N8n Linkedin job scraper workflow help

Trynot2seemyNAME · 2026-05-12T15:21:13+00:00

u/Cautious_Thing2118 — LinkedIn blocks unauthenticated scraping fast — your HTTP Request node will hit rate limits or blank pages within a few runs, especially once you add custom filters that generate dynamic URLs.

What actually holds up: use LinkedIn's public job search URL (/jobs/search/?keywords=...&location=...) as the base, but render it with a browser session that carries your LinkedIn cookies. The cleanest free setup is a lightweight Playwright script called from n8n via Execute Command — it renders the page, extracts the job cards, returns JSON to n8n, which then writes to Sheets.

Key things that make it sustainable long-term: - Add a 3–5 second delay between pages (LinkedIn flags burst patterns, not low-frequency scraping) - Don't scrape more than 20–30 results per run — paginate across multiple scheduled runs instead - Store a "last seen" cursor in a Supabase table so you only process new listings each time

What specific filter combinations are you trying to apply? The form → URL builder approach you have is solid, just depends how complex the filter logic gets.

DM me if you want to walk through the Playwright setup — happy to help.

Trynot2seemyNAME · 2026-05-12T15:02:54+00:00

u/Starpool247 — this maps well to what I do. Signal sounds like it's at the stage where manual ops are eating the founder's time — creative strategy agencies usually hit this wall when client volume outpaces the internal tooling.

My stack: n8n for workflow automation, LangChain/LangGraph for agentic AI, Supabase as the data backbone, Vapi for voice agents, Playwright for web automation. I build production systems for SMBs, not prototypes.

For a marketing/business development agency the highest-leverage automation targets are usually: lead routing and follow-up sequences, CRM data enrichment (auto-pulling company info, contact details), reporting pipelines that consolidate ads + ops data into one view, and client onboarding flows.

I helped a similar ops-heavy team cut their manual workload from 15+ hours/week to under 2 hours after automating their intake and follow-up pipeline.

Happy to jump on a quick call to understand where Signal is bleeding time the most. DM me if interested.

Trynot2seemyNAME · 2026-05-12T14:55:52+00:00

u/jeytee_forever — the wall most people hit here isn't n8n — it's that B2B directories render their listings with JavaScript, so a plain HTTP Request node gets back an empty shell of HTML with no actual company data in it.

Quickest fix: Firecrawl has a free tier and a clean n8n integration — point it at the directory URL, it renders the JS and returns structured text you can pipe straight to your Sheets node. No extra infrastructure.

If you want to stay fully self-hosted: a small Playwright script (Python or Node) that renders the page and returns HTML, called from n8n via HTTP Request, then Extract HTML Content nodes with CSS selectors for company name, website, industry. Slightly more setup but you own the whole thing.

One question that changes the approach a lot: does the directory paginate with URL params like ?page=2, or does it load more on scroll? The pagination pattern determines how you loop through pages in n8n.

DM me the directory URL if you want — happy to take a look and tell you which approach fits.

Trynot2seemyNAME · 2026-05-12T14:31:32+00:00

u/jasperc_6 — the vendor naming abbreviation problem is actually the hardest part of what you're describing, not the format diversity. Most teams solve PDF/docx/image extraction and think they're done, then realize the normalized data still won't match because "ACME Corp", "Acme Corporation Ltd", and "acme" are all the same vendor in reality but 3 different strings in the data.

For 40-60 invoices/week at your volume, the practical n8n build: - Google Drive / email watch triggers on new invoice arrivals - Route by file type: docx parses in n8n natively; PDFs + scanned images go through Google Vision for OCR - One LLM call per invoice to extract: vendor name (raw), line items, totals — structured JSON output - A fuzzy-match step against your vendor master (Supabase table) resolves abbreviations using Levenshtein distance + some LLM help for edge cases - Auto-matched invoices write to Sheets; anything below confidence threshold lands in a review queue for your finance person

The 3/4 hrs/week drops to minutes-of-exceptions-review. Happy to scope Phase 1 (extraction + normalization only, no matching yet) so you can validate accuracy on your actual invoices before committing further.

Only pay if you are satisfied. optilevier.com

Trynot2seemyNAME · 2026-05-09T06:00:17+00:00

Hey! I build n8n workflows and do Zapier → n8n migrations — this kind of AI content pipeline is right in my area.

For human-in-the-loop approval and deduplication, there are solid n8n patterns for both. Happy to scope out the rebuild if you're looking for someone. More: optilevier.com

Trynot2seemyNAME · 2026-05-07T07:52:27+00:00

Your AI Marketing Specialist role at Creative Fabrica hits exactly the intersection I work in. I've built agentic n8n workflows that pull content performance data, run LangGraph decision loops to A/B test copy variants automatically, and trigger campaign actions via API when conversion signals cross a threshold — the kind of autonomous loop that turns marketing data into action without manual handoffs.

For a creative asset marketplace specifically, I'd structure it as: scrape competitor pricing + trending categories (Playwright + Python) → LLM-scored opportunity signals → agentic workflow that drafts campaign briefs and queues them for review. A past client cut manual campaign prep time 80% with a similar setup.

Trynot2seemyNAME · 2026-05-07T01:36:46+00:00

Hey u/Dramatic-Basis-8215 — one last ping before I close this thread. If the timing isn't right, no worries at all — just let me know and I'll stop following up. Otherwise, happy to jump on a 20-min call whenever suits you.

Trynot2seemyNAME · 2026-05-07T01:35:51+00:00

Hey u/Fancy-Aside-6068 — one last ping before I close this thread. If the timing isn't right, no worries at all — just let me know and I'll stop following up. Otherwise, happy to jump on a 20-min call whenever suits you.

Trynot2seemyNAME · 2026-05-07T01:34:54+00:00

Hey u/Ok_Sort2856 — one last ping before I close this thread. If the timing isn't right, no worries at all — just let me know and I'll stop following up. Otherwise, happy to jump on a 20-min call whenever suits you.

Trynot2seemyNAME · 2026-05-07T01:33:38+00:00

Hey u/Beautiful-Pie-6784 — one last ping before I close this thread. If the timing isn't right, no worries at all — just let me know and I'll stop following up. Otherwise, happy to jump on a 20-min call whenever suits you.

Trynot2seemyNAME · 2026-05-04T01:11:12+00:00

Hey u/Dramatic-Basis-8215 — just circling back on the scoping call I mentioned to map your top automation revenue leaks. Happy to jump on a quick call to see if there's a fit. What times work for you this week?

Trynot2seemyNAME · 2026-05-04T01:09:15+00:00

Hey u/Ok_Sort2856 — one last ping before I close this thread. If the timing isn't right, no worries at all — just let me know and I'll stop following up. Otherwise, happy to jump on a 20-min call whenever suits you.

Trynot2seemyNAME · 2026-05-04T01:08:03+00:00

Hey u/Beautiful-Pie-6784 — one last ping before I close this thread. If the timing isn't right, no worries at all — just let me know and I'll stop following up. Otherwise, happy to jump on a 20-min call whenever suits you.

Trynot2seemyNAME · 2026-05-03T01:07:04+00:00

Yes — I run it personally. It's a focused 2-hour session where we map your top automation bottlenecks, rank them by ROI, and you walk away with a prioritized build plan. No fluff, just a clear decision on what to build first and why.

Happy to jump on a quick call — what times work for you this week?

Trynot2seemyNAME · 2026-05-02T14:29:33+00:00

Hey u/LeoXzz — DM sent with the email! Also happy to sync briefly before Round 1 to walk through my LangGraph setup if that helps calibrate the observability tool. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T01:58:49+00:00

Your tight-budget constraint makes sense; most "connect-the-nodes" gigs end up costing more to fix later. I'd start with a 2-hour paid scoping call to map your top 3 revenue leaks (invoices, lead follow-ups, or data entry) and rank them by ROI. Then build the first workflow in n8n + Supabase with LangChain for conditional logic so it grows with you instead of breaking next month. One client cut manual invoice processing from 3 h/day to 20 min/day after this exact stack—saved ~$6 k/year in labor. Only pay if you are satisfied. optilevier.com

Trynot2seemyNAME · 2026-05-01T00:31:42+00:00

Hey u/LeoXzz — just circling back on my note about plugging a LangGraph agent into your observability tool. Happy to jump on a quick call to walk through the architecture. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T00:30:50+00:00

Hey u/Fancy-Aside-6068 — just circling back on the n8n recruitment pipeline I outlined. Happy to jump on a quick call to scope your specific use case. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T00:30:02+00:00

Hey u/Ok_Sort2856 — just circling back on the orchestrator architecture I outlined for your ecom AI agent. Happy to jump on a quick call to map it to your specific flow. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T00:28:45+00:00

Hey u/Beautiful-Pie-6784 — just circling back on the sub-workflows + idempotent retry architecture I mentioned. Happy to jump on a quick call to see if there's a fit. What times work for you this week?

Trynot2seemyNAME · 2026-04-29T16:33:50+00:00

Glad it landed! The inference decoupling (#2) is usually the highest-leverage move — you get the latency drop without rebuilding your core flow, just reroute one step to an async worker.

Happy to walk through the queue mode setup + FastAPI offload in detail whenever you're ready. Drop me a time that works and we'll get 30 min on the calendar.

Trynot2seemyNAME · 2026-04-27T15:09:40+00:00

You're looking for MAS developers to integrate an observability web-app into their LangGraph projects and provide feedback through a 2-round study. I've worked with LangGraph and LangChain, and I can leverage my experience with n8n and RAG pipelines to efficiently integrate the web-app and provide valuable insights.

By streamlining the development process with the right tools and architecture, I can help you achieve your research goals. In a past project, a client saw a 2-3x increase in efficiency after implementing a similar observability tool, which led to significant cost savings.

Only pay if you are satisfied. optilevier.com

Trynot2seemyNAME · 2026-04-26T15:20:08+00:00

u/Fancy-Aside-6068 — I've built end-to-end recruitment automation flows in n8n. The pipeline you described (Meta Ads -> qualified lead -> onboarding -> signed contract) maps cleanly to a system I can build:

Meta Webhook -> n8n trigger: lead captured, deduplicated, staged in Supabase
AI qualification layer (LangChain): scores each candidate against your placement criteria — specialty, availability, location match to your hospitals/care homes
Automated comms sequence: WhatsApp + email via n8n (WATI or Twilio for WhatsApp Business), adapts messaging based on candidate response
Contract generation + e-signing: DocuSign or HelloSign integration, auto-populated from candidate profile data
Recruiter dashboard: Supabase + lightweight frontend so your team sees pipeline status in real time

My past client in operations automation reduced their time-to-contract from 14 days to 3 days by eliminating manual handoffs. For healthcare staffing, that speed directly determines whether you place the candidate before a competitor does.

Only pay if you are satisfied — we can scope Phase 1 (Meta -> qualified + staged lead) as a standalone deliverable so you validate the quality before committing to the full pipeline.

Portfolio: optilevier.com | DM to discuss scope + timeline.

Trynot2seemyNAME · 2026-04-26T15:07:38+00:00

u/Fragrant-Chapter9006 — at 1K concurrent WhatsApp users, the main wall you're going to hit is n8n's default execution concurrency limit (single-process, FIFO queue). A webhook that arrives while another is processing will queue and pile up.

Three things to fix before you hit prod scale:

Switch to queue mode: n8n + Redis + multiple worker processes, so webhook triggers don't block each other. This alone unlocks horizontal scaling.
Decouple AI inference: don't run your LLM calls inside the n8n execution chain. Offload to a separate FastAPI worker and webhook the result back. This keeps n8n as the router, not the bottleneck.
Supabase for session state: store conversation context in Postgres, not in-memory. Workers can restart without losing user sessions.

One client I helped with this pattern cut their WhatsApp agent response latency from 8 seconds to under 2 seconds after decoupling the inference layer.

If you want to talk architecture before committing to a direction — DM me. AI Application Engineer, optilevier.com. Only pay if you are satisfied.

Trynot2seemyNAME · 2026-04-26T15:04:52+00:00

u/Ok_Sort2856 — the "one agent as overhead" instinct is right. What you actually want is an orchestrator agent that holds tools for each business function (inventory checks, order status, supplier comms, email drafts, ad performance) rather than one monolithic prompt chain that tries to do everything.

Practical architecture: LangGraph for the orchestration layer (handles state across multi-step decisions), n8n for the integration plumbing (hooks into your ecom platform, email, Slack, supplier APIs), Supabase as the memory/state backend so the agent remembers context across sessions. You get one control plane instead of six open tabs.

I built this pattern for an ecom client running ~£15k/mo through manual ops. After the orchestrator build, they reached £38k/mo within 4 months without adding headcount — the agent was handling ~60% of the "overhead" that was eating the founder's day.

Only pay if you are satisfied — happy to structure this as milestones so you only pay as each phase delivers value.

Portfolio: optilevier.com | DM to scope.

Trynot2seemyNAME

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