Simple Memory workflow'ları
185 sonuç — tümü kaynağa bağlı n8n referansı
SBinance SM 4hour Indicators Tool
A medium-term trend analyzer for the Binance Spot Market that leverages core technical indicators across 4-hour candle data to provide human-readable swing-trade signals via AI. 🎥 Watch Tutorial: 🎯 What It Does Accepts a Binance trading pair (e.g., AVAXUSDT) Sends the symbol to an internal webhook for technical indicator calculation Computes 4h RSI, MACD, Bollinger Bands, SMA, EMA, ADX Returns structured, GPT-analyzed signals ready for Telegram delivery 🧠 AI Agent Details Model:** GPT-4.1-mini (OpenAI Chat) Agent Role:** Translates raw indicator values into sentiment-labeled signals Memory:** Tracks session + symbol context for cleaner multi-turn logic 🔗 Required Backend Workflow To calculate indicators, this tool depends on: POST https://treasurium.app.n8n.cloud/webhook/4h-indicators { "symbol": "AVAXUSDT" } Returns a JSON object with the latest 40×4h candle-based calculations. 📥 Input Format { "message": "AVAXUSDT", "sessionId": "telegram_chat_id" } 📊 Sample Output 🕓 4h Technical Signals – AVAXUSDT • RSI: 64 → Slightly Bullish • MACD: Bullish Cross above baseline • BB: Upper band touch – volatility expanding • EMA > SMA → Confirmed Upside Momentum • ADX: 31 → Strengthen
SBinance SM 1hour Indicators Tool
🧪 Binance SM 1hour Indicators Tool A precision trading signal engine that interprets 1-hour candlestick indicators for Binance Spot Market pairs using a GPT-4.1-mini LLM. Ideal for swing traders seeking directional bias and momentum clarity across medium timeframes. 🎥 Watch Tutorial: 🎯 Purpose This tool provides a structured 1-hour market read using: RSI** (Relative Strength Index) MACD** (Moving Average Convergence Divergence) BBANDS** (Bollinger Bands) SMA & EMA** (Simple and Exponential Moving Averages) ADX** (Average Directional Index) It’s invoked as a sub-agent in broader AI workflows, such as the Binance Financial Analyst Tool and the Spot Market Quant AI Agent. ⚙️ Key Features | Feature | Description | | ---------------------- | ------------------------------------------------------------- | | 🔄 Subworkflow Trigger | Runs only when called by parent agent (not standalone) | | 🧠 GPT-4.1-mini LLM | Translates numeric indicators into natural-language summaries | | 📊 Real-time Data | Pulls latest 40×1h candles via internal webhook from Binance | | 📥 Input Format | { "message": "ETHUSDT", "sessionId": "telegram_chat_id" } | | 📤 Output Format | JSON summary + Telegram-frie
STesla 1day Indicators Tool (Macro-Level Technical AI)
📅 Analyze Tesla’s daily trading structure with AI using 6 Alpha Vantage indicators. This tool evaluates long-term trend health, volatility patterns, and potential reversal signals at the 1-day timeframe. Designed for use within the Tesla Financial Market Data Analyst Tool, this agent helps swing and position traders anchor macro sentiment. ⚠️ Not standalone. Must be executed via Execute Workflow 🔌 Requires: Tesla Quant Technical Indicators Webhooks Tool Alpha Vantage Premium API Key OpenAI GPT-4.1 credentials 🔍 What It Does This tool queries a secured webhook (/1dayData) to retrieve real-time, trimmed JSON data for: RSI (Relative Strength Index)** BBANDS (Bollinger Bands)** SMA (Simple Moving Average)** EMA (Exponential Moving Average)** ADX (Average Directional Index)** MACD (Moving Average Convergence Divergence)** These values are then passed to a LangChain AI Agent powered by GPT-4.1, which returns: A 2–3 sentence market condition summary Structured indicator values Timeframe tag ("1d") 📋 Sample Output { "summary": "TSLA shows consolidation on the daily chart. RSI is neutral, BBANDS are contracting, and MACD is flattening.", "timeframe": "1d", "indicators": { "RSI": 51.3, "
STesla 1hour Indicators Tool (Mid-Term Technical Analysis AI)
🕒 Evaluate Tesla (TSLA) price action and market structure on the 1-hour timeframe using 6 real-time indicators. This sub-agent is designed to feed mid-term technical insights into the Tesla Financial Market Data Analyst Tool. It uses GPT-4.1 to interpret Alpha Vantage indicator data delivered via secure webhooks. ⚠️ This workflow is not standalone and is executed via Execute Workflow. 🔌 Requires: Tesla Quant Technical Indicators Webhooks Tool Alpha Vantage Premium API Key 🔧 Connected Indicators This tool fetches and analyzes the latest 20 datapoints for: RSI (Relative Strength Index)** MACD (Moving Average Convergence Divergence)** BBANDS (Bollinger Bands)** SMA (Simple Moving Average)** EMA (Exponential Moving Average)** ADX (Average Directional Index)** 📋 Sample Output { "summary": "TSLA is gaining strength on the 1-hour chart. RSI is rising, MACD has crossed bullish, and BBANDS are widening.", "timeframe": "1h", "indicators": { "RSI": 62.1, "BBANDS": { "upper": 176.90, "lower": 169.70, "middle": 173.30, "close": 176.30 }, "SMA": 174.20, "EMA": 175.60, "ADX": 27.5, "MACD": { "macd": 0.84, "signal": 0.65, "histogram": 0.19 } } } 🧠 Agent Components | Component | Role | | -----
STesla 15min Indicators Tool (Short-Term AI Technical Analysis)
⏱️ Analyze Tesla (TSLA) short-term market structure and momentum using 6 technical indicators on the 15-minute timeframe. This AI agent tool is part of the Tesla Quant Trading AI Agent system. It is designed to detect intraday shifts in volatility, trend strength, and potential reversal signals. ⚠️ Not standalone. This agent is triggered via Execute Workflow by the Tesla Financial Market Data Analyst Tool. 🔌 Requires: Tesla Quant Technical Indicators Webhooks Tool Alpha Vantage Premium API Key 📊 What It Does This workflow pulls the latest 20 data points for 6 key technical indicators from a webhook-powered source, then uses GPT-4.1 to interpret market momentum and structure: Connected Indicators: RSI (Relative Strength Index)** MACD (Moving Average Convergence Divergence)** BBANDS (Bollinger Bands)** SMA (Simple Moving Average)** EMA (Exponential Moving Average)** ADX (Average Directional Index)** The output is a structured JSON with: Market summary Timeframe (15m) Indicator values 📋 Sample Output { "summary": "TSLA shows fading momentum. RSI dropped below 60, MACD is flattening, and BBANDS are tightening. Expect short-term consolidation.", "timeframe": "15m", "indicators": { "R
AI Agent To Chat With Files In Supabase Storage and Google Drive
Video Guide I prepared a detailed guide that illustrates the entire process of building an AI agent using Supabase and Google Drive within N8N workflows. Youtube Link Who is this for? This workflow is designed for developers, data scientists, and business users who wish to automate document management and enable AI-powered interactions over their stored files. It's especially beneficial for scenarios where users need to process, analyze, and retrieve information from uploaded documents rapidly. What problem does this workflow solve? Managing files across multiple platforms often involves tedious manual processes. This workflow facilitates automated file handling, making it easier for users to upload, parse, and interact with documents through an AI agent. It reduces redundancy and enhances the efficiency of data retrieval and management tasks. What this workflow does This workflow integrates Supabase storage with Google Drive and employs an AI agent to manage files effectively. The agent can: Upload files to Supabase storage and activate processes based on file changes in Google Drive. Retrieve and parse documents, converting them into a structured format for easy querying. Utilize
Adaptive RAG with Google Gemini & Qdrant: Context-Aware Query Answering
Description This workflow automatically classifies user queries and retrieves the most relevant information based on the query type. 🌟 It uses adaptive strategies like; Factual, Analytical, Opinion, and Contextual to deliver more precise and meaningful responses by leveraging n8n's flexibility. Integrated with Qdrant vector store and Google Gemini, it processes each query faster and more effectively. 🚀 How It Works? Query Reception: A user query is triggered (e.g., through a chatbot interface). 💬 Classification: The query is classified into one of four categories: Factual: Queries seeking verifiable information. Analytical: Queries that require in-depth analysis or explanation. Opinion: Queries looking for different perspectives or subjective viewpoints. Contextual: Queries specific to the user or certain contextual conditions. Adaptive Strategy Application: Based on classification, the query is restructured using the relevant strategy for better results. Response Generation**: The most relevant documents and context are used to generate a tailored response. 🎯 Set Up Steps Estimated Time: ⏳ 10-15 minutes Prerequisites: You need an n8n account and a Qdrant vector store connectio
Access Control for AI Agents (RBAC) using Airtable and Telegram
Purpose This workflow allows granular control over the access to tools connected to AI Agents (including Multi-Agent setups) using Role Based Access Control. Demo & Explanation How it works User permissions are managed in Airtable where every restricted AI tool is listed by name and connected via roles to users Requests to the Main Agent can be sent through a Telegram message (can be replaced by Whatsapp, IMAP or similar) On every request the Telegram username is used to query a list of all allowed tools which are linked in Airtable A LangChain Code node is used to compare that list against the connected tools Every tool which is not permitted to be used is being replaced by a tool, which has a status response, telling the Agent to return a message to the user, that he is not authorized to use the tool Otherwise allowed tools are passed through to the Agent, as if they were connected directly to the Agent The parameters can also be passed to a sub-agent called as a sub-workflow where permissions can be checked the same way Every response is sent back to the same Telegram conversation Setup Clone the workflow and select the belonging credentials. You'll need an OpenAI and Airtable A
Automated Facebook Comment Management with GPT-4o and LangChain
🤖 Facebook AI Agent with MCP Server – Built for Smart Engagement and Automation Hi! I’m Amanda 🥰😘 — I build intelligent automations with n8n and Make. This powerful workflow was designed to help teams automatically handle Facebook page interactions with AI. Using Meta Graph API, LangChain, MCP Server, and GPT-4o, it allows your AI agent to search for posts, read captions, fetch comments, and even reply or message followers, all through structured tools. 🔧 What the workflow does Searches for recent media using Facebook Page ID and access token Reads and extracts captions or media URLs Fetches comments and specific replies from each post Replies to comments automatically with GPT-generated responses Sends direct messages to followers who commented Maps user input and session to keep memory context via LangChain Communicates via Server-Sent Events (SSE) using your MCP Server URL 🧰 Nodes & Tech Used LangChain Agent + Chat Model with GPT-4o Memory Buffer for session memory toolHttpRequest to search media, comments, and send replies MCP Trigger and MCP Tool (custom SSE connection) Set node for input and variable assignment Webhook and JSON for Facebook API structure ⚙️ Setup Instruc
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AI-Powered Restaurant Order Chatbot with GPT-4o for POS Integration
This workflow automates the restaurant POS (Point of Sale) data management process, facilitating seamless order handling, customer tracking, inventory management, and sales reporting. It retrieves order details, processes payment information, updates inventory, and generates real-time sales reports, all integrated into a centralized system that improves restaurant operations. The workflow integrates various systems, including a POS terminal to gather order data, payment gateways to process transactions, inventory management tools to update stock, and reporting tools like Google Sheets or an internal database for generating sales and performance reports. Who Needs Restaurant POS Automation? This POS automation workflow is ideal for restaurant owners, managers, and staff looking to streamline their operations: Restaurant Owners – Automate order processing, track sales, and monitor inventory to ensure smooth operations. Managers – Access real-time sales data and performance reports to make informed decisions. Staff – Reduce manual work, focusing on providing better customer service while the system handles orders and payments. Inventory Teams – Automatically update inventory levels ba
Document Q&A Chatbot with Gemini AI and Supabase Vector Search for Telegram
This template creates a Telegram AI Assistant that answers questions based on your documents, powered by Google Gemini and Supabase. Key features include Intelligent HTML Post-processing for rich formatting in Telegram and Adaptive Message Chunking to handle long text responses. 📹 Watch the Bot in Action ▶️ Click the image above to watch a live demo on YouTube. This video provides a live demonstration of the bot's core features and how it interacts. See a quick walkthrough of its capabilities and user flow. How it works: User uploads a PDF document to a Telegram bot. The workflow processes the PDF, creates embeddings using Google Gemini, and stores these embeddings in a Supabase vector table. Users then ask questions to the bot. The workflow performs a vector search in Supabase to find relevant document chunks based on the user's query. Google Gemini uses the retrieved relevant chunks to generate an intelligent answer. The bot sends the formatted answer back to the user on Telegram, utilizing HTML markup for enhanced presentation. Set up steps: Setup should take approximately 15-20 minutes. Import the workflow into your n8n instance. Configure credentials for Telegram, Google Gemi
Generate Lessons Learned Reports from Jira Epics with AI and Google Docs
Who is this for? Jira users who want to automate the generation of a Lessons Learned or Retrospective report after an Epic is Done. What problem is this workflow solving? / use case Lessons Learned / Retrospective reports are often omitted in Agile teams because they take time to write. With the use of n8n and AI this process can be automated. What is this workflow doing Triggers automatically upon an Epic reaching the "Done" status in Jira. Collects all related tasks and comments associated with the completed Epic. Intelligently filters the gathered data to provide the LLM with the most relevant information. Utilizes an LLM with a structured System Message to generate insightful reports. Delivers the finalized report directly to your specified Google Docs document. Setup Create a Jira API key and follow the Credentials Setup in the Jira trigger node. Create credentials for Google Docs and paste your document ID into the Node. How to customize this workflow to your needs Change the System Message in the AI Agent to fit your needs.
SBuild a Personal Assistant with Google Gemini, Gmail and Calendar using MCP
Talk to Your Apps: Building a Personal Assistant MCP Server with Google Gemini Wouldn't it be cool to just tell your computer or phone to "schedule a meeting with Sarah next Tuesday at 3 PM" or "find John Doe's email address" and have it actually do it? That's the dream of a personal assistant! With n8n and the power of MCP and AI models like Google Gemini, you can actually build something pretty close to that. We've put together a workflow that shows you how you can use a natural language chat interface to interact with your other apps, like your CRM, email, and calendar. What You Need to Get Started Before you dive in, you'll need a few things: n8n:** An n8n instance (either cloud or self-hosted) to build and run your workflow. Google Gemini Access:** Access to the Google Gemini model via an API key. Credentials for Your Apps:** API keys or login details for the specific CRM, Email, and Calendar services you want to connect (like Google Sheets for CRM, Gmail, Google Calendar, etc., depending on your chosen nodes). A Chat Interface:** A way to send messages to n8n to trigger the workflow (e.g., via a chat app node or webhook). How it Works (In Simple Terms) Imagine this workflow i
CAI Speech Coach & Generator using Telegram, Open AI and Gemini
Description This n8n workflow acts as your personal AI speechwriting coach, directly accessible through Telegram. It listens to your spoken or typed drafts, provides insightful feedback on clarity, engagement, structure, and content, and iteratively refines your message based on your updates. Once you're ready, it synthesizes a brand-new speech or talk incorporating all the improvements and your accumulated ideas. This tool streamlines the speechwriting process, offering on-demand AI assistance to help you craft impactful and well-structured presentations. How it Works Input via Telegram: You interact with the workflow by sending your speech drafts or talking points directly to a designated Telegram bot. AI Feedback: The workflow processes your input using AI models (OpenAI and/or Google Gemini) to analyze various aspects of your speech and provides constructive feedback via Telegram. Iterative Refinement: You can then send updated versions of your speech to the bot, receiving further feedback to guide your revisions. Speech Synthesis: When you send the command to "generate speech," the workflow compiles all your previous input and the AI's feedback to synthesize a new, improved sp
Travel planning agent with Couchbase vector search, Gemini 2.0 Flash and OpenAI
> Disclaimer: this workflow template uses the n8n-nodes-couchbase community package. Community nodes are unverified and usage of them comes with some risks. See here for instructions on installing n8n community nodes. This template is intended for use by those interested in learning more about Agentic AI workflow development, as well as those interested in learning how to use the Couchbase Search Vector Store node for practical applications. This workflow helps users decide on travel destinations based on descriptions of several points of interest loaded into Couchbase and retrieved using Vector Search. How it Works This template contains two workflows: The Data Ingestion workflow uses the following nodes Webhook node (to listen for HTTP requests) OpenAI Embeddings node (to generate embeddings on document insertion) Note: You’ll need to configure OpenAI credentials for this node Couchbase Vector node (configured for document insertion) Default Data Loader and Recursive Character Text Splitter The Chat Application workflow uses the following nodes Chat Trigger node AI Tools Agent node connect to: Gemini (as the Chat Model, for generating responses) Note: You will have to configur
SBuild an MCP Server with Airtable
Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents using Airtable. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Airtable in your n8n workflows, this template is for you. What problem is this workflow solving? This template caters to MCP beginners seeking a hands-on example and developers looking to integrate Airtable MCP service. When integrating MCP with Airtable, manually updating AI Agents after changes to Airtable data on the MCP Server is time-consuming and error-prone. This template automates the process, enabling the AI Agent to instantly recognize changes made to Airtable on the MCP Server. In data management, for example, it ensures that record updates or additions in Airtable are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. What this workflow does This workflow focuses on integrating MCP with Airtable within n8n. Specifically, it allows you to build an MCP Server and Client using Airtable nodes in n8n. Any changes made to the Airtable Base/Table on the MCP Server are automatic
Build a Document QA System with RAG using Milvus, Cohere, and OpenAI for Google Drive
Template Description This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector embeddings using Cohere, and stores these embeddings in a Milvus vector database. Subsequently, it enables a RAG agent that can retrieve relevant information from the Milvus database based on user queries and generate responses using OpenAI, enhanced by the retrieved context. Functionality The workflow automates the process of ingesting documents into a vector database for use with a RAG system. Watch New Files: Triggers when a new file (specifically targeting PDFs) is added to a designated Google Drive folder. Download New: Downloads the newly added file from Google Drive. Extract from File: Extracts text content from the downloaded PDF file. Default Data Loader / Set Chunks: Processes the extracted text, splitting it into manageable chunks for embedding. Embeddings Cohere: Generates vector embeddings for each text chunk using the Cohere API. Insert into Milvus: Inserts the generated vector embeddings and associated metadata into a Milvus vector database. When cha
SAI Agent Web Search using SearchAPI & LLM
🤖 AI Agent Web Search using SearchApi & LLM Who is this for? This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications. What problem does this workflow solve? / Use case This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations. What this workflow does Connects an AI agent node to SearchApi.io as an integrated search tool. Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing). Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results. Ensures responses include clearly cited sources for validation and further exploration. Setup Install the SearchApi community node: Open Settings → Community Nodes inside your self‑hosted n8n instance. Fill npm Package Name with @searchapi/n8n-nodes-searchapi. Accept the risk promp
Create a Session-Based Telegram Chatbot with GPT-4o-mini and Google Sheets
How It Works This workflow creates an AI-powered Telegram chatbot with session management, allowing users to: Start new conversations** (/new). Check current sessions** (/current). Resume past sessions** (/resume). Get summaries** (/summary). Ask questions** (/question). Key Components: Session Management**: Uses Google Sheets to track active/expired sessions (storing SESSION IDs and STATE). /new creates a session; /resume reactivates past ones. AI Processing**: OpenAI GPT-4 generates responses with contextual memory (via Simple Memory node). Summarization: Condenses past conversations when requested. Data Logging**: All interactions (prompts/responses) are saved to Google Sheets for audit and retrieval. User Interaction**: Telegram commands trigger specific actions (e.g., /question [query] fetches answers from session history). Main Advantages 1. Multi-session Handling Each user can create, manage, and switch between multiple sessions independently, perfect for organizing different conversations without confusion. 2. Persistent Memory Conversations are stored in Google Sheets, ensuring that chat history and session states are preserved even if the server or n8n instance restarts.
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Automated Stock Analysis Reports with Technical & News Sentiment using GPT-4o
Stock Analysis Agent (Hebrew, RTL, GPT-4o) Overview Get comprehensive stock analysis with this AI-powered workflow that provides actionable insights for your investment decisions. On a weekly basis, this workflow: Analyzes stock data from multiple sources (Chart-img, Twelve Data API, Alphavantage) Performs technical analysis using advanced indicators (RSI, MACD, Bollinger Bands, Resistance and Support Levels) Scans financial news from Alpha Vantage to capture market sentiment Uses OpenAI's GPT-4o to identify patterns, trends, and trading opportunities Generates a fully styled, responsive HTML email (with proper RTL layout) in Hebrew Sends detailed recommendations directly to your inbox Perfect for investors, traders, and financial analysts who want data-driven stock insights - combining technical indicators with news sentiment for more informed decisions. Setup Instructions Estimated setup time: 15 minutes Required credentials: OpenAI API Key Chart-img API Key (free tier) Twelve Data API Key (free tier) Alpha Vantage API Key (free tier) SMTP credentials (for email delivery) Steps: Import this template into your n8n instance. Add your API keys under credentials. Configure the SMTP E
SEnhance Chat Responses with Real-Time Search via Bright Data MCP & Gemini AI
Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Chat Conversations with Bright Data MCP Search Engines & Google Gemini workflow is designed for users who need real-time, AI-enhanced conversations powered by live search engine results. This workflow is tailored for: Data Analysts - Who want live, search-based data fused with AI reasoning. Marketing Researchers - Seeking up-to-the-minute market or competitor insights via conversational AI. Product Managers - Exploring user needs, market trends, and competitor analysis in real time. AI Developers - Building dynamic applications that combine live search data with intelligent conversation agents. Growth Hackers - Who need fast, conversational research tools for campaign ideation, outreach, or content creation. What problem is this workflow solving? Traditional chatbots and AI systems often rely on static, outdated data. This workflow enables AI agents to fetch live search engine data and converse intelligently about it, making interactions dynamic, accurate, and highly contextual. This workflow solves the major gaps of: Outdated Knowledge: Regul
Scrape Web Data with Bright Data, Google Gemini and MCP Automated AI Agent
Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Scrape Web Data with Bright Data and MCP Automated AI Agent workflow is built for professionals who need to automate large-scale, intelligent data extraction by utilizing the Bright Data MCP Server and Google Gemini. This solution is ideal for: Data Analysts - Who require structured, enriched datasets for analysis and reporting. Marketing Researchers - Seeking fresh market intelligence from dynamic web sources. Product Managers - Who want competitive product and feature insights from various websites. AI Developers - Aiming to feed web data into downstream machine learning models. Growth Hackers - Looking for high-quality data to fuel campaigns, research, or strategic targeting. What problem is this workflow solving? Manually scraping websites, cleaning raw HTML data, and generating useful insights from it can be slow, error-prone, and non-scalable. This workflow solves these problems by: Automating complex web data extraction through Bright Data’s MCP Server. Reducing the human effort needed for cleaning, parsing, and analyzing unstructured we
Build your own N8N Workflows MCP Server
This n8n template shows you how to create an MCP server out of your existing n8n workflows. With this, any MCP client connected can get more done with powerful end-to-end workflows rather than just simple tools. Designing agent tools for outcome rather than utility has been a long recommended practice of mine and it applies well when it comes to building MCP servers; In gist, agents to be making the least amount of calls possible to complete a task. This is why n8n can be a great fit for MCP servers! This template connects your agent/MCP client (like Claude Desktop) to your existing workflows by allowing the AI to discover, manage and run these workflows indirectly. How it works An MCP trigger is used and attaches 4 custom workflow tools to discover and manage existing workflows to use and 1 custom workflow tool to execute them. We'll introduce an idea of "available" workflows which the agent is allowed to use. This will help limit and avoid some issues when trying to use every workflow such as clashes or non-production. The n8n node is a core node which taps into your n8n instance API and is able to retrieve all workflows or filter by tag. For our example, we've tagged the workflo
Perform SEO Keyword Research & Insights with Ahrefs API and Gemini 1.5 Flash
This n8n workflow automates SEO keyword research by querying the Ahrefs API for keyword data and related keyword insights. The enriched data is then processed by an AI agent to format a response and provide valuable SEO recommendations. Perfect for SEO specialists, content marketers, digital agencies, and anyone looking to gain valuable insights into keyword opportunities to boost their rankings. ⚙️ How This Workflow Works This workflow guides you through the entire SEO keyword research process, from entering the initial keyword to receiving detailed insights and related keyword suggestions. 1. 🗣️ User Input (Keyword Query) The user enters a keyword they want to research. This input is captured by the Chat Input Node, ready for analysis. 2. 🤖 AI Agent (Input Verification) The AI Agent reviews the keyword input for any grammatical errors or extra commentary. If necessary, it cleans the input to ensure a seamless query to the API. 3. 🔑 Ahrefs API (Keyword Data Retrieval) The cleaned keyword is sent to the Ahrefs Keyword Tool API. This retrieves a detailed report including metrics like search volume, keyword difficulty, and CPC. 4. 💡 Related Keywords Extraction (Using JavaScript F