ClinicalAgents

ClinicalAgents is an advanced AI-powered multi-agent system designed to revolutionize how healthcare professionals and researchers interact with clinical trial data and drug safety information. By leveraging state-of-the-art Large Language Models (LLMs) and a LangGraph orchestration framework, it automates complex tasks such as trial enrollment analysis, drug safety assessment, and clinical trial discovery.

Key Features

ClinicalAgent 2.0 - LangGraph Workflow

• Intelligent Agent Orchestration: Built on LangGraph for robust, stateful multi-agent workflows
• Enrollment Agent: Smart patient-to-trial matching using ChromaDB vector search and ML-based success prediction
• Safety Agent: Real-time FDA drug safety analysis with black box warnings and adverse event tracking
• Efficacy Agent: Neo4j graph database integration for complex trial outcome analysis
• General Agent: Conversational AI for general clinical trial inquiries

Premium Chat Interface

• Dynamic Session Management: Create, rename, delete, and organize chat sessions with smart auto-generated titles
• Real-time Streaming: Live agent responses with typing indicators and smooth animations
• Stop Generation: Cancel ongoing responses with one click
• Rich Markdown Rendering: Tables, code blocks, and formatted medical insights
• Agent Visualization: See which specialist agents are processing your query
• Context-Aware Conversations: Full conversation history maintained across sessions

Clinical Trials Browser

• Advanced Search: Find relevant clinical trials by disease name
• Comprehensive Trial Details: View trial status, phases, enrollment criteria, and outcomes
• Real-time Data: Pulls from extensive clinical trials database
• Intuitive UI: Modern, responsive design with smooth animations

Secure & Personalized

• JWT Authentication: Secure user registration and login system
• User Profiles: Personalized settings and preferences
• MongoDB Persistence: All chat history and user data securely stored
• Session Isolation: Each user's conversations are private and isolated

Technology Stack

Backend (agents_server)

• Framework: FastAPI (Python 3.11+)
• LLM: Grok (Llama 3.3) via Groq API
• Orchestration: LangGraph 2.0 for advanced agent workflows
• Databases:
  • MongoDB (User data, chat sessions, audit logs)
  • ChromaDB Cloud (Enrollment vector search)
  • Neo4j AuraDB (Efficacy graph analytics)
  • FAISS (Local vector indices)
• External APIs: ClinicalTrials.gov, openFDA
• Machine Learning: Custom ML models for trial success prediction

Frontend (client)

• Framework: Next.js 16 (React 19)
• Styling: Tailwind CSS 4 with custom design system
• Animations: Framer Motion for premium UI interactions
• UI Components: Heroicons, Lucide React
• HTTP Client: Axios
• Markdown: React Markdown with GFM support

Project Structure

ClinicalAgent/
├── agents_server/              # Python FastAPI Backend
│   ├── agents/                 # Specialized AI Agents
│   │   ├── enrollment_agent.py
│   │   ├── safety_agent.py
│   │   ├── efficacy_agent.py
│   │   └── general_agent.py
│   ├── langgraph_v2/           # LangGraph 2.0 Workflow
│   │   ├── workflow.py         # Main orchestration logic
│   │   ├── state.py            # State management
│   │   ├── tools.py            # Agent tools
│   │   └── config.py           # Workflow configuration
│   ├── storage/                # Database connectors
│   ├── ml_models/              # ML prediction models
│   ├── datasets/               # Clinical trial datasets
│   ├── app.py                  # FastAPI application
│   ├── chatbot.py              # Chat logic controller
│   └── auth.py                 # Authentication & JWT
│
├── client/                     # Next.js Frontend
│   ├── app/                    # App Router Pages
│   │   ├── chat/               # Chat interface
│   │   ├── trials/             # Trials browser
│   │   └── layout.js           # Root layout
│   ├── components/             # React Components
│   │   ├── chat/               # Chat UI
│   │   ├── auth/               # Login/Register
│   │   └── ui/                 # Reusable components
│   ├── hooks/                  # Custom React hooks
│   └── services/               # API client services
│
└── docs/                       # Documentation & Assets

Getting Started

For detailed installation and setup instructions, please refer to the SETUP.md file.

Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+
• MongoDB (local or cloud)
• API Keys: Groq, ChromaDB, Neo4j (see .env.example)

Backend Setup

cd agents_server
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env      # Configure your API keys
python app.py

Frontend Setup

cd client
npm install
npm run dev

Access the application at http://localhost:3000

API Documentation

Once the backend is running, access the interactive Swagger UI at: http://localhost:8000/docs

Key Endpoints

• Chat

  • POST /chat: Send a message to the agent system
  • GET /history/{session_id}: Get message history for a session
  • POST /chat/stop: Stop ongoing response generation

• Authentication

  • POST /auth/register: Create a new user account
  • POST /auth/login: Authenticate a user
  • GET /auth/me: Get current user profile

• Sessions

  • GET /sessions: Retrieve user chat sessions
  • POST /sessions: Create a new chat session
  • PUT /sessions/{session_id}: Update session details
  • DELETE /sessions/{session_id}: Delete a chat session

• Trials

  • GET /trials/search: Search clinical trials by disease

Use Cases

1. Trial Enrollment Analysis: "Find suitable clinical trials for Type 2 Diabetes patients"
2. Drug Safety Research: "What are the safety concerns for aspirin?"
3. Efficacy Evaluation: "Show me efficacy data for recent cancer immunotherapy trials"
4. Trial Discovery: Browse and search thousands of clinical trials by disease
5. Research Organization: Manage multiple research sessions with smart session titles

How It Works

1. User Query: Type a question about clinical trials or drug safety
2. LangGraph Routing: The system analyzes your query and routes it to appropriate agents
3. Agent Processing: Specialized agents query databases, APIs, and ML models
4. Response Generation: AI synthesizes findings into comprehensive, actionable insights
5. Continuous Learning: System learns from interactions to improve future responses

Security & Privacy

• JWT-based authentication with secure token management
• Password hashing using industry-standard algorithms
• CORS configuration for frontend-backend communication
• Environment-based configuration for sensitive credentials
• User data isolation and privacy protection

Development

Running Tests

# Backend
cd agents_server
pytest

# Frontend
cd client
npm test

Contributing

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

Acknowledgments

• Built with LangGraph by LangChain
• Powered by Grok (Llama 3.3)
• Clinical trial data from ClinicalTrials.gov
• Drug safety data from openFDA

---