# CSV Diff Tool (`automation-lab/csv-diff-tool`) Actor Compare two CSV datasets and find added, removed, and modified rows. Supports key-column matching, configurable delimiters, case-insensitive comparison, and whitespace trimming. Exports a structured change report with before/after values. - **URL**: https://apify.com/automation-lab/csv-diff-tool.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## CSV Diff Tool — Compare Two CSV Files & Find Changes 🔍 **Paste two CSV datasets and instantly see what changed** — which rows were added, removed, or modified. Supports key-column matching, configurable delimiters, case-insensitive comparison, and exports a detailed change report in JSON, CSV, or Excel. --- ### What does CSV Diff Tool do? **CSV Diff Tool** compares two CSV datasets row by row and outputs the exact differences: added rows, removed rows, and modified rows with before/after values for each changed column. Unlike simple text-diff tools, this actor understands **CSV structure** — it matches rows by their key column values (e.g. `id`, `email`, or a composite `[country, city]`), not by line position. This means rows that were reordered between versions are correctly identified as unchanged, not as "removed then added." The actor handles all real-world CSV quirks: quoted fields, commas inside values, semicolon-delimited European CSVs, TSV files, headers or no headers, leading/trailing whitespace, and case-insensitive comparison. --- ### Who is CSV Diff Tool for? **📊 Data engineers and analysts** who receive periodic CSV exports from databases, ERP systems, or third-party APIs and need to audit what changed between snapshots. Instead of manually scrolling through two spreadsheets, paste both and get a structured change report in seconds. **🛒 E-commerce operators** comparing product catalog exports — tracking price changes, stock level drops, or product removals between weekly supplier feeds. The actor flags exactly which SKUs changed and shows old vs. new values side by side. **📋 Compliance and audit teams** verifying that data migrations, ETL transforms, or database imports produced the expected changes. Use the summary stats (added/removed/modified counts) as an audit checkpoint, and drill into modified rows to verify specific field changes. **🔧 Developers and QA engineers** running regression tests on data pipelines or API exports. Compare baseline and updated snapshots automatically in CI workflows via the Apify API. **📈 Operations and business intelligence teams** who track KPIs exported as CSVs (sales reports, lead lists, inventory) and need a fast way to see week-over-week deltas without writing custom scripts. --- ### Why use CSV Diff Tool? - ✅ **Intelligent row matching** — matches rows by key column values, not line position. Rows that moved keep their identity. - ✅ **Before/after column diffs** — modified rows show `price_before: 14.99, price_after: 19.99` so you see exactly what changed. - ✅ **No code required** — paste CSV, click Run. No Python, no SQL, no scripting. - ✅ **Any delimiter** — comma, semicolon, tab, pipe. Works with European CSVs out of the box. - ✅ **Handles quoted fields** — commas inside quoted values are correctly parsed, not split. - ✅ **Composite key support** — match on multiple columns (`["country", "city"]`) for datasets without a single unique identifier. - ✅ **Case-insensitive mode** — treat `APPLE` and `apple` as the same value. - ✅ **Whitespace trimming** — ignore invisible whitespace differences. - ✅ **Export anywhere** — results in JSON, CSV, Excel, or NDJSON. Schedule via API for automated monitoring. - ✅ **Pure computation** — no proxy, no browser. Runs in under a second for typical datasets. --- ### What data can you extract? The actor outputs one row per change to the Apify dataset, plus a summary in the key-value store. **Per-change row output:** | Field | Description | |-------|-------------| | `changeType` | `added`, `removed`, or `modified` | | `rowKey` | Human-readable key, e.g. `id=42` or `country=UK\|city=London` | | `changedColumns` | Comma-separated list of columns that changed (for modified rows) | | `columnCount` | Number of columns that changed | | `data` | Full row data — modified rows show `field_before` and `field_after` for each changed column | **Summary object (saved to key-value store as `DIFF_SUMMARY`):** | Field | Description | |-------|-------------| | `totalRowsA` | Total rows in CSV A (baseline) | | `totalRowsB` | Total rows in CSV B (updated) | | `addedRows` | Rows present in B but not A | | `removedRows` | Rows present in A but not B | | `modifiedRows` | Rows present in both but with different values | | `unchangedRows` | Rows identical in both datasets | | `totalChanges` | Sum of added + removed + modified | | `keyColumns` | Key columns used for matching | --- ### How much does it cost to compare CSV files? This actor uses **pay-per-event (PPE) pricing** — you only pay for what you use. | Event | Price | |-------|-------| | Actor start (covers first 1,000 rows compared) | $0.005 | | Additional 1,000 rows compared | $0.001 | **Real-world cost examples:** | Dataset size | Cost | |-------------|------| | Two 50-row product catalogs | $0.005 (start fee only) | | Two 500-row customer lists | $0.005 (start fee only) | | Two 2,000-row inventory files | ~$0.007 (start + 3 extra units) | | Two 10,000-row transaction logs | ~$0.024 | | Two 50,000-row data exports | ~$0.104 | 💡 **Free plan estimate**: Apify gives new users $5 in free credits. That covers ~500 diff runs on typical datasets, or a single comparison of two 500,000-row files. All pricing uses **tiered discounts** — heavy users automatically pay less per row. --- ### How to compare two CSV files 1. **Open [CSV Diff Tool](https://apify.com/automation-lab/csv-diff-tool)** on Apify Store. 2. Click **Try for free** to open the actor in Apify Console. 3. Paste your **baseline CSV** (the "before" snapshot) into the **CSV A** field. 4. Paste your **updated CSV** (the "after" snapshot) into the **CSV B** field. 5. Set **Key columns** to the column name(s) that uniquely identify each row (e.g. `["id"]` or `["email"]`). Leave empty to match by row position. 6. Configure **Delimiter** if you're using a non-comma separator (semicolon, tab, pipe). 7. Click **Start**. The actor typically completes in under 2 seconds. 8. View results in the **Dataset** tab — each change is a separate row with `changeType`, `rowKey`, and full `data`. 9. Export as CSV or Excel using the **Export** button. **Example input (JSON format):** ```json { "csvA": "id,name,price,stock\n1,Widget Alpha,9.99,100\n2,Widget Beta,14.99,50\n3,Widget Gamma,4.99,200", "csvB": "id,name,price,stock\n1,Widget Alpha,9.99,100\n2,Widget Beta,19.99,45\n5,Widget Epsilon,34.99,10", "keyColumns": ["id"], "delimiter": ",", "hasHeader": true, "outputFormat": "all-changes" } ```` **Example for TSV files:** ```json { "csvA": "id\tname\tvalue\n1\tAlpha\t10\n2\tBeta\t20", "csvB": "id\tname\tvalue\n1\tAlpha\t10\n2\tBeta\t25", "keyColumns": ["id"], "delimiter": "\t", "hasHeader": true } ``` **Example with composite key:** ```json { "csvA": "country,city,population\nUK,London,8900000\nDE,Berlin,3700000", "csvB": "country,city,population\nUK,London,9100000\nFR,Paris,2100000", "keyColumns": ["country", "city"], "hasHeader": true } ``` *** ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `csvA` | String | — | **Required.** The baseline (original) CSV content. | | `csvB` | String | — | **Required.** The updated (new) CSV content. | | `keyColumns` | Array | `[]` | Column names that uniquely identify rows. Empty = use row position. | | `delimiter` | String | `,` | Field separator: `,` `;` `\t` or `\|` | | `hasHeader` | Boolean | `true` | Whether the first row contains column names. | | `caseSensitive` | Boolean | `true` | Whether value comparison is case-sensitive. | | `trimWhitespace` | Boolean | `true` | Trim leading/trailing spaces from cell values. | | `outputFormat` | String | `all-changes` | What to output: `all-changes`, `added-only`, `removed-only`, `modified-only`, `summary-only` | *** ### Output examples **Modified row (price changed):** ```json { "changeType": "modified", "rowKey": "id=2", "changedColumns": "price, stock", "columnCount": 2, "data": { "id": "2", "name": "Widget Beta", "price_before": "14.99", "price_after": "19.99", "stock_before": "50", "stock_after": "45" } } ``` **Added row:** ```json { "changeType": "added", "rowKey": "id=5", "changedColumns": "id, name, price, stock", "columnCount": 4, "data": { "id": "5", "name": "Widget Epsilon", "price": "34.99", "stock": "10" } } ``` **Removed row:** ```json { "changeType": "removed", "rowKey": "id=4", "changedColumns": "id, name, price, stock", "columnCount": 4, "data": { "id": "4", "name": "Widget Delta", "price": "24.99", "stock": "25" } } ``` **Summary (from key-value store, key `DIFF_SUMMARY`):** ```json { "totalRowsA": 4, "totalRowsB": 4, "addedRows": 1, "removedRows": 1, "modifiedRows": 1, "unchangedRows": 2, "totalChanges": 3, "keyColumns": ["id"], "hasHeader": true, "delimiter": "," } ``` *** ### Tips for best results - 🔑 **Always specify key columns** for datasets that have a unique identifier. Without key columns, the actor uses row position — which means adding a row at the top makes every subsequent row appear as "modified." - 📋 **Use composite keys** when no single column is unique. E.g. `["country", "city"]` for geographic data or `["year", "month", "product_id"]` for time-series exports. - ✂️ **Keep trimWhitespace enabled** (the default). Spreadsheet exports often add invisible trailing spaces that would otherwise cause false "modified" detections. - 🔠 **Use case-insensitive mode** when comparing data from different systems that may use different capitalization conventions. - 📤 **Use `outputFormat: modified-only`** when you only care about value changes, not structural additions/removals. This keeps datasets small and focused. - 📊 **Check `DIFF_SUMMARY` in the key-value store** for aggregate counts before diving into individual rows. If `totalChanges` is 0, both CSVs are identical. - 🔄 **Schedule weekly runs** to automate monitoring of recurring CSV exports. Combine with webhooks to alert you when changes exceed a threshold. - 📦 **Large files**: CSVs up to hundreds of thousands of rows work fine. The actor uses batched processing to handle large datasets efficiently. *** ### Integrations **📊 CSV Diff Tool → Google Sheets** Export diff results directly to a Google Sheets spreadsheet using Apify's Google Sheets integration. Create a live "change log" sheet that updates every time you re-run the diff. Useful for sharing change reports with non-technical stakeholders without manual copy-paste. **🔔 CSV Diff Tool → Slack/Discord alerts** Combine with an Apify webhook that fires when the run completes. If `totalChanges > 0`, send a Slack message with the counts: "Product catalog updated: 3 prices changed, 1 item removed." Keeps your team informed without manual monitoring. **🔁 CSV Diff Tool → Make (formerly Integromat) or Zapier** Trigger a Make scenario when the Apify run completes. Read the dataset items and route each `changeType` to a different action — e.g. `added` rows go to a CRM import, `removed` rows trigger a deactivation workflow, `modified` rows trigger a price update. **📅 Scheduled monitoring of recurring exports** Use Apify's scheduler to run the diff daily or weekly against the latest export from your ERP, PIM, or data warehouse. Compare against the previous run's output (stored in a dataset) to build an automated audit trail. **🔗 Webhook-triggered diff in CI/CD pipelines** Call the Apify API from your CI/CD pipeline after running a data migration. Diff the pre-migration and post-migration exports to verify the transform produced the expected changes. Fail the pipeline if unexpected rows were modified. *** ### Using the Apify API #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('automation-lab/csv-diff-tool').call({ csvA: 'id,name,price\n1,Alpha,9.99\n2,Beta,14.99', csvB: 'id,name,price\n1,Alpha,9.99\n2,Beta,19.99', keyColumns: ['id'], outputFormat: 'all-changes', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient(token='YOUR_APIFY_TOKEN') run = client.actor('automation-lab/csv-diff-tool').call(run_input={ 'csvA': 'id,name,price\n1,Alpha,9.99\n2,Beta,14.99', 'csvB': 'id,name,price\n1,Alpha,9.99\n2,Beta,19.99', 'keyColumns': ['id'], 'outputFormat': 'all-changes', }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items) ``` #### cURL ```bash curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~csv-diff-tool/runs?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "csvA": "id,name,price\n1,Alpha,9.99\n2,Beta,14.99", "csvB": "id,name,price\n1,Alpha,9.99\n2,Beta,19.99", "keyColumns": ["id"], "outputFormat": "all-changes" }' ``` *** ### Use with AI agents via MCP CSV Diff Tool is available as a tool for AI assistants that support the [Model Context Protocol (MCP)](https://docs.apify.com/platform/integrations/mcp). Add the Apify MCP server to your AI client — this gives you access to all Apify actors, including this one: #### Setup for Claude Code ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/csv-diff-tool" ``` #### Setup for Claude Desktop, Cursor, or VS Code Add this to your MCP config file: ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com?tools=automation-lab/csv-diff-tool", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` **Example prompts for AI agents:** - *"Compare these two CSV exports and tell me which product prices changed."* - *"I have a customer list from January and one from February — find who was added or removed."* - *"Diff these two CSVs using 'email' as the key column and show me only the modified rows."* *** ### Is it legal to compare CSV data? Yes — this actor performs **local computation only** on data you provide. It makes no external HTTP requests, accesses no third-party websites, and stores nothing beyond the run's dataset and key-value store (which you control). **Your data, your responsibility**: Ensure you have the right to process any personal data contained in the CSVs you compare (GDPR, CCPA, etc.). The actor does not transmit your data to any third parties. *** ### FAQ **How fast is the comparison?** For typical datasets (up to 10,000 rows), the comparison completes in under 2 seconds. Even 100,000-row CSVs typically finish in under 10 seconds since the algorithm runs in O(n) time with hash-based key lookups. **How much does it cost?** The start fee is $0.005, which covers the first 1,000 rows compared. Additional rows cost $0.001 per 1,000. Two 500-row CSVs cost $0.005 total. See the [Pricing section](#how-much-does-it-cost-to-compare-csv-files) for a full table. **What if my CSV has duplicate key values?** The last row with a given key value wins in the lookup map. If your data has genuine duplicates (e.g., multiple orders with the same `order_id`), use a composite key that makes rows unique, or leave `keyColumns` empty to use row-position matching. **How does it differ from a plain text diff (e.g. `diff` command)?** A text diff treats each line as a unit and is sensitive to row order — adding one row at the top makes every subsequent row appear as "changed." CSV Diff Tool understands column structure and uses key-based matching, so reordered rows are correctly identified as unchanged, and you see column-level before/after values for modified rows. **Why are some rows showing as modified when they look identical?** The most common cause is invisible whitespace — extra spaces before or after cell values. Enable `trimWhitespace: true` (it's on by default). The second most common cause is case differences — `Apple` vs `apple`. Enable `caseSensitive: false` to ignore case. **Why are all rows showing as modified when I expect 0 changes?** Check that both CSVs use the same delimiter. If one is comma-separated and the other is semicolon-separated, the parser will misread column boundaries and everything will look different. Also verify that `hasHeader` matches — if one CSV has a header and you set `hasHeader: false`, the header row becomes a data row and shifts all comparisons. **Can I compare CSVs with different columns?** Yes. The actor takes the union of all column names from both CSVs. Columns present in one but not the other will have empty values for the dataset they're missing from. *** ### Other data tools from automation-lab 🔗 [**Apache Log Parser**](https://apify.com/automation-lab/apache-log-parser) — Parse Apache and Nginx access logs into structured datasets with IP, path, status code, and response time fields. 🔗 [**Base64 Converter**](https://apify.com/automation-lab/base64-converter) — Encode or decode Base64 strings and file content, with URL-safe variant support. 🔗 [**Barcode Generator**](https://apify.com/automation-lab/barcode-generator) — Generate QR codes and 1D barcodes (EAN-13, Code128, etc.) in bulk from a list of values. 🔗 [**Accessibility Checker**](https://apify.com/automation-lab/accessibility-checker) — Audit web pages for WCAG accessibility violations using automated axe-core analysis. 🔗 [**Broken Link Checker**](https://apify.com/automation-lab/broken-link-checker) — Crawl a website and find all broken internal and external links with their HTTP status codes. 🔗 [**Fake Test Data Generator**](https://apify.com/automation-lab/fake-test-data-generator) — Generate bulk fake/test data with realistic names, addresses, emails, and more using Faker.js. 🔗 [**Bulk Image Optimizer**](https://apify.com/automation-lab/bulk-image-optimizer) — Compress and resize images in bulk from URLs, with WebP conversion and quality controls. # Actor input Schema ## `csvA` (type: `string`): Paste the baseline (original) CSV content here. This is the 'before' dataset. ## `csvB` (type: `string`): Paste the updated (new) CSV content here. This is the 'after' dataset. ## `keyColumns` (type: `array`): Column names that uniquely identify each row (e.g. 'id', or \['country', 'city']). If empty, row position (1, 2, 3...) is used for matching. ## `delimiter` (type: `string`): Field separator character. Use comma for standard CSV, tab (\t) for TSV, semicolon for European formats. ## `hasHeader` (type: `boolean`): Enable if the first row contains column names. Disable if the CSV has no header row (columns will be named col1, col2, etc.). ## `caseSensitive` (type: `boolean`): Enable to treat 'Apple' and 'apple' as different values. Disable for case-insensitive matching. ## `trimWhitespace` (type: `boolean`): Trim leading/trailing spaces from cell values before comparison. Recommended to avoid false positives from invisible whitespace. ## `outputFormat` (type: `string`): Controls what gets written to the dataset. 'all-changes' outputs one row per changed record. 'summary-only' outputs only the summary statistics. ## Actor input object example ```json { "csvA": "id,name,price,stock\n1,Widget Alpha,9.99,100\n2,Widget Beta,14.99,50\n3,Widget Gamma,4.99,200\n4,Widget Delta,24.99,25", "csvB": "id,name,price,stock\n1,Widget Alpha,9.99,100\n2,Widget Beta,19.99,45\n3,Widget Gamma,4.99,200\n5,Widget Epsilon,34.99,10", "keyColumns": [ "id" ], "delimiter": ",", "hasHeader": true, "caseSensitive": true, "trimWhitespace": true, "outputFormat": "all-changes" } ``` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "csvA": `id,name,price,stock 1,Widget Alpha,9.99,100 2,Widget Beta,14.99,50 3,Widget Gamma,4.99,200 4,Widget Delta,24.99,25`, "csvB": `id,name,price,stock 1,Widget Alpha,9.99,100 2,Widget Beta,19.99,45 3,Widget Gamma,4.99,200 5,Widget Epsilon,34.99,10`, "keyColumns": [ "id" ], "delimiter": ",", "hasHeader": true, "caseSensitive": true, "trimWhitespace": true, "outputFormat": "all-changes" }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/csv-diff-tool").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "csvA": """id,name,price,stock 1,Widget Alpha,9.99,100 2,Widget Beta,14.99,50 3,Widget Gamma,4.99,200 4,Widget Delta,24.99,25""", "csvB": """id,name,price,stock 1,Widget Alpha,9.99,100 2,Widget Beta,19.99,45 3,Widget Gamma,4.99,200 5,Widget Epsilon,34.99,10""", "keyColumns": ["id"], "delimiter": ",", "hasHeader": True, "caseSensitive": True, "trimWhitespace": True, "outputFormat": "all-changes", } # Run the Actor and wait for it to finish run = client.actor("automation-lab/csv-diff-tool").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "csvA": "id,name,price,stock\\n1,Widget Alpha,9.99,100\\n2,Widget Beta,14.99,50\\n3,Widget Gamma,4.99,200\\n4,Widget Delta,24.99,25", "csvB": "id,name,price,stock\\n1,Widget Alpha,9.99,100\\n2,Widget Beta,19.99,45\\n3,Widget Gamma,4.99,200\\n5,Widget Epsilon,34.99,10", "keyColumns": [ "id" ], "delimiter": ",", "hasHeader": true, "caseSensitive": true, "trimWhitespace": true, "outputFormat": "all-changes" }' | apify call automation-lab/csv-diff-tool --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/csv-diff-tool", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CSV Diff Tool", "description": "Compare two CSV datasets and find added, removed, and modified rows. Supports key-column matching, configurable delimiters, case-insensitive comparison, and whitespace trimming. Exports a structured change report with before/after values.", "version": "0.1", "x-build-id": "EReANMQyjZUxiDPLK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~csv-diff-tool/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-csv-diff-tool", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~csv-diff-tool/runs": { "post": { "operationId": "runs-sync-automation-lab-csv-diff-tool", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~csv-diff-tool/run-sync": { "post": { "operationId": "run-sync-automation-lab-csv-diff-tool", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "csvA", "csvB" ], "properties": { "csvA": { "title": "📄 CSV A (baseline)", "type": "string", "description": "Paste the baseline (original) CSV content here. This is the 'before' dataset." }, "csvB": { "title": "📄 CSV B (updated)", "type": "string", "description": "Paste the updated (new) CSV content here. This is the 'after' dataset." }, "keyColumns": { "title": "🔑 Key columns", "type": "array", "description": "Column names that uniquely identify each row (e.g. 'id', or ['country', 'city']). If empty, row position (1, 2, 3...) is used for matching.", "items": { "type": "string" }, "default": [] }, "delimiter": { "title": "🔣 Delimiter", "enum": [ ",", ";", "\t", "|" ], "type": "string", "description": "Field separator character. Use comma for standard CSV, tab (\\t) for TSV, semicolon for European formats.", "default": "," }, "hasHeader": { "title": "📋 First row is header", "type": "boolean", "description": "Enable if the first row contains column names. Disable if the CSV has no header row (columns will be named col1, col2, etc.).", "default": true }, "caseSensitive": { "title": "🔠 Case-sensitive comparison", "type": "boolean", "description": "Enable to treat 'Apple' and 'apple' as different values. Disable for case-insensitive matching.", "default": true }, "trimWhitespace": { "title": "✂️ Trim whitespace", "type": "boolean", "description": "Trim leading/trailing spaces from cell values before comparison. Recommended to avoid false positives from invisible whitespace.", "default": true }, "outputFormat": { "title": "📊 Output format", "enum": [ "all-changes", "added-only", "removed-only", "modified-only", "summary-only" ], "type": "string", "description": "Controls what gets written to the dataset. 'all-changes' outputs one row per changed record. 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