Workflow Templates

This n8n workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. 💪 🚀 Setup Run the top part of the workflow once. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook.db). 🗣️ Chatting with Your Data Send a message in a chat window. Locally saved SQLite database loads automatically. User's chat input is combined with the binary data. The LangChain Agend node gets both data and begins to work. The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. 🗄️ 🌟 Example Queries Try these sample queries to see the AI Agent in action: "Please describe the database" - Get a high-level overview of the database structure, only one or two queries are needed. "What are the revenues by genre?" - Retrieve revenue information grouped by genre, LangChain agent iterates several time before producing the answer. The AI Agent will store the final answer in its memory, allowing for context-aware conversations. 💬 Read the full article: 👉 https://blog.n8n.io/ai-agents/

AI-Powered Product Assistant for E-commerce Transform your online store customer service with an intelligent AI assistant that automatically processes customer inquiries, searches your product database, and provides personalized responses about product availability, pricing, and specifications. Perfect for shoe stores, fashion retailers, and any business with extensive product catalogs - this workflow eliminates manual customer service while increasing response speed and accuracy. How it works Customer sends product inquiry via webhook (Instagram DM, website chat, or messaging app) AI extracts key product details (brand, model, size, color) from natural language text System searches your Google Sheets product database with smart filtering AI generates friendly, personalized response with availability, pricing, and stock information Automatic response sent back to customer with product details or alternatives Screenshots: Customer inquiry: "Do you have Nike Air Max 40 size?" AI response: "Nike Air Max 90, size 40 - in stock 3 pieces, price 120$" Set up steps Prepare your product database - Create Google Sheets with columns: Brand, Model, Size, Color, Price, Quantity Configure AI settings - Connect OpenAI API for natural language processing Set up webhook endpoint - Configure trigger for your messaging platform (Instagram, Telegram, website chat) Test with sample inquiries - Verify AI correctly parses requests and finds products Deploy and monitor - Launch your automated assistant and track performance Time investment: 30-45 minutes setup, works immediately with any product catalog up to 1000+ items.

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.

This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

AI Proposal Generator System Categories* Sales Automation Document Generation AI Business Tools This workflow creates a complete AI-powered proposal generation system that transforms simple form inputs into professional, personalized proposals in under 30 seconds and can be deployed during live sales calls, allowing you to send polished proposals before the call even ends. Benefits* Instant Proposal Generation - Convert 30-second form inputs into professional proposals automatically High-Value Business Tool - Generates $1,500-$5,000 per client implementation Live Sales Integration - Generate and send proposals during active sales calls Complete Automation Pipeline - From form submission to email delivery with zero manual work Professional Presentation - Produces proposals indistinguishable from manually crafted documents Dual Platform Support - Works with both Google Slides (free) and PandaDoc (premium) integration How It Works* Smart Form Interface: Simple N8N form captures essential deal information Collects prospect details, problems, solutions, scope, timeline, and budget Designed for rapid completion during live sales conversations Advanced AI Processing: Uses sophisticated GPT-4 prompting with example-based training Converts basic form inputs into professionally written proposal sections Applies consistent tone, formatting, and business language automatically Dynamic Document Generation: Creates duplicate proposal templates for each new prospect Replaces template variables with AI-generated personalized content Maintains professional formatting and visual consistency Automated Email Delivery: Sends personalized email with proposal link immediately after generation Includes professional messaging and clear next steps Optionally includes invoice for immediate payment processing Premium PandaDoc Integration: Advanced version includes built-in payment processing Combines proposal, agreement, and invoice in single document Enables immediate signature and payment collection Business Use Cases* Service-Based Businesses - Generate proposals for consulting, agencies, and professional services Automation Agencies - Offer proposal generation as a high-value service to clients Sales Teams - Accelerate proposal creation and improve close rates Freelancers - Professionalize client interactions with instant custom proposals Consultants - Streamline business development with automated proposal workflows B2B Companies - Scale personalized proposal generation across entire sales organization Difficulty Level: Intermediate Estimated Build Time: 2-3 hours Monthly Operating Cost: $20-150 (depending on Google Slides vs PandaDoc) Watch My Complete Live Build* Want to see me build this entire $2,485 proposal system from scratch? I walk through every component live - including the AI prompting strategies, form design, Google Slides integration, and the advanced PandaDoc setup that enables payment collection. 🎥 See My Live Build Process: "I Built A $2,485 AI Proposal Generator In N8N (Copy This)" This comprehensive tutorial shows the real development process - including advanced AI prompting, template design, API integrations, and the exact pricing strategy that generates $1,500-$5,000 per client. Required Template Setup* Google Slides Template: Create a professional proposal template with these variable placeholders (wrapped in double curly braces): {{proposalTitle}} - Main proposal heading {{descriptionName}} - Project subtitle/description {{oneParagraphProblemSummary}} - Problem analysis section {{solutionHeadingOne}}, {{solutionHeadingTwo}}, {{solutionHeadingThree}} - Solution titles {{shortScopeTitleOne}} through {{shortScopeTitleThree}} - Scope sections {{milestoneOneDay}} through {{milestoneFourDay}} - Timeline milestones {{cost}} - Project pricing Form Field Requirements: The N8N form must include these exact field labels: First Name, Last Name, Company Name, Email, Website Problem (textarea) - Client's current challenges Solution (textarea) - Your proposed approach Scope (textarea) - Specific deliverables Cost - Project pricing How soon? - Timeline expectations PandaDoc Setup (Premium): Configure PandaDoc template with token placeholders matching the AI-generated content structure. Template must include pricing tables and signature fields for complete proposal-to-payment automation. Set Up Steps* Form Design & Integration: Create N8N form with optimized fields for proposal generation Design form flow for rapid completion during sales calls Configure form triggers and data validation AI Content Generation Setup: Configure OpenAI API for sophisticated proposal writing Implement example-based training with input/output pairs Set up JSON formatting for structured content generation Google Slides Integration (Free Version): Create professional proposal templates with variable placeholders Set up Google Cloud Console API access and credentials Configure template duplication and text replacement workflows Email Automation Setup: Configure Gmail integration for automated proposal delivery Design professional email templates with proposal links Set up dynamic content insertion and personalization PandaDoc Integration (Premium Version): Set up PandaDoc API for advanced document generation Configure payment processing and signature collection Implement proposal-to-payment automation workflows Testing & Quality Control: Test complete workflow with various proposal scenarios Validate AI output quality and professional presentation Optimize form fields and content generation based on results Advanced Features* Premium system includes: Payment Processing Integration: Collect payments immediately after proposal acceptance Digital Signature Collection: Streamline agreement execution with electronic signatures Custom Branding: Apply company branding and visual identity automatically Multi-Template Support: Generate different proposal types based on service offerings CRM Integration: Automatically sync proposal data with existing sales systems Why This System Works* The competitive advantage lies in speed and professionalism: 30-second generation time vs. hours of manual proposal writing Professional presentation that matches or exceeds manual proposals Live sales integration - send proposals during active conversations Consistent quality - eliminates human error and formatting inconsistencies Immediate follow-up - maintain sales momentum with instant delivery System Architecture* The workflow follows a simple but powerful 6-step process: Form Trigger - Captures essential deal information AI Processing - Converts inputs to professional content Template Duplication - Creates unique document for each prospect Content Replacement - Populates template with AI-generated content Email Delivery - Sends proposal with professional messaging Payment Collection (PandaDoc) - Enables immediate signature and payment Check Out My Channel* For more high-value automation systems and proven business-building strategies, explore my YouTube channel where I share the exact systems used to build successful automation businesses and scale to $72K+ monthly revenue.

How it works Use Telegram to send in new phrases (flashcard front) You can also manually input phrase in the workflow itself ChatGPT generates provided phrase description (in English but you can change it) including multiple meanings & generates examples of using the phrase in a sample sentence (flashcard back) Steps to setup Provide your Telegram bot API key (optional) Provide your OpenAI key Provide Google Sheets credentials How to import flashcards from Google Sheets into Anki Use Google Sheets to Anki add-on: 1871608121 In Anki simply click Sync Decks and you're done :) Enjoy

This workflow auto-fetches top financial headlines, cleans the content, and uses AI to summarize it into a short investor-friendly email. Good to know The workflow runs daily and relies on stable webpage access; check the URL (e.g., https://www.ft.com/) for availability. AI costs may apply depending on the LLM model used (e.g., GPT-4 or Gemini); refer to provider pricing. How it works Trigger the workflow daily with the Schedule Daily Trigger node. Fetch financial news from a webpage using the Fetch Webpage News node. Add a Delay to Ensure Page Load node to ensure content is fully loaded. Extract and clean headlines with the Extract News Headlines & Clean Extracted Data node. Process the data with the LLM Chat Model node to generate a summary. Send the summarized report via email using the Email Daily Financial Summary node. How to use Import the workflow into n8n and configure the nodes with your webpage URL and email credentials. Test the workflow to verify content fetching and email delivery. Requirements Webpage access (e.g., financial news site API or RSS) Email service (e.g., SMTP or API) LLM model credentials (e.g., GPT-4 or Gemini) Customising this workflow Adjust the Fetch Webpage News node to target different news sources or modify the LLM Chat Model prompt for a different summary style.

Who is this for? This workflow is designed for: Database administrators and developers working with MongoDB Content managers handling movie databases Organizations looking to implement AI-powered search and recommendation systems Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: The complexity of writing MongoDB aggregation pipelines The need for natural language interaction with movie databases The challenge of maintaining user preferences and favorites The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: Accepts natural language queries about movies Translates user requests into MongoDB aggregation pipelines Queries a movie database containing detailed information including: Plot summaries Genre classifications Cast and director information Runtime and release dates Ratings and awards Provides contextual responses using OpenAI's language model Allows users to save favorite movies to the database Maintains conversation context using a window buffer memory Setup Required Credentials: OpenAI API credentials MongoDB connection details Node Configuration: Configure the MongoDB connection in the MongoDBAggregate node Set up the OpenAI Chat Model with your API key Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: A MongoDB collection named "movies" with the specified document structure Proper indexes for efficient querying Appropriate user permissions for read/write operations How to customize this workflow Modify the Document Structure: Update the tool description in the MongoDBAggregate node to match your collection schema Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: Customize the prompt in the "AI Agent - Movie Recommendation" node Modify the window buffer memory size based on your context needs Add additional tools for more functionality Extend Functionality: Add more MongoDB operations beyond aggregation Implement additional workflows for different types of queries Create custom error handling and validation Add user authentication and rate limiting Integration Options: Connect to external APIs for additional movie data Add webhook endpoints for different platforms Implement caching mechanisms for frequent queries Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

This n8n workflow template is designed for developers, system administrators, and IT professionals who manage Linux VPS environments. It leverages an AI chatbot powered by the OpenAI model to interpret and execute SSH commands on a Linux VPS directly from chat messages. The workflow triggers when a specific chat message is received, which is then processed by the AI SysAdmin ReAct Agent to execute predefined SSH commands securely. How It Works Chat Trigger: The workflow starts when a chat message is received via a supported platform (like Slack, Telegram, etc.). AI Processing: The message is passed to the AI SysAdmin ReAct Agent, which uses an embedded OpenAI model to interpret the command and map it to a corresponding SSH action. Command Execution: The interpreted command is securely executed on the target Linux VPS using SSH, with login credentials managed through a secure method embedded within the workflow. Setup Instructions Import the Workflow: Download and import the workflow into your n8n instance. Configure Chat Integration: Set up the chat trigger node by connecting it to your preferred chat platform and configuring the trigger conditions. Set SSH Credentials: Securely input your SSH credentials in the designated SSH login credentials node. Deploy and Test: Deploy the workflow and perform tests to ensure that commands are executed correctly and securely on your VPS. Embrace the future of VPS management with our AI-driven SysAdmin for Linux VPS template. This innovative solution transforms how system administrators interact with and manage their servers, offering a streamlined, secure, and efficient method to handle routine tasks through simple chat commands. With the power of AI at your fingertips, enhance your operational efficiency, reduce response times, and manage your Linux environments more effectively. Get started today to experience a smarter way to manage your systems directly through your chat tool.

Learning something new? Endlessly searching to find the best resources? This workflow finds top community-recommended learning resources on any topic from Hacker News, delivered to your inbox. How it works User submits a topic they want to learn via a simple form. The workflow searches for relevant "Ask HN" posts on Hacker News and extracts top-level comments. An LLM analyzes the comments and identifies the best learning resources. A personalized email is sent to the user with a Markdown formatted list of top recommendations, categorized by resource type (e.g., book, course, article) and difficulty level. Set up steps Add your Google Gemini API credentials. You'll need to create a project and enable the Generative Language API. Add your SMTP credentials for sending emails. Customize the Form and email subject (optional) Activate the workflow Screenshots for Workflow, Form and Email Built on Day-03 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!

Transform raw customer feedback into powerful testimonial quotes automatically. This intelligent n8n workflow monitors feedback forms, uses AI to identify and extract the most emotionally engaging testimonial content, and organizes everything into a searchable database for your marketing campaigns. 🔄 How It Works This streamlined 4-step automation turns feedback into marketing assets: Step 1: Continuous Feedback Monitoring The workflow monitors your Google Sheets (connected to feedback forms) every minute, instantly detecting new customer submissions and triggering the extraction process. Step 2: Intelligent Quote Extraction Google Gemini AI analyzes each feedback submission using specialized prompts designed to: Identify emotionally engaging phrases and statements Extract short, impactful testimonial quotes from longer feedback Filter out neutral, irrelevant, or negative content Focus on marketing-ready, quotable customer experiences Preserve the authentic voice and emotion of the original feedback Step 3: Automated Database Population Extracted testimonials are automatically written back to your Google Sheets in a dedicated "Testimony" column, creating an organized, searchable database of customer quotes ready for marketing use. Step 4: Instant Team Notification Email alerts are sent immediately to your marketing team with each new extracted testimonial, ensuring no valuable social proof goes unnoticed or unused. ⚙️ Setup Steps Prerequisites Google Workspace account for Forms, Sheets, and Gmail Google Gemini API access for intelligent quote extraction n8n instance (cloud or self-hosted) Basic understanding of Google Forms and customer feedback collection Required Google Forms Structure Create a customer feedback form with these essential fields: 📝 Required Form Fields: Name (Short answer text) Email Address (Email field with validation) Feedback (Paragraph text - this is where testimonials are extracted from) Testimony (Leave blank - will be auto-populated by AI) Form Design Best Practices: Use open-ended questions to encourage detailed responses Ask specific questions about customer experience and outcomes Include questions about before/after results for powerful testimonials Make the feedback field prominent and easy to complete Configuration Steps 1. Credential Setup Google Sheets OAuth2**: Monitor feedback responses and update testimonial database Google Gemini API Key**: Extract intelligent, emotionally engaging quotes from feedback Gmail OAuth2**: Send automated notifications to marketing team Google Forms Integration**: Ensure seamless data flow from feedback forms 2. Google Sheets Configuration Verify your feedback response sheet contains proper column structure: | Timestamp | Name | Email | Feedback | Testimony | 3. AI Extraction Optimization The default prompt extracts impactful testimonials, but can be customized for: Industry-Specific Language**: Healthcare, technology, finance, retail terminology Quote Length Preferences**: Short punchy quotes vs longer detailed testimonials Emotional Tone Targeting**: Excitement, relief, satisfaction, transformation Content Focus**: Results-oriented, process-focused, or relationship-based testimonials 4. Notification Customization Email alerts can be configured for: Multiple Recipients**: Marketing team, sales team, customer success Custom Subject Lines**: Include customer name, product type, or urgency indicators Rich Content**: Include full feedback alongside extracted testimonial Categorization**: Different alerts for different product lines or service types 5. Quality Control Implementation Extraction Confidence**: Set minimum quality thresholds for extracted quotes Manual Review Process**: Flag testimonials for human review before publication Approval Workflows**: Add approval steps for high-value or sensitive testimonials Version Control**: Track original feedback alongside extracted quotes 🚀 Use Cases E-commerce & Retail Product Reviews**: Extract compelling quotes from detailed product feedback Customer Success Stories**: Identify transformation narratives from user experiences Social Proof Collection**: Build testimonial libraries for product pages and ads Review Mining**: Turn long reviews into short, shareable testimonial quotes SaaS & Technology Companies User Experience Feedback**: Extract quotes about software usability and impact ROI Testimonials**: Identify statements about business results and efficiency gains Feature Feedback**: Capture specific praise for product capabilities and benefits Customer Success Metrics**: Extract quantifiable results and outcome statements Professional Services Client Success Stories**: Transform project feedback into powerful case study quotes Service Quality Testimonials**: Extract praise for expertise, communication, and results Consulting Impact**: Identify statements about business transformation and growth Relationship Testimonials**: Capture quotes about trust, partnership, and collaboration Healthcare & Wellness Patient Experience**: Extract quotes about care quality and health outcomes Treatment Success**: Identify statements about symptom improvement and recovery Provider Relationships**: Capture testimonials about bedside manner and communication Wellness Journey**: Extract quotes about lifestyle changes and health transformations Education & Training Student Success Stories**: Extract quotes about learning outcomes and career impact Course Effectiveness**: Identify statements about skill development and knowledge gains Instructor Praise**: Capture testimonials about teaching quality and support Career Transformation**: Extract quotes about professional growth and opportunities 🔧 Advanced Customization Options Multi-Category Extraction Enhance extraction with specialized processing: Product-Specific: Extract testimonials for different product lines separately Service-Based: Customize extraction for various service offerings Demographic-Focused: Tailor extraction for different customer segments Journey-Stage: Extract testimonials for awareness, consideration, and retention phases Quality Enhancement Features Implement advanced quality control: Sentiment Scoring**: Rate extracted testimonials for emotional impact Authenticity Verification**: Cross-reference testimonials with customer records Duplicate Detection**: Prevent similar testimonials from the same customer Content Enrichment**: Add context and customer details to extracted quotes Marketing Integration Extensions Connect to marketing and sales tools: Social Media Publishing**: Auto-post testimonials to Facebook, LinkedIn, Twitter Website Integration**: Push testimonials to website testimonial sections Email Marketing**: Include fresh testimonials in newsletter campaigns Sales Enablement**: Provide sales team with relevant testimonials for prospects Analytics and Reporting Generate insights from testimonial data: Testimonial Performance**: Track which quotes generate most engagement Customer Satisfaction Trends**: Analyze testimonial sentiment over time Product/Service Insights**: Identify most praised features and benefits Competitive Advantages**: Extract testimonials highlighting differentiators 📊 Extraction Examples Before (Raw Feedback): "I was really struggling with managing my team's projects and keeping track of all the deadlines. Everything was scattered across different tools and I was spending way too much time just trying to figure out what everyone was working on. Since we started using your project management software about 6 months ago, it's been a complete game changer. Now I can see everything at a glance, our team communication has improved dramatically, and we're actually finishing projects ahead of schedule. The reporting features are amazing too - I can finally show my boss concrete data about our team's productivity. I honestly don't know how we managed without it. The customer support team has been fantastic as well, always quick to help when we had questions during setup." After (AI Extracted Testimonial): "Complete game changer - now I can see everything at a glance, our team communication has improved dramatically, and we're actually finishing projects ahead of schedule." Healthcare Example: Before (Raw Feedback): "I had been dealing with chronic back pain for over 3 years and had tried everything - physical therapy, medication, different doctors. Nothing seemed to help long-term. When I found Dr. Martinez, I was honestly pretty skeptical because I'd been disappointed so many times before. But after our first consultation, I felt hopeful for the first time in years. She really listened to me and explained everything clearly. The treatment plan she developed was comprehensive but manageable. Within just 2 months, I was experiencing significant pain reduction, and now after 6 months, I'm practically pain-free. I can play with my kids again, sleep through the night, and even started hiking on weekends. Dr. Martinez didn't just treat my symptoms - she helped me get my life back." After (AI Extracted Testimonial): "Within just 2 months, I was experiencing significant pain reduction, and now I'm practically pain-free. Dr. Martinez didn't just treat my symptoms - she helped me get my life back." 🛠️ Troubleshooting & Best Practices Common Issues & Solutions Low-Quality Extractions Improve Feedback Questions**: Ask more specific, outcome-focused questions Refine AI Prompts**: Adjust extraction criteria for better quote selection Set Minimum Length**: Ensure feedback has sufficient content for meaningful extraction Quality Scoring**: Implement rating system for extracted testimonials Insufficient Feedback Volume Multiple Feedback Channels**: Collect testimonials through various touchpoints Incentivized Feedback**: Offer small rewards for detailed feedback submissions Follow-up Automation**: Send feedback requests to satisfied customers Timing Optimization**: Request feedback at optimal moments in customer journey Privacy and Consent Issues Permission Management**: Ensure customers consent to testimonial use Attribution Control**: Allow customers to specify how they want to be credited Approval Workflows**: Implement customer approval before publishing testimonials Data Protection**: Maintain compliance with privacy regulations Optimization Strategies Extraction Quality Enhancement Prompt Engineering**: Continuously refine AI prompts based on output quality A/B Test Extractions**: Test different extraction approaches for effectiveness Human Review Integration**: Combine AI extraction with human editorial oversight Context Preservation**: Maintain customer context alongside extracted quotes Marketing Integration Campaign Alignment**: Extract testimonials that support specific marketing campaigns Audience Segmentation**: Categorize testimonials for different target audiences Channel Optimization**: Format testimonials for specific marketing channels Performance Tracking**: Monitor which testimonials drive best marketing results Process Automation Multi-Stage Processing**: Implement multiple extraction and refinement steps Quality Gates**: Add checkpoints for testimonial quality and relevance Workflow Branching**: Route different types of feedback to appropriate processes Error Handling**: Implement fallbacks for failed extractions or poor-quality feedback 📈 Success Metrics Extraction Efficiency Processing Speed**: Reduce time from feedback submission to usable testimonial Success Rate**: Percentage of feedback submissions yielding quality testimonials Quote Quality**: Average rating of extracted testimonials by marketing team Volume Increase**: Growth in testimonial collection and database size Marketing Impact Testimonial Usage**: Frequency of extracted testimonials in marketing campaigns Conversion Rates**: Impact of AI-extracted testimonials on sales metrics Social Proof Effectiveness**: Engagement rates on testimonial-based content Customer Acquisition**: Attribution of new customers to testimonial-driven campaigns 📞 Questions & Support Need help implementing your AI Testimonial Extractor Agent? 📧 Specialized Technical Support Email**: Yaron@nofluff.online Response Time**: Within 24 hours on business days Expertise**: AI testimonial extraction, feedback form optimization, marketing automation 🎥 Comprehensive Learning Library YouTube Channel**: https://www.youtube.com/@YaronBeen/videos Complete setup guides for feedback form design and AI extraction Advanced prompt engineering techniques for testimonial quality Integration tutorials for marketing platforms and social media Best practices for customer feedback collection and testimonial usage Troubleshooting common extraction and quality issues 🤝 Professional Marketing Community LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Connect for ongoing testimonial marketing automation support Share your customer success story automation achievements Access exclusive templates for feedback forms and testimonial campaigns Join discussions about social proof marketing and customer experience automation 💬 Support Request Guidelines Include in your support message: Your industry and typical customer feedback patterns Current testimonial collection process and challenges Specific marketing channels where testimonials will be used Volume expectations and quality requirements Integration needs with existing marketing tools Ready to turn every customer feedback into marketing gold? Deploy this AI Testimonial Extractor Agent and build a powerful testimonial database that drives sales and builds trust with prospects automatically!

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This workflow is a simple AI Agent that connects to Langfuse so send tracing data to help monitor LLM interactions. The main idea is to create a custom LLM model that allows the configuration of callbacks, which are used by langchain to connect applications such Langfuse. This is achieves by using the "langchain code" node: Connects a LLM model sub-node to obtain the model variables (model name, temp and provider) - Creates a generic langchain initChatModel with the model parameters. Return the LLM to be used by the AI Agent node. 📋 Prerequisites Langfuse instance (cloud or self-hosted) with API credentials LLM API key (Gemini, OpenAI, Anthropic, etc.) n8n >= 1.98.0 (required for LangChain code node support in AI Agent) ⚙️ Setup Add these to your n8n instance: Langfuse configuration LANGFUSE_SECRET_KEY=your_secret_key LANGFUSE_PUBLIC_KEY=your_public_key LANGFUSE_BASEURL=https://cloud.langfuse.com # or your self-hosted URL LLM API key (example for Gemini) GOOGLE_API_KEY=your_api_key Alternative: Configure these directly in the LangChain code node if you prefer not to use environment variables Import the workflow JSON Connect your preferred LLM model node Send a test message to verify tracing appears in Langfuse