Host your own AI deep research agent with n8n, Apify and OpenAI o3
This template attempts to replicate OpenAI's DeepResearch feature which, at time of writing, is only available to their pro subscribers. > An agent that uses reasoning to synthesize large amount of online information and complete multi-step research tasks for you. Source Though the inner workings of DeepResearch have not been made public, it is presumed the feature relies on the ability to deep search the web, scrape web content and invoking reasoning models to generate reports. All of which n8n is really good at! Using this workflow, n8n users can enjoy a variation of the Deep Research experience for themselves and their teams at a fraction of the cost. Better yet, learn and customise this Deep Research template for their businesses and/or organisations. Check out the generated reports here: https://jimleuk.notion.site/19486dd60c0c80da9cb7eb1468ea9afd?v=19486dd60c0c805c8e0c000ce8c87acf How it works A form is used to first capture the user's research query and how deep they'd like the researcher to go. Once submitted, a blank Notion page is created which will later hold the final report and the researcher gets to work. The user's query goes through a recursive series of web serches and web scraping to collect data on the research topic to generate partial learnings. Once complete, all learnings are combined and given to a reasoning LLM to generate the final report. The report is then written to the placeholder Notion page created earlier. How to use Duplicate this Notion database template and make sure all Notion related nodes point to it. Sign-up for APIFY.com API Key for web search and scraping services. Ensure you have access to OpenAI's o3-mini model. Alternatively, switch this out for o1 series. You must publish this workflow and ensure the form url is publically accessible. On depth & breadth configuration For more detailed reports, increase depth and breadth but be warned the workflow will take exponentially longer and cost more to complete. The recommended defaults are usually good enough. Depth=1 & Breadth=2 - will take about 5 - 10mins. Depth=1 & Breadth=3 - will take about 15 - 20mins. Dpeth=3 & Breadth=5 - will take about 2+ hours! Customising this workflow I deliberately chose not to use AI-powered scrapers like Firecrawl as I felt these were quite costly and quotas would be quickly exhausted. However, feel free to switch web search and scraping services which suit your environment. Maybe you don't decide to source the web and instead, data collection comes from internal documents instead. This template gives you freedom to change this. Experiment with different Reasoning/Thinking models such as Deepseek and Google's Gemini 2.0. Finally, the LLM prompts could definitely be improved. Refine them to fit your use-case. Credits This template is largely based off the work by David Zhang (dzhng) and his open source implementation of Deep Research: https://github.com/dzhng/deep-research
By JimleukE-commerce product fine-tuning with Bright Data and OpenAI
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automates the process of scraping product data from e-commerce websites and using it to fine-tune a custom OpenAI GPT model for generating high-quality marketing copy and product descriptions. Main Use Cases Fine-tune OpenAI models with real product data from hundreds of supported e-commerce websites for marketing content generation. Create custom AI models specialized in writing compelling product descriptions across different industries and platforms. Automate the entire pipeline from data collection to model training using Bright Data's extensive scraper library. Generate marketing copy using your custom-trained model via an interactive chat interface. How it works The workflow operates in two main phases: model training and model usage, organized into these stages: Data Collection & Processing Manually triggered to start the fine-tuning process. Uses Bright Data's web scraper to extract product information from any supported e-commerce platform (Amazon, eBay, Shopify stores, Walmart, Target, and hundreds of other websites). Collects product titles, brands, features, descriptions, ratings, and availability status from your chosen platform. Easily customizable to scrape from different websites by simply changing the dataset configuration and product URLs. Training Data Preparation A Code node processes the scraped product data to create training examples in OpenAI's required JSONL format. For each product, generates a complete training example with: System message defining the AI's role as a marketing assistant. User prompt containing specific product details (title, brand, features, original description snippet). Assistant response providing an ideal marketing description template. Compiles all training examples into a single JSONL file ready for OpenAI fine-tuning. Model Fine-Tuning Uploads the training file to OpenAI using the OpenAI File Upload node. Initiates a fine-tuning job via HTTP Request to OpenAI's fine-tuning API using the GPT-4o-mini model as the base. The fine-tuning process runs on OpenAI's servers to create your custom model. Interactive Chat Interface Provides a chat trigger that allows real-time interaction with your fine-tuned model. An AI Agent node connects to your custom-trained OpenAI model. Users can chat with the model to generate product descriptions, marketing copy, or other content based on the training. Custom Model Integration The OpenAI Chat Model node is configured to use your specific fine-tuned model ID. Delivers responses trained on your product data for consistent, high-quality marketing content. Summary Flow: Manual Trigger → Scrape E-commerce Products (Bright Data) → Process & Format Training Data (Code) → Upload Training File (OpenAI) → Start Fine-Tuning Job (HTTP Request) | Parallel: Chat Trigger → AI Agent → Custom Fine-Tuned Model Response Benefits: Fully automated pipeline from raw product data to trained AI model. Works with hundreds of different e-commerce websites through Bright Data's extensive scraper library. Creates specialized models trained on real e-commerce data for authentic marketing copy across various industries. Scalable solution that can be adapted to different product categories, niches, or websites. Interactive chat interface for immediate access to your custom-trained model. Cost-effective fine-tuning using OpenAI's most efficient model (GPT-4o-mini). Easily customizable with different websites, product URLs, training prompts, and model configurations. Setup Requirements: Bright Data API credentials for web scraping (supports hundreds of e-commerce websites). OpenAI API key with fine-tuning access. Replace placeholder credential IDs and model IDs with your actual values. Customize the product URLs list and Bright Data dataset for your specific website and use case. The workflow can be adapted for any e-commerce platform supported by Bright Data's scraping infrastructure.
By Daniel ShashkoNews-to-blog automation with GPT, Leonardo AI & WordPress publishing
This workflow automates the end-to-end process of generating and publishing blog posts from live news headlines. Fetch Headlines – Collects the latest top stories from Google News and GDELT, merges them, and removes duplicates. Headline Selection & Classification – Picks top headlines, checks relevance, and applies style rules. Draft Generation – Uses GPT to create an initial blog article in a natural, human tone. Tone & Expansion – Refines the draft for clarity, length, and style (customized to your own writing voice). Image Generation – Sends the article topic to Leonardo AI, waits for the image to finish rendering, and retrieves the final asset. Publish to WordPress – Uploads the generated image, assigns alt-text, creates a WordPress post with the article and image, and logs the publication to Google Sheets for tracking. Purpose Designed for hands-off content automation, this workflow continuously produces SEO-ready blog posts enriched with AI-generated images and publishes them directly to WordPress. It’s ideal for: Running an automated blog that reacts to trending news. Keeping websites updated with fresh, styled content without manual effort. Creating a repeatable content engine that combines research, writing, and media assets in one pipeline. Setup Instructions: Add Your Credentials Go to Credentials in n8n and create: OpenAI (for article generation) Leonardo AI (for image generation) WordPress (to publish posts) (Optional) Google Sheets (to log published articles) Attach these credentials to the matching nodes in the workflow. Check the WordPress Node Update the WordPress site URL to your own blog. Make sure the category, tags, and status (publish/draft) are set the way you want.
By Dean GallopReal-time currency conversion via webhook & Google search parsing
This automated n8n workflow provides real-time currency conversion by capturing GET requests via a webhook, parsing exchange rate data from Google Search, and returning a formatted response. The system handles query parameter validation and error cases to ensure reliable conversions. What is Real-Time Currency Conversion? Real-time currency conversion involves fetching the latest exchange rates from Google Search via HTTP requests, processing the data, and delivering a user-friendly conversion result based on a provided query parameter. Good to Know The workflow requires a valid query parameter (q) for conversion requests Google Search parsing depends on the availability and structure of search results Error handling is included for missing query parameters Responses are formatted for easy integration How It Works Webhook - Captures GET requests with query parameter q Check Query Parameter - Validates that the required query parameter exists Fetch Exchange Rate - Makes HTTP request to Google search for exchange rates Error Response - Handles missing query parameter errors Extract Conversion Data - Processes HTML response to extract conversion data Format Currency Response - Formats the result into a user-friendly response Send Conversion Response - Returns the formatted response How to Use Import the workflow into n8n Configure the webhook to receive GET requests with a query parameter (q) Test the workflow with sample conversion queries (e.g., "1 USD to INR") Monitor for error responses and adjust query handling if needed Requirements Webhook configuration Internet access for Google Search requests Customizing This Workflow Adjust the query parameter validation in the Check Query Parameter node to support additional formats Modify the Format Currency Response node to change the output format based on user needs
By Oneclick AI SquadGenerate verified job offer letters with OpenAI, Gmail and Slack
📄 AI-Powered Verified Job Offer Letter Generator Description Creating job offer letters manually is time-consuming, error-prone, and difficult to scale. This AI-powered workflow automates the entire job offer letter process — from validating candidate emails to generating and delivering professional PDF offer letters. This intelligent workflow eliminates repetitive drafting, reduces human errors, and ensures offer letters are sent only to verified email addresses, helping HR teams move faster while maintaining professionalism and accuracy. --- What This Workflow Does Transforms manual offer letter creation into a seamless, automated HR process: 📝 Capture Candidate & Job Details – Receives candidate name, email, job role, salary, joining date, and company details via webhook or form. 📧 Email Verification – Validates the candidate’s email address before sending any communication to prevent delivery errors. 🧠 AI-Powered Offer Letter Generation – Uses AI to generate a clear, professional, and structured job offer letter. 📄 HTML Offer Letter Formatting – Converts the AI-generated content into a clean and readable HTML layout. 📑 PDF Generation – Automatically converts the offer letter into a professional PDF document. 📧 Offer Letter Delivery – Sends the PDF offer letter directly to the verified candidate email. 🗂️ Document Storage – Saves a copy of the offer letter for internal records and future reference. 🔁 Confirmation Response – Returns a success response confirming completion. --- Key Features 🤖 AI Offer Letter Writing – Generates professional, ready-to-send offer letters automatically. 📧 Email Verification Built-In – Ensures offer letters are only sent to valid email addresses. 📑 PDF Generation – Creates clean, official-looking offer letters. ⚙️ End-to-End Automation – No manual drafting, formatting, or sending required. 📂 Centralized Record Keeping – Keeps copies of all generated offer letters. 🔄 Flexible Triggering – Can be triggered from HR systems, forms, or internal tools. --- Perfect For 🏢 HR & Recruitment Teams – Automate offer letter creation and delivery. 🚀 Startups & Growing Companies – Send professional offer letters without extra admin work. 🏫 Staffing & Hiring Agencies – Generate offer letters quickly for multiple candidates. 💻 Remote & Distributed Teams – Ensure consistent communication across locations. 🧠 Operations Teams – Maintain accurate records and reduce manual errors. --- What You’ll Need Required Integrations 🌐 Webhook Trigger – Receives candidate and job details. 🤖 OpenAI – Generates offer letter content. 📧 VerifyEmail – Validates candidate email addresses. 📄 HTMLCSS to PDF – Converts HTML into PDF offer letters. 📧 Gmail – Sends the offer letter email. ☁️ Google Drive (optional) – Stores generated offer letters. --- Optional Enhancements 🎨 Brand Customization – Add company logo, colors, and formatting to offer letters. 🧾 HR System Integration – Connect with ATS or HR tools for automatic triggering. 🌍 Multilingual Offer Letters – Generate offer letters in different languages. 🔐 Approval Step – Add internal approval before sending offer letters. 📊 Audit Logging – Store offer letter data in Google Sheets or databases. 📎 Additional Attachments – Include policies or onboarding documents with the offer letter. --- Quick Start 1️⃣ Import the workflow template into your n8n workspace 2️⃣ Connect credentials for OpenAI, VerifyEmail, Gmail, and HTMLCSS to PDF 3️⃣ Send test candidate data to the webhook 4️⃣ Review the generated PDF offer letter 5️⃣ Activate the workflow and start sending offer letters automatically --- Customization Options 1️⃣ Offer Letter Tone – Adjust AI prompt for formal or friendly tone. 2️⃣ Company Branding – Customize HTML layout and styling. 3️⃣ Email Content – Modify subject line and email message. 4️⃣ PDF Layout – Adjust spacing, fonts, and structure. 5️⃣ Storage Location – Change where offer letters are saved. 6️⃣ Validation Rules – Extend email or input checks. --- Expected Results ⚡ Faster Hiring Process – Generate offer letters in minutes. 🤖 Consistent Quality – Every offer letter follows a professional format. 📧 Error-Free Delivery – Verified emails reduce failed communication. 🗂️ Organized Records – All offer letters stored automatically. 🏢 Professional Candidate Experience – Clean, official documents every time. --- Workflow Structure Visualization 📝 Candidate & Job Details ↓ 📧 Email Verification ↓ 🧠 AI Offer Letter Generation ↓ 📄 HTML Formatting ↓ 📑 PDF Conversion ↓ 📧 Email Delivery ↓ 🔁 Confirmation Response --- 🚀 Ready to Automate Job Offer Letters? Import this template today and let AI handle offer letter creation, verification, and delivery — so your team can focus on hiring the right talent faster. ✨ ---
By Jitesh Dugar