Block 1 - OpenRouter Chat Model
- Type / Role
- @n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
- Config choices
- Version 1
This workflow is provided as-is. Please review and test before using in production.
AI Powered Job Application Screening Workflow Who Is This For? This template is ideal for: HR teams Recruiters Hiring managers Who want to automate the screening of job applicants. If you collect c...
@n8n/n8n-nodes-langchain.lmchatopenrouter, @n8n/n8n-nodes-langchain.agent, n8n-nodes-base.merge, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.noop, n8n-nodes-base.jotformtrigger, n8n-nodes-base.httprequest, n8n-nodes-base.code
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Roshan Ramani.
Original n8n.io sourceThis template is ideal for:
Who want to automate the screening of job applicants. If you collect candidate data via JotForm and want an AI‑powered, multi‑source evaluation—without manual copy‑paste—this workflow is for you.
Manually reviewing:
Is time‑consuming and error‑prone. This workflow:
Captures candidate name, email, social URLs, resume link, and job description
(Pro Tip: Add hidden field in JotForm to pass job requirements)
Parses LinkedIn, GitHub, and LeetCode handles
Uses LangChain agents on OpenRouter to:
Consolidates scores and feedback → writes new row to Google Sheets with hyperlinks and totals
job_descriptionjob_description field in JotFormUse Edit Fields node to map your form questions
Tweak agent prompts:
linkedin evaluationgithub evaluationInsert new branches (e.g., Stack Overflow) by following existing patterns
Transforms raw candidate submissions into actionable insights—evaluating applicants against specific job requirements and reducing screening time from hours to minutes with consistent, unbiased evaluations.
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 41 workflow blocks. Download the JSON for the full node graph.
| Workflow | TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using AI |
|---|---|
| Complexity | advanced |
| Nodes | 41 |
| Categories | HR, AI Summarization |
| Author | Roshan Ramani |
| Published | 21 Jun 2025 |
Use the JSON export at /data/workflows/5099/5099.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
AI Powered Job Application Screening Workflow Who Is This For? This template is ideal for: HR teams Recruiters Hiring managers Who want to automate the screening of job applicants. If you collect c...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your HR, AI Summarization use case.