This article provides a complete, practical walkthrough of the Code Readpdf Send 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:
   Automate PDF Processing from Gmail to Google Drive with OpenAI Using n8n
   
   Meta Description:
   Discover how to automate email PDF attachment filtering and upload specific documents like payslips or invoices to Google Drive using OpenAI and n8n. Boost productivity by streamlining manual email processing.
   
   Keywords:
   n8n workflow, Gmail automation, PDF processing, OpenAI API, Google Drive upload, email attachment filter, automate payslips, invoice management, document automation, n8n OpenAI integration
   
   Third-Party APIs Used:
   
   - OpenAI API (GPT for content analysis)
   - Gmail API (for receiving emails and downloading attachments)
   - Google Drive API (for uploading matched PDF documents)
   
   Article:
   
   Automatically Filter and Route PDF Attachments from Gmail to Google Drive Using OpenAI and n8n
   
   In a world overflowing with digital paperwork, managing email attachments efficiently is a game changer. Whether it's invoices, receipts, payslips, or contracts, manually sifting through your inbox can consume valuable time and result in cluttered document storage. Fortunately, n8n — an extendable workflow automation tool — makes it easy to automatically process specific PDF attachments in Gmail using OpenAI and send them directly to a chosen Google Drive folder.
   
   In this article, we’ll break down how an n8n-powered workflow can intelligently scan PDF attachments, understand their content using OpenAI’s language model, and route the relevant documents to your cloud storage — all without any manual intervention.
   
   Use Case: Route Payslips Automatically
   
   The sample workflow focuses on identifying “payslip” PDFs sent to your Gmail inbox and uploading matched files to a preset Google Drive folder. However, thanks to its modular design, you can easily customize the keyword (e.g., “invoice” or “contract”) or target folder as needed.
   
   How the Workflow Works (Step-by-Step)
   
   1. Email Monitoring via Gmail Trigger
   The automation begins with the n8n Gmail trigger node, On email received, which checks for new emails every minute. It is configured to fetch email attachments upon detection.
   
   2. Check for Attachments
   An If node (Has attachments?) validates whether the email has any attachments. If there are none, the workflow stops gracefully.
   
   3. Iterate Over Each Attachment
   A JavaScript code node (Iterate over email attachments) loops over each incoming binary file (attachment) and separates them into individual items for processing.
   
   4. Filter by File Type
   A conditional node (Is PDF) checks whether the attachment's file extension is .pdf. Non-PDF files are pushed aside to prevent unnecessary processing.
   
   5. Extract Text from PDFs
   For each identified PDF, a Read PDF node extracts the textual information embedded within the file.
   
   6. Validate PDF Content Size
   The workflow ensures that the extracted text falls within OpenAI’s token limits using a token limit check node (Is text within token limit?). This is important to prevent issues related to prompt or response size.
   
   7. Evaluate Content Relevance with OpenAI
   Now comes the AI magic. Using OpenAI’s GPT model, the OpenAI matches PDF textual content node evaluates whether the attachment’s content matches a user-defined keyword such as "payslip". This is controlled via a Configure node, which sets:
   - What keyword to look for
   - The OpenAI token limits
   - The Google Drive folder URL for uploads
   
   The prompt used for OpenAI is carefully structured:
   "Does this PDF file look like a [keyword]? Return 'true' if it is a [keyword] and 'false' if not. Only reply with lowercase letters 'true' or 'false'."
   
   This makes parsing the language model’s response trivial for downstream logic.
   
   8. Merge Textual and AI Results
   A Merge node combines the PDF data with OpenAI’s response, aligning it for the next conditional check.
   
   9. Match Verification
   An If node (Is matched) filters PDFs based on whether OpenAI’s response was "true". Non-matching files are discarded silently.
   
   10. Upload to Google Drive
   If the match condition is met, the Upload file to folder node pushes the matched PDF to a predefined Google Drive folder. The destination folder ID is parsed from a full Drive URL configured by the user.
   
   Key Customization Points
   
   Through the Configure node, users can easily change:
   - The keyword (e.g., “invoice”, “receipt”) that determines relevance
   - Target Google Drive folder
   - Max token size for OpenAI prompts/responses
   
   This makes the workflow versatile and expandable for a variety of document processing tasks.
   
   Why Use AI in PDF Filtering?
   
   Conventional automation typically uses filenames or file metadata to route documents. By employing OpenAI's advanced natural language processing capabilities, this workflow evaluates the actual contents of the documents, allowing for a higher degree of precision and flexibility. For instance, even if the filename is unclear, the AI can still understand if the body of the document contains payslip details or contract terms.
   
   Important Considerations
   
   - File Size: Large PDFs that surpass token limits are skipped. You may customize the token threshold in the Configure node.
   - AI Misclassification: There is a small chance that OpenAI may incorrectly classify a document. Manual validation is advised for critical use cases.
   - Privacy: Be cautious when sending sensitive data like payslips or contracts to external APIs like OpenAI. Always adhere to your organization's data policy.
   
   Conclusion
   
   This powerful n8n workflow showcases the synergy of OpenAI’s intelligence and the seamless automation capabilities of n8n. Automating document triage based on actual content — not just metadata — helps save time, ensures accuracy, and reduces clutter in our digital lives.
   
   Whether you're a freelancer managing invoices or a business automating HR paperwork, this system can be tailored to meet your needs with just a few clicks.
   
   This workflow was contributed by David Sha — explore more at https://davidsha.me.

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.