# Capture a full-height screenshot of a webpage In just a few lines of JSON inlined in a single cURL command, you can automate: * Dismissing Wikipedia’s EU cookie consent banner (if present) * Waiting for the main heading on the Artificial Intelligence article * Scrolling through every section (lazy-loaded images and all) * Capturing a full-page PNG for archiving, visual regression, or documentation All without installing Playwright or managing headless browsers, Gaffa handles it for you server-side via the[ Browser Requests API](https://gaffa-1.gitbook.io/gaffa/features/browser-requests). ### Prerequisites * A valid Gaffa API key * A simple HTTP client (cURL, Postman, axios, etc.). * Familiarity with the[ API Playground](https://gaffa.dev/dashboard/playground) for testing browser requests. * Target URL for this tutorial, for this we'll use Wikipedia: {% stepper %} {% step %} ### Execute the Request Use cURL with the full JSON payload inlined to ensure Gaffa receives exactly what you intend: ```sh curl https://api.gaffa.dev/v1/browser/requests \ --request POST \ --header 'Content-Type: application/json' \ --header 'X-API-Key: YOUR_API_KEY' \ --data '{ "url": "https://en.wikipedia.org/wiki/Artificial_intelligence", "async": false, "max_cache_age": 0, "settings": { "actions": [ { "type": "wait", "selector": "#cookie-policy-notice", "timeout": 10000, "continue_on_fail": true }, { "type": "click", "selector": "#cookie-policy-notice", "continue_on_fail": true }, { "type": "wait", "selector": "#firstHeading", "timeout": 10000 }, { "type": "scroll", "percentage": 100 }, { "type": "capture_screenshot", "size": "fullscreen" } ] } }' ``` Replace YOUR\_API\_KEY with your actual token from your [Dashboard.](https://gaffa.dev/dashboard/api-keys) This command has the following actions: 1. **Wait** (optional): Detect and accept Wikipedia’s cookie banner if it appears. If it fails, that simply means no banner was present, or it did not load in time. Since continue\_on\_fail defaults to true, Gaffa will continue without halting the workflow, ensuring the remaining steps still execute. 2. **Wait**: Ensure the main heading (#firstHeading) is loaded. 3. **Scroll**: Scroll through the entire page to trigger any lazy-loaded content. 4. **Capture** Screenshot: Produce a full-page PNG. {% endstep %} {% step %} ### Retrieve Your Screenshot A successful response returns JSON like: {% code lineNumbers="true" %} ```json { "data": { "id": "brq_VJX3mbESLiyCFYvZQEUih9RdDYovog", "url": "https://en.wikipedia.org/wiki/Artificial_intelligence", "proxy_location": null, "state": "completed", "credit_usage": 2, "http_status_code": 200, "from_cache": false, "started_at": "2025-06-09T15:55:46.4235903Z", "completed_at": "2025-06-09T15:56:27.9381332Z", "running_time": "00:00:40.7348244", "page_load_time": "00:00:02.2087117", "actions": [ { "id": "act_VJX3memaue6YUgFcn44uNscZbVUpYg", "type": "wait", "query": "wait?selector=%23cookie-policy-notice%2C%20.mw-cookie-consent-container&timeout=10000&continue_on_fail=true", "timestamp": "2025-06-09T15:55:48.6323091Z", "error": "action_timed_out" }, { "id": "act_VJX3mkwfwNPdGiMUpqKr34Tm5xzyUU", "type": "click", "query": "click?selector=%23cookie-policy-notice%20button%2C%20.mw-cookie-consent-container%20button&continue_on_fail=true&timeout=5000", "timestamp": "2025-06-09T15:55:58.7949275Z", "error": "action_timed_out" }, { "id": "act_VJX3mkSJ3sevWRXUCjFy6zwfD172fV", "type": "wait", "query": "wait?selector=%23firstHeading&timeout=10000&continue_on_fail=false", "timestamp": "2025-06-09T15:56:03.9581113Z" }, { "id": "act_VJX3mbq9Jgj8EwADszW2AqdeJJXJiY", "type": "scroll", "query": "scroll?percentage=100&max_scroll_time=20000&scroll_speed=medium&continue_on_fail=false", "timestamp": "2025-06-09T15:56:03.9691994Z" }, { "id": "act_VJX3mjBQYv8zTsXv1SkgUnBkzNFmJU", "type": "capture_screenshot", "query": "capture_screenshot?size=fullscreen&continue_on_fail=false", "timestamp": "2025-06-09T15:56:20.0727905Z", "output": "https://storage.gaffa.dev/brq/image/brq_VJX3mbESLiyCFYvZQEUih9RdDYovog/act_VJX3mjBQYv8zTsXv1SkgUnBkzNFmJU_full.png" } ] }, "error": null } ``` {% endcode %} The response contains the following information: * **data.id**: Unique request identifier. * **data.state**: "completed" means the workflow finished (even if some steps timed out). * **data.credit\_usage**: Credits consumed for this run. * **data.started\_at** / **data.completed\_at**: Workflow timing. * **data.running\_time** and **data.page\_load\_time**: Performance metrics. * **data.actions**: Each action’s details, including successes, timeouts, and final screenshot URL. Within the list of actions, you'll be able to see the capture\_screenshot action, which contains an output parameter containing the full-size screenshot that was captured. {% endstep %} {% endstepper %} If you don't want to use cURL, you can also run this query in the [Gaffa API Playground](https://gaffa.dev/dashboard/playground), which is an easy way to get started. ### Use Cases Gaffa's screenshot action could be used for a huge number of use cases, but here are a few ideas: * **Visual Regression**: Integrate into your CI pipeline to compare changes over time. * **Archival**: Schedule daily captures for audit or compliance purposes. * **Monitoring**: Automate periodic checks to detect visual bugs or layout shifts. #### All this is powered by Gaffa’s hosted headless browsers with no local setup required. Experiment with more actions and easily build complex browser workflows. Refer to the full[ Browser Requests API documentation](https://gaffa-1.gitbook.io/gaffa/features/browser-requests) for additional capabilities.