n8n is an open-source workflow automation platform that you can self-host or use in the cloud. It's a powerful alternative to Zapier and Make for processing Browse AI data, especially when you need more control over your pipelines, want to avoid per-task pricing, or need to keep data on your own infrastructure.
Combined with AI nodes, n8n lets you build sophisticated data processing workflows: receive scraped data from Browse AI, enrich or analyze it with any LLM (Claude, OpenAI, Gemini, Mistral), and route the results to any destination.
📖 New to connecting Browse AI with AI models? Start with our beginner's guide to using Browse AI data with AI assistants for the simplest approach, or see our LLM integration guide for API-based methods.
Why n8n for Browse AI workflows
Advantage | Details |
Self-hosted option | Run on your own server. Scraped data never leaves your infrastructure. |
No per-execution pricing | Process thousands of Browse AI tasks without worrying about per-task costs. |
Code nodes | Write JavaScript or Python directly in your workflow for custom data transformations. |
Native AI nodes | Built-in nodes for OpenAI, Anthropic, Google Gemini, Mistral, Ollama (local models), and more. |
Branching and logic | Advanced IF/switch nodes, loops, error handling, and retry logic out of the box. |
400+ integrations | Connect to databases, CRMs, messaging platforms, spreadsheets, and APIs. |
Getting started with n8n
You can use n8n in two ways:
n8n Cloud: Hosted version at n8n.io. Sign up and start building immediately.
Self-hosted: Run on your own server with Docker. Free and open-source.
# Quick self-hosted setup with Docker docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n n8nio/n8n
Method 1: Webhook trigger (real-time)
The most common pattern. Browse AI sends data to n8n the moment a task completes, and n8n processes it through your workflow.
Step 1: Create the webhook trigger in n8n
Create a new workflow in n8n.
Add a Webhook node as the trigger.
Set the HTTP method to POST.
Copy the webhook URL that n8n generates (it will look like
https://your-n8n.com/webhook/abc123).Set Response Mode to "Immediately" so Browse AI doesn't time out waiting.
Step 2: Configure the webhook in Browse AI
Open your robot in Browse AI and go to the Integrations tab.
Click Add Webhook.
Paste your n8n webhook URL.
Select the events to listen for:
Task finished successfully for processing new data
Task captured data changed for monitor change detection
Save the webhook.
⚠️ Browse AI does not support webhook signature verification. If your n8n instance is publicly accessible, add an IF node after the webhook to verify the request came from Browse AI's IP address: 3.228.254.190. See our webhook IP allowlisting guide.
Step 3: Add an AI processing node
After the webhook trigger, add an AI node to process the scraped data:
Option A: Use a built-in AI node
Add an OpenAI, Anthropic, or Google Gemini node.
Connect your API credentials.
In the prompt, reference the webhook data using expressions:
{{ $json.task.capturedTexts }}
Option B: Use the HTTP Request node (any LLM)
For providers without a native n8n node, use the HTTP Request node to call any LLM API directly:
Add an HTTP Request node.
Set the method to POST and enter the API endpoint (e.g.,
https://api.anthropic.com/v1/messages).Add your authentication headers.
Build the request body with your prompt and the Browse AI data.
Step 4: Route the output
Add destination nodes after the AI processing step. Common patterns:
Google Sheets node to append enriched data to a spreadsheet
Slack node to post analysis to a channel
Postgres/MySQL node to store in a database
Email node to send reports to your team
HTTP Request node to push to any API (CRM, project management, etc.)
Method 2: Scheduled polling
Use n8n's Schedule Trigger to periodically pull data from Browse AI's API and process it in batch.
Add a Schedule Trigger node. Set your interval (e.g., every hour, daily at 8 AM).
Add an HTTP Request node to call Browse AI's API:
Header:
Authorization: Bearer YOUR_BROWSE_AI_API_KEY
Add a Code node to filter for new/successful tasks since the last run.
Add your AI processing node.
Route results to your destination.
Example workflows
Here are complete workflow patterns you can build in n8n:
Competitor price monitoring with smart alerts
Nodes: Webhook → IF (check event type) → Code (extract price changes) → Anthropic (analyze significance) → IF (is significant?) → Slack (alert team)
The Code node extracts and compares prices:
// n8n Code node (JavaScript)
const event = $input.all()[0].json;
const changes = event.task.capturedDataChanges || {};
const current = event.task.capturedTexts || {};// Extract price-related changes
const priceChanges = Object.entries(changes)
.filter(([key]) => key.toLowerCase().includes('price'))
.map(([key, value]) => ({
field: key,
previous: value.previous,
current: value.current
}));return [{ json: { priceChanges, allData: current } }];
Daily content digest
Nodes: Schedule Trigger (daily 8 AM) → HTTP Request (Browse AI API) → Code (collect articles) → OpenAI (generate digest) → Email (send to team)
Lead enrichment pipeline
Nodes: Webhook → Code (parse lead data) → Anthropic (qualify and score) → IF (score > threshold) → HubSpot (create contact) + Slack (notify sales)
Multi-source research aggregator
Nodes: Schedule Trigger → Multiple HTTP Request nodes (one per robot) → Merge → Claude (synthesize report) → Google Docs (save report) + Slack (share link)
Tips for n8n + Browse AI workflows
Tip | Details |
Use the Code node for data shaping | Clean and restructure Browse AI's webhook payload before sending it to an LLM. This keeps your prompts focused and reduces token usage. |
Add error handling | Use n8n's Error Trigger node to catch failures (LLM API timeouts, rate limits) and retry or alert. |
Use credentials properly | Store API keys in n8n's credential manager rather than hardcoding them in nodes. |
Test with webhook replay | n8n lets you pin webhook data and replay it through your workflow. Use this to iterate on your AI prompts without triggering new Browse AI tasks. |
Use sub-workflows | Break complex pipelines into reusable sub-workflows. For example, have one sub-workflow for "enrich with Claude" that multiple triggers can call. |
✅ Tip: n8n has a built-in AI Agent node that can chain multiple LLM calls with tools and memory. This is useful for complex analysis where a single prompt isn't enough, like researching a competitor across multiple data points before producing a summary.
n8n vs. Zapier / Make
n8n | Zapier / Make | |
Pricing | Free (self-hosted) or flat monthly (cloud) | Per-task pricing |
Data privacy | Self-host option keeps data on your servers | Data processed on their cloud |
Code support | Full JavaScript/Python nodes | Limited (Zapier Code, Make functions) |
AI nodes | Native nodes for all major LLMs + AI Agents | Separate apps per provider |
Setup effort | Moderate (especially self-hosted) | Low (fully managed) |
Best for | Technical teams, high volume, data-sensitive | Non-technical users, quick setups |
Next steps
Browse AI webhooks: Learn how to configure webhooks in our webhook setup guide.
Browse AI API: See the full Browse AI API documentation for all available endpoints.
n8n documentation: Explore n8n's full capabilities at docs.n8n.io.
AI enrichment patterns: See our Claude enrichment guide for prompt patterns you can adapt in n8n.