This article provides a complete, practical walkthrough of the Telegram Code Create 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 Resume Parsing and PDF Generation with Telegram, OpenAI, and n8n
   
   ---
   
   **Meta Description:**  
   Discover how to build a fully automated resume parser workflow using n8n, Telegram Bot, OpenAI GPT-4, and Gotenberg. Extract structured data from PDF resumes and generate beautiful PDFs on the fly.
   
   ---
   
   **Keywords:**  
   n8n workflow, resume parser, Telegram bot automation, OpenAI GPT-4, PDF generation, Gotenberg, n8n tutorial, automation tools, extract resume data, JSON resume parsing, PDF to JSON, AI resume scanning
   
   ---
   
   **Third-Party APIs Used:**
   
   1. OpenAI GPT-4 – for intelligent parsing and structuring of resume content.
   2. Telegram Bot API – to receive resumes and send back finalized PDF documents to users.
   3. Gotenberg – for powerful server-side HTML-to-PDF conversion.
   
   ---
   
   **Article:**
   
   ### Automating Resume Parsing and PDF Output with n8n, OpenAI, and Telegram
   
   In the modern job market, recruiting and staffing processes are increasingly automated. Yet, one common bottleneck remains—efficiently extracting structured, searchable data from resumes. This is where automation platforms like n8n shine.
   
   In this article, we’ll walk through an advanced yet elegantly designed n8n workflow that takes a user-submitted resume PDF file via Telegram, extracts its contents using AI, structures the data into clean JSON, transforms it into HTML, and finally generates a professional PDF—all automatically.
   
   Let’s break down the key components of this powerful workflow.
   
   ---
   
   ### 1. Collecting Resumes via Telegram Bot
   
   The journey begins when a user uploads a resume to a Telegram bot. The workflow uses the Telegram Trigger node to listen for incoming messages. It checks whether the message is a `/start` command (which it ignores) and verifies that the user is authorized to use the bot by checking the chat ID.
   
   Once authorized and a document (PDF) is received, n8n proceeds to download the file using the Telegram "Get File" node.
   
   ---
   
   ### 2. Extracting Text from PDF
   
   The downloaded resume is passed to the “Extract text from PDF” node using n8n's built-in file extractor. This generates raw, unstructured textual content from the resume, ready for parsing.
   
   ---
   
   ### 3. Parsing the Resume Using OpenAI GPT-4
   
   Here's where the magic happens. n8n interfaces with OpenAI’s GPT-4 (via the Turbo Preview model) to intelligently parse the resume's text. A specific instruction prompt asks the model to output structured JSON containing:
   
   - Personal info (name, email, GitHub, LinkedIn)
   - Employment history
   - Education
   - Projects
   - Volunteering
   - Programming and foreign languages
   
   The output is strictly parsed to match a predefined JSON Schema using LangChain’s structured output parser and an auto-fixer in case the AI deviates from the structure.
   
   ---
   
   ### 4. Formatting Parsed Data as HTML
   
   Once structured JSON data is obtained, multiple custom JavaScript code nodes run to convert each section (employment history, education, projects, volunteering, technologies, and personal info) into HTML-friendly formats.
   
   Each section is wrapped in `<b>` and `<u>` tags for titles, with appropriate `<br />` tags for line breaks and bullet points for lists. These outputs mimic a basic styled resume layout when compiled into an HTML document.
   
   ---
   
   ### 5. Merging HTML Sections
   
   To prepare for converting to PDF, all the HTML sections are merged into a single HTML body. This involves three levels of n8n’s Merge nodes:
   
   - First, combine personal info and technologies.
   - Then, combine education with employment history and projects with volunteering.
   - Finally, all are merged into a single payload using a “Set final data” node.
   
   ---
   
   ### 6. Creating a PDF Resume
   
   The final structured HTML output is base64-encoded and converted into an actual `index.html` file. Then, using Gotenberg (an open-source PDF rendering API), the HTML is converted into a polished PDF file through an HTTP Request node.
   
   This PDF reflects all the structured resume data in clean, human-readable, and printable format.
   
   ---
   
   ### 7. Sending the PDF Back to the Telegram User
   
   After the PDF is generated, the Telegram node sends it back to the user who uploaded the original resume. The bot crafts a filename using the candidate’s name (sanitized for URL safety), completing the resume transformation loop.
   
   ---
   
   ### Additional Notes and Customization
   
   - The automation was presented during an n8n Community Hangout on March 7, 2024.
   - The Gotenberg service is necessary for HTML-to-PDF conversion but can be substituted with paid options like PDFMonkey or ApiTemplate.
   - The JSON Schema used in the OpenAI response validator is highly customizable, allowing tailoring for different resume structures or company needs.
   - Source code includes sticky notes for better visualization and onboarding for maintainers.
   
   ---
   
   ### Why This Workflow Matters
   
   Automating the extraction and formatting of resume content saves countless hours otherwise spent on manual data entry. This system not only reads resumes but truly understands and structures them—enabled by the latest in natural language understanding (OpenAI) and open-source automation (n8n).
   
   Whether you're an HR tech startup, a recruiter, or simply love automation, this workflow shows what's possible when combining powerful tools in a creative, structured way.
   
   ---
   
   Want to dive deeper or try it yourself? Subscribe to the creator’s [YouTube channel](https://www.youtube.com/@workfloows) or [newsletter](https://workfloows.com) for regular automation tutorials, behind-the-scenes guides, and community insights.
   
   ---
   
   **Final Thoughts**
   
   Resume automation has long been considered a luxury or difficult task. Thanks to n8n and tools like OpenAI and Gotenberg, building an intelligent and elegant solution is now within reach—no heavy coding required.
   
   Harness this workflow and stay ahead in the future of smart, scalable recruiting.

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.