This article provides a complete, practical walkthrough of the Http Extractfromfile 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:**  
   Automated CV Screening with OpenAI and n8n: A Seamless Workflow for Smarter Hiring
   
   **Meta Description:**  
   Discover how to automate and streamline resume screening using OpenAI and n8n. This powerful workflow helps HR teams and recruiters assess candidate-job matches with AI precision and store insights in Supabase.
   
   **Keywords:**  
   CV screening automation, OpenAI resume analysis, n8n workflow hiring, recruitment automation, supabase candidate storage, resume parsing AI, HR tech tools, AI hiring assistant, GPT-4 resume evaluation, no-code hiring workflow
   
   ---
   
   ### Automated CV Screening with OpenAI and n8n: A Seamless Workflow for Smarter Hiring
   
   Hiring the right candidate is a time-consuming challenge, especially when you’re flooded with resumes. What if you could automate the first step—screening CVs for relevance, fit, and quality—using the power of AI? That’s exactly what you can do with this n8n-based workflow, which integrates OpenAI's GPT-4, PDF extraction, and Supabase to provide intelligent, structured candidate evaluations.
   
   This automation is ideal for recruiters, hiring managers, tech-focused HR teams, and recruitment agencies dealing with large application volumes. With a few customizable parameters, this workflow screens resumes based on direct job requirements, evaluates candidate strengths and weaknesses, and stores results for further use—and it’s all done automatically.
   
   ---
   
   ### Overview of the Workflow
   
   Let’s break down how this recruitment automation works step-by-step:
   
   #### 1. Manual Trigger & Input Setup
   The journey starts with a Manual Trigger in n8n that launches the workflow. Immediately after that, a "Set Variables" node assigns the key parameters:
   - A direct link to the candidate’s CV (PDF)
   - A detailed job description
   - A custom prompt that instructs OpenAI on how to perform the analysis
   - A JSON schema that defines the structure of the AI’s response
   
   This ensures that every candidate is evaluated consistently, based on the specific needs of the job.
   
   #### 2. File Download & Text Extraction
   Next, the CV is downloaded via an HTTP request to the provided file URL. Once downloaded, an “Extract Document PDF” node pulls out the raw text content from the resume file. This extracted text becomes the input to be analyzed by OpenAI.
   
   #### 3. AI-Powered Resume Analysis Using OpenAI GPT-4
   Here’s where the magic happens. The candidate’s resume text, along with the job description and recruiter prompt, is sent to OpenAI’s GPT-4 model via the chat/completions API. OpenAI acts like a hypercritical recruiter—it checks for job jumping, misalignment in skills, and any lack of qualifications.
   
   The prompt instructs GPT-4 to return a structured response with four main outputs:
   - A matching score (in 10% increments)
   - A simple summary and decision (Yes or No)
   - Key reasons why the candidate is a good fit
   - Key reasons why the candidate might not be a fit
   
   The GPT-4 model's output is JSON-formatted to conform to a predefined schema. This ensures clarity, consistency, and easy parsing in downstream applications or dashboards.
   
   #### 4. Parsing and Cleanup
   After receiving the structured result from OpenAI, the “Parsed JSON” node in n8n uses JavaScript’s JSON.parse to convert the stringified response back into proper JSON format. This clean structured data can then be passed into storage systems like Supabase or displayed in apps for recruiter review.
   
   ---
   
   ### Why This Workflow Matters
   
   Traditional resume screening forces recruiters to read through dozens—or even hundreds—of CVs manually. This leads to subjectivity, bias, and exhaustion. With this AI-driven workflow built in n8n, you gain:
   - Consistent evaluation criteria across all candidates
   - Immediate matching scores and critical insights
   - Structured feedback that plugs into ATS or CRM systems
   - Huge time savings, especially in large-scale hiring scenarios
   
   Additionally, it’s transparent. The structured schema includes reasons both for and against the candidate—so no black boxes, just explainable decisions based on inputs.
   
   ---
   
   ### Use Case: Hiring a Software Engineer for a Cutting-Edge Startup
   
   To showcase the workflow, the hiring scenario centers on a Software Engineer role at Melange, a venture-backed startup revolutionizing patent search through NLP and ML. The job listing is robust and includes required experience with cloud infrastructure, AI libraries like Langchain, and tools like Kubernetes.
   
   When a candidate submits a CV via a form or upload link, their resume is fed into this n8n workflow. The GPT-4 model evaluates their fit and returns a detailed report with matching percentage, strengths (e.g., experience with Langchain or AWS), and weaknesses (e.g., lack of NLP experience). Recruiters get a ready-to-use summary that can be stored and presented to hiring managers.
   
   ---
   
   ### Third-Party APIs Used
   
   The workflow uses the following third-party APIs:
   
   1. **OpenAI GPT-4 API**  
      Endpoint: https://api.openai.com/v1/chat/completions  
      Purpose: To evaluate the candidate’s resume in relation to the job description by returning structured insights.
   
   2. **Supabase Storage (optional/future extension)**  
      Source/URL: https://cflobdhpqwnoisuctsoc.supabase.co  
      Purpose: To host candidate CVs and possibly to store analysis results in a future step.
   
   3. **n8n ExtractFromFile Node**  
      It's an internal tool in n8n, but relies on libraries like pdf-parse to handle non-API based document extraction.
   
   ---
   
   ### How You Can Extend This Workflow
   
   This base workflow opens the door to multiple improvements:
   - Connect it to a Supabase database or Airtable to track all applications visually.
   - Automate triggering via webhook form submissions (e.g. from Typeform or Jotform).
   - Send analysis results to Slack, Email, or Notion for hiring team review.
   - Add multi-CV evaluation loops for batch processing.
   
   ---
   
   ### Final Thoughts
   
   This CV screening workflow is a powerful example of how no-code automation with AI can simplify and enhance recruitment tasks. Built with n8n and fueled by OpenAI, it turns every resume into structured, actionable insight—immediately. For busy HR teams and modern recruitment agencies, this is not just time-saving; it’s game-changing.
   
   You can preview or modify this workflow from the [5minAI community](https://www.skool.com/5minai-2861), and even watch an [8-min setup video](https://youtu.be/TWuI3dOcn0E) to get started.
   
   Hiring smarter, faster, and more fairly has never been so accessible.
   
   ---
   
   Article by:  
   Mark Shcherbakov from [5minAI](https://www.skool.com/5minai-2861)  
   Workflow link and support: [LinkedIn Profile](https://www.linkedin.com/in/marklowcoding/)

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.