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 Flask + frontend with smooth user interaction, Dockerized deployment for scalability, and an integrated CI/CD pipeline ensuring continuous updates, reliability and capable of context-aware, factual, and empathetic medical consultations.
1.1.mp4
You can interact with the live AI-powered medical assistant here: 👉 https://medigenius.onrender.com/
| 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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Rural Health Access Providing preliminary medical advice in rural or underserved areas where certified doctors may not be immediately available.
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Mental Health First Aid Offering supportive conversations for users dealing with stress, anxiety, or medical confusion.
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Patient Pre-screening Collecting and analyzing symptoms before a user visits a doctor, reducing clinical workload.
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Home Care Guidance Guiding patients and caregivers on medication usage, symptoms, or recovery advice.
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Educational Assistant Helping medical students or patients understand medical topics in simpler language.
- 🤖 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
- 🌐 Flask backend with custom HTML, CSS, and JavaScript frontend for smooth UX
- 🔁 CI/CD pipeline integration for automated testing and deployment
| 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 | Flask (REST API + application logic) |
| Frontend | Custom HTML, CSS, JavaScript UI |
| 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 |
MediGenius/
├── .github/
│ └── workflows/
│ └── main.yml
│
├── agents/
│ ├── __init__.py
│ ├── duckduckgo_agent.py
│ ├── executor_agent.py
│ ├── explanation_agent.py
│ ├── llm_agent.py
│ ├── memory_agent.py
│ ├── planner_agent.py
│ ├── retriever_agent.py
│ └── wikipedia_agent.py
│
├── biogpt-merged/ # Fine Tuning Model
│
├── core/
│ ├── __init__.py
│ ├── langgraph_workflow.py
│ └── state.py
│
├── data/
│ └── medical_book.pdf
│
├──── medical_db/
│ └── chroma.sqlite3
│
├──── chat_db/
│ └── medigenius_chats.db
│
├── notebook/
│ ├── Experiments.ipynb
│ ├── Fine Tuning LLM.ipynb
│ └── Model Train.ipynb
│
├── static/
│ ├── css/
│ │ └── style.css
│ └── js/
│ └── main.js
│
├── templates/
│ └── index.html
│
├── tests/
│ └── test_app.py
│
├── tools/
│ ├── __init__.py
│ ├── llm_client.py
│ ├── pdf_loader.py
│ └── vector_store.py
│
├── .gitignore
├── api.py
├── app.png
├── app.py
├── demo.mp4
├── Dockerfile
├── Fine Tuning LLM.py
├── LICENSE
├── main.py
├── README.md
├── render.yaml
├── requirements.txt
└── setup.py
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
http://localhost:8000
Process a medical question and return AI response
Request:
POST /chat HTTP/1.1
Content-Type: application/json
Host: localhost:8000
{
"message": "What are diabetes symptoms?",
"conversation_id": "optional_existing_id"
}Parameters:
message(required): The medical question to processconversation_id(optional): Existing conversation ID for context
Response:
{
"response": "Diabetes symptoms include increased thirst, frequent urination...",
"timestamp": "12:30",
"conversation_id": "20240615123045"
}Status Codes:
- 200: Successful response
- 400: Invalid request (missing message)
- 500: Internal server error
POST /chat
{
"message": "What causes migraines?"
}Response:
{
"response": "Migraines may be caused by genetic factors, environmental triggers...",
"timestamp": "14:25",
"conversation_id": "20240615142500"
}- 🎙️ Add voice input/output
- 🖼️ Add image upload for reports or prescriptions
- 🧬 Integrate with real-time medical APIs (e.g., WebMD)
- 🔐 Add user authentication & role-based chat memory
Md Emon Hasan
📧 Email: [email protected]
💬 WhatsApp: +8801834363533
🔗 GitHub: Md-Emon-Hasan
🔗 LinkedIn: Md Emon Hasan
🔗 Facebook: Md Emon Hasan
MIT License. Free to use with credit.