AI Agent workflows
518 results — all source-linked n8n references
Run Bunker Guard NPC chat and RAG lore checks with Google Gemini and Google Drive
Quick overview This workflow powers dynamic character interactions for investigative games. It ingests lore from Google Drive into a vector database, using Google Gemini agents to generate daily briefings, spawn NPCs, and roleplay interrogations via a webhook. How it works Runs on a manual trigger to download a lore PDF from Google Drive, split it into chunks, create Google Gemini embeddings, and store them in an in-memory vector store for retrieval. Receives a POST webhook request from the game client containing game state and an action value (briefing, init, or chat). Routes the request based on action to either generate a morale-based daily intel briefing, create a new NPC profile for the day, or generate an in-character reply to the player’s interrogation message. When factual lore is needed (routes, locations, prices, rules, professions), the Gemini agent queries the Database_Bunker vector-search tool to ground the response in the ingested PDF. Maintains short conversation context per player using a buffer memory keyed by playerId for ongoing chat and NPC interactions. Returns the agent output to the caller as clean JSON via the webhook response. Optionally, triggers on an n8n
Mask PII in hosted chat with OpenAI, Claude, and Saklam Bridge
Quick overview This workflow provides a public hosted chat that routes messages through the Saklam Bridge to mask PII on-premises before calling models like Claude, GPT, or Mistral, then restores the original values in the response. How it works Exposes a public hosted chat interface and receives each user message. Sends the message to an AI agent that uses an OpenAI-compatible chat model endpoint. Routes the model request through the self-hosted Saklam Bridge, which masks PII locally before forwarding the prompt to your chosen LLM provider. Receives the provider’s response back through Saklam Bridge, which unmasks the placeholders to restore the original PII. Returns the final unmasked answer to the hosted chat user. Setup Deploy the Saklam Bridge container in the same network as n8n and configure it with your Saklam license and your LLM provider API key. Create an OpenAI API credential in n8n that points its Base URL to your Saklam Bridge endpoint (for example, http://saklam-bridge:4000/v1) and uses the Bridge master key as the API key. Select that OpenAI credential in the chat model node and set the model name to the provider model you want to use (for example, claude-haiku-4-5,
Re-engage dormant real estate leads with Google Sheets, Gemini and Gmail
Quick Overview This workflow runs daily to find dormant real estate leads in Google Sheets, uses Google Gemini to draft personalized winback emails, sends them via Gmail, updates the lead records, and then posts a run summary digest to Gmail and optionally to Slack. How it works Runs every day at 8am on a schedule. Pulls all lead records from a Google Sheets spreadsheet. Filters to leads that have an email address, are not marked “Do Not Contact,” and have not been contacted in at least 30 days. Processes each dormant lead in turn by prompting Google Gemini to generate a short re-engagement email subject and body based on the lead’s name, property interest, and days since last contact. Sends the generated message to the lead via Gmail and updates the corresponding row in Google Sheets (for example, tracking status and re-engagement attempt count). Aggregates the run results and sends a summary digest to a specified Gmail address and to a Slack channel. Setup Connect credentials for Google Sheets, Gmail, and Google Gemini (PaLM) API. Select the Google Sheets document and sheet name for both the lead fetch and lead update steps, and ensure your sheet includes fields like Email, Statu
Analyze LinkedIn profiles and posts with Apify and OpenAI GPT-4
Quick overview This workflow collects a LinkedIn profile URL via an n8n form, scrapes the profile’s recent posts with Apify, analyzes the profile and each post using OpenAI models, and stores the resulting profile summary and per-post insights in n8n Data Tables. How it works Receives a LinkedIn profile URL when a user submits the built-in n8n form. Extracts the LinkedIn username from the URL and runs an Apify actor to scrape the profile’s posts into a dataset. Generates an overall competitive-intelligence style profile analysis from the scraped posts using an OpenAI chat model and parses the result into structured JSON. Saves the structured profile summary, themes, messaging patterns, and content ideas to an n8n Data Table. Loops through each scraped post, stores the raw post text and engagement metrics in an n8n Data Table, and marks it as unprocessed. Analyzes each post with an OpenAI chat model to extract structured insights (topic, takeaway, engagement insight, and suggested use) and updates the corresponding Data Table row as processed. Setup Configure the n8n Form Trigger and share the form URL with users who will submit LinkedIn profile links. Add an Apify API token in n8n
WRun a Telegram productivity assistant with OpenRouter, Google Calendar, and Todoist
Quick overview This workflow turns Telegram into a personal assistant that manages your Google Calendar and Todoist, sends daily briefings, and alerts you when meetings are booked or things break. How it works Morning briefing - Every day at a set time, the workflow checks your Google Calendar for today's events and your Todoist for open tasks, then sends you two Telegram messages: a calendar rundown and a task summary grouped by project with priority flags. Cal.com booking alerts - When someone books, reschedules, or cancels a call on Cal.com, or a meeting ends, the workflow catches the webhook and sends you a Telegram notification with the attendee's name, email, meeting title, and time. Talk to your assistant on Telegram - Message the bot with text or a voice note. Voice notes get transcribed automatically. Your message goes to an AI agent that can read, create, update, or delete calendar events, and create, find, update, complete, or delete Todoist tasks, all through natural conversation. The agent replies back in the same chat. Error alerts - If any part of the workflow fails, you get a Telegram message with the failed node, the error, and a link to the execution, so you catch
Screen candidate CVs with Google Gemini, Google Sheets, and Gmail
Quick overview This workflow collects candidate applications via an n8n form, extracts text from uploaded PDF CVs, compares each CV to a job description stored in Google Sheets using Google Gemini (via OpenRouter), logs screening results back to Google Sheets, and creates a Gmail draft email for HR review. How it works Candidate submits the n8n-hosted form with their details and a PDF CV. Two operations run in parallel: CV text is extracted from the uploaded PDF, and the matching Job Description is fetched from Google Sheets using the role the candidate entered. Gemini Flash evaluates the CV against the JD and returns a structured recommendation: Strong Interview, Interview, Borderline, or Do Not Proceed, along with a score, matched skills, gaps, and a plain-text summary. The full screening record is logged to a Screening Results tab in Google Sheets. A Gmail draft is created for HR to review and send: a shortlist notification for Interview and above, a rejection for Borderline and below. Setup Open the Config node and fill in spreadsheet_id, jd_sheet_gid, results_sheet_gid, and company_name. Create two tabs in your Google Sheet: Job Description (columns: job_id, title, department,
WMatch packing list PDFs to purchase orders with docling-serve and Ollama
Quick overview This workflow checks an SFTP “pending” folder for packing list PDFs, converts them to OCR text with docling-serve, and uses Ollama (Gemma) to extract structured line items and match them to a purchase order, logging results and moving files to completed or error folders. How it works Runs every hour and lists files in the SFTP pending folder. Filters for PDFs, downloads each file from SFTP, and logs a PENDING status to an n8n Data Table. Uploads the PDF to docling-serve for OCR conversion (RapidOCR, English) and stops with an error status if conversion fails. Sends the converted Markdown text to an Ollama Gemma chat model to extract a normalized packing-list JSON (PO number, parties, and line items). Loads the matching purchase order data and cross-references PO vs packing-list SKUs and quantities to determine whether the document matches. If SKU count matches but SKU text differs, uses an Ollama-based healing step to detect likely OCR-misread SKUs, applies the corrections, and re-runs the cross-reference. Writes the final status (COMPLETED or ERROR) to the n8n Data Table and moves the PDF in SFTP into a folder named after the status (for example, completed/ or error
Summarize Zoom meeting recordings in Notion with Smallest.ai and Gemini
Quick Overview This workflow receives Zoom recording webhooks, downloads the meeting recording, transcribes it with Smallest.ai, summarizes it with a Google Gemini-powered AI Agent, and saves the summary and action items as a new page in a Notion database. How it works Receives a POST webhook from Zoom and checks whether the event is Zoom’s endpoint URL validation challenge. For URL validation events, computes the Zoom CRC HMAC SHA-256 token and responds with the plain and encrypted tokens to verify the endpoint. For recording events, immediately acknowledges Zoom with a 200 OK response, then extracts the meeting topic, metadata, and an audio recording download URL from the webhook payload. Downloads the recording file from Zoom using the provided download token for authorization. Sends the downloaded audio to Smallest.ai to generate a full transcription. Uses a LangChain AI Agent with Google Gemini to produce a short meeting summary and a list of action items from the transcript. Creates a new page in a Notion database using the meeting topic as the title and the generated summary as the page content. Setup Create and configure a Zoom Event Notifications webhook (for a recording-c
Reply to Telegram voice messages with Smallest.ai and Google Gemini
Quick Overview This workflow listens for Telegram bot messages, detects voice audio, downloads the audio file, transcribes it with Smallest.ai, and uses a Google Gemini–powered AI agent to draft a helpful reply that is sent back to the same Telegram chat. How it works Triggers when a new message is received by your Telegram bot. Checks whether the incoming message contains a voice/audio attachment. Downloads the audio file from Telegram using the message’s file ID. Sends the audio to Smallest.ai Speech-to-Text to generate a transcription. Passes the transcription and chat ID to a LangChain AI agent backed by Google Gemini to generate a suggested reply. Sends the formatted response back to the originating Telegram chat. Setup Create a Telegram bot with BotFather, then add your Telegram credentials/token to the Telegram Trigger, file download, and Telegram reply tool nodes. Add your Smallest.ai API credentials to the transcription step. Add your Google Gemini (Google AI) credentials to the chat model used by the AI agent. Start the workflow and message your Telegram bot with a voice note to test the end-to-end transcription and reply.
Qualify and route free signups with Twain, Claude, Slack, HubSpot and Gmail
Quick overview This workflow receives free-signup events via webhook, enriches the contact with Twain research (and optional Findymail LinkedIn lookup), uses Anthropic Claude to score ICP fit, then routes outcomes to Slack and, for high-fit leads, to HubSpot and Gmail. How it works Receives a POST webhook request for a new signup and authenticates it via a required header. Normalizes the signup fields (uid, email, name, LinkedIn URL) and deduplicates records across runs to avoid reprocessing the same user. If the signup has no LinkedIn URL but has an email, optionally calls Findymail reverse-email lookup to resolve a LinkedIn profile URL and merges it back into the contact. If the lead has neither a work email nor a LinkedIn URL, posts a “skipped” message to Slack and stops. Sends the contact to the Twain Generate/Research API to create research and add the contact to the specified Twain campaign, and posts a Slack alert if the research request fails. If Twain flags a hard disqualification (persona mismatch or incomplete profile), posts a disqualification message to Slack; otherwise, Anthropic Claude evaluates the research against the defined ICP tiers and returns a structured fit
Screen and route candidate CVs with OpenRouter, Google Drive, Sheets, and Gmail
Quick overview This workflow receives candidate CVs via a webhook, extracts text from PDF, DOCX files, looks up the matching job description in Google Sheets, runs a three-agent evaluation using OpenRouter models, then uploads the CV to Google Drive, logs results to Google Sheets, and sends or drafts emails in Gmail. How it works Receives a CV submission via a POST webhook that includes candidate details, an applied job ID, and a CV file. Normalizes the intake data, timestamps the submission, and fetches the matching job description from Google Sheets using the applied job ID. Extracts CV text by either parsing the PDF directly or converting a DOCX/DOC to a Google Doc in Google Drive and exporting it as plain text. Detects whether the CV is Vietnamese or not and uses OpenRouter (Qwen for Vietnamese or Google Gemini for others) to parse the CV into a structured Candidate Profile JSON. Uses OpenRouter (Anthropic Claude Sonnet) to compare the Candidate Profile against the job description, then generates a hiring-manager style review and recommendation based on the ATS evaluation. Uploads the original CV to a recommendation-based Google Drive folder, logs the full screening record (inc
Generate and publish AI short videos from Telegram using Blotato and OpenAI
Quick overview This workflow accepts a content idea via Telegram, uses OpenAI to write a short cross-platform caption, generates a vertical AI video with Blotato, and publishes it to TikTok, Instagram, Facebook, and X (Twitter), then confirms the result back in Telegram. How it works Receives a message (your content idea) via a Telegram bot trigger. Uses the incoming text to set the video prompt and posting options like TikTok privacy and the AI-generated flag. Sends the prompt to OpenAI to generate a single short caption (max 280 characters) to reuse across all platforms. Creates a 9:16 AI video in Blotato using a selected template and predefined template inputs (for example profile name and image). Waits and repeatedly checks Blotato until the video render status is marked as done. Publishes the rendered video and the generated caption to TikTok, Instagram Reels, Facebook Reels, and X (Twitter) via Blotato. Sends a Telegram message confirming the video has been published. Setup Install and enable the Blotato community node in your n8n instance (or enable Verified Community Nodes on n8n Cloud) and add a Blotato API credential. Connect your Blotato account to TikTok, Instagram, Fac
CCreate daily ClickUp task reports with OpenRouter o3-mini
Quick overview This workflow runs each morning, pulls all tasks from a specified ClickUp list, merges their names and descriptions, and uses an OpenRouter-hosted OpenAI chat model to generate a daily report. It then creates a new ClickUp list for the current run and adds the report as a task. How it works Runs every morning at the configured hour using a schedule trigger. Fetches all tasks from the specified ClickUp list. Merges the retrieved tasks into a single formatted text block for summarization. Checks that task data exists and, if not, creates an error list in ClickUp and stops with an error. Creates a new ClickUp list named with the current date/time to store the report. Sends the merged task text to an AI Agent backed by an OpenRouter (OpenAI) chat model to generate the report content. Creates a ClickUp task named “Report” in the newly created list and stores the AI-generated summary in the task description. Setup Add ClickUp OAuth2 credentials in n8n and select the correct Team/Space/Folder/List details in the ClickUp nodes (including the source list to read from). Add an OpenRouter credential and choose the chat model in the AI Chat Model node. Set the schedule time (hou
Route patients to the best nearby hospital using Google Maps, GPT-4.1 and Twilio
Quick overview This workflow accepts emergency location requests (or runs on a 5-minute schedule), finds and ranks nearby hospitals/clinics using Google Places, capacity data, and Google Distance Matrix, generates a routing message with OpenAI, sends the recommendation via Twilio SMS, logs the event to Google Sheets, and returns a JSON response. How it works Receives an emergency request via a webhook POST (or runs every 5 minutes on a schedule) and normalizes latitude, longitude, severity, phone number, and a request ID. Validates the GPS coordinates, maps severity to a numeric score, and sets a search radius, returning an error response if the location is invalid. Queries the Google Places API for nearby hospitals and clinics, then merges and deduplicates the results into a single facility list. Looks up each facility’s ED capacity metrics from a capacity API and calculates real-time driving time to each facility with the Google Distance Matrix API. Scores and ranks facilities using a severity-adjusted composite score (travel time, available beds, wait time, and ED diversion penalties) and selects the best match plus two alternatives. Uses OpenAI to generate a calm routing messag
Validate startup ideas and generate business plans with Claude and SendGrid
Quick overview This workflow receives a startup idea via webhook, uses Anthropic Claude to expand it, run market research, competitor analysis, and demand validation, then generates a structured business plan, emails it through SendGrid, logs a summary row to Google Sheets, and returns a JSON response to the webhook caller. How it works Receives a startup idea via an HTTP POST webhook request. Normalizes the submission details (idea, founder, industry, budget) and uses Anthropic Claude to expand the idea into a structured concept with an initial score. Filters out ideas below the viability threshold (ideaScore < 4) to stop low-quality submissions early. Uses Anthropic Claude to produce market sizing and persona research, then parses the results into structured market data. Uses Anthropic Claude to analyze direct and indirect competitors and compute a competitive position score. Uses Anthropic Claude to validate demand signals, risks, and go-to-market recommendations, then calculates a composite score and PROCEED/VALIDATE_FURTHER/PIVOT_OR_ABANDON recommendation. Uses Anthropic Claude to generate a 10-section business plan, appends a tracking row to Google Sheets, emails the report v
CResearch Reddit trends and publish LinkedIn posts with Gemini and PostPeer
Quick overview This workflow runs daily, scrapes a selected subreddit with Apify, researches sources via Brave Search and Jina Reader, and uses Eden AI (DeepSeek) plus Google Gemini to draft a LinkedIn post with an image. It logs state in Google Sheets/Drive, sends approval emails, and publishes via PostPeer. How it works Runs daily on a schedule, loads Global Constants, and picks an ACTIVE subreddit row from Google Sheets using a random ID. Scrapes recent posts from that subreddit using an Apify Reddit scraper, then normalizes, scores, and deduplicates candidates. Uses an Eden AI (DeepSeek) agent to vet the candidates and either selects one post idea or stops and records a NO_VIABLE_IDEA entry in Google Sheets and emails the rationale. For a selected idea, a second Eden AI (DeepSeek) agent generates a research brief and up to five Brave Search queries, then Brave Search and Jina Reader collect and extract a small set of non-social sources. A third Eden AI (DeepSeek) agent writes the LinkedIn draft and an accompanying image prompt, then Google Gemini generates the image. Uploads the image to Google Drive, writes the draft and metadata to a Google Sheets post log, and emails a two-s
WReview GitHub pull requests with Mistral and send decisions to Slack
Quick overview This workflow receives GitHub webhook events for pull requests, pushes, and branch/tag creates, filters out actions from the repo owner, and posts alerts to Slack (optionally Discord). For pull requests, it uses Mistral to generate an AI review from the PR diff and supports a human approval form. How it works Receives a GitHub webhook request and filters for push, create, and pull_request events coming from non-owner accounts (and non-deleted branches). Routes the event by type and, for push and create events, extracts key details and posts a notification to Slack (optionally Discord). For pull request opened, reopened, or synchronize events, extracts PR metadata such as title, URLs, branch refs, and change counts. Uses Mistral (via an AI agent) to fetch the PR unified diff over HTTP and generate a structured JSON review, optionally consulting existing GitHub PR reviews for synchronize re-reviews. Posts the AI recommendation to Slack with a link to a hosted PR review form (optionally Discord) and waits up to 24 hours for a reviewer decision. Applies the reviewer decision back to GitHub by approving the PR and merging it, requesting changes with a comment, or closing
Predict sprint slip risk in Jira with OpenAI via OpenRouter and Slack alerts
Quick Overview This workflow runs on a weekday morning schedule (or manually) to pull the active Jira sprint and its issues, compute sprint health metrics, use an OpenRouter-hosted OpenAI model to assess slip risk, and post a motivating alert to a Slack channel when the sprint is behind. How it works Runs every weekday at 08:00 (or when executed manually). Calls the Jira Software Cloud Agile API to fetch the board’s active sprint and all issues in that sprint, including status, labels, and story points. Calculates sprint timing, completion percentage, slip margin, velocity gap, and a list of potentially blocked issues based on labels or status. Sends the computed metrics to an OpenRouter (OpenAI) chat model to return a structured JSON risk assessment (on_track, at_risk, or critical) with recommendations and tickets to watch. Parses the model output into JSON and merges it with the computed sprint metrics for reporting. If the risk level is not on_track, a second OpenRouter (OpenAI) agent drafts a team-focused status update and posts it to the configured Slack channel. If the risk level is on_track (or parsing fails and defaults to on_track), the workflow ends without posting to Sla
Generate vendor negotiation strategies from pricing data with GPT-4 and SendGrid
Quick overview This workflow ingests vendor pricing via a webhook or a weekly schedule, analyzes variance against historical and market benchmarks, uses OpenAI to draft negotiation strategies for high-priority cases, then emails recommendations through SendGrid and appends the analysis to a Google Sheets tracker. How it works Receives vendor pricing data from a POST webhook endpoint or runs every Monday at 8:00 to process a scheduled input. Normalizes incoming fields (vendor, pricing, benchmarks, volume, currency, renewal date, and notes) into a consistent schema. Calculates price variance versus historical and market benchmarks, estimates potential savings, derives a confidence score, and flags whether the opportunity is high priority. Filters to only high-priority opportunities and waits briefly to help manage API rate limits. Uses OpenAI to generate a structured, under-300-word negotiation strategy including tactics, target and walk-away prices, projected savings range, and risk flags. Formats the AI output into a report, emails it via the SendGrid API, and appends the analyzed record to a Google Sheets tab for tracking. Setup Add an OpenAI API credential for the OpenAI Chat Mod
Generate rich character bibles for writers with GPT-4.1 and Google Sheets
Quick overview This workflow accepts character details via webhook or manual trigger, uses OpenAI to generate psychology, backstory, and emotional arc drafts in parallel, then synthesizes them into a structured “Character Bible” JSON response and optionally appends a log row to Google Sheets. How it works Receives a POST request on an n8n webhook (or runs manually) with character parameters like name, age, archetype, world setting, story role, traits, and constraints. Normalizes the incoming fields, validates the archetype, and derives genre tags from the world setting using Python logic. Sends the enriched character inputs to OpenAI three times in parallel to generate a psychology profile, a backstory, and an emotional arc document. Combines the three AI documents into a single context object and passes it to OpenAI again to synthesize a unified Character Bible with defined sections. Parses the Character Bible into structured JSON fields (identity snapshot, personality core, backstory highlights, emotional arc, voice, and story hooks) and adds metadata like request ID and timestamp. Returns the completed character profile as a JSON webhook response and optionally appends a summary
Audit website SEO with Groq, Google PageSpeed and Google Sheets
Quick overview This workflow audits a submitted website URL by extracting on-page SEO signals from the HTML, pulling Google PageSpeed Insights scores, and using Groq (Llama 3.3 70B) to generate a scored SEO report, then logs the results to Google Sheets and returns the audit via webhook. How it works Receives a POST webhook request containing a JSON body with a target url to audit. Fetches the page HTML for the provided URL and stops with an error response if no HTML is returned. Extracts on-page SEO signals from the HTML (title, meta description/keywords, headings, image alt coverage, links, canonical, robots, viewport, Open Graph/Twitter tags, schema, word count, and language). Calls the Google PageSpeed Insights API for mobile to retrieve performance, accessibility, SEO, and best-practices results. Combines the extracted on-page data with the PageSpeed results and sends it to Groq via a LangChain agent to produce a strict, structured JSON audit with scores, findings, fixes, and top priorities. Parses and validates the AI JSON output, appends/updates the audit record in Google Sheets, and returns the final audit JSON to the webhook caller (or returns a 400 error if parsing fails)
Generate personalized cold email drafts from Google Sheets with Groq and Tavily
Quick overview This scheduled workflow reads leads from Google Sheets, researches each company with Tavily Search, uses Groq (Llama 3.3 70B) to generate a short personalized cold email, and saves the result as a Gmail draft while updating the lead status back in the sheet. How it works Runs on an hourly schedule trigger. Reads lead rows from Google Sheets and keeps only rows that have an email address and are not marked with status "sent". Processes leads one at a time and uses the Tavily Search API to fetch a brief company overview based on the lead’s company name. Sends the lead details plus the Tavily research summary to Groq (Llama 3.3 70B) to generate a JSON-formatted email subject, body, and personalization hook. Parses and validates the AI output to ensure a subject and body are present, and routes failures to an error log. Creates a Gmail draft with the generated subject and body, updates the corresponding row in Google Sheets with the new status and generated fields, and waits briefly before continuing to the next lead. Setup Create and connect a Google Sheets OAuth2 credential, then select the target spreadsheet and sheet in both the Google Sheets read and update steps. C
Manage KlickTipp contact tags with GPT-5-mini via chat input
Quick overview This workflow uses an n8n chat trigger, OpenAI (gpt-5-mini), and KlickTipp community nodes to interpret chat instructions and create, apply, or remove tags for an existing KlickTipp contact, returning a structured JSON result. How it works Receives a chat message containing a contact email address and explicit tag add/remove instructions. Uses OpenAI (gpt-5-mini) to extract the email plus the requested tags to apply and/or remove, enforcing strict rules to avoid inventing tags. Looks up the contact in KlickTipp by email and reads the contact’s currently assigned tag IDs. Retrieves the full tag list from KlickTipp and creates any missing tags that were explicitly requested for applying. Applies the resolved tag IDs to the contact in KlickTipp when an explicit apply action is present and the contact does not already have them. Removes specific tag IDs (or all currently assigned tag IDs when requested) from the contact in KlickTipp when an explicit remove action is present. Returns a structured JSON response summarizing what was requested, which tags were created, and which tag IDs were applied or removed. Setup Add KlickTipp credentials for the community KlickTipp node
Enrich KlickTipp contact fields using OpenAI GPT-5.5 and context
Quick Overview This workflow receives an email and a context block, uses OpenAI to extract and validate enrichment details against the existing KlickTipp subscriber record, and then updates approved mapped data fields in KlickTipp. How it works Receives an incoming request from another workflow containing a contact email address and a free-text context block. Fetches the existing subscriber record from KlickTipp using the provided email. Uses OpenAI to extract enrichment attributes from the context, compare them to the current KlickTipp values, and decide final values using a confidence threshold for overwriting non-empty fields. Updates the KlickTipp subscriber with the final approved values for the mapped fields (for example name, company, address, phone numbers, website, birthday, and social profile URLs). Setup Add KlickTipp credentials and ensure the target subscriber fields (including any custom fields for LinkedIn, Instagram, Facebook, and job title) exist in your KlickTipp account. Add an OpenAI API key credential and select the model you want to use in the OpenAI chat model configuration. Ensure the incoming payload sent to this workflow includes at least email and context