This article provides a complete, practical walkthrough of the Http Filter Monitor Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Stripe Checkout Session Filtering with n8n: A Step-by-Step Workflow
   
   Meta Description:  
   Learn how to use n8n to retrieve and filter Stripe checkout sessions by custom fields, such as nickname and job title, using a no-code automation workflow complete with pagination and data splitting.
   
   Keywords:  
   n8n workflow, Stripe API, Stripe checkout sessions, no-code automation, custom field filtering, data splitting, API pagination, n8n tutorial, automation tools, workflow automation
   
   Third-Party APIs Used:  
   - Stripe API
   
   —
   
   Article:
   
   Simplifying Stripe Checkout Data Filtering Using n8n
   
   Automating data workflows is at the heart of operational efficiency, especially for teams handling online transactions and CRM data. In this article, we’ll walk through an n8n workflow designed to automatically retrieve Stripe checkout session data from the past 20 days, split the dataset into manageable records, and filter those records based on specific custom fields — namely "nickname" and "job title".
   
   Whether you're a developer looking to streamline data analytics or a marketer trying to segment customers based on their input, this guide will help you leverage n8n's nodes and the Stripe API effectively.
   
   Overview of the Workflow
   
   At a high level, the workflow includes the following steps:
   
   1. Fetch all checkout sessions from the Stripe API from the past 20 days.
   2. Handle pagination to retrieve all available session data.
   3. Split the session list into individual records.
   4. Extract and loop through the custom fields from each record.
   5. Filter for sessions where specific custom fields (nickname and job_title) are present.
   
   Let’s take a deeper dive into each stage.
   
   Step 1: Retrieve Stripe Checkout Sessions
   
   The workflow begins with the HTTP Request node labeled "Stripe | Get latest checkout sessions1", which fetches data from the Stripe API endpoint https://api.stripe.com/v1/checkout/sessions using a GET request. The node includes a custom JSON query to limit the results to sessions created within the last 20 days:
   
   ```json
   {
     "created": {
       "gte": {{ $today.minus(20, 'days').toSeconds() }},
       "lte": {{ $today.toSeconds() }}
     }
   }
   ```
   
   Pagination is enabled to ensure you get all results, not just the first page. Stripe's “has_more” boolean flag is used to iterate through results until completion.
   
   Tip: You can easily adjust the date range by modifying the "created" parameter depending on your reporting needs.
   
   Step 2: Split Data into Individual Records
   
   Once the full dataset is retrieved, the node “split all data” takes the "data" field (which contains the list of session objects) and outputs each item as a separate execution path. This is essential for complex data manipulations or field filtering in later steps.
   
   Step 3: Extract Custom Fields
   
   The next node, “split custom_fields”, dives deeper into each checkout session object by extracting the custom_fields property. It creates new execution threads for each key-value pair in the custom_fields array, which makes it easier to apply conditional filters.
   
   Step 4: Filter Based on Specific Fields
   
   The final processing node, “Filter by custom_field”, applies a strict filter using n8n’s conditional logic. It checks for presence and equality of two specified field keys:
   
   - custom_fields.key == "nickname"
   - custom_fields.key == "job_title"
   
   Both conditions are strung together using the "and" combinator, which ensures that only records containing both custom fields are passed through the workflow. This is helpful if you're only interested in users who provided both a nickname and job title during checkout.
   
   Optional Enhancements
   
   While the core workflow serves most needs, you can enhance it further depending on your goals:
   
   - Add a Set or HTTP Request node to forward filtered data to another CRM like HubSpot, Mailchimp, or Airtable.
   - Transform the data with a Function node to match your internal schema.
   - Set up a Scheduler node to automate the workflow to run daily or weekly.
   
   Key Benefits
   
   - No Coding Required: All logic is built using native nodes in n8n, eliminating the need for custom scripts.
   - Scalable: Supports Stripe’s pagination system, so you're not limited by dataset size.
   - Customizable: Easily target any custom fields relevant to your business needs.
   
   Conclusion
   
   This n8n workflow highlights the power of low-code automation when paired with powerful APIs like Stripe. By fetching, splitting, and filtering checkout session data, you can automate complex data segmentation tasks into simple, repeatable workflows—saving both time and effort.
   
   Whether you're building a user segmentation strategy, preparing marketing data, or simply trying to understand customer behavior, this approach puts versatile data handling capabilities at your fingertips.
   
   Start leveraging automation today, and let your workflows work for you.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.