This article provides a complete, practical walkthrough of the Noop Telegram Automate Triggered 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:  
   Building a Telegram Moderation Bot with n8n and Google Perspective API
   
   Meta Description:  
   Learn how to automate moderation on Telegram by building a smart bot using n8n, Telegram Trigger, and the Google Perspective API to detect and respond to toxic messages.
   
   Keywords:  
   Telegram moderation bot, n8n workflow, Google Perspective API, Telegram bot automation, profanity filter, AI content moderation, Telegram AI bot, toxic comment detection
   
   List of Third-Party APIs Used:
   
   1. Telegram Bot API
   2. Google Perspective API
   
   Article:
   
   In today’s digital age, moderating online communities is an essential yet challenging task. Toxic language, identity attacks, and threatening messages can disrupt healthy conversation and discourage engagement. To tackle this challenge, developers are turning to automation to help manage and maintain community guidelines. In this article, we demonstrate how to build a smart Telegram moderation bot using n8n—a powerful workflow automation tool—and Google’s Perspective API, which analyzes the toxicity of text in real time.
   
   Overview
   
   This bot automatically monitors a Telegram group or channel, evaluates new messages using Google Perspective API, and takes action when a message contains toxic language. Specifically, the bot will reply to the sender with a warning whenever profanity exceeds a certain threshold. All of this is accomplished through a simple n8n workflow, requiring no custom code.
   
   Let’s break down how it works.
   
   Workflow Summary
   
   The n8n workflow is composed of five interconnected nodes:
   
   1. Telegram Trigger — Listens for incoming messages.
   2. Google Perspective — Analyzes message toxicity.
   3. IF Node — Conditionally checks profanity score.
   4. Telegram Message — Responds if the message is toxic.
   5. NoOp — A placeholder for clean messages.
   
   Step-by-Step Walkthrough
   
   1. Telegram Trigger
   
   The workflow starts with a Telegram Trigger node. This node listens to incoming Telegram messages in a specific group or channel. It reacts to events like new messages, edited messages, or channel posts. Once triggered, it passes the message content to the next node.
   
   Key configuration:
   - Listens to: message, edited_message, channel_post, edited_channel_post
   - Connected using Telegram Bot credentials.
   
   This ensures that all interactions in the group are monitored, whether they are posted or edited.
   
   2. Google Perspective API
   
   The next stop is the Google Perspective node. This node sends the text from each incoming message to the Perspective API, which evaluates it using machine learning models. We request analysis on three toxicity attributes:
   - identity_attack
   - threat
   - profanity
   
   In this workflow, we specifically use the profanity score to determine whether a message breaks community standards.
   
   Key configuration:
   - Language: English (en)
   - Attribute Scores returned: Identity_attack, Threat, Profanity
   - Credentials: Google OAuth2 setup for Perspective API access.
   
   3. IF Node: Checking for Toxicity
   
   Once we have the toxicity scores back from Perspective, we use an IF node to examine the result. This condition checks if the profanity score exceeds a certain threshold—in this case, 0.7 (70% likelihood of the message being profane).
   
   Condition logic:
   - If PROFANITY score > 0.7 → True path
   - Else → False path
   
   Why 0.7? While subjective, this threshold helps strike a balance between tolerating casual language and intercepting highly offensive content.
   
   4. Telegram Response
   
   If the IF node evaluates to true—meaning the message is detected as likely profane—the bot sends a warning reply to the specific user who sent the message.
   
   Sample bot response:
   “I don’t tolerate toxic language!”
   
   The message also replies directly to the offending message using Telegram's "reply_to_message_id" option, providing context and encouraging accountability.
   
   Configuration highlights:
   - Text: Warning message
   - Chat ID and Message ID dynamically pulled from the trigger
   - Telegram credentials reused for message delivery
   
   5. NoOp Node
   
   If the profanity score is under the threshold, the message is simply ignored, and the NoOp node is used to represent this outcome. It doesn’t perform any action; it just acts as a placeholder to keep the workflow valid and logically complete.
   
   Benefits of This Workflow
   
   This simple yet powerful automation offers multiple benefits:
   
   - Real-time Moderation: Responses happen instantly after each message is posted.
   - Consistency: No user biases or subjective judgment—moderation is based on machine-learning scores.
   - Scalable: Can monitor groups of any size without human intervention.
   - Extensible: Easily add more conditions, such as checking for threats or identity-based attacks.
   - No Coding Required: Visual workflow builder makes it accessible to non-developers.
   
   Limitations and Considerations
   
   - False Positives: Like any automated moderation system, false positives or negatives can occur. Adjusting the profanity threshold can help.
   - Contextual Nuance: AI cannot always capture tone, sarcasm, or cultural variations in language.
   - Language Support: This example is limited to English. Additional language support may require model tuning or alternative analysis.
   
   Conclusion
   
   Using n8n alongside Google Perspective API allows you to build a functional and intelligent Telegram moderation bot. With no coding required and scalable logic, this setup empowers community managers to maintain respectful and healthy communication spaces on their Telegram channels or groups.
   
   Whether you manage a small online community or operate a public-facing support channel, integrating automated moderation via n8n is a smart and efficient way to keep the conversation civil.
   
   Get started by signing up with n8n, setting up your Telegram bot, and requesting access to Google’s Perspective API today.
   
   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.