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:**  
   Automated Research Report Generation Using n8n, OpenAI, Google, and PDF Automation
   
   **Meta Description:**  
   Learn how a powerful n8n workflow combines OpenAI, Google Search, News APIs, Wikipedia, and PDF conversion tools to automatically generate comprehensive research reports and distribute them via Gmail and Telegram.
   
   **Keywords:**  
   n8n workflow, research report automation, OpenAI GPT-4, Google Search API, Wikipedia API, NewsAPI, PDFShift, Google Drive, Telegram automation, Gmail automation, research bot, AI-powered reporting, GPT-4o mini, automated research pipeline, AI report generator, scholarly insights, automatic PDF creation
   
   ---
   
   ### Automating the Research Report Process with n8n, GPT-4, and PDF Tools
   
   In today's fast-paced digital environment, the ability to generate, synthesize, and distribute high-quality research quickly is a game-changer. This is exactly what a highly sophisticated n8n workflow achieves by orchestrating a blend of advanced AI models, data aggregation tools, and automated document generation—all without writing a single manual report.
   
   Let’s break down how this advanced research bot workflow works and the key components that make it uniquely powerful.
   
   ---
   
   ### Purpose of the Workflow
   
   At its core, the workflow automates the process of creating detailed, customized research reports. Users simply input a topic—such as "The Best AI Models 2025"—and receive a professionally formatted PDF report sent directly to their email and Telegram chat.
   
   This is achieved through sequential stages: query validation and refinement, web and academic data collection, report formatting, PDF generation, and final delivery.
   
   ---
   
   ### Step-by-Step Breakdown
   
   #### 1. Input Validation and Query Enhancement
   The workflow begins by validating that the input query is meaningful (at least three characters long). A dedicated Query Refiner powered by OpenAI GPT-4o improves the original topic by generating five research-friendly queries that explore various aspects like applications, ethical concerns, latest developments, and industry impact.
   
   #### 2. Multi-Source Research Aggregation
   The enhanced queries are handed off to the “ResearchBot” AI agent. This agent conducts broad-spectrum searches using multiple data sources:
   
   - Wikipedia for foundational knowledge.
   - Google Custom Search API for general web information.
   - NewsAPI for news articles published between 2024 and 2025.
   - SerpAPI querying Google Scholar for academic insights from 2020 to 2025.
   
   These sources are collected and parsed into a standardized JSON structure containing:
   
   - Introduction  
   - Summary  
   - Key Findings  
   - News Highlights  
   - Scholarly Insights  
   - Wikipedia Summary  
   - Source URLs  
   
   #### 3. Data Formatting and Parsing
   The extracted research elements are split, parsed, and reassembled into a single structured dataset. This enables further processing such as date formatting, capitalization of acronyms (like “AI”), and proper HTML escape sequences to safeguard against rendering issues.
   
   #### 4. PDF Generation
   A custom HTML report template is created using the aggregated research. This highly stylized HTML document includes:
   
   - A cover page
   - Section headlines
   - Page numbers and date stamps
   - Highlighted key insights
   - Hyperlinked sources
   
   The template is then passed to the PDFShift API, which converts the HTML into a downloadable, printable PDF.
   
   #### 5. Delivery & Storage
   Once the PDF is generated:
   
   - It's sent via Telegram using the Telegram Bot API.
   - A customized email with the PDF attached is sent using Gmail OAuth2.
   - The research metadata (topics, queries, sources, timestamp) is stored in Google Sheets.
   - Optional storage to Google Drive is implemented based on folder presence.
   
   ---
   
   ### Benefits of This Automation
   
   - ⚡ Speed: Entire cycle from topic entry to delivery takes minutes.
   - 🎯 Accuracy: GPT-4-backed research retains contextual relevance.
   - 📄 Quality: Professionally styled PDF makes it boardroom-ready.
   - 📤 Ease of Distribution: Instant email and Telegram delivery.
   - 📚 Versatility: Works across topics—tech, health, finance, more.
   
   ---
   
   ### Third-Party APIs Used
   
   This workflow heavily relies on the following APIs:
   
   | API/Service       | Purpose                                                                 |
   |-------------------|-------------------------------------------------------------------------|
   | OpenAI API (GPT-4o) | Natural language processing, topic refinement, summarization           |
   | NewsAPI            | Fetching real-time news articles related to the topic                 |
   | Wikipedia API      | Retrieving structured topic summaries from Wikipedia                  |
   | Google Custom Search API | Searching across general web sources                          |
   | SerpAPI (Google Scholar) | Discovering academic papers and citations                     |
   | PDFShift API       | Converting HTML documents to PDF                                       |
   | Gmail API (OAuth2) | Sending the final research report via email                            |
   | Telegram Bot API   | Sending the PDF document via Telegram chat                             |
   | Google Sheets API  | Logging research metadata for reporting and tracking                   |
   | Google Drive API   | (Optional) Storing PDFs in a specific Drive folder                     |
   
   ---
   
   ### In Summary
   
   This n8n-based pipeline exemplifies the practical power of low-code automation. Blending cutting-edge AI from OpenAI with real-time data sources, scholarly research, and automated delivery, this research workflow saves hours of manual work—transforming a single-line query into a polished document suitable for professional use.
   
   If you need to produce research reports on the fly, streamline your information gathering, and deliver results seamlessly, this workflow could be your new AI-powered research assistant.
   
   ---
   
   Interested in implementing this system for your projects or organization? The workflow is scalable, customizable, and extendable—fitting perfectly into any research-driven environment.

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.