This article provides a complete, practical walkthrough of the Telegram Splitout Automate 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:
   Building an AI-Powered Chatbot for Confluence Pages Using n8n and GPT-4
   
   Meta Description:
   Learn how to automate Confluence page retrieval and enable an AI chatbot to provide human-like responses using n8n, OpenAI, and the Confluence REST API. This low-code solution allows effortless knowledge querying from your Confluence documents.
   
   Keywords:
   n8n, Confluence API, OpenAI, GPT-4, chatbot, automation, REST API, no-code, markdown, knowledge base, Atlassian, AI chatbot workflow, conversational AI, internal documentation automation
   
   Third-Party APIs Used:
   
   - Atlassian Confluence Cloud REST API (v2)
   - OpenAI API (GPT-4)
   - Telegram Bot API
   
   — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
   
   Article:
   
   Creating an AI Chatbot to Answer Questions from Confluence Pages with n8n and OpenAI
   
   In today’s fast-paced work environment, accessing internal documentation quickly can significantly improve productivity. What if your team could query documentation in natural language and get accurate responses instantly? With a combination of Confluence’s REST API, OpenAI’s GPT-4, and n8n’s low-code automation capabilities, that’s now possible — and easier than you might think.
   
   In this article, we explore an n8n workflow that fetches content from Confluence pages, processes it into markdown, applies GPT-4 for response generation, and even delivers the answer directly via Telegram. Let’s break down this AI-powered automated assistant that bridges your internal documentation and conversational interfaces.
   
   📘 Overview: The Use Case
   
   This workflow creates a chatbot using OpenAI’s GPT-4 that answers user questions based on specific Confluence pages. The bot:
   
   - Listens for incoming chat messages.
   - Retrieves and converts the requested Confluence page content.
   - Parses the content into markdown.
   - Provides AI-generated, context-restricted answers.
   - Sends the response via Telegram.
   
   Perfect for internal support or onboarding, this solution can accelerate knowledge delivery and reduce employee friction when navigating documentation.
   
   🧠 Tools and APIs Involved
   
   To build this workflow, we leveraged the following:
   
   1. Atlassian Confluence Cloud REST API
      - To authenticate, search for documents, and retrieve page content.
   2. OpenAI GPT-4 API
      - For generating answers based only on the provided context.
   3. Telegram Bot API
      - Optional, used here to send a response to users via chat.
   
   Low-code scripting is done using n8n, which acts as the automation orchestrator and integrates these external services smoothly.
   
   🔐 Confluence API Authentication
   
   Confluence requires basic authentication using an email and API key combination, Base64-encoded, and passed via HTTP headers. The StickyNote documentation embedded in the workflow provides simple shell/Powershell commands to create the auth string. Once completed, this credential is stored in n8n as a genericHeaderAuth credential named “Confluence API.”
   
   💡 Workflow Breakdown
   
   Let’s drill into the key components of the automation:
   
   1. Trigger: Chat Message Received
      - The flow begins when a message containing a question is received by the AI assistant.
   
   2. Globals: Defining Page IDs
      - A set node predefines which Confluence page IDs can be referenced (e.g., page_id_n8n, page_id_tekla). These can represent different technical documentation pages.
   
   3. Search and Fetch Page Data
      - An HTTP request node Search By ID accesses page metadata using Confluence’s search API via CQL (Content Query Language). The page schema is extracted, including URL, title, and ID.
   
   4. Retrieve Page Content
      - Using the page ID, another HTTP node Confluence Page Storage View fetches detailed content from the /pages/{id} endpoint with the format parameter set to storage. This provides rich content HTML.
   
   5. Convert HTML to Markdown
      - To make the content easier to consume for GPT-4, the HTML is transformed to markdown via the HTML to Markdown node.
   
   6. AI Processing with GPT-4
      - The markdown content is fed to a custom GPT-4 conversational agent using the Langchain-based “AI Agent” node in n8n.
      - Memory is enabled using a “Window Buffer Memory” node to maintain session context.
      - A prompt instructs the agent to answer only using the provided documentation — if the answer is out of scope, the reply defaults to “I don’t know.”
   
   7. Send the Answer
      - The response is formatted and delivered either back into the chat interface or over Telegram using the Telegram Bot API.
   
   📎 Telegram Integration (Optional)
   
   To make the process truly interactive, this workflow includes a Telegram channel output, pushing the AI’s answer directly to users. Whether for internal chatbots or OPS notifications, this makes information retrieval effortless and immediate.
   
   🧪 Benefits and Use Cases
   
   - Internal Documentation Access: Ask natural questions and get automated answers from internal guides.
   - Developer/IT Support: Automate repetitive queries about deployment steps, tool usage, or onboarding.
   - Productivity Bot: Combine with Slack, Discord, or any platform to build an intelligent internal assistant.
   
   🚀 Next Steps
   
   To adapt this for your environment:
   
   1. Replace example.atlassian.net with your own Confluence instance.
   2. Use your Confluence API token and email to create new API credentials.
   3. Add your own page IDs to the Globals node.
   4. Customize the chat input channel or interface as needed (could use Slack, webhooks, or live apps).
   5. Switch OpenAI GPT-4 to a model that fits your pricing and latency needs (e.g. GPT-3.5).
   
   💬 Final Thoughts
   
   This n8n-powered AI chatbot provides a simple, scalable way to democratize internal knowledge through intelligent automation. Within a few hours, you can convert static documentation into a responsive AI assistant that meets your team where they are — making knowledge as accessible as a text message.
   
   By combining no-code with powerful AI and document APIs, this system elegantly showcases what’s possible when automation meets information retrieval.
   
   Are you ready to turn your documentation system into a conversational powerhouse?
   
   Let’s build it.

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.