Workflow Templates

This n8n workflow provides a comprehensive solution for user authentication and management, leveraging Airtable as the backend database. It includes flows for user sign-up and login, aswell as the sample crud operations retrieving user details, and updating user information. Youtube Video of me explaining the flow: https://www.youtube.com/watch?v=gKcGfyq3dPM How it Works User Sign-Up Flow Receives POST request: A webhook listens for POST requests containing new user details (email, first name, last name, password). Checks for existing email: The workflow queries Airtable to see if the submitted email already exists. Handles email in use: If the email is found, it responds with {"response": "email in use"}. Creates new user: If the email is unique, the password is SHA256 hashed (Base64 encoded), and the user's information (including the hashed password) is stored in Airtable. A successful response of {"response": "success"} is then sent. User Login Flow Receives POST request: A webhook listens for POST requests with user email and password for login. Verifies user existence: It checks Airtable for a user with the provided email. If no user is found, it responds with a failure message ("wrong email"). Compares passwords: If a user is found, the submitted password is hashed (SHA256, Base64 encoded) and compared with the stored hashed password in Airtable. Responds with JWT or error: If passwords match, a JWT token containing the user's ID and email is issued. If they don't match, a "wrong password" response is sent. Flows for a Logged-In User These flows require a JWT-authenticated request. Get User Details:** Webhook (GET): Receives a JWT-authenticated request. Airtable (Read): Fetches the current user’s record using the jwtPayload.id. Set Node ("Specify Current Details"): Maps fields like "First Name," "Last Name," "Email," and "Date" from Airtable to a standard output format. Update User Details:** Webhook (POST): Receives updated user data (email, name, password). Airtable (Upsert): Updates the record matching jwtPayload.id using the submitted fields. Set Node ("Specify New Details"): Outputs the updated data in a standard format. Set Up Steps (Approx. 5 Minutes) Step 1: Set up your Airtable Base and Table You'll need an Airtable Base and a table to store your user data. Ensure your table has at least the following columns: Email** (Single Line Text) First Name** (Single Line Text) Last Name** (Single Line Text) Password** (Single Line Text - this will store the hashed password) Date** (Date - optional, for user sign-up date) Step 2: Obtain an Airtable Personal Access Token Go to the Airtable website and log in to your account. Navigate to your personal access token page (usually found under your developer settings or by searching for "personal access tokens"). Click "Create new token." Give your token a name (e.g., "n8n User Management"). Grant necessary permissions: Scope: data.records:read, data.records:write for the specific base you will be using. Base: Select the Airtable base where your user management table resides. Generate the token and copy it immediately. You won't be able to see it again. Store it securely. Step 3: Create a JWT Auth Credential in n8n In your n8n instance, go to "Credentials" (usually found in the left-hand sidebar). Click "New Credential" and search for "JWT Auth". Give the credential a name (e.g., "UserAuthJWT"). For the "Signing Secret," enter a strong, random string of characters. This secret will be used to sign and verify your JWT tokens. Keep this secret highly confidential. Save the credential. Customization Options This workflow is designed to be highly adaptable: Database Integration**: Easily switch from Airtable to other databases like PostgreSQL, MySQL, MongoDB, or even Google Sheets by replacing the Airtable nodes with the appropriate database nodes in n8n. Authentication Methods**: Extend the authentication to include multi-factor authentication (MFA), social logins (Google, Facebook), or integrate with existing identity providers (IdP) by adding additional nodes. User Profile Fields**: Add or remove user profile fields (e.g., phone number, address, user roles) by adjusting the Airtable table columns and the Set nodes in the workflow. Notification System**: Integrate notification systems (e.g., email, SMS) for events like new user sign-ups, password resets, or account changes. Admin Panel**: Build an admin panel using n8n to manage users directly, including functionalities for adding, deleting, or updating user records, and resetting passwords. This workflow provides a solid foundation for building robust user management systems, adaptable to a wide range of applications and security requirements. Need Assistance or Customization? Do you have specific integrations in mind, or are you looking to add more user management features to this workflow? If you need help setting this up, or want to adapt it for a unique use case, don't hesitate to reach out! You can contact me directly at nanabrownsnr@gmail.com. I'd be glad to assist you.

What does this template do? This workflow sets a small "lock" value in Redis so that only one copy of a long job can run at the same time. If another trigger fires while the job is still busy, the workflow sees the lock, stops early, and throws a clear error. This protects your data and keeps you from hitting rate limits. Because the workflow also stores simple progress flags ("working", "loading", "finishing"), you can poll the current status and show live progress for very long jobs. Use Case Great when the same workflow can be called many times in parallel (for example by webhooks, cron jobs, or nested Execute Workflow calls) and you need an "only run once at a time" guarantee without building a full queue system. What the Workflow Does ⚡ Starts through Execute Workflow Trigger called by another workflow 🔄 A Switch sends the run to Get, Set, or Unset actions 💾 Redis reads or writes a key named process_status_<key> with a time‑to‑live (default 600 s) 🚦 If nodes check the key and decide to continue or stop ⏱️ Wait nodes stand in for the slow part of your job (replace these with your real work) 📈 Updates the key with human‑readable progress values that another workflow can fetch with action = get 🏁 When done, the lock is removed so the next run can start Apps & Services Used Redis Core n8n nodes (Switch, If, Set, Wait, Stop and Error) Pre‑requisites A Redis server that n8n can reach Redis credentials stored in n8n A second workflow that calls this one and sends: action set to get, set, or unset key set to a unique name for the job Optional timeout in seconds Customization Tips Increase or decrease the TTL in the Set Timeout node to match how long your job usually runs Add or rename status values ("working", "loading", "finishing", and so on) to show finer progress Replace Stop and Error with a Slack or email alert, or even push the extra trigger into a queue if you prefer waiting instead of failing Use different Redis keys if you need separate locks for different tasks Build a small "status endpoint" workflow that calls this one with action = get to display real‑time progress to users Additional Use Cases 🛑 Telegram callback spam filter If a Telegram bot sends many identical callbacks in a burst, call this workflow first to place a lock. Only the first callback will proceed; the rest will exit cleanly until the lock clears. This keeps your bot from flooding downstream APIs. 🧩 External API rate‑limit protection Run heavy API syncs one after the other so parallel calls do not break vendor rate limits. 🔔 Maintenance window lock Block scheduled maintenance tasks from overlapping, making sure each window finishes before the next starts.

Overview Transform your customer support operations with an intelligent WhatsApp automation system that handles text, voice, and image messages across multiple languages. This comprehensive solution uses advanced AI to provide instant, accurate responses by accessing your company's knowledge base, while maintaining conversation context and supporting both English and Roman Urdu communications. Perfect for businesses serving diverse markets who need 24/7 customer support without the overhead costs. Key Benefits 🤖 Multi-Modal AI Processing Handle text messages, voice notes, and images seamlessly. The system automatically transcribes audio, analyzes images, and processes all content types through a single intelligent pipeline. 🌍 True Multilingual Support Native support for English and Roman Urdu with intelligent language detection and matching responses. The AI automatically detects the customer's language and responds accordingly, making it perfect for South Asian markets. 📚 Dynamic Knowledge Base Integration Real-time access to your Google Docs knowledge base ensures customers always receive current, accurate information about your products and services. No more outdated responses or manual updates needed. 💭 Conversation Memory & Context Advanced memory system maintains conversation history for natural, contextual interactions. Customers can have flowing conversations without repeating information, improving satisfaction rates. ⚡ Instant Response Times Automated responses within seconds of receiving messages, dramatically improving customer satisfaction and reducing response time from hours to seconds. 🔄 Zero Manual Intervention Fully automated system that works 24/7 without human oversight. Handles inquiries, provides information, and maintains professional communication standards automatically. 📊 Scalable Architecture Built on enterprise-grade n8n platform with robust error handling and retry mechanisms. Can handle thousands of concurrent conversations without performance degradation. 💰 Cost-Effective Operations Replace expensive customer support teams with intelligent automation. Reduce operational costs by up to 80% while improving response quality and availability. How It Works Phase 1: Message Reception & Classification The system begins with a WhatsApp webhook trigger that captures all incoming messages in real-time. An intelligent switch node immediately analyzes each message to determine its content type - whether it's a text message, voice note, or image with optional caption. This classification is crucial as each media type requires different processing approaches to extract meaningful information. Phase 2: Advanced Media Processing For voice messages, the system retrieves the audio file URL, downloads the content using authenticated requests, and processes it through OpenAI's Whisper transcription service to convert speech to text. Image messages follow a similar pattern - the system downloads the image and uses GPT-4 Vision to analyze and describe the visual content in detail. Text messages are processed directly, while all media types are ultimately converted to standardized text format for consistent AI processing. Phase 3: Intelligent Response Generation The processed content is fed into a sophisticated AI agent powered by Claude Sonnet 4 via OpenRouter. This agent operates with a comprehensive system prompt that defines its role as a professional customer support representative with specific instructions for tone, language handling, and response protocols. The agent has access to a Google Docs tool that allows it to retrieve real-time information from your company's knowledge base. Phase 4: Contextual Memory Management A memory buffer system maintains conversation history for each unique phone number, allowing for natural, flowing conversations where the AI remembers previous interactions and can reference earlier parts of the conversation. This creates a more human-like experience and reduces customer frustration from having to repeat information. Phase 5: Response Delivery Generated responses are automatically sent back to the customer's WhatsApp number using the WhatsApp Business API, completing the conversation loop. The system maintains proper formatting and ensures message delivery confirmation. Required Setup & Database Requirements API Credentials Needed: WhatsApp Business API**: For receiving and sending messages OpenAI API**: For audio transcription and image analysis OpenRouter API**: For Claude Sonnet 4 language model access Google Docs API**: For knowledge base integration n8n Cloud/Self-hosted instance**: For workflow execution Knowledge Base Setup: Google Docs Document**: Structured company information document Document Permissions**: Shared with the Google service account Content Organization**: FAQ format with clear sections for products, services, pricing, and contact information WhatsApp Configuration: Business Phone Number**: Verified WhatsApp Business account Webhook URL**: Configured to point to n8n webhook endpoint Message Templates**: Pre-approved for business communications Business Use Cases E-commerce Support: Handle product inquiries, order status checks, and return policies across multiple languages, perfect for businesses serving diverse customer bases. Service Business Automation: Appointment scheduling, service explanations, and pricing inquiries for consultancies, agencies, and professional services. Restaurant & Food Industry: Menu inquiries, order modifications, and delivery status updates with support for local language preferences. Real Estate: Property inquiries, showing appointments, and market information with ability to process property images sent by clients. Healthcare & Wellness: Appointment booking, service explanations, and general inquiries while maintaining professional communication standards. Education & Training: Course information, enrollment processes, and student support with multilingual capabilities for international programs. Revenue Potential Direct Cost Savings: $3,000-8,000/month in customer support staff salaries Increased Conversion: 25-40% improvement in lead response rates due to instant replies Extended Availability: 24/7 operation captures international and after-hours inquiries worth $2,000-5,000/month additional revenue Scalability: Handle 10x more inquiries without proportional cost increases Customer Satisfaction: Improved response times lead to 15-30% increase in customer retention ROI Timeline: Typical payback period of 2-3 months with ongoing monthly savings of $4,000-12,000 depending on business size. Difficulty Level & Build Time Complexity: Intermediate to Advanced (7/10) Estimated Build Time: 4-6 hours for experienced n8n users Setup Time: 2-3 hours for API configurations and testing Maintenance: Minimal - primarily updating knowledge base content Skills Required: n8n workflow building experience API credential management WhatsApp Business API familiarity Basic understanding of AI language models Detailed Setup Steps 1. API Account Setup (60 minutes) Create and configure accounts for WhatsApp Business, OpenAI, OpenRouter, and Google Cloud Platform. Obtain all necessary API keys and configure proper permissions for each service. 2. n8n Credential Configuration (30 minutes) Add all API credentials to your n8n instance using the credential manager. Test each connection to ensure proper authentication and access permissions. 3. WhatsApp Business Integration (45 minutes) Configure your WhatsApp Business account with webhook URLs pointing to your n8n instance. Set up phone number verification and message template approvals. 4. Knowledge Base Creation (90 minutes) Structure your Google Docs knowledge base with comprehensive information about your business. Include FAQs, product details, pricing, and contact information in an organized format. 5. Workflow Import & Testing (60 minutes) Import the n8n workflow, configure all node parameters with your specific credentials and settings, then conduct thorough testing with different message types and languages. 6. Production Deployment (30 minutes) Deploy the workflow to production, monitor initial performance, and fine-tune system prompts based on real customer interactions. Advanced Customization Options Custom Language Support: Extend beyond English and Roman Urdu by modifying the system prompt and adding language detection for additional languages like Arabic, Hindi, or French. Integration Expansions: Connect additional data sources like CRM systems, databases, or e-commerce platforms to provide more comprehensive customer information. Advanced Analytics: Add logging nodes to track conversation metrics, response times, and customer satisfaction scores for continuous improvement. Multi-Channel Support: Extend the system to handle Telegram, Facebook Messenger, or other messaging platforms using similar processing logic. Escalation Protocols: Implement human handoff triggers for complex queries that require personal attention, with automatic notification systems for support teams. Custom AI Models: Swap Claude Sonnet 4 for other models like GPT-4, Gemini, or open-source alternatives based on your specific needs and budget requirements. This automation system represents the future of customer support - intelligent, scalable, and incredibly cost-effective while maintaining the personal touch that customers expect from quality businesses.

WordPress Blog to Google Sheets Sync Posts • Categories • Tags • Media 🧩 Overview This n8n workflow automatically syncs your WordPress website content — including posts, categories, tags, and media — into Google Sheets. It helps automate content reporting, SEO analysis, and data backups. The workflow can run on schedule or on demand via a webhook. 💡 Use cases Maintain a live database of blog posts in Google Sheets. Create dashboards in Google Data Studio or Looker Studio. Track new articles for newsletters or social media scheduling. Backup all WordPress content and media outside of your CMS. ⚙️ Prerequisites Before importing the workflow, ensure you have: A WordPress website with the REST API enabled (default in WP 4.7+). Authentication: either Application Passwords or Basic Auth credentials. A Google Sheet with the following tabs: Posts Categories Tags Media The following credentials configured in n8n: HTTP Basic Auth (for WordPress) Google Sheets OAuth2 🚀 Setup instructions Import the workflow into your n8n instance. Replace all example WordPress API URLs with your domain, for example: https://yourdomain.com/wp-json/wp/v2/ Connect your HTTP Basic Auth credentials (WordPress username + Application Password). Connect your Google Sheets OAuth2 account. Update the spreadsheet ID in each Google Sheets node with your own. Adjust the Schedule Trigger (e.g. run daily at 2:00 AM). Run once manually to verify data sync. 🧠 Workflow structure | Section | Description | |----------|--------------| | Schedule / Webhook Trigger | Starts the workflow manually or automatically | | Variables & Loop Vars | Initialize pagination for REST API requests | | Get Posts → Split Out → Update Posts | Fetch and update all WordPress posts | | Get Categories → Update Categories | Sync WordPress categories | | Get Tags → Update Tags | Sync WordPress tags | | Get Media → Split Out → Update Media | Sync media library (images, videos, etc.) | | IF Loops | Handles pagination logic until all items are retrieved | ⚠️ Notes & Limitations Works with standard WordPress REST API endpoints only. Custom post types require editing endpoint URLs. The per_page value defaults to 10; increase for faster syncs. For large sites, consider increasing n8n memory or adding execution logs. Avoid running the workflow too frequently to prevent API rate limits. 🎥 Video Tutorial A step-by-step setup guide is available here: 👉 https://www.youtube.com/watch?v=czSMWyD6f-0 Please subscribe to my YouTube channel to support me: 👉 https://www.youtube.com/@gregfrolovpersonal 👨‍💻 Author Created by: Grigory Frolov SEO & Automation Specialist — helping businesses integrate WordPress, AI, and data tools with n8n. 🧾 License This workflow is provided under the MIT License. Feel free to use, modify, and share improvements with the community.

This workflow can be used to save all of your workflows in: a raw state (as a json file in Dropbox) an Airtable base, in a pre-designed format. It runs periodically (currently, every 30 minutes) and either updates (if already existing in Airtable) or creates a new record in Airtable for each workflow. Here's the Airtable base to give you an idea: View Airtable base Note: This workflows uses the "http://localhost:5678/rest" API which the UI editor uses but is still not officially supported. Hence, it may suffer breaking changes at some point in the future and the workflow might become dysfunctional then.

This workflow automatically converts incoming RSS/news articles into structured, AI-enriched content records. It uses a local AI model to summarize articles, extract metadata, and classify content before storing it in PostgreSQL. A human-in-the-loop approval step via Telegram ensures only validated content is published to LinkedIn, maintaining quality and brand safety. ⚙️ How it works Fetch RSS feed articles → AI summarizes content using Ollama → Extracts category, keywords, sentiment, and key points → Stores structured data in PostgreSQL → Sends content to Telegram for human approval → User approves or rejects → Approved content is marked in database → Automatically publishes approved posts to LinkedIn 📦 Requirements n8n (latest version) PostgreSQL database Telegram Bot API credentials LinkedIn Developer App (OAuth2) Ollama (local AI runtime) with model like qwen2.5:3b RSS feed source (e.g., Ars Technica or any news feed) 📊 Database Table Required: rss_feed_articles Stores AI-processed articles, metadata, and approval status for controlled publishing workflow.

This workflow synchronizes MySQL database table schemas with a vector database in a controlled, idempotent manner. Each database table is indexed as a single vector to preserve complete schema context for AI-based retrieval and reasoning. The workflow prevents duplicate vectors and automatically handles schema changes by detecting differences and re-indexing only when required. How it works The workflow starts with a manual trigger and loads global configuration values. All database tables are discovered and processed one by one inside a loop. For each table, a normalized schema representation is generated, and a deterministic hash is calculated. A metadata table is checked to determine whether a vector already exists for the table. If a vector exists, the stored schema hash is compared with the current hash to detect schema changes. When a schema change is detected, the existing vector and metadata are deleted. The updated table schema is embedded as a single vector (without chunking) and upserted into the vector database. Vector identifiers and schema hashes are persisted for future executions. Setup steps Set the MySQL database name using mysql_database_name. Configure the Pinecone index name using pinecone_index. Set the vector namespace using vector_namespace. Configure the Pinecone index host using vector_index_host. Add your Pinecone API key using pinecone_apikey. Select the embedding model using embedding_model. Configure text processing options: chunk_size chunk_overlap Set the metadata table identifier using dataTable_Id. Save and run the workflow manually to perform the initial schema synchronization. Limitations This workflow indexes database table schemas only. Table data (rows) are not embedded or indexed. Each table is stored as a single vector. Very large or highly complex schemas may approach model token limits depending on the selected embedding model. Schema changes are detected using a hash-based comparison. Non-structural changes that do not affect the schema representation will not trigger re-indexing.

This workflow streamlines the entire inventory replenishment process by leveraging AI for demand forecasting and intelligent logic for supplier selection. It aggregates data from multiple sources—POS systems, weather forecasts, SNS trends, and historical sales—to predict future demand. Based on these predictions, it calculates shortages, requests quotes from multiple suppliers, selects the optimal vendor based on cost and lead time, and executes the order automatically. 🚀 Who is this for? Retail & E-commerce Managers** aiming to minimize stockouts and reduce overstock. Supply Chain Operations** looking to automate procurement and vendor selection. Data Analysts** wanting to integrate external factors (weather, trends) into inventory planning. 💡 How it works Data Aggregation: Fetches data from POS systems, MySQL (historical sales), OpenWeatherMap (weather), and SNS trend APIs. AI Forecasting: Formats the data and sends it to an AI prediction API to forecast demand for the next 7 days. Shortage Calculation: Compares the forecast against current stock and safety stock to determine necessary order quantities. Supplier Optimization: For items needing replenishment, the workflow requests quotes from multiple suppliers (A, B, C) in parallel. It selects the best supplier based on the lowest total cost within a 7-day lead time. Execution & Logging: Places the order via API, updates the inventory system, and logs the transaction to MySQL. Anomaly Detection: If the AI's confidence score is low, it skips the auto-order and sends an alert to Slack for manual review. ⚙️ Setup steps Configure Credentials: Set up credentials for MySQL and Slack in n8n. API Keys: You will need an API key for OpenWeatherMap (or a similar service). Update Endpoints: The HTTP Request nodes use placeholder URLs (e.g., pos-api.example.com, ai-prediction-api.example.com). Replace these with your actual internal APIs, ERP endpoints, or AI service (like OpenAI). Database Prep: Ensure your MySQL database has a table named forecast_order_log to store the order history. Schedule: The workflow is set to run daily at 03:00. Adjust the Schedule Trigger node as needed. 📋 Requirements n8n** (Self-hosted or Cloud) MySQL** database Slack** workspace External APIs for POS, Inventory, and Supplier communication (or mock endpoints for testing).

An end-to-end Retrieval-Augmented Generation (RAG) customer support workflow for n8n, using a cache-first strategy (LangCache) combined with a Redis vector store powered by OpenAI embeddings. This template is designed for fast, accurate, and cost-efficient customer support chatbots, internal help desks, and knowledge-base assistants. Overview This workflow implements a production-ready RAG architecture optimized for customer support use cases. Incoming chat messages are processed through a structured pipeline that prioritizes cached answers, falls back to semantic vector search when needed, and validates response quality before returning a final answer. The workflow supports: Multi-question user inputs Intelligent query decomposition Cache reuse to reduce latency and cost High-precision retrieval from a Redis vector database Quality evaluation and controlled retries Final answer synthesis into a single, coherent response Key Features Chat-based RAG pipeline** using n8n’s Chat Trigger Query decomposition** for multi-topic questions LangCache integration** (search + save) Redis Vector Store** for semantic retrieval OpenAI embeddings and chat models** Quality scoring** with retry logic Session memory buffers** for contextual continuity Fallback-safe behavior** (no hallucinations) How the Workflow Works 1. Chat Trigger The workflow starts when a new chat message is received. 2. Configuration Setup A centralized configuration node defines: LangCache base URL Cache ID Similarity threshold (default: 0.75) Maximum retrieval iterations (default: 2) 3. Query Decomposition The user message is analyzed and decomposed into: A single focused question, or Multiple independent sub-questions This improves retrieval accuracy and cache reuse. 4. Cache-First Retrieval Each sub-question is processed independently: The workflow first searches LangCache If a high-similarity cached answer is found, it is reused immediately 5. Vector Retrieval (Cache Miss) If no cache hit exists: The query is embedded using OpenAI embeddings A semantic search is executed against the Redis vector index Retrieved knowledge-base documents are passed to a research-only agent 6. Knowledge-Only Answering The research agent: Answers strictly from the retrieved knowledge Returns "no info found" if no relevant data exists 7. Quality Evaluation Each generated answer is evaluated by a dedicated quality-check node: Outputs a numerical SCORE (0.0 – 1.0) Provides textual feedback Low scores can trigger limited retries 8. Cache Update High-quality answers are saved back to LangCache for future reuse. 9. Aggregation & Synthesis All sub-answers are aggregated and synthesized into: One final, user-facing response, or A polite fallback message if information is insufficient Main Nodes & Responsibilities When Chat Message Received** — Entry point for user messages LangCache Config** — Centralized configuration values Decompose Query (LangChain Agent)** — Splits complex queries Structured Output Parser** — Ensures valid JSON output Search LangCache** — Cache lookup via HTTP Redis Vector Store** — Semantic retrieval from Redis Embeddings OpenAI** — Vector generation Research Agent** — KB-only answering (no hallucinations) Quality Evaluator** — Scores answer relevance Save to LangCache** — Stores validated answers Memory Buffers** — Session context handling Response Synthesizer** — Final message generation Setup Instructions 1. Configure Credentials Create the following credentials in n8n: OpenAI API** Redis** HTTP Bearer Auth** (for LangCache) 2. Prepare the Knowledge Base Embed your documents using OpenAI embeddings Insert them into the configured Redis vector index Ensure documents are concise and well-structured 3. Configure LangCache Update the configuration node with: langcacheBaseUrl langcacheCacheId Optional tuning for similarity threshold and iterations 4. Test the Workflow Use the example data loader or schedule trigger Send test chat messages Validate cache hits, vector retrieval, and final responses Recommended Tuning Similarity Threshold:** 0.7 – 0.85 Max Iterations:** 1 – 3 Quality Score Cutoff:** 0.7 Model Choice:** Use faster models for low latency, stronger models for accuracy Cache Policy:** Cache only high-confidence answers Security & Compliance Notes Store API keys securely using n8n credentials Avoid caching sensitive or personally identifiable information Apply least-privilege access to Redis and LangCache Consider logging cache writes for audit purposes Common Use Cases Customer support chatbots Internal help desks Knowledge-base assistants Self-service support portals AI-powered FAQ systems Template Metadata (Recommended) Template Name:** AI Customer Support — Redis RAG (LangCache + OpenAI) Category:** Customer Support / AI / RAG Tags:** customer-support, RAG, knowledge-base, redis, openai, langcache, chatbot, n8n-template Difficulty Level:** Intermediate Required Integrations:** OpenAI, Redis, LangCache

Automating WhatsApp replies in Go High Level with Redis and Anthropic Description Integrates GHL + Wazzap with Redis and an AI Agent using ClientInfo to process messages, generate accurate replies, and send them via a custom field trigger. Who’s it for This workflow is for businesses using GoHighLevel (GHL), including the Wazzap plugin for WhatsApp, who want to automate inbound SMS/WhatsApp replies with AI. It’s ideal for teams that need accurate, data-driven responses from a predefined ClientInfo source and want to send them back to customers without paying for extra inbound automations. How it works / What it does Receive message in n8n via Webhook from GHL (Customer Replied (SMS) automation). WhatsApp messages arrive the same way using the Wazzap plugin. Filter message type: If audio → skip processing and send fallback asking for text. If text → sanitize by fixing escaped quotes, escaping line breaks/carriage returns/tabs, and removing invalid fields. Buffer messages in Redis to group multiple messages sent in a short window. Run AI Agent using the ClientInfo tool to answer only with accurate service/branch data. Sanitize AI output before sending back. Update GHL contact custom field (IA_answer) with the AI’s response. Send SMS reply automatically via GHL’s outbound automation triggered by the updated custom field. How to set up In GHL, create: Inbound automation: Trigger on Customer Replied (SMS) → Send to your n8n Webhook. Outbound automation: Trigger when IA_answer is updated → Send SMS to the contact. Create a custom field named IA_answer. Connect Wazzap in GHL to handle WhatsApp messages. Configure Redis in n8n (host, port, DB index, password). Add your AI model credentials (Anthropic, OpenAI, etc.) in n8n. (Optional) Set up the Google Drive Excel Merge sub-workflow to enrich ClientInfo with external data. Requirements GoHighLevel sub-account API key**. Anthropic (Claude)** API key or another supported LLM provider. Redis database** for temporary message storage. GHL automations: one for inbound messages to n8n, one for outbound replies when **IA\_answer is updated. GHL custom field: **IA\_answer to store and trigger replies. Wazzap plugin** in GHL for WhatsApp message handling. How to customize the workflow Add more context or business-specific data to the AI Agent prompt so replies match your brand tone and policies. Expand the ClientInfo dataset with additional services, branches, or product details. Adjust the Redis wait time to control how long the workflow buffers messages before replying.

This n8n template builds an automated daily news digest powered by Claude AI. It monitors RSS feeds, Reddit, and Hacker News, extracts full article text, analyzes each piece with AI, and delivers a polished briefing to Discord and Slack. Stop drowning in newsletters -- Claude reads everything and surfaces only what matters, scored and ranked by importance. Good to know Estimated cost is $0.03-0.10 per daily run using Claude Haiku + Sonnet. See Anthropic pricing for current rates. Works without a database out of the box. Optionally enable PostgreSQL for article history and cross-day deduplication. How it works Schedule trigger fires daily and fetches articles from 10 configurable sources (RSS, Atom, Reddit JSON, Hacker News API) Articles are deduplicated by URL hash and fuzzy title matching Jina Reader extracts full article text for deeper analysis Claude Haiku scores each article 1-10 for importance, assigns categories, and writes a "why it matters" summary Claude Sonnet compiles the top articles into a structured digest with lead story, top stories, quick hits, and trend detection Formatted output is delivered to Discord (rich embeds) and Slack (Block Kit) How to use Add your Anthropic API key as an n8n credential and set your Discord webhook URL in the config node -- that's the minimum to get running Edit the feed list in "Build feed source list" to add your own sources Requirements Anthropic API key (setup guide) Discord webhook URL (setup guide) and/or Slack credential Customizing this workflow Swap feed sources for any topic -- finance, gaming, research papers, industry news Adjust topic importance weights to prioritize what you care about Modify the Claude system prompt to change the digest's tone and style

How it works Your WhatsApp AI Assistant automatically handles customer inquiries by linking your Google Docs knowledge base to incoming WhatsApp messages. The system instantly processes customer questions, references your business documentation, and delivers AI-powered responses through OpenAI or Gemini - all without you lifting a finger. Works seamlessly in individual chats and WhatsApp groups where the assistant can respond on your behalf. Set up steps Time to complete: 15-30 minutes Step 1: Create your WhapAround account and connect your WhatsApp number (5 minutes) Step 2: Prepare your Google Doc with business information and add the document ID to the system (5 minutes) Step 3: Configure the WhatsApp webhook and map message fields (10 minutes) Step 4: Connect your OpenAI or Gemini API key (3 minutes) Step 5: Send a test message to verify everything works (2 minutes) Optional: Set up PostgreSQL database for conversation memory and configure custom branding/escalation rules (additional 15-20 minutes) Detailed technical configurations, webhook URLs, and API parameter settings are provided within each workflow step to guide you through the exact setup process.