Suggest meeting slots using AI
The purpose of this n8n workflow is to automate the process of identifying incoming Gmail emails that are requesting an appointment, evaluating their content, checking calendar availability, and then composing and sending a response email. Note that to use this template, you need to be on n8n version 1.19.4 or later.
By n8n TeamFind Instagram collaboration leads using Apify scraping and GPT-4o evaluation
This no-code n8n workflow finds recent Instagram posts by hashtag, scrapes profile data, and uses an AI agent to evaluate whether each account is a good collaboration lead. The workflow filters based on the number of followers and the content of their bio, and outputs structured reasoning for outreach decisions. Perfect for creators, marketers, or business developers looking to automate influencer or community partnership prospectingβespecially in niche ecosystems like n8n. --- β Key Features π Hashtag Discovery: Finds recent Instagram posts from a specified hashtag (e.g., n8n) π€ Account Scraping: Retrieves profile details such as follower count and biography π§ AI Evaluation: Uses OpenAI and LangChain to determine if the profile is a good fit for outreach π¦ Structured Output: Returns a JSON object with "Yes/No" lead status and reasoning π οΈ Manual Execution: Run on demand using the manual trigger --- π§° What You'll Need | Tool / API | Purpose | Setup Steps | |-------------------------|------------------------------------------|-------------| | Apify Account | To access Instagram scraping actors | Create account β Generate API Token β Use in httpQueryAuth credential in n8n | | OpenAI API Key | To power the AI decision-making agent | Sign up at OpenAI β Create API key β Paste into OpenAI credential in n8n | | LangChain Plugin for n8n | AI Orchestration with System Message | Install LangChain nodes from Community Nodes (already installed in this workflow) | --- π§ Step-by-Step Setup 1οΈβ£ Manual Trigger Node: When clicking βExecute workflowβ Use: Allows you to run the workflow manually while testing. 2οΈβ£ Define Hashtag Node: Create Search Term Value: Sets "n8n" as the default Instagram hashtag to scan. You can edit this to any other hashtag you'd like. 3οΈβ£ Find Recent Posts Node: Find Recent Posts API: Apify Instagram Hashtag Scraper Auth Setup: Go to your Apify Console Click βCreate new tokenβ In n8n, create a new HTTP Query Auth credential Set token in the token query param (e.g., ?token=yourTokenHere) Choose the credential in this node 4οΈβ£ Scrape Each Profile Node: Scrape Accounts API: Apify Instagram Profile Scraper Body: JSON with usernames from the hashtag search Note: Uses the same httpQueryAuth credential as the previous node. 5οΈβ£ Extract Fields Node: Set bio and follower count What it does: Extracts biography and followersCount from the profile JSON and stores them in clean variables for AI input. 6οΈβ£ AI Lead Scoring Node: AI Agent Purpose: Uses GPT-4o-mini to analyze the bio and follower count Prompt Details: 7οΈβ£ AI Model Node: OpenAI Chat Model Model: gpt-4o-mini Credential: Connect your OpenAI account via API Key. Go to OpenAI API Keys Copy your key and create a new OpenAI API credential in n8n. 8οΈβ£ Output Parser Node: Structured Output Parser What it does: Parses the response from the AI into structured JSON for further use (e.g., storing leads, sending to Airtable, etc.) --- π§ͺ Sample Output json { "lead status": "Yes", "Reasoning": "The user has 3.5k followers and their bio shows they build automations with n8n." } --- π¬ Need More Help? If you'd like assistance setting this up, customizing it to your niche, or expanding it to score and store leads automatically β I can help! π€ Robert Breen Automation Consultant | AI Workflow Designer | n8n Expert π§ robert@ynteractive.com π ynteractive.com π LinkedIn ---
By Robert BreenTelegram AI chatbot with document-based answers using OpenAI and PGVector RAG
π€ AI Q&A Chatbot Workflow β Build Your Own AI Agent Trained on Private Documents This powerful AI automation add-on upgrades your Telegram Bot Starter Template by integrating a fully functional AI chatbot and a context-aware AI agent that answers user questions using your internal documents. Unlike generic bots, this chatbot uses your own data to respond with deeply personalized, context-relevant information β perfect for support, onboarding, internal knowledge access, and client-facing interactions. It connects to any PostgreSQL database β including Neon.tech, Supabase, or a self-hosted Postgres setup β allowing you to build custom AI-powered FAQ assistants, internal support bots, or knowledge-based customer service tools. --- π§ Why It Works: Contextual Retrieval The secret is Contextual Retrieval β a powerful technique where your documents are stored in a way that preserves meaning and context. This allows the AI to fetch highly relevant, source-backed responses, eliminating hallucinations and guesswork. > Data is embedded, chunked, and saved in a vector database (Postgres + PGVector), enabling smart semantic search tailored to your needs. π Learn more about this approach in this article by Anthropic β --- β¨ Key Features Chat with your internal documents: Uses your content to answer questions with precision Built-in document vectorization: Pre-configured Google Drive ingestion flow (Notion, Airtable, Dropbox available separately) Contextual memory: Past chats stored in PostgreSQL for personalized conversations Plug-and-play architecture: Connect Supabase, OpenAI, custom APIs via n8nβs interface --- π€ Who Can Use This Workflow? Entrepreneurs & startups building branded AI chatbots without code Customer support teams automating answers using documentation Ops teams creating internal FAQ bots for onboarding and training No-code developers using n8n to build Telegram bots with AI features --- βοΈ Setup Instructions You'll find step-by-step instructions inside the workflow. Quick Setup Overview: Import the workflow into n8n (cloud or self-hosted) Add your Telegram Bot credentials Connect your PostgreSQL DB (Neon, Supabase, etc.) Set up document ingestion from Google Drive Activate the workflow and start chatting --- 𧩠Extensibility This workflow is modular and ready to expand. Build powerful assistants by connecting additional workflows: Vectorization modules for Notion, Airtable, Dropbox, and others Any vector DB: Neon.tech, Supabase, or self-hosted PGVector βπ» Telegram User Registration Module β π΅ Telegram Payment, Invoicing & Refund Module β --- π§ More Smart AI Agents Explore more AI workflows and agents on my Gumroad β π Agent: Find in the Internet β fetches live info from the web π Agent: Search Internal Docs β queries Notion, Google Drive, etc. π¦ Agent: Check Order Status β reads status from Airtable or CRM π° Agent: Calculate Cost or Quote β builds pricing logic from inputs π¨ Submit your idea here for a custom AI agent β
By Victor GoldGenerate articles from Google Sheets to WordPress with web scraping and GPT-4 Mini
π€ Automated AI Article Generation from Google Sheets to WordPress π Short Description Transform a Google Sheet into an automated content factory! This workflow reads article topics, scrapes source content, uses AI to create original articles, and publishes drafts to WordPress automatically. π π Full Description This workflow automates the entire content creation pipeline by connecting Google Sheets, web scraping, AI content generation, and WordPress publishing. It's designed for content marketers, bloggers, and news publishers who need to scale their content production efficiently. πͺ The system monitors a Google Sheet for new article ideas, processes source URLs through a dual-AI system for summarization and content creation, then automatically generates WordPress drafts while tracking everything back to the spreadsheet. πβπ€βπ π₯ Who's It For π Content marketing agencies managing multiple clients βοΈ Bloggers looking to scale their content output π° News publishers automating article aggregation π SEO specialists creating keyword-optimized content π― Digital marketers running content campaigns βοΈ How It Works π Sheet Monitoring: Watches Google Sheets for rows marked "New" in the Flow Status column π Content Processing: Fetches and analyzes source articles using dual AI agents π€ Article Generation: Creates SEO-optimized articles with proper formatting and structure π WordPress Integration: Automatically publishes drafts to your WordPress site β Status Tracking: Updates the sheet with progress and final draft links π οΈ How to Set Up π Prerequisites π Google Sheets API access (OAuth2) π§ OpenAI API key π WordPress REST API credentials π Source URLs for article inspiration β‘ Configuration Steps π Clone the workflow into your n8n instance π Connect credentials for Google Sheets, OpenAI, and WordPress π Update the Google Sheet ID in all Sheet nodes to point to your document π Configure the sheet columns to match: Topic, Source, Flow Status, Publish Status, Publish Link π§ͺ Test with one row marked as "New" in your sheet π Requirements 𧩠n8n Nodes Used π Google Sheets (read/update operations) π HTTP Request (web scraping) π€ OpenAI/LangChain (AI content processing) π WordPress (draft creation) π» Code node (content formatting) βοΈ If node (error handling) π SplitInBatches (item processing) βοΈ External Services π Google Sheets with specific column structure π§ OpenAI API access π WordPress installation with REST API enabled π¨ How to Customize the Workflow βοΈ Content Style Adjustments Modify the "Article Creator" AI node's system prompt to change: π Writing tone and style π SEO keyword density π Article structure and headings π¬ Call-to-action format π Source Processing Adjust the "Article Summarizer" node to: πΈοΈ Handle different website structures π Extract specific content elements π Modify markdown output format π€ Publishing Options Customize the "Create a Draft" WordPress node to: π Change post status from "draft" to "publish" π₯ Assign different authors or categories π·οΈ Add custom fields or tags β οΈ Error Handling Modify the conditional logic in the "If" node to handle different failure scenarios or add additional validation steps. --- βΉοΈ Note: This workflow uses community nodes (LangChain/OpenAI) and requires a self-hosted n8n instance. β¨ Workflow features comprehensive error handling, real-time status tracking, and batch processing for efficient content pipeline management.
By Automation for youLet AI agents handle issues with the Jira MCP Server πͺ all 20 operations
Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing all Jira Software Tool operations to AI agents. Zero configuration needed - all 20 operations pre-built. β‘ Quick Setup Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL π§ How it Works β’ MCP Trigger: Serves as your server endpoint for AI agent requests β’ Tool Nodes: Pre-configured for every Jira Software Tool operation β’ AI Expressions: Automatically populate parameters via $fromAI() placeholders β’ Native Integration: Uses official n8n Jira Software Tool tool with full error handling π Available Operations (20 total) Every possible Jira Software Tool operation is included: π Issue (8 operations) β’ Get an issue changelog β’ Create an issue β’ Delete an issue β’ Get an issue β’ Get many issues β’ Create an email notification for an issue β’ Get the status of an issue β’ Update an issue π§ Issueattachment (4 operations) β’ Add an attachment to an issue β’ Get an attachment from an issue β’ Get many issue attachments β’ Remove an attachment from an issue π§ Issuecomment (5 operations) β’ Add a comment β’ Get a comment β’ Get many comments β’ Remove a comment β’ Update a comment π€ User (3 operations) β’ Create a user β’ Delete a user β’ Get a user π€ AI Integration Parameter Handling: AI agents automatically provide values for: β’ Resource IDs and identifiers β’ Search queries and filters β’ Content and data payloads β’ Configuration options Response Format: Native Jira Software Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic π‘ Usage Examples Connect this MCP server to any AI agent or workflow: β’ Claude Desktop: Add MCP server URL to configuration β’ Custom AI Apps: Use MCP URL as tool endpoint β’ Other n8n Workflows: Call MCP tools from any workflow β’ API Integration: Direct HTTP calls to MCP endpoints β¨ Benefits β’ Complete Coverage: Every Jira Software Tool operation available β’ Zero Setup: No parameter mapping or configuration needed β’ AI-Ready: Built-in $fromAI() expressions for all parameters β’ Production Ready: Native n8n error handling and logging β’ Extensible: Easily modify or add custom logic > π Free for community use! Ready to deploy in under 2 minutes.
By David Ashby