HTTP Request Workflows
458 Ergebnisse — ausschließlich quellenverknüpfte n8n-Referenzen
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Research 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
Triage and retry failed workflow executions with Anthropic, Jira and OpenTelemetry
Quick overview This workflow triggers on n8n execution errors, uses Anthropic (Claude) to classify the failure as transient or logic, and then either retries the failed execution via the n8n API with exponential backoff or creates a Jira issue, while also sending incident telemetry to an OpenTelemetry collector. How it works Triggers automatically when an n8n workflow execution errors. Extracts key telemetry like workflow name, failing node, error message/stack, execution URL, and any detected HTTP status code. Sends the error context to Anthropic (Claude) to return structured JSON with a category, confidence, and remediation guidance. Combines the AI diagnostics with telemetry and tracks a per-incident retry counter (up to three attempts) to decide if the failure is eligible for automated retry. If the error is transient with sufficient confidence and retry budget remains, waits using exponential backoff and calls the n8n API to retry the failed execution, then records the retry outcome. If the error is not retry-eligible (or the retry budget is exhausted), creates a Jira issue with the diagnostics, execution link, and failure details. Posts an OpenTelemetry trace span to your col
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
WScore customer health and churn risk with Salesforce, Gemini, and Gmail
Quick Overview This workflow runs every minute to pull accounts from Salesforce, enrich each customer with order, payment, support, and marketing data via HTTP APIs, analyze the combined profile with Google Gemini, then update the Salesforce account and send Gmail alerts for high-risk customers and execution outcomes. How it works Runs every minute on a schedule and retrieves up to five Salesforce Account records. Processes accounts in batches and initializes a retry counter to control fault-tolerant data fetching. Fetches customer order data via an HTTP API, retrying up to three times with a wait delay and falling back to a backup HTTP endpoint if needed. Fetches payments, support tickets, and marketing engagement for the same customer via HTTP API requests and normalizes everything into a single customer profile. Sends the unified customer profile to Google Gemini to generate a JSON assessment with health score, churn risk, customer type, a summary, and a recommended action. Parses the AI output and emails a high-risk alert via Gmail when the churn risk equals HIGH. Routes by customer type, updates the Salesforce Account description with the AI summary, then logs the result and e
Send multi-carrier shipping quotes via WhatsApp with Gemini and ShipEngine
Quick overview Youtube Video: https://youtu.be/jIGBUW_1Sx0?si=BNXzk95soO0Nefan This workflow responds to incoming WhatsApp messages, validates the sender in HighLevel, uses Google Gemini with Redis memory to collect shipment details, fetches multi-carrier rates from ShipEngine, applies a commission markup, logs the quote to Google Sheets, and sends the best options back via WhatsApp. How it works Triggers when a new WhatsApp message is received. Looks up the sender’s phone number in HighLevel and stops outreach if the contact is marked Do Not Disturb. Uses a Google Gemini-powered agent with Redis chat memory to collect and confirm shipment details, creating the contact in HighLevel if needed or marking them DND if they opt out. If shipment data collection is complete, requests shipping rates from the ShipEngine Rates API for the configured carriers. Calculates a commission markup, filters for valid rates, and selects the cheapest and fastest options to format a quote message. Appends the quote text and metadata to a Google Sheets spreadsheet and sends the quote back to the customer via WhatsApp. Setup Connect WhatsApp Business credentials for both the WhatsApp trigger and send acti
WAnswer HR and IT policy questions on Telegram with OpenAI and Postgres PGVector
Quick overview This workflow builds a searchable HR policy knowledge base from a PDF and runs a Telegram helpdesk bot that answers employee questions from that content, handling both text messages and voice notes via OpenAI transcription and retrieval from a Postgres PGVector vector store. How it works Manually runs once to download an HR policy PDF over HTTP and extract its text. Splits the extracted policy text into chunks, generates OpenAI embeddings, and inserts the vectors into a Postgres PGVector store. Triggers on new Telegram messages sent to your bot. Routes incoming Telegram updates by type, sending text directly to the assistant, transcribing voice messages with OpenAI, or replying that other formats are unsupported. Uses an OpenAI chat model with Postgres-backed chat memory and a PGVector retrieval tool to answer questions grounded in the stored policy content. Sends the final answer back to the user in Telegram. Setup Add an OpenAI API key for embeddings, chat completions, and audio transcription. Set up a Postgres database with the PGVector extension enabled and add the Postgres credentials for both the vector store and chat memory. Create a Telegram bot with BotFathe
Create social posts from RSS feeds with OpenAI, Unsplash, and Buffer
Quick overview This workflow polls an RSS feed for new articles, uses OpenAI to generate platform-specific copy for X, LinkedIn, and Instagram, pulls a related random image from the Unsplash API, and creates queued posts in Buffer for each social channel. How it works Runs every hour and reads the latest items from the configured RSS feed. Sends each article title and snippet to OpenAI to generate three ready-to-post messages for X, LinkedIn, and Instagram. Requests a random related image from the Unsplash API using the article title as the search query. Creates an image post in Buffer for X using the generated Twitter text and the Unsplash image URL. Creates an image post in Buffer for LinkedIn using the generated LinkedIn text and the Unsplash image URL. Creates an image post in Buffer for Instagram using the generated Instagram caption and the Unsplash image URL. Setup Add an OpenAI API credential in n8n (or replace the included OpenAI credential) for the OpenAI node. Create a Buffer account, connect your X/Twitter, LinkedIn, and Instagram profiles, and add your Buffer API credential in n8n. Create an Unsplash developer app and replace the client_id value in the HTTP Request URL
Monitor n8n security advisories from GitHub, NVD, JPCERT and JVN with Claude and Slack
Quick overview This workflow runs hourly to monitor n8n-related security information from GitHub Advisories, NVD (NIST) CVE data, GitHub n8n security release notes, and JPCERT/JVN RSS feeds, then summarizes new findings with Anthropic Claude and posts an alert to Slack. How it works Runs every hour on a schedule. Fetches the latest items from the GitHub Advisory API, the NVD CVE API, the n8n GitHub Releases Atom feed, and the JPCERT and JVN RSS feeds. Parses the XML feeds, normalizes all sources into a single list of n8n-related security items, and removes duplicates and previously notified items using stored IDs. Checks whether any new items were found and stops if there are none. Sends the new items to Anthropic Claude to generate a concise Japanese summary including importance, affected product, and urgency. Formats a Slack Block Kit message and posts the alert to the configured Slack channel. Saves the notified item IDs to workflow static data to prevent repeat notifications in future runs. Setup Create an NVD API key and add it as an HTTP Header Auth credential named (or mapped to) NVD_API_Key with header name apiKey. Add an Anthropic API credential for the Claude model used i
Create blog drafts, images, and LinkedIn posts with Tavily, Gemini, and OpenAI
Quick overview This workflow receives a blog request via webhook, researches the topic with Tavily, generates a long-form HTML article using Google Gemini, creates two images via the kie.ai API, stores assets in Google Drive, updates a Google Sheets tracker, and notifies a Slack user with links. How it works Receives a POST webhook request containing a Google Sheets row payload with an “AI Blog Title” and related metadata. Searches the web for supporting sources using Tavily and passes the research plus the title to Google Gemini to generate the full article as HTML. Converts the generated content to HTML, creates a temporary HTML file in Google Drive, downloads it to set the correct text/html MIME type, re-uploads it as the final Drive file, and deletes the temporary file. Sends the finished article to OpenAI to generate a short LinkedIn post, then updates the matching row in Google Sheets with a Done flag, the article folder/link value, and the LinkedIn post text. Creates a cover image and an in-article infographic by submitting two text-to-image jobs to the kie.ai API, polling until each job succeeds, downloading each resulting image, and uploading both images to Google Drive. W
CScrape Tunisia IT job listings with Groq, Firecrawl, Airtable, and Resend
Quick overview This workflow runs daily or via chat to scrape Tunisia-focused IT job listings from predefined company career pages using Firecrawl, saves results into Airtable tables, and emails a daily digest through Resend, with job parsing and orchestration handled by a Groq-powered AI agent. How it works Triggers either on a daily 7AM schedule or when an n8n Chat message is received. On scheduled runs, rotates through a fixed list of companies, builds a prompt for the chosen company (including pagination rules), and sets the Airtable table name. Uses a Groq (Llama 3.3 70B) AI agent to decide which allowed career-page URLs to scrape and how to extract job listings into structured fields. Scrapes the requested career pages via the Firecrawl API and parses the returned main content into jobs with title, company, location, description, and URL. When saving is requested, uses Airtable’s API to check for the target table, create it if missing, and insert the job records. For scheduled runs, emails the AI-generated job digest to the configured recipient using the Resend API; otherwise it outputs the response for the chat session. Setup Add credentials and values for Firecrawl (API key
Run a multi-agent research and publishing pipeline with GPT-4o, Tavily and Notion
Quick overview Three specialized AI agents: Researcher, Writer, and Reviewer, collaborate autonomously to research a topic, draft content, and quality-check it through a self-correcting review loop with a circuit breaker. Approved content is published directly to Notion with full audit metadata. How it works A form submission captures the research topic, content format (blog post, exec summary, or LinkedIn post), target audience, and maximum revision cycles allowed before the circuit breaker fires. The Researcher Agent uses GPT-4o and Tavily Search to gather authoritative sources, extract key facts and contradictions, and produce a validated research brief. Search depth and result count are automatically scaled to the requested format. The Writer Agent receives the research brief and produces a structured draft in the correct format. On revision cycles it also receives the Reviewer's specific rejection notes and must address them directly. A Circuit Breaker sits between the Writer and Reviewer. If the number of Reviewer rejections reaches the user-defined maximum, the draft is force-approved and the pipeline proceeds to publishing without another LLM call. The Reviewer Agent evalua
Create SEO blog posts from YouTube videos with Gemini and Google Docs
Quick overview Submit a YouTube URL and get a publish-ready SEO blog post. Three Gemini agents research the video, write the article, and score it. Articles scoring 7+ auto-publish to Google Docs. Lower scores route to Telegram for review. How it works The workflow starts when a POST request hits the webhook with a YouTube URL, optional target keyword, and tone. A Code node extracts the video ID and validates the URL format, then an HTTP Request node calls YouTube Data API v3 to pull the video title, description, channel name, and tags. Three Basic LLM Chain nodes run in sequence, each powered by a Google Gemini Flash sub-node connected via the native ai_languageModel port. The first chain (Research Agent) receives the video context and returns a structured JSON brief containing an SEO title, four H2 headings, primary keyword, secondary keywords, key insights, target audience, and a content angle. A Code node parses this output with a JSON fallback in case Gemini adds unexpected formatting. The second chain (Writer Agent) takes the research brief and writes a complete 1,000–1,400 word blog post following strict rules: flowing paragraphs over bullet lists, primary keyword used 3–5 t
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Index and query financial documents with Qdrant and Mistral
Quick Overview This workflow ingests local financial documents into a Qdrant vector database using Mistral Cloud embeddings, keeping the index in sync when files are added, changed, or deleted. How it works Runs when you manually start the workflow (for testing) and sets the working folder path and Qdrant collection name. Routes the run based on whether a file was added, changed, or deleted. For changed or deleted files, searches Qdrant for existing vectors matching the file path and deletes the corresponding points. For added files (and changed files after cleanup), reads the file from disk and prepares a text payload that includes file metadata and contents. Splits the text into chunks, generates embeddings with Mistral Cloud, and inserts the vectors into the specified Qdrant collection. Setup Create a Qdrant API credential in n8n and ensure your Qdrant instance is reachable at the host/port used in the HTTP requests (for example, http://qdrant:6333). Add a Mistral Cloud API key credential for the Mistral embeddings node. Update the folder path in the variables (for example, /home/node/BankStatements) and make sure n8n has filesystem access to that directory. Create (or update) t
BCWScrape Hacker News hiring threads with OpenAI GPT-4o-mini and Airtable
Quick overview This workflow manually runs a scraper that finds the latest Hacker News “Ask HN: Who is hiring?” thread via the Algolia HN Search API, pulls each job comment from the Hacker News Firebase API, uses OpenAI to structure postings into fields, and saves the results to Airtable. How it works Starts when you manually execute the workflow. Queries the Algolia Hacker News Search API for recent “Ask HN: Who is hiring?” stories and keeps only the relevant thread metadata. Filters the results to the most recent thread (created within the last 30 days) and fetches the full thread from the Hacker News Firebase API. Iterates through the thread’s comment IDs, fetching each individual job comment from the Hacker News Firebase API. Extracts and cleans the job text to remove HTML/entities and normalize links and whitespace. Sends the cleaned text to OpenAI (GPT-4o-mini) to extract a structured JSON record (company, title, location, type, salary, description, and URLs). Creates a new record in Airtable for each structured job posting. Setup Add an Airtable credential and set the target base and table in the Airtable create step. Add an OpenAI credential for the GPT-4o-mini chat model.
Detect visual regressions with Apify, Google Gemini, Sheets and Linear
Quick overview This workflow generates baseline website screenshots with Apify, stores them in Google Drive, and logs the file IDs in Google Sheets, then runs scheduled visual regression checks by comparing new screenshots against the baselines with Google Gemini Vision and creating a consolidated Linear issue when changes are detected. How it works Manually starts to backfill missing baselines by reading URLs from Google Sheets that do not yet have a stored base image. For each missing baseline URL, calls Apify’s screenshot actor, downloads the rendered image, uploads it to Google Drive, and updates the matching Google Sheets row with the Drive file ID. Runs weekly on a schedule and reads the list of webpages to test from Google Sheets. For each webpage, downloads the baseline image from Google Drive and captures a fresh screenshot via Apify. Sends both images to Google Gemini (vision) to detect visual differences and returns a structured list of regressions (text, number, image, color, or position). Filters out pages with no detected changes, aggregates the remaining results, and creates a Linear issue containing the regression report. Setup Add an Apify API token as HTTP Query A
Sync Google Drive documents to Pinecone RAG with Google Gemini embeddings
Quick overview This workflow runs on a schedule to sync files from a Google Drive folder into a Pinecone vector index for RAG, extracting text from PDFs, XLSX, Google Docs, and spreadsheets, generating embeddings with Google Gemini, and tracking file state in a Google Sheets log to handle updates and deletions. How it works Runs on a schedule and fetches the current file list from a target Google Drive folder and the existing file log from Google Sheets. Compares Google Drive files with the Google Sheets log to detect new/updated files to ingest and files that were deleted from Drive. For new or updated files, deletes any existing vectors in Pinecone for the file ID, downloads the file from Google Drive, and routes it by MIME type. Extracts text from PDFs, XLSX/Google Sheets, and plain text/Google Docs files and maps the extracted content with file metadata (file ID, name, modified time, and MIME type). Chunks the document text, generates embeddings with Google Gemini, and inserts the resulting vectors and metadata into a Pinecone index. Appends or updates the Google Sheets log with the latest file metadata, and for deleted Drive files it deletes matching vectors in Pinecone and re
WTurn Telegram into a French fitness coach with Claude, OpenAI and Google Sheets
Quick overview This workflow turns a Telegram chat into a French-speaking fitness and nutrition coach that can handle text or voice messages, use Anthropic for responses, log and read training/nutrition data in Google Sheets, and schedule workouts in Google Calendar. How it works Triggers when a new Telegram message is received. Detects whether the incoming message is a voice note and, if so, downloads it from Telegram and transcribes it with OpenAI. Normalizes the user message, chat ID, and current date fields for downstream processing. Uses an Anthropic chat model with short-term session memory to generate a coaching response and decide when to call tools. Reads from and writes to Google Sheets to fetch or update the user profile, log workouts and meals, retrieve daily nutrition totals, and create or update weekly training and nutrition plans. Optionally creates a workout event in Google Calendar when the user asks to schedule a session. Sends the generated response back to the user in Telegram. Setup Create and connect credentials for Telegram, Anthropic, OpenAI (for audio transcription), Google Sheets OAuth2, and Google Calendar OAuth2. Replace YOUR_SHEET_ID_HERE and select the
Create vertical AI videos from web articles with OpenAI, Seedance and Blotato
Quick overview This workflow accepts an article URL from Telegram, extracts the page text, uses OpenAI to generate a Seedance 2.0 video prompt plus platform-specific captions, creates a vertical video via the AtlasCloud Seedance API, then publishes it to TikTok, Instagram, and YouTube through Blotato and confirms back on Telegram. How it works Receives a message in Telegram containing a web article URL. Fetches the web page HTML, strips it to plain text, and truncates the extracted content for prompting. Uses OpenAI to turn the URL and extracted text into a Seedance 2.0 text-to-video prompt plus TikTok and Instagram captions and a YouTube title and description. Submits the generated prompt to the AtlasCloud Seedance 2.0 generateVideo API with the configured duration, resolution, and FPS. Polls the AtlasCloud prediction endpoint on a wait interval until the video status returns completed/succeeded. Publishes the resulting video URL to TikTok, Instagram, and YouTube via Blotato using the generated captions and YouTube metadata. Sends a Telegram confirmation message that includes the video title and the final video URL. Setup Create a Telegram bot with @BotFather, add Telegram credent
Run a multi-tool Telegram AI assistant with Groq Llama 3.3
Quick overview This workflow turns a Telegram bot into a personal assistant J.A.R.V.I.S - powered by Groq (Llama 3.3 70B) and a LangChain agent with chat memory, using tools for web search, weather, news, Wikipedia lookups, calculations, date/time, jokes, and URL reading. How it works Triggers when a new Telegram message is received by your bot. Sends the message text to a LangChain agent running on Groq’s chat model, using the Telegram chat ID to keep a rolling memory of the conversation. When needed, the agent calls external tools such as Tavily (web search), OpenWeatherMap (weather), NewsAPI (news), Wikipedia, JokeAPI, and a URL fetcher to gather or summarize information. For math and date/time questions, the agent runs built-in code tools to compute results or return the current date and time. Returns the agent’s final response to the same Telegram chat. Setup Create a Telegram bot with @BotFather, add a Telegram API credential in n8n, and select it in both the Telegram Trigger and Telegram send-message step. Add a Groq credential in n8n and ensure the Groq chat model is selected in the workflow. Replace placeholders with your API keys: YOUR_TAVILY_API_KEY (Tavily), YOUR_OPENWE