Workflow Templates

Who’s it for This template is designed for traders, investors, and finance enthusiasts who want quick stock insights and technical analysis delivered directly in Telegram. It’s ideal for anyone who wants to explore candlestick patterns, MACD signals, volume trends, and market sentiment without switching between multiple tools. What it does A Telegram Trigger starts the workflow whenever a user sends a message. The AI Agent (powered by OpenAI) engages in a professional yet friendly conversation about stocks and finance. If a ticker symbol is provided, the agent uses the Get Chart workflow to fetch and analyze the chart. The workflow generates candlestick, MACD, and volume insights, returning both the chart image and a plain-language breakdown of key patterns, support/resistance levels, and momentum signals. Results are sent back to the user in Telegram. How to set up Connect your Telegram account in the Telegram Trigger and Send nodes. Add your Chart-IMG API key in the HTTP Request node (Get Chart URL). Connect your OpenAI account to power the AI Agent. Deploy the workflow and start interacting with your Telegram bot. Requirements Telegram bot credentials OpenAI API key Chart-IMG API key How to customize the workflow Adjust the technical indicators (e.g., timeframe, studies like RSI or Bollinger Bands) in the Get Chart URL node. Fine-tune the AI Agent’s tone or level of detail in its system prompt. Modify sticky notes for clarity or to add setup instructions.

Overview This n8n workflow automates the creation of viral CCTV-style animal videos using AI, perfect for TikTok content creators looking to capitalize on the popular security camera animal footage trend. The workflow generates realistic surveillance-style videos featuring random animals in humorous situations, complete with authentic CCTV aesthetics. How It Works The workflow runs on a 4-hour schedule and automatically: AI Prompt Generation: Uses GPT-5 to create hyper-realistic CCTV-style prompts with random animals, locations, and funny actions Video Creation: Generates videos using Veo3 AI with authentic security camera aesthetics (black & white, grainy, timestamp overlay) Content Optimization: AI creates viral TikTok titles and hashtags optimized for maximum engagement Multi-Platform Publishing: Automatically uploads to TikTok via Blotato and sends to Telegram Data Tracking: Stores all content in a data table for analytics and management Key Features Authentic CCTV Style**: Black & white, grainy quality, timestamp overlays, night vision effects Random Content**: 50+ animals, 30+ locations, 50+ hilarious actions for endless variety AI-Powered Titles**: GPT-4 generates compelling, SEO-optimized titles and viral hashtags Automated Publishing**: Direct TikTok posting with proper AI-generated content labeling Multi-Channel Distribution**: TikTok + Telegram for maximum reach Content Management**: Built-in data tracking and status management Perfect For TikTok content creators Social media managers AI automation enthusiasts Viral content strategists Pet/animal content creators Requirements OpenAI API key (for GPT-5 and GPT-4) Veo3 AI API access Blotato account (for TikTok posting) Telegram bot token n8n Cloud or self-hosted instance Customization Options Modify animal lists, locations, and actions Adjust scheduling frequency Change video aspect ratios Add more social platforms Customize AI prompts for different styles Categories Content Creation AI Automation Social Media Multimodal AI Tags #AI #TikTok #VideoGeneration #CCTV #Animals #ViralContent #Automation #SocialMedia

🎙️ Audio-to-Insights Workflow (Form Upload + Google Drive Link) Description This workflow enables seamless speech-to-text transcription, AI-powered summarization, sentiment analysis, and automated email delivery. It supports two different input modes: Form Upload (Local File)** Form Submission (Google Drive Link)** How it Works Input Form 1: Upload an audio file (e.g., .mp3,.wav,.mp4) Form 2: Submit a Google Drive link File Handling Local uploads go directly to AssemblyAI. Drive links are parsed → File ID extracted → File fetched → Sent to AssemblyAI. Transcription AssemblyAI generates transcript text with punctuation and highlights. Workflow loops with Wait + If until transcript status = completed. AI Analysis Transcript is passed to OpenAI. Generates a structured output including: Executive summary Sentiment label & score Key points Action items Notable quotes Topics Email Delivery A formatted email is sent via Gmail with the summary and insights. Features Dual input support: Google Drive OR direct upload Handles long-running jobs with Wait + If polling AI-powered transcript analysis with structured JSON Automated sentiment scoring and summary generation Professional HTML email reports Requirements AssemblyAI API Key – transcription Google Drive OAuth2 – file fetch OpenAI API Key – summarization & sentiment analysis Gmail OAuth2 – email delivery How to Use Import this workflow into your n8n instance. Add and configure the required credentials. Update placeholders for: AssemblyAI API Key Google Drive Link Gmail ID Trigger via either form (local file or Google Drive link). 5.For long recordings, split before uploading (10–20 min per chunk, 2–5s overlap).Keep audio consistent (e.g., WAV/MP3, 16 kHz mono if possible).Process chunks sequentially and combine summaries/action items at the end. Customising this Workflow Adjust the OpenAI prompt to fit your reporting style (executive summary, bullet points, etc.). Extend email formatting with logos or branding. Add Slack, CRM, or Notion integrations for distribution. Use Cases Meeting or lecture transcription with summaries Podcast analysis with highlights and quotes Business call reviews with action item extraction Academic seminar notes emailed automatically

Revenue Growth Strategy with CRO-led Multi-Agent Team using O3 & GPT-4.1-mini 🔥 Powered by OpenAI O3 & GPT-4.1-mini Multi-Agent System \#RevOps #n8nWorkflows #AIRevenue #OpenAI #GrowthHacking ⚡ Section 1: Start & Orchestrator 💬 Chat Trigger* → Listens for revenue-related requests (e.g., *“Optimize our sales funnel”). 🤖 CRO Agent (O3)* → Acts as the *Chief Revenue Officer**. Thinks strategically with the Think Node. Decides which specialist agents to call. 🧠 OpenAI O3 Model** → Provides advanced reasoning for CRO decisions. Benefit: Central orchestration ensures every request gets a strategic, executive-level response before delegation. 🛠️ Section 2: Specialist Agents Each specialist agent uses GPT-4.1-mini for fast, cost-effective execution. They receive the CRO’s instructions and return insights. 📈 Sales Pipeline Analyst Funnel optimization, conversion tracking, bottleneck fixes. Outputs: Pipeline health, drop-off points, recommendations. 🎯 Revenue Attribution Specialist Multi-touch attribution, ROI analysis, campaign efficiency. Outputs: Attribution models, marketing ROI. 📊 Revenue Forecasting Analyst Predictive modeling, scenario planning, growth projections. Outputs: Forecast reports, “what-if” scenarios. ⚙️ Revenue Operations Manager CRM optimization, territory planning, sales automation. Outputs: Process improvements, efficiency boosts. 💰 Pricing & Packaging Strategist Competitive pricing analysis, packaging strategy, revenue optimization. Outputs: Price models, package recommendations. 🧠 Revenue Intelligence Analyst BI dashboards, performance tracking, KPI insights. Outputs: Reports with actionable intelligence. Benefit: Breaks complex revenue problems into specialized tasks handled by domain experts. 🔄 Section 3: Feedback & Integration Each agent → sends results back to CRO Agent. CRO Agent → compiles a comprehensive revenue strategy. Can integrate with CRM, BI dashboards, or Slack/Email for delivery. Benefit: Clear, actionable insights delivered in one place — like having a virtual RevOps team on demand. 📊 Workflow Overview | Section | Key Nodes | Purpose | Benefit | | ----------------------- | ----------------------------------- | ------------------------------------------------- | ------------------------------------ | | ⚡ Start & Orchestration | Chat Trigger, CRO Agent, O3 Model | Capture request & assign to CRO | Centralized leadership | | 🛠️ Specialists | 6 Agent Nodes + GPT-4.1-mini models | Analyze pipeline, pricing, ops, attribution, etc. | Specialized, cost-efficient insights | | 🔄 Feedback Loop | CRO Agent aggregation | Compiles strategy from multiple agents | Unified, data-driven revenue plan | 💡 Use Cases Pipeline Optimization** → Identify bottlenecks, improve conversions. Attribution Modeling** → Know exactly where revenue comes from. Revenue Forecasting** → Plan growth scenarios and projections. Ops Excellence** → Automate CRM, streamline sales ops. Pricing Strategy** → Compete smarter with optimized pricing models. Revenue Intelligence** → Ongoing tracking and performance monitoring. 💸 Cost Optimization O3 only for CRO decisions** → Strategic layer. GPT-4.1-mini for specialists** → Low-cost execution (\~90% cheaper). Parallel processing** → All agents can run simultaneously. ✅ Final Result: A virtual AI-powered RevOps team that turns any revenue-related question into a comprehensive growth strategy — instantly.

Adaptive LLM Router for Optimized AI Chat Responses Elevate your AI chatbots with intelligent model selection: automatically route simple queries to cost-effective LLMs and complex ones to powerful ones, balancing performance and expenses seamlessly. What It Does This workflow listens for chat messages, uses a lightweight Gemini model to classify query complexity, then selects and routes to the optimal LLM (Gemini 2.5 Pro for complex, OpenAI GPT-4.1 Nano for simple) to generate responses—ensuring efficient resource use. Key Features Complexity Classifier** - Quick assessment using Gemini 2.0 Flash Dynamic Model Switching** - Routes to premium or budget models based on needs Chat Trigger** - Webhook-based for real-time conversations Current Date Awareness** - Injects $now into system prompt Modular Design** - Easy to add more models or adjust rules Cost Optimization** - Reserves heavy models for demanding tasks only Perfect For Chatbot Developers**: Build responsive, cost-aware AI assistants Customer Support**: Handle routine vs. technical queries efficiently Educational Tools**: Simple facts vs. in-depth explanations Content Creators**: Quick ideas vs. detailed writing assistance Researchers**: Basic lookups vs. complex analysis Business Apps**: Optimize API costs in production environments Technical Highlights Harnessing n8n's LangChain nodes, this workflow demonstrates: Webhook triggers for instant chat handling Agent-based classification with strict output rules Conditional model selection for AI chains Integration of multiple LLM providers (Google Gemini, OpenAI) Scalable architecture for expanding model options Ideal for minimizing AI costs while maximizing response quality. No coding required—import, configure credentials, and deploy!

Template presentation This template generates a sales follow-up presentation in Google Slides after a sales call recorded in Claap. The workflow is simplified to showcase the main use case. You can customize and enrich this workflow by connecting to the CRM, researching data online or adding more files in the presentation. The presentation template used in this workflow is available here. Workflow configuration Create a webhook in Claap, by following this article. Edit the labels that trigger the workflow and route on the relevant presentation. Fill your Open AI credentials by creating an API Key in OpenAI Platform Edit the presentation personalization details (user set as editor, content, title) Fill your Slack credentials by following steps in this video.

This n8n workflow automates the process of collecting, storing, and summarizing customer reviews from the Apple App Store for multiple apps. It fetches daily reviews, stores them in a Pinecone vector database, and generates a weekly summary using OpenAI, which is then posted to a Slack channel. Key Features Fetches daily customer reviews for a list of Apple App Store apps using the App Store Connect API. Stores reviews in Pinecone, with separate namespaces for each app and automatic weekly cleanup. Uses OpenAI to generate a summary of reviews, including positive/negative highlights and average star rating. Posts the summary to a specified Slack channel every week. How to use Set your Apple App Store app IDs and names in the provided Set nodes. Configure your Apple API, Pinecone, OpenAI, and Slack credentials. Adjust the schedule triggers as needed for daily fetching and weekly summarization. Deploy the workflow to automate review monitoring and reporting for your apps.

Build a powerful AI chatbot that provides precise answers from your own company's knowledge base. This template provides a smart AI agent that connects to Lookio, a platform where you can easily upload your documents (from Notion, Jira, Slack, etc.) to create a dedicated knowledge source. What makes this agent "smart" is its efficiency. It's configured to handle simple greetings and small talk on its own, only using its powerful (and paid) knowledge retrieval tool when a user asks a genuine question. This cost-saving logic makes it perfect for building production-ready internal helpdesks, customer support bots, or any application where you need accurate, source-based answers. Who is this for? Customer support teams:** Build internal bots that help agents find answers instantly from your support documentation and knowledge bases. Product & engineering teams:** Create a chatbot that can answer technical questions based on your product documentation or internal wikis. HR departments:** Deploy an internal assistant that can answer employee questions based on company handbooks, policies, and procedures. Any business with a knowledge base:** Provide an interactive, conversational way for employees or customers to access information locked away in your documents. What problem does this solve? Provides accurate, grounded answers:** Ensures the AI agent's responses are based on your trusted, private documents, not the open internet, which prevents factual errors and "hallucinations." Makes your knowledge accessible:** Transforms your static documents and knowledge bases into an interactive, 24/7 conversational resource. Optimizes for cost and efficiency:** The agent is intelligent enough to handle simple small talk without making unnecessary API calls to your knowledge base, saving you credits and money. Simplifies RAG setup:** Provides a ready-to-use template for a common RAG (Retrieval-Augmented Generation) pattern, with the complexities of document management and retrieval handled by the Lookio platform. How it works First, build your knowledge base in Lookio: The process starts on the Lookio platform. You upload your documents (from Notion, Jira, PDFs, etc.) and create an "assistant" which becomes your secure, queryable knowledge base. A user asks a question: The n8n workflow begins when a user sends a message via the Chat Trigger. The agent makes a decision: The AI Knowledge Agent, guided by its system prompt, analyzes the user's message. If it's a simple greeting like "hi," it will respond directly. If it's a substantive question that requires specific knowledge, it decides to use its "Query knowledge base" tool. Query the Lookio knowledge base: The agent passes the user's question to the HTTP Request Tool. This tool securely calls the Lookio API with your specific Assistant ID and API key. Deliver the fact-based answer: Lookio searches your documents, synthesizes a precise answer, and sends it back to the workflow. The n8n agent then presents this answer to the user in the chat interface. Architectural Approaches to RAG in n8n with Lookio From a workflow perspective, integrating RAG natively in n8n involves orchestrating multiple nodes for data handling, embedding, and vector searches. This method provides high visibility and control over each step. An alternative architectural pattern is to use an external RAG service like Lookio, which consolidates these steps into a single HTTP Request node. This simplifies the workflow's structure by abstracting the multi-stage RAG process into one API endpoint. Setup Set up your Lookio assistant (Prerequisite): First, go to Lookio, sign up (you get 50 free credits), create an assistant with your documents, and from your settings, copy your API Key and Assistant ID. Configure the Lookio tool: In the Query knowledge base (HTTP Request Tool) node: Replace the <your-assistant-id> placeholder with your actual Assistant ID. Replace the <your-lookio-api-key> placeholder with your actual API Key. Connect your AI model: In the OpenAI Chat Model node, connect your AI provider credentials. Activate the workflow. Your smart knowledge base agent is now live and ready to chat! Taking it further Adjust retrieval quality:* In the *Query knowledge base** node, you can change the query_mode from flash (fastest) to deep for higher quality but slightly slower answers, depending on your needs. Add more tools:** Enhance your agent by giving it other tools, like a web search for when the internal knowledge base doesn't have an answer, or a calculator for performing computations. Deploy it anywhere:* Swap the *Chat Trigger* for a *Slack* or *Discord** trigger to deploy your agent right where your team works.

Supercharge your sales and marketing efforts with this powerful automation that transforms a list of LinkedIn profiles into a fully enriched, personalized outreach campaign. This workflow is designed for sales teams, growth marketers, and business development professionals looking to scale their lead generation without sacrificing personalization. It seamlessly integrates LinkedIn scraping, email enrichment with Hunter.io, AI-powered message generation with OpenAI, and data organization in Google Sheets. How It Works Start with Leads: The workflow begins with a list of target LinkedIn profile URLs. Scrape Profile Data: It automatically scrapes each LinkedIn profile to extract key professional information such as name, title, company, and location. A built-in delay helps manage rate limits. Find Verified Emails: Using the scraped company and name, the workflow queries ==Hunter.io to find a verified work email address== for the lead. AI-Powered Personalization: If an email is found, the lead's data is sent to OpenAI (GPT-4), which generates a highly personalized, conversational outreach message based on their role, company, and your value proposition. Sync to CRM/Sheet: Finally, all the enriched data—including the custom AI message—is neatly organized and saved as a new row in your designated Google Sheet. Stop wasting hours on manual lead research and generic outreach. Implement this automated workflow to focus on building relationships and closing deals.

Overview This workflow automates static security analysis for JavaScript, PHP, and Python codebases. It’s designed for bug bounty hunters and security researchers who need fast, structured, and AI-assisted vulnerability detection across multiple sources. Features 🤖 AI-Powered Analysis: Specialized agents for each language: AI JavaScript Expert AI PHP Expert AI Python Expert Each agent detects only exploitable vulnerabilities (AST + regex heuristics). Returns strict JSON with: { "results": [ { "url": "file or URL", "code": "lines + snippet", "severity": "medium|high|critical", "vuln": "vulnerability type" } ] } 🧩 Post-Processing: Cleans, formats, and validates JSON results. Generates HTML tables with clear styling for quick visualization. Output ✅ JSON vulnerability reports per file. 📊 HTML table summaries grouped by language and severity. Usage Import the workflow into n8n. Configure credentials: OpenAI API key GitHub API Key Google Drive API Key Run via the provided webhook form. Select analysis mode and input target. View structured vulnerability reports directly in n8n or Google Drive. Notes Performs static analysis only (no code execution). Detects exploitable findings only; ignores low-impact issues.

Multi-Agent Cold Email Campaign Generator with O3 Director & GPT-4.1 Specialists 🌍 Overview This workflow simulates a virtual sales & marketing team where each AI agent has a role: A Director Agent (O3) who manages strategy. Multiple Specialist Agents (GPT-4.1-mini) for research, writing, personalization, deliverability, sequencing, and analytics. Everything is triggered automatically when a new chat message request comes in. 🟢 Section 1: Entry & Director 🔗 Nodes: 1️⃣ When chat message received (Trigger) 💬 Starts the workflow when a new request arrives (e.g., “Create a cold email campaign for SaaS CTOs”). 2️⃣ Outreach Director Agent (O3 model) 🎯 The “manager” agent. Decides what kind of campaign is needed and assigns tasks. 3️⃣ Think (Planning Node) 🧠 Helps the Director structure thoughts before delegating. 💡 Why useful? Director uses O3 (strong reasoning model) only where strategy is needed → reduces cost. Provides a single point of control to coordinate all other agents. 🔵 Section 2: Specialist Agents Each is powered by GPT-4.1-mini (cheaper + faster). 🔍 Prospect Research Specialist → researches target companies, roles, pain points. ✍️ Cold Email Copywriter → drafts subject lines, hooks, and persuasive body copy. 🎯 Personalization Specialist → inserts custom variables for each recipient. 📅 Email Sequence Strategist → designs follow-ups, timing, nurture flows. 📬 Email Deliverability Expert → ensures emails land in inbox, not spam. 📊 Outreach Analytics Specialist → tracks performance, runs A/B tests, optimizes campaigns. 💡 Why useful? Each agent is a specialist → just like a real marketing team. Parallel execution** in n8n means faster results. Modular → you can remove or add more specialists. 🟣 Section 3: Execution Flow Request comes in via chat trigger Director (O3) interprets and delegates → calls specialists as tools Specialists generate their pieces (research → copy → personalization → sequence → deliverability → analytics) Director integrates results into a cohesive cold email campaign 🟡 Section 4: Documentation & Notes There are two Sticky Notes inside the workflow: Header Note** → Support info + tutorials (YouTube & LinkedIn by Yaron Been) Main Note** → Full documentation (overview, use cases, cost optimization, tags) 📊 Final Overview | Section | What Happens | Why It’s Useful | | -------------- | ------------------------ | --------------------------- | | 🟢 Director | Trigger + O3 strategy | Ensures smart coordination | | 🔵 Specialists | GPT-4.1-mini agents | Faster, cheaper execution | | 🟣 Flow | Delegation + Integration | Automated campaign building | | 🟡 Docs | Sticky Notes | In-workflow guide for users | 🚀 Benefits ✅ AI-powered cold email team without hiring humans ✅ Cost-optimized (O3 only for strategy, GPT-4.1-mini for tasks → \~90% cheaper) ✅ End-to-end coverage (research → writing → personalization → sequencing → analytics) ✅ Scalable: can run multiple campaigns in parallel ✅ Customizable: swap models, add tools, or expand team

Name: AI Chatbot for Max Messenger with Voice Recognition (GigaChat + Sber) Description: How it works This workflow powers an intelligent, conversational AI bot for Max messenger that can understand and respond to both text and voice messages. The bot uses GigaChat AI with built-in memory, allowing it to remember the conversation history for each unique user and answer follow-up questions. Voice messages are transcribed using Sber SmartSpeech. It's a complete solution for creating an engaging, automated assistant within your Max bot, using Russian AI services. Step-by-step Max Trigger:* The workflow starts when the *Max Trigger** node receives a new message sent to your Max bot. Access Control:* The *Check User** node verifies the sender's user ID against an allowed list. This prevents unauthorized users from accessing your bot. Access Denied Response:* If the user is not authorized, the *Access Denied** node sends a polite rejection message. Message Type Routing:* The *Text/Attachment** (Switch) node checks if the message contains plain text or has attachments (voice, photo, file). Attachment Processing:* If an attachment is detected, the *Download Attachment* (HTTP Request) node retrieves it, and the *Attachment Router** (Switch) node determines its type (voice, photo, or file). Voice Transcription:* For voice messages, the workflow gets a Sber access token via *Get Access Token* (HTTP Request), merges it with the audio file, and sends it to *Get Response** (HTTP Request) which uses Sber SmartSpeech API to transcribe the audio to text. Input Unification:* The *Voice to Prompt* node converts transcribed text into a prompt, while *Text to Prompt* does the same for plain text messages. Both paths merge at the *Combine** node. AI Agent Processing:* The unified prompt is passed to the *AI Agent, powered by **GigaChat Model and using Simple Memory to retain the last 10 messages per user (using Max user_id as the session key). Response Delivery:* The AI-generated response is sent back to the user via the *Send Message** node. Set up steps Estimated set up time: 15 minutes Get Max bot credentials: Visit https://business.max.ru/ to create a bot and obtain API credentials. Add these credentials to Max Trigger, Send Message, and Access Denied nodes. Add GigaChat credentials: Register for GigaChat API access and add your credentials to the GigaChat Model node. Add Sber credentials: Obtain Sber SmartSpeech API credentials and add them to Get Access Token and Get Response nodes (HTTP Header Auth). Configure access control: Open the Check User node and change the user_id value (currently 50488534) to your own Max user ID. This ensures only you can use the bot during testing. Customize bot personality: Open the AI Agent node and edit the system message to change the bot's name, behavior, and add your own contact information or links. Test the bot: Activate the workflow and send a text or voice message to your Max bot to verify it responds correctly. Notes This workflow is specifically designed for Russian-speaking users and uses Russian AI services (GigaChat and Sber SmartSpeech) as alternatives to OpenAI. Make sure you have valid API access to both services before setting up this workflow.