This article provides a complete, practical walkthrough of the Extractfromfile Form Automate 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:  
   Automating HR Recruitment with AI: An End-to-End Candidate Evaluation Workflow in n8n
   
   Meta Description:  
   Discover how a fully automated HR pipeline built with n8n and AI transforms candidate screening. This intelligent workflow extracts data from CVs, evaluates applicants using AI, and stores insights seamlessly in Google Sheets.
   
   Keywords:  
   n8n workflow, HR automation, AI in recruiting, CV analysis, job application automation, Google Sheets integration, OpenAI, LangChain, candidate evaluation, AI resume screening, Google Drive integration, HR tech, data extraction
   
   Third-Party APIs Used:
   
   1. OpenAI (GPT-4 via LangChain) – For text summarization, skill extraction, and candidate evaluation.
   2. Google Sheets API – To store and organize candidate data.
   3. Google Drive API – For uploading and saving CV files securely.
   4. LangChain – For structured data extraction and interaction with LLMs.
   
   ---
   
   Article:
   
   Harnessing AI Automation for Smarter HR Recruitment with n8n
   
   In today's fast-paced job market, HR departments are under increasing pressure to process large volumes of candidate applications swiftly and accurately. Manual screening of CVs can be time-consuming, prone to bias, and lacks scalability—especially for tech talent hiring where specific skills and experiences are critical. Enter n8n, the open-source workflow automation platform, with a powerful AI-driven workflow designed to revolutionize candidate evaluation.
   
   This article explores an end-to-end recruitment workflow engineered in n8n that automates CV processing, extracts relevant candidate information, evaluates applicant profiles against job requirements, and stores insights in Google Sheets—all enhanced by the intelligent capabilities of AI tools like OpenAI and LangChain.
   
   A Smarter, Faster First Step: CV Submission Automation
   
   The process begins the moment a candidate uploads their CV using a web form configured in n8n. This form collects basic information including Name, Email, and a PDF version of the CV. Once the form is submitted, the workflow automatically triggers a sequence of actions that move the CV forward in the evaluation pipeline.
   
   The uploaded resume is instantly saved to a secure Google Drive folder, ensuring long-term access and easy retrieval. This storage strategy also makes it easier for HR teams to organize applications by date and candidate name.
   
   AI-Powered Extraction of Candidate Data
   
   At the heart of the workflow lies AI-powered data parsing. Using LangChain's nodes connected to OpenAI’s GPT-4 model, the workflow extracts structured and unstructured data from the PDF resume. This includes:
   
   - Educational Qualification: An academic snapshot, including schools, degrees, and grades.
   - Job History: A summary of the applicant’s work experience.
   - Technical Skills: A bullet-point list identifying software, languages, and frameworks mastered.
   
   Simultaneously, another LangChain extractor parses personal details such as city of residence, date of birth, and contact number.
   
   Data from these two chains are merged before passing into a summarization node, which compresses the findings into a concise and conversational summary of the applicant's profile—useful for quick HR overviews.
   
   Intelligent Fit Assessment: AI as Your Virtual HR Expert
   
   The next stage of the workflow compares the candidate's summarized profile with a pre-configured job description. In this case, the target role is a full-stack web developer with knowledge of PHP, Python, and JavaScript, and based in Northern Italy.
   
   This task is handled by a dedicated LangChain node that functions as a virtual HR expert. Using intelligent prompt engineering, the model evaluates the fit between the candidate and the profile. It assigns a numerical score from 1 to 10—where 10 means the perfect candidate—and provides a detailed “consideration” that explains the rationale behind the score.
   
   Storing Insights for Easy Access
   
   The final step submits all collected and computed data to a Google Spreadsheet. Each submission logs the candidate’s name, location, birthdate, degree, work history, technical skills, AI-generated summary, HR evaluation score, and written feedback. This streamlined record-keeping serves as an always-updated database for hiring managers and removes the need for manual tracking.
   
   Benefits: Efficiency, Accuracy, and Scalability
   
   This n8n-powered HR automation pipeline eliminates countless hours of admin work while ensuring that decision-making is data-driven and consistent. Key benefits include:
   
   - Immediate CV processing and storage via Google Drive.
   - Structured and unstructured data extraction using OpenAI via LangChain.
   - AI-driven fit score and justifications by simulating expert HR analysis.
   - Centralized insights with Google Sheets for easy cross-team access.
   
   Final Thoughts
   
   The integration of AI into HR functions through platforms like n8n is a game-changer. By automating repetitive tasks and elevating candidate screening with smart insights, HR teams can focus more on meaningful engagement and strategic hiring decisions.
   
   Whether you’re a startup looking to scale hiring or an established company optimizing your recruitment funnel, this n8n workflow is a practical blueprint for next-gen HR automation.
   
   Ready to streamline your hiring? Start building your own intelligent recruitment pipeline with n8n today!

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.