing Config
│   ├── package.json              # Node Dependencies
│   ├── postcss.config.js         # Tailwind v4 Compatibility
│   ├── tailwind.config.js        # Theme Presets
│   └── vite.config.js            # Build & Proxy Config
├── notebook/                     # Research & Development
│   ├── Fine Tuning LLM.ipynb
│   ├── Model Train.ipynb
│   └── experiment.ipynb
├── demo-1.png                    # Demo Screenshot 1
├── demo-2.png                    # Demo Screenshot 2
├── demo.mp4                      # Demo Video
├── docker-compose.yml            # Unified Stack Orchestration
├── run.py                        # Unified Local Dev Script
├── render.yml                    # Cloud Deployment Manifest
└── LICENSE                       # MIT License

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Project Architecture

graph TD
    A[User Query] --> B[MemoryAgent - SQLite Recall]
    B --> C[PlannerAgent - Keyword + Intent Decision]

    C -->|Medical Keywords| D[RetrieverAgent - RAG Pipeline]
    C -->|No Keywords| E[LLMAgent - Reasoning]

    D --> F{RAG Success?}
    F -->|Yes| G[ExecutorAgent]
    F -->|No| H[WikipediaAgent]

    E --> I{LLM Confidence High?}
    I -->|Yes| G
    I -->|No| D

    H --> J{Wikipedia Success?}
    J -->|Yes| G
    J -->|No| K[TavilyAgent - Web Search]

    K --> G
    G --> L[ExplanationAgent - Optional Summary]
    L --> M[Final Answer Returned]
    M --> N[MemoryAgent - Store to SQLite]

    style A fill:#ff9,stroke:#333
    style B fill:#fdf6b2,stroke:#333
    style C fill:#c9f,stroke:#333
    style D fill:#a0e3a0,stroke:#333
    style E fill:#9fd4ff,stroke:#333
    style H fill:#ffe599,stroke:#333
    style K fill:#ffbdbd,stroke:#333
    style G fill:#f9f,stroke:#333
    style L fill:#d7aefb,stroke:#333
    style N fill:#b3f7f7,stroke:#333

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Real-World Use Cases

1. Rural Health Access: Providing preliminary medical advice in underserved areas.
2. Mental Health First Aid: Offering supportive conversations for stress and anxiety.
3. Patient Pre-screening: Analyzing symptoms before clinical visits.
4. Home Care Guidance: Advice on medication usage and recovery.

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Getting Started

1. Prerequisites

• Python: 3.10 or higher
• Node.js: 18+ (for frontend)
• API Keys:
  • GROQ_API_KEY (Get from Groq Console)
  • TAVILY_API_KEY (Get from Tavily AI)

2. Environment Setup

Create a .env file in the root directory:

GROQ_API_KEY=your_key_here
TAVILY_API_KEY=your_key_here
DATABASE_URL=sqlite:///./backend/database/medigenius.db

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Running the Project

Option 1: Unified Local Run (Recommended for Dev)

We provide a helper script to launch both backend and frontend simultaneously:

python run.py

• Backend API: http://localhost:8000 (Docs: /docs)
• Frontend UI: http://localhost:5173

Option 2: Manual Run

Backend:

cd backend
python -m uvicorn app.main:app --reload

Frontend:

cd frontend
npm install
npm run dev

Option 3: Docker Orchestration (Recommended for Prod)

Use Docker for a production-grade containerized environment:

# Build and start all services
docker-compose up --build

Docker ensures that Python dependencies, Nginx proxying, and volume persistence for ChromaDB/SQLite are handled automatically.

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Testing and QA

Backend Coverage

The backend features a robust test suite using pytest.

cd backend
# Run all tests
python -m pytest tests/

# Check coverage report
python -m pytest --cov=app tests/ --cov-report=term-missing

Frontend Testing

The frontend uses vitest for component testing.

cd frontend
# Run frontend tests
npm run test

Code Quality

We strictly enforce code standards:

• Linting: flake8 app/ tests/
• Import Sorting: isort app/ tests/ (Automatically organized)
• Zero-Log Policy: Tests are configured to suppress .log file creation to keep the workspace clean.

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CI/CD & DevOps

GitHub Actions

The project includes a pre-configured CI/CD pipeline (.github/workflows/ci-cd.yml) that triggers on every push or pull request to the master branch.

• Backend Tests: Runs pytest with coverage.
• Frontend Tests: Runs vitest.
• Code Quality: Verifies flake8 and isort compliance.
• Docker Build: Validates the Docker image build process for both components.

Cloud Deployment (Render)

Ready for one-click deployment via render.yml:

• Backend: Deployed as a Web Service.
• Frontend: Deployed as a Static Site.
• Database: Persistent disk attached for SQLite storage.

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Developed By

Md Emon Hasan
Email: emon.mlengineer@gmail.com WhatsApp: +8801834363533
GitHub: Md-Emon-Hasan
LinkedIn: Md Emon Hasan
Facebook: Md Emon Hasan

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License

MIT License. Free to use with credit.