Generate Brave Search Goggles with DataForSEO, Firecrawl, OpenAI and Postgres

Workflow preview

100%

Open on n8n.io

$20/month : Unlimited workflows

2500 executions/month

Try free

THE #1 IN WEB SCRAPING

Scrape any website without limits

Try free

HOSTINGER

Early Deal
DISCOUNT 20%

Self-hosted n8n

Unlimited workflows - from $4.99/mo

Try free

#1 hub for scraping, AI & automation

6000+ actors - $5 credits/mo

Try free

1. Workflow Overview

Who’s it for Business owners, industry specialists, and AI developers building domain specific search experiences. It generates custom search filters to boost technical authorities and block lead g...

Best for

Market Research automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.stickynote, n8n-nodes-dataforseo.dataforseo, n8n-nodes-base.manualtrigger, n8n-nodes-base.code, n8n-nodes-base.merge, n8n-nodes-base.postgres, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.httprequest

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Stephan Koning.

Original n8n.io source

1.1 Workflow description

Title: Generate Brave Search Goggles with DataForSEO, Firecrawl, OpenAI and Postgres
Workflow name: Generate Brave Search Goggles with DataForSEO, Firecrawl, OpenAI and Postgres

Who’s it for

Business owners, industry specialists, and AI developers building domain-specific search experiences. It generates custom search filters to boost technical authorities and block lead-gen aggregators, requiring no advanced SEO expertise. Pre-configured for the Dutch plastering industry, but universally adaptable.

What it does

Automates the creation of custom Brave Search Goggles to ground LLMs and AI agents in noise-free, high-authority industry content. This strict filtering ensures RAG pipelines consume pre-optimized, expert-verified data for accurate question answering and reduced hallucinations.

How it works

Domain Intersection: Uses DataForSEO to identify high-value search terms by extracting exact keyword overlaps between top competitors.
SERP & Mapping: Fetches the Top 100 Google results for these terms and uses Firecrawl to map the internal architecture ("Power Folders") of the ranking domains.
AI Auditing: An OpenAI GPT-4o agent evaluates each domain's structure, classifying them (e.g., Manufacturer, Retailer, Aggregator) and assigning strict Goggle rules (boost, downrank, discard) based on technical depth.
Live Deployment: Stores rules in Postgres, formats them into valid Brave Goggle syntax via a custom SQL View, and automatically syncs to a public GitHub Gist.

Requirements

Postgres: Stores rules, audit trails, and generates the Goggle syntax.
DataForSEO API: Extracts competitor intersections and live SERP data.
OpenAI API: Powers the domain classification agent.
Firecrawl API: Maps internal domain architectures.
GitHub Account: Hosts the live Goggle file via Gists.

How to adapt the AI Auditor for other niches

To pivot this workflow to a new industry (e.g., Healthcare or Legal), update the System Prompt inside the Information Extraction Agent. Adjust the AI's persona, redefine which domain types represent true authority (to boost), and explicitly define your industry's specific noise (to discard).

PS

Goggles must be submitted to Brave Search before they can be used with the API. Visit search.brave.com/goggles/create to register your Goggle.

1.2 Logical Blocks

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.

2. Block-by-Block Analysis

Block 1 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 2 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 3 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 4 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 5 - Sticky Note4

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 6 - Sticky Note5

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 7 - Sticky Note6

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 8 - Get SERP Data for SEO

Type / Role: n8n-nodes-dataforseo.dataForSeo - dataForSeo
Config choices: Version 1

Block 9 - Manual Start Trigger

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 10 - Parse Domain and Path

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 11 - SQL-Based Data Merge

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Block 12 - Execute Postgres Query

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 13 - Use OpenAI GPT-4o

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.3

Block 14 - Post to DataForSEO API

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.4

Block 15 - Process Intersection Results

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 16 - Run SQL Data Fetch

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 17 - Information Extraction Agent

Type / Role: @n8n/n8n-nodes-langchain.informationExtractor - informationExtractor
Config choices: Version 1.2

Block 18 - If Audit Status Pending

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 19 - Split Rules

Type / Role: n8n-nodes-base.splitOut - splitOut
Config choices: Version 1

Block 20 - Format Data for SQL

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 21 - Optimize AI Prompts

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 22 - Verify AI Process

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 23 - If Complete Status Is True

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 24 - Set Environment Variables

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Showing the first 24 of 61 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Generate Brave Search Goggles with DataForSEO, Firecrawl, OpenAI and Postgres
Complexity	advanced
Nodes	61
Categories	Market Research, AI RAG
Author	Stephan Koning
Published	29 Apr 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/15390/15390.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

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.

Download workflow JSON Original n8n.io source Market Research workflows AI RAG workflows

Frequently asked questions

What does Generate Brave Search Goggles with DataForSEO, Firecrawl, OpenAI and Postgres do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

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 Market Research, AI RAG use case.

Stephan Koning

15 workflows

Nodes

n8n-nodes-base.stickynote n8n-nodes-dataforseo.dataforseo n8n-nodes-base.manualtrigger n8n-nodes-base.code n8n-nodes-base.merge n8n-nodes-base.postgres @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.httprequest

Complexity

advanced

Published 29 Apr 2026

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/15390/15390.json

Share Your Workflow

Have a useful automation to share? Publish it and help the community.

Submit Your Template How to Submit

Related Workflows

Answer BigQuery questions in Microsoft Teams with Claude 3.5 dual agents

## Quick overview Turn Microsoft Teams into a natural language BI tool. This dual-agent Claude workflow intelligently classifies user questions, generates safe BigQuery SQL, executes the query, and returns a plain-English summary alongside a data preview directly to your Teams channel. ## How it works 1. The workflow listens for new messages in a designated Microsoft Teams channel or chat. 2. It checks a 24-hour static data cache for your BigQuery schema. If the cache is missing or expired, it automatically fetches the latest INFORMATION_SCHEMA metadata. 3. A main Claude 3.5 Sonnet agent classifies the user's intent (analytical, conversational, or unsafe) and identifies the relevant database tables. 4. If the intent is analytical, a specialized Claude SQL sub-agent generates a read-only SELECT query tailored exactly to your schema. 5. The workflow executes the query against BigQuery (safely constrained to a maximum of 100 rows). 6. A final Claude agent translates the raw query results into a concise, plain-English summary. 7. The answer is posted back to Teams as an Adaptive Card, complete with a formatted data preview and the generated SQL code. 8. Non-analytical questions or pipeline errors are intelligently routed to receive standard conversational replies in Teams. ## Setup 1. CRITICAL SECURITY: Restrict your Google Cloud Service Account IAM roles to BigQuery Data Viewer and BigQuery Job User ONLY. Do not use a highly privileged account. 2. Configure your Microsoft Teams trigger and posting credentials, specifying the target team and channel for the bot to operate in. 3. Configure your Google BigQuery credentials to allow reading metadata and executing queries. 4. Configure your Anthropic credentials for the three Claude LangChain nodes. 5. Update the "Configure Variables" node with your specific GCP Project ID and BigQuery Dataset ID. ## Requirements - A Google Cloud project with BigQuery enabled and existing structured datasets. - An Anthropic API key with access to the Claude 3.5 Sonnet model. - Microsoft Teams administrator access to authorize the n8n integration. ## Customization - Distributed Environments: If you are running n8n with multiple worker nodes (Queue Mode), you must swap the $getWorkflowStaticData() schema cache logic to read/write from a dedicated Key-Value store (like Redis or Supabase) to ensure state synchronization. - UI Layout: Modify the Create Teams Adaptive Card node to add corporate branding, custom colors, or alter the FactSet data preview structure. ## Additional info This architecture features built-in pipeline protections, including strict 40,000-character payload limits to prevent LLM context window blowouts, and fallback handling for "zero-row" query results to ensure users are never left without a response.

View

Evaluate LinkedIn profiles with Apify and Groq Llama chat model

## Quick overview This workflow collects a LinkedIn profile URL via an n8n form, scrapes the public profile data using the Apify LinkedIn Profile Scraper actor, analyzes the results with a Groq-hosted Llama model, and displays an HTML-formatted evaluation back to the user. ## How it works 1. Receives a LinkedIn Profile URL when a user submits the n8n form. 2. Prepares the LinkedIn URL along with the Apify API token and Groq API key used for downstream requests. 3. Sends the profile URL to the Apify `harvestapi/linkedin-profile-scraper` actor and retrieves the scraped dataset items. 4. Passes the scraped profile JSON to a Groq chat model via an LLM chain to generate an HTML evaluation with a score and recommendations. 5. Displays the generated HTML evaluation to the user as the form completion message. ## Setup 1. Add a valid Apify API token and ensure the Apify actor endpoint (`harvestapi/linkedin-profile-scraper`) is accessible for your account. 2. Add Groq API credentials in n8n and confirm the selected model (meta-llama/llama-4-scout-17b-16e-instruct) is available to your Groq account. 3. Replace the placeholder API key values in the workflow (or move them to n8n credentials/environment variables) and verify the form field label matches `LinkedIn Profile URL`.

View

Validate startup ideas with Google Gemini, Groq, and Gmail

## Quick overview This workflow collects a startup idea via an n8n form, uses Google Gemini to generate a SWOT-style analysis, assigns a viability score, uses Groq to turn the results into a structured validation report, and emails the final report through Gmail. ## How it works 1. Receives a startup idea, industry, and target audience from an n8n Form trigger. 2. Sends the submission to Google Gemini to generate a professional analysis covering the problem, business model, and SWOT factors. 3. Generates a startup viability score (currently a random value from 80–100) and packages it with the Gemini analysis. 4. Sends the score and analysis to Groq (Llama 3.3 70B) to compile a full validation report with an executive summary, market potential, recommendations, and final verdict. 5. Emails the generated validation report to the configured recipient using Gmail. ## Setup 1. Add credentials for Google Gemini (PaLM) API, Groq API, and a Gmail OAuth2 connection. 2. Replace the placeholder recipient address in the Gmail step ("[email protected]") with your target email address before activating the workflow. 3. Publish the n8n form, then share the form URL with the users who should submit their startup ideas for validation.

View

Need Custom Automation?

Get help designing a custom n8n workflow that connects your stack and fits your process.

Generate Brave Search Goggles with DataForSEO, Firecrawl, OpenAI and Postgres

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Who’s it for

What it does

How it works

Requirements

How to adapt the AI Auditor for other niches

PS

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Sticky Note

Block 2 - Sticky Note1

Block 3 - Sticky Note2

Block 4 - Sticky Note3

Block 5 - Sticky Note4

Block 6 - Sticky Note5

Block 7 - Sticky Note6

Block 8 - Get SERP Data for SEO

Block 9 - Manual Start Trigger

Block 10 - Parse Domain and Path

Block 11 - SQL-Based Data Merge

Block 12 - Execute Postgres Query

Block 13 - Use OpenAI GPT-4o

Block 14 - Post to DataForSEO API

Block 15 - Process Intersection Results

Block 16 - Run SQL Data Fetch

Block 17 - Information Extraction Agent

Block 18 - If Audit Status Pending

Block 19 - Split Rules

Block 20 - Format Data for SQL

Block 21 - Optimize AI Prompts

Block 22 - Verify AI Process

Block 23 - If Complete Status Is True

Block 24 - Set Environment Variables

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions