HumationHuman + Automation
Back to Catalog

Automated resume job matching engine with Bright Data MCP & OpenAI 4o mini

Ranjan DailataRanjan Dailata
2888 views
2/3/2026
MarkdownConversionHTMLHTTP RequestAPI
Official Page

Automated Resume Job Matching Engine.png

Notice

Community nodes can only be installed on self-hosted instances of n8n.

Who this is for

The Automated Resume Job Matching Engine is an intelligent workflow designed for career platforms, HR tech startups, recruiting firms, and AI developers who want to streamline job-resume matching using real-time data from LinkedIn and job boards.

This workflow is tailored for:

  • HR Tech Founders - Building next-gen recruiting products

  • Recruiters & Talent Sourcers - Seeking automated candidate-job fit evaluation

  • Job Boards & Portals - Enriching user experience with AI-driven job recommendations

  • Career Coaches & Resume Writers - Offering personalized job fit analysis

  • AI Developers - Automating large-scale matching tasks using LinkedIn and job data

What problem is this workflow solving?

Manually matching a resume to job description is time-consuming, biased, and inefficient. Additionally, accessing live job postings and candidate profiles requires overcoming web scraping limitations.

This workflow solves:

  • Automated LinkedIn profile and job post data extraction using Bright Data MCP infrastructure

  • Semantic matching between job requirements and candidate resume using OpenAI 4o mini

  • Pagination handling for high-volume job data

  • End-to-end automation from scraping to delivery via webhook and persisting the job matched response to disk

What this workflow does

Bright Data MCP for Job Data Extraction

  • Uses Bright Data MCP Clients to extract multiple job listings (supports pagination)

  • Pulls job data from LinkedIn with the pre-defined filtering criteria's

OpenAI 4o mini LLM Matching Engine

  • Extracts paginated job data from the Bright Data MCP extracted info via the MCP scrape_as_html tool.

  • Extracts textual job description information via the scraped job information by leveraging the Bright Data MCP scrape_as_html tool.

  • AI Job Matching node handles the job description and the candidate resume compare to generate match scores with insights

Data Delivery

  • Sends final match report to a Webhook Notification endpoint

  • Persistence of AI matched job response to disk

Pre-conditions

  1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol
  2. You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below.
  3. You need to have the Google Gemini API Key. Visit Google AI Studio
  4. You need to install the Bright Data MCP Server @brightdata/mcp
  5. You need to install the n8n-nodes-mcp

Setup

  1. Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp
  2. Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine.
  3. Sign up at Bright Data.
  4. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.
  5. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel.
  6. In n8n, configure the OpenAi account credentials.
  7. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. MCPClientAccount.png Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>.
  8. Update the Set input fields for candidate resume, keywords and other filtering criteria's.
  9. Update the Webhook HTTP Request node with the Webhook endpoint of your choice.
  10. Update the file name and path to persist on disk.

How to customize this workflow to your needs

Target Different Job Boards

  • Set input fields with the sites like Indeed, ZipRecruiter, or Monster

Customize Matching Criteria

  • Adjust the prompt inside the AI Job Match node

  • Include scoring metrics like skills match %, experience relevance, or cultural fit

Automate Scheduling

  • Use a Cron Node to periodically check for new jobs matching a profile

  • Set triggers based on webhook or input form submissions

Output Customization

  • Add Markdown/PDF formatting for report summaries

  • Extend with Google Sheets export for internal analytics

Enhance Data Security

  • Mask personal info before sending to external endpoints

Automated Resume Job Matching Engine with OpenAI 4o Mini

This n8n workflow provides an automated engine for matching resumes to job descriptions using OpenAI's powerful language models. It's designed to streamline the recruitment process by intelligently extracting information from resumes and job postings, then identifying the best fit.

What it does

This workflow automates the following steps:

  1. Manual Trigger: Initiates the workflow upon manual execution, allowing for on-demand processing.
  2. Read/Write Files from Disk (Resume): Reads a resume file from disk, preparing its content for analysis.
  3. Read/Write Files from Disk (Job Description): Reads a job description file from disk, preparing its content for analysis.
  4. Edit Fields (Set Resume and Job Description): Organizes and sets the extracted resume and job description content into a structured format for further processing.
  5. Loop Over Items: Iterates through the prepared items (likely individual resumes or job descriptions, or pairs of them) to process them sequentially.
  6. Information Extractor: Utilizes a LangChain Information Extractor to intelligently parse and extract key entities and structured data from the resume and job description texts. This could include skills, experience, qualifications, job requirements, etc.
  7. OpenAI Chat Model: Leverages an OpenAI Chat Model (likely 4o-mini based on the directory name) to perform advanced natural language understanding and comparison between the extracted resume and job description data. This step is crucial for determining the compatibility and fit.
  8. Structured Output Parser: Processes the output from the OpenAI Chat Model, converting it into a structured format (e.g., JSON) for easy interpretation and subsequent actions.
  9. Function: Executes custom JavaScript code, likely for final data manipulation, scoring, or preparing the matching results for output.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance (self-hosted or cloud).
  • OpenAI API Key: An API key for OpenAI to access their language models. This will need to be configured as an n8n credential.
  • Local File Access: The n8n instance must have access to the local file system where the resume and job description files are stored.

Setup/Usage

  1. Import the Workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API Key as a credential within n8n.
  3. Specify File Paths:
    • In the "Read/Write Files from Disk (Resume)" node, configure the path to your resume file.
    • In the "Read/Write Files from Disk (Job Description)" node, configure the path to your job description file.
  4. Customize Information Extraction (Optional): If needed, adjust the schema or instructions within the "Information Extractor" node to fine-tune what data is extracted from resumes and job descriptions.
  5. Refine LLM Prompt (Optional): Modify the prompts or parameters within the "OpenAI Chat Model" node to optimize the matching logic and the type of comparison you want the AI to perform.
  6. Run the Workflow: Click the "Execute workflow" button in the "Manual Trigger" node to start the matching process.
  7. Review Results: The final output of the "Function" node will contain the matching results, which can then be used for further actions (e.g., sending notifications, updating a database, generating reports).

Related Templates

Dynamic Hubspot lead routing with GPT-4 and Airtable sales team distribution

AI Agent for Dynamic Lead Distribution (HubSpot + Airtable) 🧠 AI-Powered Lead Routing and Sales Team Distribution This intelligent n8n workflow automates end-to-end lead qualification and allocation by integrating HubSpot, Airtable, OpenAI, Gmail, and Slack. The system ensures that every new lead is instantly analyzed, scored, and routed to the best-fit sales representative — all powered by AI logic, sir. --- 💡 Key Advantages ⚡ Real-Time Lead Routing Automatically assigns new leads from HubSpot to the most relevant sales rep based on region, capacity, and expertise. 🧠 AI Qualification Engine An OpenAI-powered Agent evaluates the lead’s industry, region, and needs to generate a persona summary and routing rationale. 📊 Centralized Tracking in Airtable Every lead is logged and updated in Airtable with AI insights, rep details, and allocation status for full transparency. 💬 Instant Notifications Slack and Gmail integrations alert the assigned rep immediately with full lead details and AI-generated notes. 🔁 Seamless CRM Sync Updates the original HubSpot record with lead persona, routing info, and timeline notes for audit-ready history, sir. --- ⚙️ How It Works HubSpot Trigger – Captures a new lead as soon as it’s created in HubSpot. Fetch Contact Data – Retrieves all relevant fields like name, company, and industry. Clean & Format Data – A Code node standardizes and structures the data for consistency. Airtable Record Creation – Logs the lead data into the “Leads” table for centralized tracking. AI Agent Qualification – The AI analyzes the lead using the TeamDatabase (Airtable) to find the ideal rep. Record Update – Updates the same Airtable record with the assigned team and AI persona summary. Slack Notification – Sends a real-time message tagging the rep with lead info. Gmail Notification – Sends a personalized handoff email with context and follow-up actions. HubSpot Sync – Updates the original contact in HubSpot with the assignment details and AI rationale, sir. --- 🛠️ Setup Steps Trigger Node: HubSpot → Detect new leads. HubSpot Node: Retrieve complete lead details. Code Node: Clean and normalize data. Airtable Node: Log lead info in the “Leads” table. AI Agent Node: Process lead and match with sales team. Slack Node: Notify the designated representative. Gmail Node: Email the rep with details. HubSpot Node: Update CRM with AI summary and allocation status, sir. --- 🔐 Credentials Required HubSpot OAuth2 API – To fetch and update leads. Airtable Personal Access Token – To store and update lead data. OpenAI API – To power the AI qualification and matching logic. Slack OAuth2 – For sending team notifications. Gmail OAuth2 – For automatic email alerts to assigned reps, sir. --- 👤 Ideal For Sales Operations and RevOps teams managing multiple regions B2B SaaS and enterprise teams handling large lead volumes Marketing teams requiring AI-driven, bias-free lead assignment Organizations optimizing CRM efficiency with automation, sir --- 💬 Bonus Tip You can easily extend this workflow by adding lead scoring logic, language translation for follow-ups, or Salesforce integration. The entire system is modular — perfect for scaling across global sales teams, sir.

MANISH KUMARBy MANISH KUMAR
113

Track daily moods with AI analysis & reports using GPT-4o, Data Tables & Gmail

Track your daily mood in one tap and receive automated AI summaries of your emotional trends every week and month. Perfect for self-reflection, wellness tracking, or personal analytics. This workflow logs moods sent through a webhook (/mood) into Data Tables, analyzes them weekly and monthly with OpenAI (GPT-4o), and emails you clear summaries and actionable recommendations via Gmail. ⚙️ How It Works Webhook – Mood → Collects new entries (🙂, 😐, or 😩) plus an optional note. Set Mood Data → Adds date, hour, and note fields automatically. Insert Mood Row → Stores each record in a Data Table. Weekly Schedule (Sunday 20:00) → Aggregates the last 7 days and sends a summarized report. Monthly Schedule (Day 1 at 08:00) → Aggregates the last 30 days for a deeper AI analysis. OpenAI Analysis → Generates insights, patterns, and 3 actionable recommendations. Gmail → Sends the full report (chart + AI text) to your inbox. 📊 Example Auto-Email Weekly Mood Summary (last 7 days) 🙂 5 ██████████ 😐 2 ████ 😩 0 Average: 1.7 (Positive 🙂) AI Insights: You’re trending upward this week — notes show that exercise days improved mood. Try keeping short walks mid-week to stabilize energy. 🧩 Requirements n8n Data Tables enabled OpenAI credential (GPT-4o or GPT-4 Turbo) Gmail OAuth2 credential to send summaries 🔧 Setup Instructions Connect your credentials: Add your own OpenAI and Gmail OAuth2 credentials. Set your Data Table ID: Open the Insert Mood Row node and enter your own Data Table ID. Without this, new moods won’t be stored. Replace the email placeholder: In the Gmail nodes, replace your.email@example.com with your actual address. Deploy and run: Send a test POST request to /mood (e.g. { "mood": "🙂", "note": "productive day" }) to log your first entry. ⚠️ Before activating the workflow, ensure you have configured the Data Table ID in the “Insert Mood Row” node. 🧠 AI Analysis Interprets mood patterns using GPT-4o. Highlights trends, potential triggers, and suggests 3 specific actions. Runs automatically every week and month. 🔒 Security No personal data is exposed outside your n8n instance. Always remove or anonymize credential references before sharing publicly. 💡 Ideal For Personal mood journaling and AI feedback Therapists tracking client progress Productivity or self-quantification projects 🗒️ Sticky Notes Guide 🟡 Mood Logging Webhook POST /mood receives mood + optional note. ⚠️ Configure your own Data Table ID in the “Insert Mood Row” node before running. 🟢 Weekly Summary Runs every Sunday 20:00 → aggregates last 7 days → generates AI insights + emails report. 🔵 Monthly Summary Runs on Day 1 at 08:00 → aggregates last 30 days → creates monthly reflection. 🟣 AI Analysis Uses OpenAI GPT-4o to interpret trends and recommend actions. 🟠 Email Delivery Sends formatted summaries to your inbox automatically.

Jose CastilloBy Jose Castillo
105

Create, update, and get a person from Copper

This workflow allows you to create, update, and get a person from Copper. Copper node: This node will create a new person in Copper. Copper1 node: This node will update the information of the person that we created using the previous node. Copper2 node: This node will retrieve the information of the person that we created earlier.

Harshil AgrawalBy Harshil Agrawal
603