This article provides a complete, practical walkthrough of the Filter Summarize Send Scheduled 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:
   Automated Client Usage Tracking and Invoicing for AI Services Using n8n and Langchain
   
   Meta Description:
   Learn how to use n8n and the Langchain Code Node to build a fully automated workflow for tracking AI usage metrics, calculating token-based costs, and emailing monthly client invoices — all stored conveniently in Google Sheets.
   
   Keywords:
   n8n tutorial, Langchain code node, OpenAI GPT-4o, AI billing automation, track token usage, client invoice automation, Google Sheets API, resume extraction, LLM cost tracking, self-hosted n8n, AI service usage logging, GPT usage metrics
   
   Third-Party APIs Used:
   
   1. OpenAI API (via Langchain) – for calling the GPT-4o model to process resume data.
   2. Google Sheets API – to record and retrieve usage metrics for invoicing.
   3. Gmail API – to send automated invoices to clients.
   
   Article:
   
   Automated Client Usage Tracking and Invoicing for AI Services Using n8n and Langchain
   
   If you're offering AI-powered services to clients—such as resume parsing, document summarization, or content generation—keeping track of each request's usage and cost can quickly become overwhelming. Thankfully, n8n's self-hosted automation platform, paired with Langchain’s powerful library and Google tools, offers a no-code/low-code solution to automate this process effectively.
   
   In this article, we’ll take a detailed look at an n8n workflow template that does the following:
   
   - Offers a client-facing form to upload PDF resumes.
   - Parses and processes uploaded files using OpenAI's GPT-4o model.
   - Logs token usage and cost data to a Google Sheet.
   - Aggregates usage for each client at the end of the month.
   - Sends out automated invoices via Gmail.
   
   All of this is done using a fully self-contained and repeatable workflow, customizable for your AI use case.
   
   📥 Step 1: Offer Your AI Service With a Form
   The workflow begins with a Form Trigger node labeled “CV Parsing Service.” Clients can upload their resumes in PDF format and acknowledge a billing agreement. This interface acts as your AI service entry point and neatly collects client submissions.
   
   📄 Step 2: Parse and Process the Resume Contents
   Using the Extract From File node, the uploaded PDF is converted into text. This parsed content serves as input for the Langchain "Information Extractor" node, which organizes the resume data into structured JSON based on a predefined schema—including fields like name, skills, experience, and education.
   
   📊 Step 3: Logging Usage Data with Langchain Code Node
   This is where the real automation magic happens.
   
   A powerful feature of this workflow is the “Custom LLM Subnode” built using the Langchain Code node. This node wraps around the AI interaction and intercepts a rich piece of metadata: token usage. It captures how many input/output tokens a query consumed, calculates the cost based on your OpenAI pricing (in this case, $0.15/M input tokens and $0.60/M output tokens), and computes a total cost per client request.
   
   Instead of hardcoding another node connection or adding a database, the node cleverly uses n8n’s built-in Google Sheets Tool (attached as an “ai_tool”) to append the metadata to a shared Google Sheet. Each request logs the following:
   
   - Date
   - Workflow and execution ID
   - Client ID
   - Token counts
   - Individual and total costs
   
   You can view a sample here: Client Usage Log Sheet.
   
   📨 Step 4: Showing Results and Transparency
   After processing, clients receive a visual JSON representation of their parsed resume—offering instant feedback for their upload. This orderly transition also adds credibility and trust, proving your AI service works and is transparently billed.
   
   💰 Step 5: Aggregate and Send Invoices Automatically (Optional)
   At the end of each month, a Schedule Trigger node initiates a sequence that retrieves each client’s logs via the Google Sheets node. A Filter node confines the data to the current month, and a Summarize node calculates total tokens and costs.
   
   Then, the Gmail node takes over and sends a formatted invoice email to each client. The message includes:
   
   - Total tokens used
   - Cost calculation
   - VAT amount (added at 20%)
   - Total amount payable
   
   All communication is handled programmatically, freeing you from manual calculations or email updates.
   
   🔒 Notes on Compatibility
   Important: This workflow requires the self-hosted version of n8n, as the Langchain Code node isn’t currently supported in n8n Cloud. Additionally, the GPT-4o model is used in this template, but you can swap it for any OpenAI-compatible model that provides usage metadata.
   
   🎯 Why This Workflow Matters
   This n8n template demonstrates a realistic end-to-end business use case where AI costs need to be tracked closely for billing and analytics. Whether you’re running a SaaS business, a freelance service, or an internal tool, keeping tabs on how much each client uses and costs you is mission-critical for sustainability.
   
   By logging usage in real-time and automating monthly invoicing, this template saves hours of administrative time while ensuring transparency and profitability.
   
   🔌 Technologies and APIs Used
   Here’s a quick summary of tools and APIs integrated in this workflow:
   
   - OpenAI’s GPT-4o via Langchain – to extract structured data from resume PDFs and return usage metadata.
   - Google Sheets API – for storing daily usage logs cost-effectively.
   - Gmail API – for sending monthly invoices to clients via email.
   
   🚀 Final Thoughts
   If you're managing any AI infrastructure used by external or internal clients, this n8n workflow keeps things scalable, automated, and accountable. Even better, it runs in a self-hosted environment, giving you full control over access, credentials, and system behavior.
   
   Test it, tweak it, and make it your own—it’s a brilliant example of combining no-code automation with powerful AI capabilities.
   
   Need help setting it up? Visit the n8n Community Forum or join the Discord for hands-on support from the developer community!
   
   Useful links:
   - Langchain Code Node Docs
   - Try the Live Client Usage Log Sheet
   - Get started with self-hosted n8n
   
   Happy automating! ⚙️🤖

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.