This article provides a complete, practical walkthrough of the Http Telegram Automation 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:  
   Automate Image and Text Generation with Telegram and NeurochainAI Using n8n
   
   Meta Description:  
   Learn how to integrate Telegram with NeurochainAI through an n8n workflow to build a chatbot capable of responding with AI-generated images and text, using models like LLaMA and Mistral.
   
   Keywords:  
   n8n, Telegram bot, NeurochainAI, image generation, text generation, AI integration, chatbot automation, LLaMA model, Mistral model, API workflow, automation bot, n8n workflow
   
   Third-Party APIs Used:
   
   1. Telegram Bot API
   2. NeurochainAI API (https://ncmb.neurochain.io/tasks)
   
   Article:
   
   Building an AI-Powered Chatbot Using n8n, Telegram, and NeurochainAI
   
   Combining automation and artificial intelligence allows developers to create intuitive and engaging user experiences. In this tutorial, we'll walk through an n8n workflow that integrates Telegram with NeurochainAI's API to create a chatbot capable of producing both AI-generated text responses and high-quality images on command. Whether you're building a creative project or simply exploring workflow automation, this setup demonstrates the powerful synergy between messaging platforms, AI models, and low-code solutions like n8n.
   
   Overview of the Workflow
   
   The core idea of the n8n workflow is simple: Take messages from a Telegram bot, process them through NeurochainAI APIs based on user intent (text vs image), and send the corresponding AI-generated content back to the user automatically. The system responds to different triggers including user commands like /flux for image generation or direct messages for general AI text responses.
   
   Step-by-Step Explanation of the Workflow
   
   1. User Interaction via Telegram  
      At the core of this workflow is the Telegram Trigger node, which listens to all incoming messages sent to your Telegram bot. Once a message is received, it is validated through a Switch node to decide how to handle the input.
   
      - If the message starts with /flux, it is treated as an image generation request.
      - If the message mentions the bot (e.g., @NCNAI_BOT) or is sent in a private chat, it is treated as a text prompt for chat-based responses.
   
   2. Preprocessing User Input  
      A simple Code node is used to sanitize the incoming message by removing the /flux command prefix and excess whitespace. This ensures clean input for the NeurochainAI API.
   
   3. Visual Feedback to Users  
      Before sending the message to the AI service, a Telegram node sends an emoji (⌛) to indicate the request is being processed. This enhances the user experience by providing visual feedback immediately.
   
   4. Communicating with NeurochainAI API  
      - For image tasks, the NeurochainAI - Flux node sends a POST request to https://ncmb.neurochain.io/tasks/tti with parameters like the cleaned prompt, image size, quality, and chosen image model.
      - For text generation, the NeurochainAI - REST API node sends the user’s input to the endpoint https://ncmb.neurochain.io/tasks/message aligned with chosen text-based LLMs such as Meta-Llama-3.1 or Mistral.
   
   5. Handling AI Responses and Errors  
      Once a response is received:
      - Text responses are parsed and sent back to the user using a Telegram node with proper formatting.
      - Image links returned as JSON strings are extracted, downloaded with an HTTP Request node, and posted as Telegram photos with captions.
      - Error output is intelligently managed. If an AI model fails to respond or the prompt is invalid, specific error messages or retry options are shown via inline keyboards.
   
   6. Customization and Flexibility  
      The workflow is highly customizable. Sticky Notes in the n8n interface provide instructions for:
      - Replacing the default AI models with alternatives from the NeurochainAI dashboard.
      - Configuring your own Telegram Bot.
      - Managing API keys and plugin authentication for secure operation.
   
   Supported AI Models
   
   The workflow supports a various models, which users can specify in HTTP request bodies:
   
   - Meta-Llama-3.1-8B-Instruct-Q6_K.gguf
   - Mistral-7B-Instruct-v0.2-GPTQ-Neurochain-custom
   - ingredient-extractor-mistral-7b
   - flux1-schnell-gguf (for image generation)
   
   These models can be customized and scaled based on the requirements, offering developers maximal control over output style, quality, and response structure.
   
   Error-Proofing and Retry Mechanism
   
   The workflow includes a sophisticated fallback system using Switch nodes and Telegram alerts. If the AI service times out or sends an error message (e.g., “No response from worker” or “Prompt too short”), the user is notified, and optional retry buttons are presented to minimize frustration.
   
   Final Thoughts
   
   By combining Telegram, NeurochainAI, and n8n, this automated chatbot showcases how anyone—developer or non-developer—can build advanced AI-powered applications. Whether you're looking to generate creative content, build conversational agents, or streamline support via Telegram, this workflow is a powerful foundation that can be expanded with minimal code.
   
   Not only does it automate responses, but it intelligently routes different kinds of tasks to specific AI endpoints, ensuring that users get appropriate outputs for both text and image content. Plug in your API keys, set your models, and you’re ready to launch a smart assistant directly in your Telegram ecosystem.
   
   With a user-friendly UI, modular structure, and clearly labeled steps, this n8n workflow is a compelling blueprint for building cost-efficient AI integrations on messaging platforms.
   
   🧠💬📸 Build smart, talk smarter—right from your Telegram chat.

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.