Auto-respond to Instagram, Facebook & WhatsApp with Llama 3.2

5696 views

2/3/2026

Google Sheets Data Processing Automation HTTP Request CSV

This automated n8n workflow enables AI-powered responses across multiple social media platforms, including Instagram DMs, Facebook messages, and WhatsApp chats using Meta's APIs. The system provides intelligent customer support, lead generation, and smart engagement at scale through AI-driven conversation management and automated response routing.

Good to Know

Supports multi-platform messaging across Instagram, Facebook, and WhatsApp
Uses AI Travel Agent and Ollama Chat Model for intelligent response generation
Includes platform memory for maintaining conversation context and history
Automatic message processing and routing based on platform and content type
Real-time webhook integration for instant message detection and response

How It Works

WhatsApp Trigger - Monitors incoming WhatsApp messages and initiates automated response workflow
Instagram Webhook - Captures Instagram DM notifications and processes them for AI analysis
Facebook Webhook - Detects Facebook Messenger interactions and routes them through the system
Message Processor - Analyzes incoming messages from all platforms and prepares them for AI processing
AI Travel Agent - Processes messages using intelligent AI model to generate contextually appropriate responses
Ollama Chat Model - Provides advanced language processing for complex conversation scenarios
Platform Memory - Maintains conversation history and context across multiple interactions for personalized responses
Response Router - Determines optimal response strategy and routes messages to appropriate sending mechanisms
Instagram Sender - Delivers AI-generated responses back to Instagram DM conversations
Facebook Sender - Sends automated replies through Facebook Messenger API
Send Message (WhatsApp) - Delivers personalized responses to WhatsApp chat conversations

How to Use

Import workflow into n8n
Configure Meta's Instagram Graph API, Facebook Messenger API, and WhatsApp Business Cloud API
Set up approved Meta Developer App with required permissions
Configure webhook endpoints for real-time message detection
Set up Ollama Chat Model for AI response generation
Test with sample messages across all three platforms
Monitor response accuracy and adjust AI parameters as needed

Requirements

Access to Meta's Instagram Graph API, Facebook Messenger API, and WhatsApp Business Cloud API
Approved Meta Developer App
Webhook setup and persistent token management for real-time messaging
Ollama Chat Model integration
AI Travel Agent configuration

Customizing This Workflow

Modify AI prompts for different business contexts (customer service, sales, support)
Adjust response routing logic based on message content or user behavior
Configure platform-specific message templates and formatting
Set up custom memory storage for enhanced conversation tracking
Integrate additional AI models for specialized response scenarios
Add message filtering and content moderation capabilities

n8n Workflow: AI-Powered Auto-Responder for Messaging Platforms

This n8n workflow provides a robust, AI-driven solution for automatically responding to incoming messages on various platforms, including WhatsApp, Instagram, and Facebook. Leveraging the power of an AI Agent and a local Ollama Chat Model, it intelligently processes messages and generates contextually relevant replies, enhancing customer engagement and support efficiency.

What it does

This workflow automates the following steps:

Listens for Incoming Messages: It acts as a webhook listener, waiting for new messages from connected platforms (e.g., WhatsApp, Instagram, Facebook).
Extracts Message Content: Upon receiving a message, it extracts the relevant text and sender information.
Determines Response Channel: It uses a Switch node to identify the originating platform of the message (e.g., WhatsApp, Instagram, Facebook) to ensure the reply is sent back through the correct channel.
Generates AI Response: The core of the workflow uses an AI Agent, powered by a local Ollama Chat Model, to process the incoming message. It leverages a "Simple Memory" to maintain conversational context, allowing for more coherent and relevant replies.
Sends AI-Generated Reply: Based on the identified channel, it sends the AI-generated response back to the user via the appropriate platform (e.g., WhatsApp Business Cloud).

Prerequisites/Requirements

To use this workflow, you will need:

n8n Instance: A running n8n instance (self-hosted or cloud).
WhatsApp Business Cloud Account: For sending and receiving messages via WhatsApp.
Instagram/Facebook Integration: Depending on your needs, you might need to configure webhooks and API access for Instagram and Facebook within your n8n instance or via an upstream service that forwards messages to this workflow.
Ollama Installation: A local or accessible Ollama server running your preferred Large Language Model (e.g., Llama 3.2, as hinted by the directory name). The Ollama Chat Model node will connect to this server.
n8n Credentials: Configured credentials for WhatsApp Business Cloud and any other messaging platforms you intend to integrate.

Setup/Usage

Import the Workflow: Download the provided JSON and import it into your n8n instance.
Configure Webhook Trigger:
- The Webhook node (ID: 47) serves as the entry point. Copy its webhook URL.
- Configure your messaging platforms (WhatsApp, Instagram, Facebook) to send incoming message events to this webhook URL.
- For WhatsApp, you can also use the WhatsApp Trigger node (ID: 1260) directly if you prefer, configuring it with your WhatsApp Business Cloud credentials.
Configure Credentials:
- Set up your WhatsApp Business Cloud credentials in n8n.
- Ensure any other necessary API keys or credentials for Instagram/Facebook are configured.
Configure Ollama Chat Model:
- In the Ollama Chat Model node (ID: 1151), ensure the connection details (e.g., endpoint URL) point to your running Ollama server.
- Specify the model name you want to use (e.g., llama3:8b).
Configure AI Agent:
- The AI Agent node (ID: 1119) is pre-configured to use the Ollama Chat Model and Simple Memory. Review its settings if you wish to customize the agent's behavior or prompt.
Activate the Workflow: Once configured, activate the workflow in n8n.

The workflow is now ready to automatically respond to messages received on the configured platforms, using the intelligence of your chosen Llama model.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

By Ranjan Dailata

161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

By Daniel Nkencho

601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

By Le Nguyen

942