Multi-agent healthcare assistant with WhatsApp, GPT-4 & Google Sheets
Multi-Agent AI Healthcare Assistant Demo
⚠️ EDUCATIONAL DEMONSTRATION ONLY - NOT FOR PRODUCTION MEDICAL USE ⚠️
A comprehensive demonstration of n8n's advanced multi-agent AI orchestration capabilities, showcasing how to build sophisticated conversational AI systems with specialized agent coordination.
🎯 What This Demo Shows
Advanced Multi-Agent Architecture:
- Main Orchestrator Agent - Traffic controller and decision maker
- Patient Registration Agent - Specialized data collection and validation
- Appointment Scheduler Agent - Complex multi-step booking workflows
- Medical Report Analyzer - Document processing and analysis
- Prescription Medicine Analyzer - Medicine verification and safety checks
Technical Learning Objectives:
- Multi-agent coordination patterns
- Conditional agent routing and tool selection
- Memory management across conversations
- Multi-modal input processing (text, audio, images, documents)
- Complex state management in AI workflows
- External system integration (Google Sheets, WhatsApp, OpenAI)
🏗️ Architecture Highlights
Multi-Modal Processing Pipeline:
- Text Messages → Direct agent processing
- Audio Messages → Transcription → Text processing → Audio response
- Images → Vision analysis → Context integration
- Documents → PDF extraction → Content analysis
Agent Specialization:
- Each agent has focused responsibilities and constraints
- Intelligent document classification and routing
- Context-aware tool selection
- Error handling and recovery mechanisms
Memory & State Management:
- Session-based conversation persistence
- Context sharing between specialized agents
- Multi-step workflow state tracking
🔧 Technical Implementation
Key n8n Features Demonstrated:
@n8n/n8n-nodes-langchain.agent- Main orchestrator@n8n/n8n-nodes-langchain.agentTool- Specialized sub-agents@n8n/n8n-nodes-langchain.memoryPostgresChat- Conversation memoryn8n-nodes-base.googleSheetsTool- External data integration- Complex conditional logic and routing
Integration Patterns:
- WhatsApp Business API integration
- OpenAI GPT-4 model orchestration
- Google Sheets as data backend
- PostgreSQL for conversation memory
- Multi-step document processing
📚 Learning Value
For n8n Developers:
- Enterprise-grade workflow architecture patterns
- AI agent orchestration best practices
- Complex conditional logic implementation
- Memory management in conversational AI
- Multi-modal data processing techniques
- Error handling and recovery strategies
For AI Engineers:
- Agent specialization and coordination
- Tool calling and function integration
- Context management across conversations
- Multi-step workflow design
- Production workflow considerations
⚙️ Setup Requirements
Required Credentials:
- OpenAI API Key (GPT-4 access recommended)
- WhatsApp Business API credentials
- Google Sheets OAuth2 API
- PostgreSQL database connection
External Dependencies:
- Google Sheets database (template structure provided)
- WhatsApp Business Account
- PostgreSQL database for conversation memory
🚨 Important Disclaimers
Educational Use Only:
- This is a DEMONSTRATION of n8n capabilities
- NOT suitable for actual medical use
- NOT HIPAA compliant
- Use only with fictional/test data
Production Considerations:
- Requires proper security implementation
- Needs compliance review for medical use
- Consider HIPAA-compliant alternatives for healthcare
- Implement proper data encryption and access controls
🎓 Educational Applications
Perfect for Learning:
- Advanced n8n workflow patterns
- Multi-agent AI system design
- Complex automation architecture
- Integration pattern best practices
- Conversational AI development
Workshop & Training Use:
- AI automation workshops
- n8n advanced training sessions
- Multi-agent system demonstrations
- Integration pattern tutorials
🔄 Workflow Components
Main Flow:
- WhatsApp message reception and media processing
- Input classification and routing
- Main agent orchestration and tool selection
- Specialized agent execution
- Response formatting and delivery
Sub-Agents:
- Registration Tool - Patient data collection
- Scheduler Tool - Appointment booking logic
- Report Analyzer - Medical document analysis
- Medicine Analyzer - Prescription verification
💡 Customization Ideas
Extend the Demo:
- Add more specialized agents
- Implement different communication channels
- Integrate with other healthcare APIs
- Add more sophisticated document processing
- Implement advanced analytics and reporting
Adapt for Other Industries:
- Customer service automation
- Educational assistance systems
- E-commerce support workflows
- Technical support orchestration
🎯 Perfect for: Learning advanced n8n patterns, AI system architecture, multi-agent coordination
⏱️ Setup Time: 30-45 minutes (with credentials)
📈 Skill Level: Intermediate to Advanced
🏷️ Tags: AI Agents, Multi-Agent Systems, Healthcare Demo, Educational, Advanced Workflows
Multi-Agent Healthcare Assistant with WhatsApp, GPT-4, and Google Sheets
This n8n workflow automates a multi-agent healthcare assistant that interacts with users via WhatsApp, leverages an AI agent for complex queries, and can potentially integrate with external data sources like Google Sheets (though not explicitly configured in this JSON, the directory name suggests this capability). It provides a conversational interface for healthcare-related questions, utilizing an AI agent with tools like a calculator to provide comprehensive responses.
What it does
This workflow orchestrates a sophisticated AI assistant:
- Listens for WhatsApp Messages: It acts as a webhook, waiting for incoming messages from WhatsApp.
- Initial Message Processing: The received WhatsApp message content is extracted and prepared for the AI agent.
- Routes to AI Agent: The cleaned message is passed to an AI Agent configured with an OpenAI Chat Model.
- AI Agent with Tools: The AI Agent is equipped with a
Calculatortool, allowing it to perform mathematical operations if needed to answer user queries. It also appears to have a "Postgres Chat Memory" for conversational context and an "AI Agent Tool" which suggests it can delegate tasks or use other specialized AI agents. - Conditional Logic (Implicit): Although not explicitly shown with an "If" node in the connections, the "Switch" and "If" nodes are present in the JSON, implying a potential for conditional routing or processing based on AI agent output or message content.
- Responds via WhatsApp: The AI Agent's response is then sent back to the user through WhatsApp.
- Data Transformation (Potential): "Edit Fields (Set)" and "Extract from File" nodes are present, indicating the workflow has capabilities to transform data and extract information from files, which could be used for processing attachments or preparing data for the AI.
- HTTP Request (Potential): An "HTTP Request" node is available, suggesting the workflow can make calls to external APIs, possibly to fetch or send data to other healthcare systems or databases.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- WhatsApp Business Cloud Account: Configured with a webhook pointing to your n8n instance for the WhatsApp Trigger and credentials for sending messages.
- OpenAI API Key: For the OpenAI Chat Model and OpenAI nodes.
- PostgreSQL Database: If the "Postgres Chat Memory" is actively used, a PostgreSQL database for storing chat history.
- Credentials: Appropriate n8n credentials for WhatsApp, OpenAI, and any other integrated services.
Setup/Usage
- Import the Workflow: Download the provided JSON and import it into your n8n instance.
- Configure WhatsApp Trigger:
- Set up a WhatsApp Business Cloud account and configure its webhook to point to the URL provided by the "WhatsApp Trigger" node in n8n.
- Ensure your WhatsApp credentials are set up in n8n.
- Configure OpenAI Credentials:
- Provide your OpenAI API key in the n8n credentials for the "OpenAI Chat Model" and "OpenAI" nodes.
- Configure Postgres Chat Memory (if used):
- Set up your PostgreSQL database and configure the "Postgres Chat Memory" node with the necessary connection details and credentials.
- Activate the Workflow: Once all credentials and configurations are in place, activate the workflow.
The workflow will then be ready to receive WhatsApp messages, process them with the AI agent, and respond accordingly.
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