This article provides a complete, practical walkthrough of the Telegram Executecommand Process Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating Telegram Weather Reports with n8n and R: A Step-by-Step Workflow Breakdown
   
   Meta Description:
   Learn how to build a Telegram bot that fetches weather data for European capitals, generates a visual image using R's ggplot2, and sends it back to users—all automated with n8n.
   
   Keywords:
   n8n Telegram bot, R scripting, ggplot2 weather chart, OpenWeatherMap API, chatbot automation, n8n workflow tutorial, Telegram weather bot, n8n data visualization, R integration, API automation
   
   Third-Party APIs Used:
   
   - Telegram Bot API (via n8n Telegram and Telegram Trigger nodes)
   - OpenWeatherMap API (to fetch weather data)
   
   Article:
   
   In the realm of automation, combining messaging platforms like Telegram with robust open-source tools such as n8n and R can lead to powerful, intelligent bots that perform tasks on your behalf. One such example is a fully automated Telegram bot that returns a visual weather report of several major European capitals via a ggplot2-generated plot. Below, we break down how the n8n workflow makes this process possible—from triggering user commands to fetching data, rendering images using R, and sending a rich response back to Telegram.
   
   Understanding the Use Case
   
   The goal of this workflow is straightforward: when a Telegram user sends a command like /getweather, the bot retrieves current weather data for key European capitals, processes it, and returns a single informative image in response. Users can also initiate conversation with /start, and the bot will introduce itself and explain its functionalities.
   
   Let’s explore how this is achieved using n8n, an open-source workflow automation tool.
   
   1. Workflow Trigger: Telegram Commands
   
   The workflow begins with a Telegram Trigger node that listens for incoming messages. The two supported commands are:
   
   - /start — to greet and inform the user about the bot’s purpose.
   - /getweather — to initiate the weather processing routine.
   
   A Switch node checks which of these commands was sent and routes the logic accordingly. If the command is unrecognized, a fallback message is sent informing the user of valid options.
   
   2. Preparing the Processing Flow
   
   Upon receiving a /getweather message, the bot replies immediately with a polite "Please wait" message before continuing. This enriches the user experience by indicating background processing is underway.
   
   3. Defining Cities and Building API Requests
   
   A JavaScript Function node, "City List", defines an array of cities, including their OpenWeatherMap city ID and relevant metadata such as name and country. Examples include:
   - London (GB)
   - Berlin (DE)
   - Paris (FR)
   - Rome (IT)
   - Belgrade (RS)
   
   The workflow then loops through this list and sends HTTP requests to the OpenWeatherMap API using each city's ID to retrieve weather data in metric units.
   
   4. Error Handling for API Calls
   
   The API response is validated in the “Any errors API?” node. In case of an issue (e.g., server error, invalid city ID), the bot sends an error message back to the user via Telegram. If the data is good, the workflow continues.
   
   5. Transforming and Exporting Data
   
   Next, the workflow reformats the JSON data returned from the weather API into a structure suitable for CSV export. This reformatted data includes:
   - City Name and Country
   - Current Temperature
   - Minimum and Maximum Temperature
   
   These records are then saved to a uniquely named CSV file, generated based on the Telegram user ID and timestamp using the DateTime object.
   
   6. Calling R to Generate the Weather Chart
   
   With the CSV file prepared, the workflow executes an R script via the "Run R script" node. This script uses ggplot2 to create a dumbbell-style comparison plot showcasing temperatures for each European capital.
   
   To see how the R script is structured, check out the public Gist:
   https://gist.github.com/ed-parsadanyan/0561cd12d545e642fcef17dcb0872b00
   
   If the R script encounters errors (e.g., dependency issues or malformed CSV), the bot notifies the user accordingly.
   
   7. Fetching and Sending the Image
   
   Assuming the R script runs successfully, the resulting PNG image is read in binary format. It is then uploaded through the Telegram API using the “sendPhoto” operation, accompanied by a caption addressing the user by name.
   
   The user receives a rich, visual weather report—without leaving Telegram or issuing further commands.
   
   8. Graceful Error Handling
   
   If anything goes wrong during processing—whether during the API call or R script execution—dedicated error-handling branches ensure that the user receives meaningful feedback.
   
   Why This Workflow Matters
   
   This setup is a terrific use case showing how modular and powerful n8n workflows can be. With the right combination of scripting, APIs, and conditional logic, you can create compact yet highly functional bots suitable for business or personal use. And because every step is modular, it's easy to swap APIs, customize city lists, or update the R script for different types of visualization.
   
   Conclusion
   
   This project beautifully showcases the synergy of low-code platforms like n8n with statistical computing tools like R. By automating the entire process—from command handling and API integration to data transformation and messaging—you can build a responsive chatbot that not only communicates with your users but processes and visualizes complex data on demand.
   
   So whether you're a data enthusiast, developer, or business owner looking to automate customer reporting, this workflow is a valuable and adaptable starting point.
   
   Want to extend this bot further? Consider adding hourly forecasts, integrating more cities, or switching from images to animations—all with the same scalable architecture.
   
   Happy automating!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.