Block 1 - Website overview
- Type / Role
- @n8n/n8n-nodes-langchain.chainLlm - chainLlm
- Config choices
- Version 1.7
This workflow automatically researches a lead’s website, understands what the business actually does, and generates a highly personalized cold outreach subject line and icebreaker using AI. Instead...
@n8n/n8n-nodes-langchain.chainllm, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.set, n8n-nodes-base.httprequest, n8n-nodes-base.splitinbatches, n8n-nodes-base.stickynote, n8n-nodes-base.airtable, n8n-nodes-base.if
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Muhammad Faaz Khan.
Original n8n.io sourceInstead of guessing or relying on shallow placeholders, it scrapes real website content, summarizes it intelligently, and feeds that context into an LLM to produce outreach that feels relevant and human.
If a website is broken or unreachable, the workflow safely flags it so you can identify faulty leads early.
no content, helping you identify broken or invalid leads.The workflow starts by pulling leads from Baserow, including company name and website URL.
The lead’s website is fetched and converted into HTML. If the site fails to load or respond, the workflow records no content and continues without breaking.
All links are extracted from the page, then filtered so only links belonging to the same website are kept.
The workflow scrapes up to five pages in total, including the main website page and up to four internal pages. This provides enough context while avoiding unnecessary data.
Each page is converted to markdown to reduce token usage and trimmed to a maximum of 5,000 characters to control LLM costs.
All processed markdown content is combined into a single structured input.
An LLM analyzes the aggregated content and generates a concise overview of the company and its offering.
A second LLM uses the company name, lead name where available, and the generated business overview to create a highly personalized subject line and icebreaker for outreach.
The final outputs are written back to the database, keeping each lead enriched and ready for outreach.
Most outreach fails because it is generic. This workflow solves that by grounding every message in real website content while staying fast, efficient, and cost-conscious.
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 58 workflow blocks. Download the JSON for the full node graph.
| Workflow | Generate personalized cold email icebreakers with Apify, Baserow and OpenRouter GPT-4.1 |
|---|---|
| Complexity | advanced |
| Nodes | 58 |
| Categories | Lead Generation, AI Summarization |
| Author | Muhammad Faaz Khan |
| Published | 17 Jan 2026 |
Use the JSON export at /data/workflows/12778/12778.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.
This workflow automatically researches a lead’s website, understands what the business actually does, and generates a highly personalized cold outreach subject line and icebreaker using AI. Instead...
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 Lead Generation, AI Summarization use case.