Build a Facebook Messenger customer service AI chatbot with Google Gemini

🤖 Build a customer service AI chatbot for Facebook Messenger with Google Gemini --- 📌 Overview A streamlined Facebook Messenger chatbot powered by AI with conversation memory. This is a simplified version designed for quick deployment, learning, and testing — not suitable for production environments. Base workflows: - Smart message batching AI-powered Facebook Messenger chatbot use Data Table - Smart human takeover & auto pause AI-powered Facebook Messenger chatbot --- 🎯 What This Workflow Does ✅ Core Features: - Receives messages from Facebook Messenger via webhook - Processes user messages with Google Gemini AI - Maintains conversation context using Simple Memory node - Automatically responds with AI-generated replies - Handles webhook verification for Facebook setup - Send image or video to customer through Facebook Messenger 🔹 Simplified Approach: - Memory: Simple Memory node (10-message window) - Format: Cleans text, strips markdown, truncates >1900 chars - Response: Single message delivery ⚠️ Limitations & Trade-offs: 1. No Smart Batching → fragmented user messages cause spam-like replies 2. No Human Takeover Detection → bot continues even when admin joins 3. Basic Memory Management → no persistence, not reliable in production 4. Basic Text Formatting → strips markdown, truncates brutally, no smart splitting --- 🚀 When to Upgrade Upgrade to full workflows when you need: - Production deployment with reliability & persistence - Analytics & tracking (query history, reports) - Professional formatting (bold, italic, lists, code blocks) - Handling long messages (>2000 chars) - Smart batching for fragmented inputs - Human handoff detection - Full conversation persistence Key upgrades available: - Smart message batching workflow - Smart human takeover workflow --- ⚙️ Setup Requirements Facebook Setup 1. Create Facebook App at developers.facebook.com 2. Add Messenger product 3. Configure webhook: - URL: https://your-domain.com/webhook/your-path - Verify token: secure string - Subscribe to: messages, messagingpostbacks 4. Generate Page Access Token 5. Copy token to "Set Context" node n8n Setup 1. Import workflow 2. Edit "Set Context" node → update pageaccesstoken 3. Configure "Gemini Flash" node credentials 4. Deploy workflow (must be publicly accessible) --- 🔄 How It Works User Message → Facebook Webhook → Validation ↓ Set Context (extract userid, message, token) ↓ Mark Seen → Show Typing ↓ AI Agent (Gemini + 10-message memory) ↓ Format Output (remove markdown, truncate) ↓ Send Response via Facebook API --- 🏗️ Architecture Overview Section 1: Webhook & Initial Processing - Facebook Webhook: handles GET (verification) & POST (messages) - Confirm Webhook: returns challenge / acknowledges receipt - Filters text messages only - Blocks echo messages from bot itself Section 2: AI Processing with Memory - Set Context: extracts userid, message, token - Seen & Typing: user feedback - Conversation Memory: 10-message window, per-user isolation - Process Merged Message: AI Agent with Jenix persona - Gemini Flash: Google’s AI model for response generation Section 3: Format & Delivery - Cuts replies >2000 chars, strips markdown - Sends text via Facebook Graph API --- 🎨 Customisation Guide - Bot Personality: edit system prompt in "Process Merged Message" node - Memory: adjust contextWindowLength (default 10), change sessionKey if needed - AI Model: replace Gemini Flash with OpenAI, Anthropic Claude, or other LLMs --- 📌 Important Notes ⚠️ Production Warning: testing only, memory lost on n8n restart in queue mode 📊 No Analytics: no history storage, no reporting 🔧 Format Limitations: responses ≤1800 chars, markdown stripped, no complex formatting --- 🛠️ Troubleshooting - Bot not responding → check token, webhook accessibility, event subscriptions - Memory not working → verify session key, ensure not in queue mode, restart workflow - Messages truncated → adjust system prompt for conciseness, reduce response length --- 📜 Licens