Track expenses from receipt photos with AI, Google Sheets & Slack reports
Who’s it for
This template is for individuals or households who want to:
- Easily digitize and track their spending from physical receipts.
- Automate budget management and financial reporting.
- Get quick insights into their spending habits on a daily and monthly basis.
- Stay informed about their remaining budget through Slack notifications.
What it does
This workflow automates your household budget tracking in several steps:
- Receipt Photo Upload: You upload a photo of your receipt to a webhook.
- Parse Receipt: An AI agent extracts the date, store name, purchased items, and total amount from the receipt text.
- Add to Budget Sheet: The extracted data is then appended to your designated Google Sheet.
- Daily Budget Report: After each receipt entry, the workflow calculates your current month's spending, remaining budget, and sends a summary to Slack.
- Monthly Budget Report: Once a day (triggered by a cron job), it reads all budget data for the current month from Google Sheets, performs an analysis (total spending, daily average, top stores, items, and spending days), and sends a comprehensive report to Slack.
How to set up
Requirements
- n8n Account: Self-hosted or Cloud.
- Google Sheets: A Google Sheet set up with columns for "Date", "Store", "Items", and "Amount".
- Slack Workspace: A Slack channel where you want to receive budget updates.
- OpenRouter Account: An API key for OpenRouter to use their chat models.
Steps
-
Google Sheets Setup:
- Create a new Google Sheet (or use an existing one) and name it "Household Budget".
- In the first sheet (e.g., "Sheet1"), set up the following headers in the first row: "Date", "Store", "Items", "Amount".
- Share the Google Sheet with the service account email associated with your n8n Google Sheets credentials, granting "Editor" access.
- In the 'Add to Budget Sheet' and 'Get Budget Sheet (Daily)' nodes, select your Google Sheet and the appropriate sheet name.
-
OpenRouter Credentials:
- Sign up or log in to OpenRouter (https://openrouter.ai/).
- Generate an API key.
- In n8n, create a new "OpenRouter" credential using your API key. Apply this credential to the 'OpenRouter Chat Model1', 'OpenRouter Chat Model2', and 'OpenRouter Chat Model' nodes.
-
Slack Credentials:
- In n8n, create a new "Slack" credential. Follow the instructions to connect your Slack workspace.
- In the 'Send a message' and 'Send monthly report' nodes, select the Slack channel where you want to receive messages.
- Make sure the Slack app has permission to post to the selected channels.
-
Webhook URLs:
- After activating the workflow, the 'Receipt Photo Upload' node will generate a webhook URL. Copy this URL. You will use this URL to send receipt text (e.g., from a mobile app that scans text).
-
Monthly Budget Adjustment:
- In the 'Code in JavaScript2' node, locate the line
const budget = 30000;and change30000to your desired monthly budget in JPY.
- In the 'Code in JavaScript2' node, locate the line
How to customize the workflow
Daily Report Trigger
The 'Daily Report Trigger' node is set to run once a day. You can modify its schedule to trigger more or less frequently by adjusting its cron settings.
AI Model
You can change the AI models used in the 'OpenRouter Chat Model' nodes to any other large language model supported by n8n, such as OpenAI, Anthropic, or custom hosted models, by updating the credentials and model names.
Output Formatting
The Slack messages generated by the 'Report Budget' and 'Monthly Report' nodes can be customized by editing the systemMessage and text parameters in those nodes to change the tone, content, or language of the reports.
Additional Integrations
You can extend this workflow by adding more nodes:
- Integrate with other accounting software.
- Send notifications to different platforms (e.g., email, Discord, Telegram).
- Add sentiment analysis to your spending habits.
- Categorize expenses automatically based on items or stores using another AI node.
n8n Workflow: Expense Tracking from Receipts with AI, Google Sheets & Slack Reports
This n8n workflow automates the process of extracting expense details from receipt photos, recording them in Google Sheets, and providing real-time updates via Slack. It leverages AI to intelligently parse receipt data and streamlines expense management for individuals or small businesses.
What it does
This workflow simplifies expense tracking by:
- Receiving Receipt Data: It starts by waiting for incoming receipt data via a webhook, typically triggered by an external system (e.g., a mobile app, email forwarding, or another automation).
- Extracting Expense Details with AI: It uses an AI Agent (powered by an OpenRouter Chat Model) to intelligently parse the receipt data (likely image content or OCR output passed to the webhook) and extract key information such as vendor, amount, date, and category.
- Formatting Data: A Code node processes the AI-extracted data, ensuring it's in the correct format for Google Sheets.
- Recording in Google Sheets: The extracted and formatted expense details are then appended as a new row to a specified Google Sheet.
- Reporting to Slack: Finally, a summary of the recorded expense is posted to a designated Slack channel, providing immediate notification and transparency.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- Google Sheets Account: A Google account with access to Google Sheets.
- You'll need to create a new Google Sheets credential in n8n.
- Slack Account: A Slack workspace.
- You'll need to create a new Slack credential in n8n.
- OpenRouter API Key: An API key for OpenRouter to power the AI Chat Model.
- You'll need to create a new OpenRouter credential in n8n.
- External System to Trigger Webhook: An external system or process that can send receipt data to the n8n webhook URL.
Setup/Usage
- Import the Workflow:
- Download the provided JSON workflow.
- In your n8n instance, go to "Workflows" and click "New".
- Click the "Import from JSON" button and paste the workflow JSON.
- Configure Credentials:
- Google Sheets: Edit the "Google Sheets" node. Select or create a new "Google Sheets API" credential. Follow the n8n instructions to authenticate with your Google account.
- Slack: Edit the "Slack" node. Select or create a new "Slack API" credential. Follow the n8n instructions to authenticate with your Slack workspace.
- OpenRouter Chat Model: Edit the "OpenRouter Chat Model" node. Select or create a new "OpenRouter API" credential. Enter your OpenRouter API key.
- Configure Google Sheet:
- In the "Google Sheets" node, specify the "Spreadsheet ID" and "Sheet Name" where you want to record expenses. Ensure the sheet has appropriate header columns (e.g., "Date", "Vendor", "Amount", "Category").
- Configure Slack Channel:
- In the "Slack" node, specify the "Channel" where you want to receive expense notifications.
- Configure Webhook:
- The "Webhook" node will generate a unique URL when the workflow is activated. This URL is where your external system should send the receipt data.
- The incoming data to the webhook should contain the raw receipt text or a reference to the receipt image that the AI agent can process. You may need to adjust the "Code" node and "AI Agent" node based on the exact format of your incoming data.
- Activate the Workflow:
- Once all credentials and settings are configured, activate the workflow by toggling the "Active" switch in the top right corner of the n8n editor.
- Trigger the Workflow:
- Send receipt data to the generated Webhook URL from your external system. The workflow will then process the data, record it, and send a Slack notification.
Note on AI Agent and Code Node: The specific implementation of the AI Agent and Code node will depend on the exact format of the incoming receipt data and the desired output structure. You may need to customize these nodes to perfectly match your use case and the capabilities of your chosen OCR/receipt scanning solution.
Related Templates
Automate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte
By Wessel Bulte🎓 How to transform unstructured email data into structured format with AI agent
This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
By DavideAutomate RSS to social media pipeline with AI, Airtable & GetLate for multiple platforms
Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (to-share-linkedin, to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags social-media-automation, ai-content-generation, rss-to-social, multi-platform-posting, getlate-api, airtable-database, langchain, workflow-automation, content-marketing
By Mikal Hayden-Gates