This article provides a complete, practical walkthrough of the Http Extractfromfile Process 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 Scalable Recruitment Workflow
   
   Meta Description:
   Discover how to automate your CV screening process using OpenAI, n8n, and Supabase. This low-code workflow helps HR professionals analyze resumes, score candidates, and streamline hiring decisions with AI-powered insights.
   
   Keywords:
   CV screening automation, resume parsing, OpenAI resume analysis, n8n workflow, recruiting automation, Supabase, GPT CV scoring, HR tech, low-code recruiting tools, AI recruitment software
   
   Third-Party APIs and Services Used:
   
   - OpenAI API – for analyzing resumes, generating fit scores, and summarizing candidate suitability.
   - Supabase – used for storing resumes and structured responses from OpenAI (based on sticky notes, even if not represented in full logic).
   - n8n – not a third-party API but the core low-code automation platform hosting and executing the workflow.
   
   Article:
   
   Automated CV Screening with OpenAI and n8n: A Scalable Recruitment Workflow
   
   In the age of automation and talent wars, recruiters face an overwhelming volume of resumes to process and evaluate. To solve this modern challenge, low-code tools like n8n—combined with powerful large language models like OpenAI—offer a practical solution for automating the initial stages of candidate screening. By extracting, analyzing, and summarizing key candidate information using AI, recruiters and HR teams can focus on decision-making rather than tedious, manual reviews.
   
   In this article, we explore a powerful CV screening workflow built with n8n, OpenAI, and Supabase. Created by Mark Shcherbakov from the 5minAI community, this automation is designed for tech-driven hiring managers, recruitment agencies, and HR professionals looking to scale their recruiting process quickly and intelligently.
   
   Use Case: Automating the First Step in Talent Evaluation
   
   This workflow is designed for one clear goal: analyze a candidate’s resume against a specific job description and automatically return a concise evaluation. The scoring includes:
   
   - A percentage match (in 10% increments),
   - A short, plain-English summary of the candidate’s overall fit,
   - Reasons for the candidate’s suitability,
   - Reasons the candidate may not be a good fit.
   
   This makes it especially helpful for companies screening hundreds of applicants and needing a fast, standardized evaluation method powered by OpenAI's GPT-4o Mini model.
   
   Workflow Overview
   
   Let’s break down how this n8n workflow operates step-by-step:
   
   1. Manual Trigger & Input Initialization
   
   The workflow starts with a Manual Trigger node, ideal for testing or triggering manually through the n8n UI. Immediately after, it uses a Set node to define key variables:
   - A direct file URL to a resume PDF
   - A detailed job description
   - A structured evaluation prompt for OpenAI
   - A JSON schema defining the format of the desired AI response
   
   2. Resume Download and Extraction
   
   Using the built-in HTTP Request node in n8n, the workflow downloads the candidate’s resume from the provided file_url. The document is then passed through the “Extract Document PDF” node, which pulls out the raw readable text from the resume.
   
   This text acts as the primary data payload to be analyzed later by OpenAI.
   
   3. OpenAI Analysis: GPT-Powered Candidate Evaluation
   
   Once the text is extracted, the main innovation kicks in: a customized HTTP Request sends the resume and job description to OpenAI's Chat Completions API (GPT-4o Mini).
   
   The prompt is tailor-made for a recruiter persona and instructs the AI to play the role of a detailed and strict talent assessor. The AI is asked to:
   - Compare the candidate’s background to the job description
   - Offer a percentage match
   - Summarize their strengths and weaknesses
   - Use clear and concise language for easier HR review
   
   The prompt ensures the AI returns its evaluation in a strict, structured JSON format using a schema. This prepares the data for storage and future usage.
   
   4. Parsing and Structuring the AI Response
   
   After OpenAI responds, the following node, “Parsed JSON,” properly converts the raw AI message into structured n8n data. This makes it readable, usable, and exportable to other systems, such as a front-end database or CRM like Supabase.
   
   5. Optional: Saving to Supabase (Not Implemented in Flow, But Documented)
   
   While the actual implementation of data storage isn't shown in the working nodes, sticky notes in the flow suggest that Supabase can be used to store the final JSON analysis output. This allows seamless retrieval, dashboard integration, or syncing with a talent management system.
   
   Why This Workflow Is a Game-Changer
   
   For recruiters drowning in emails and PDFs, this automation offers enormous value. It provides:
   - Speed: Reduces resume processing to under a minute per candidate.
   - Consistency: Ensures every résumé is judged by the same logical standard.
   - Scalability: Easily expand to thousands of candidates with minimal infrastructure changes.
   - Insight: Extracts actionable feedback for hiring teams to decide whether or not to proceed.
   
   All of this is made possible using open-source and freemium services like n8n (for orchestration), OpenAI (for intelligence), and Supabase (for data storage).
   
   Conclusion
   
   The integration of resume parsing, AI-powered analysis, and automated data structuring enables a faster, fairer recruiting workflow. With this approach, HR and recruitment teams can quickly identify top applicants, flag red flags, and make data-informed decisions—effortlessly.
   
   Whether you're a solo HR manager at a startup or a recruiter handling high-volume tech hiring, this intelligent screening system can be an indispensable addition to your hiring toolbox.
   
   Want to try building it yourself? Head over to the 5minAI community for detailed tutorials, or simply duplicate the workflow into your own n8n instance.
   
   Final Pro Tip: Replace the resume URL and job description in the Set node, and the system does the rest!
   
   —
   
   Made by Mark Shcherbakov from the 5minAI Community  
   Join us at 5minAI to bootstrap smarter workflows in minutes.
   
   Resources:
   - Video tutorial (8 mins): How to Set Up This Workflow
   - GitHub / n8n flow (分享链接 if available)
   - 5minAI Community: https://www.skool.com/5minai-2861
   - Mark Shcherbakov on LinkedIn: 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.