Scrape, search and browse the web with a Firecrawl AI agent webhook
VerifiedWebhook triggers AI agent to scrape and structure web data via prompts.
What this workflow does
This n8n workflow accepts prompts through a webhook and returns structured web data by combining an AI Agent with OpenRouter Chat Model and Structured Output Parser.
It suits developers and teams needing automated data enrichment, lead generation, market research, or content aggregation from web sources.
Who is this for?
Data analysts, growth marketers, sales ops teams, and researchers who need structured web data without building scrapers.
What problem it solves
Manually searching, scraping, and structuring web content is slow and inconsistent. This workflow turns a natural-language prompt into reliable JSON via an AI agent.
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What it automates
Data Enrichment
Send company names from your CRM and receive firmographics like industry, funding, and tech stack in structured format.
Lead Generation
Query competitor pricing pages or contact details and get clean JSON ready for your outreach list.
Market Research
Extract pricing plans or feature comparisons from multiple sites and receive them in a predefined schema.
How the workflow works
The 4 nodes in this automation, in order.
- 1Codecode
- 2AI Agent@n8n/n8n-nodes-langchain.agent
- 3Structured Output Parser@n8n/n8n-nodes-langchain.outputParserStructured
- 4OpenRouter Chat Model@n8n/n8n-nodes-langchain.lmChatOpenRouter
Apps & integrations used
How to set up Scrape, search and browse the web with a Firecrawl AI agent webhook
- 1Add a Webhook node set to POST /webhook/scrape-agent and capture prompt plus optional output_schema.
- 2Insert an If node to validate the schema; route malformed schemas to a Return Error node.
- 3Connect an AI Agent node using the OpenRouter Chat Model and attach Firecrawl search, scrape, and interact tools.
- 4Add a Structured Output Parser node after the agent to enforce the requested schema or default.
- 5Link the parser output to a Respond to Webhook node that returns the final JSON.
How to customize this workflow
- →Swap OpenRouter for any supported chat model node
- →Change the trigger from Webhook to Schedule for recurring research
- →Add a Google Sheets or Airtable node to store results automatically
- →Extend the agent with extra Firecrawl interact tools for deeper page navigation
Scrape, search and browse the web with a Firecrawl AI agent webhook: pros & cons
Pros
- +Natural-language prompts replace custom scrapers
- +Optional JSON schema guarantees consistent output
- +Agent can interact with dynamic pages via Firecrawl
- +Single webhook endpoint for easy integration
Cons
- –Requires a paid Firecrawl account for production volume
- –Output quality depends on the chosen LLM
- –Complex schemas may need prompt tuning
Frequently asked questions
It accepts a prompt and optional schema via webhook and returns structured data scraped from the web by a Firecrawl-powered AI agent.
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