# Cross-Platform Ad Intelligence - Meta + Google + TikTok (`seibs.co/ad-library-intel`) Actor Competitor ad creatives, formats, run-dates, and active-vs-stopped signal across Meta Ad Library + Google Ads Transparency + TikTok, in one schema keyed by advertiser/brand/domain. Creative hook/offer tagging, competitor-set rollups, new-creative monitor mode. For marketers, DTC, agencies. - **URL**: https://apify.com/seibs.co/ad-library-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** Business, Marketing, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 ad records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Cross-Platform Ad Intelligence (Meta + Google + TikTok) > **TL;DR for performance marketers, DTC brands, and agencies:** Pull a competitor's live ad creatives, formats, run-dates, and **active-vs-stopped** signal across **Meta Ad Library + Google Ads Transparency Center + TikTok** in one run, normalized into a single cross-platform schema keyed by **advertiser / brand / domain**. On top of the raw ads it adds the "what's working now" layer the ad-spy SaaS tools gate behind subscriptions: **creative hook/offer tagging**, **per-advertiser rollups** (active vs stopped, format mix, top angles, longest-running creative), and a **cross-advertiser competitor comparison**. The transparency archives are public-by-design but have no clean API and the paid stack (AdSpy $149/mo + BigSpy $99/mo + Pipiads $77/mo ≈ $325/mo) gates the cross-platform research - this undercuts it at pay-per-use. Logged-out public archives, no login, no token, PII minimized to the advertising business entity. ### Run it in 30 seconds ```python ## Via the Apify Python SDK from apify_client import ApifyClient client = ApifyClient("") run = client.actor("seibs.co/ad-library-intel").call(run_input={ "mode": "competitor_set", "advertisers": ["Allbirds", "Rothy's"], "platforms": ["meta", "google", "tiktok"], "country": "US" }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ```` Or via curl: ```bash curl -X POST "https://api.apify.com/v2/acts/seibs.co~ad-library-intel/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"mode": "advertiser_ads", "advertisers": ["Allbirds"], "platforms": ["meta","google","tiktok"]}' ``` Or click "Try for free" on this page if you prefer the no-code UI. ### What you get Each run produces: - A clean dataset, filterable in the Apify console and downloadable as CSV or JSON - An OUTPUT.html dashboard preview of your top records - A sample-output preview at [`.actor/sample-output.json`](./.actor/sample-output.json) - An `access_notes` record up top documenting each archive's access method, anti-bot tier, and escalation telemetry ### What does Ad Intelligence do? It queries each selected ad-transparency archive for your advertisers (or keywords) and normalizes every creative into one schema: `platform`, `advertiser_name`, `advertiser_domain` (the cross-platform join key), `ad_format` (normalized to `image` / `video` / `carousel` / `text`), `headline`, `body_text`, `cta_text`, `landing_url`, `media_urls`, `start_date` / `end_date`, `is_active`, `days_running`, `spend_band` / `impressions_band` where the archive exposes it, `regions`, and `publisher_platforms`. Then it runs the **value layer**: - **Creative analysis** - tags each creative's **hook** (question / social-proof / urgency / curiosity / problem-solution / direct-benefit), **offer** (% discount / dollar-off / free shipping / free trial / BOGO / bundle / subscription / lead magnet / sale event), **themes**, and normalized **CTA**. This is the "what angle is working" signal. - **Advertiser tracking** - a per-advertiser footprint rollup: active vs stopped counts, format mix, spend-band mix, top hooks/offers, and the longest-running (proven) creative. - **Competitor set** - a cross-advertiser comparison: who's running the most, the shared angles everyone is using, and each advertiser's unique lean. ### Modes | Mode | What it returns | |---|---| | `advertiser_ads` (default) | Every matching creative per platform for your advertiser/brand/domain queries, normalized + (optionally) creative-tagged. | | `keyword_search` | Creatives discovered by keyword/term across the archives - for "what ads run for *this offer*" research. | | `competitor_set` | `advertiser_ads` plus the cross-advertiser `competitor_set` comparison and per-advertiser rollups (defaults `include_advertiser_tracking` on). | ### Platform coverage + access notes The three archives are public-by-design but none offers a clean API, and they fingerprint automated clients differently. Coverage is honestly described per surface: | Platform | Archive | Access | Notes | |---|---|---|---| | **meta** | Meta Ad Library (Facebook + Instagram) | browser-first | The commercial-ads view is public, but the internal `search_ads` endpoint is gated by an `lsd` token + `datr` cookie minted by a real page load - so the browser tier loads the public results page and captures the page's *own* search XHR (no forged token, no login). The official Graph `ads_archive` API only covers political/social-issue ads **and** needs a token, so it is intentionally not used (commercial coverage is the point). | | **google** | Google Ads Transparency Center | http-first | Free-text + region search hits the internal `SearchService/SearchCreatives` RPC (`f.req` form body, `)]}'` JSON prefix); reachable via curl\_cffi TLS impersonation, with the browser tier capturing the same XHR as a fallback. | | **tiktok** | TikTok Commercial Content Library / Creative Center | http-first | The Commercial Content Library (`library.tiktok.com`) exposes a JSON advertiser/keyword search (strongest for EU regions); the Creative Center Top-Ads API is a region-gated trend fallback. | Where a logged-out request is blocked or returns no parseable ads, the platform fails soft with a documented `platform_pending` / `fetch_error` note (the run still finishes SUCCEEDED) rather than fabricating data. ### Anti-bot escalation (residential + browser) Each archive request runs an automatic escalation ladder: 1. **httpx** over the DATACENTER proxy - cheapest, used first for the JSON surfaces (Google, TikTok). 2. **curl\_cffi** with real Chrome TLS impersonation over the RESIDENTIAL proxy - defeats JA3/TLS-fingerprint WAFs (Meta's edge, Google's RPC). 3. **Playwright** (patchright stealth) over RESIDENTIAL - loads the archive page and replays/captures its own API call (carrying the live token). This is what makes the token-locked Meta surface return data. 4. **Fail-soft** - documented note, run stays SUCCEEDED. Set `use_browser_fallback=false` to use plain httpx only (Meta then returns a `platform_pending` note). For the most reliable Meta/Google clearance, point the browser tier at a warm anti-detect browser via `browser_cdp_url` (or the `BROWSER_CDP_URL` env var); otherwise run on the `apify/actor-python-playwright` image so a headless Chromium is available. ### Monitor mode (new-creative alerts) Run this actor on an Apify **Schedule** and it switches to monitor mode: it diffs this run's creatives against the last scheduled run and emits a `monitor_digest` of **new creatives** and **newly-stopped creatives** per advertiser, optionally posting the digest to a Slack-compatible `monitor_webhook_url`. Charges one `scheduled_delta_run` per scheduled run. This is the "alert me when a competitor launches a new ad" use case. ### Responsible use / data scope Official public transparency archives, accessed logged-out, no account creation, no paid API token, no login walls bypassed. We minimize PII: the identity we keep is the **advertising business entity** (the page/brand/advertiser the archive publishes by design) plus the public creative - we never resolve a page admin or any natural-person contact. Respect each platform's terms and your local regulations for how you use competitive ad data. ### AI / RAG / Agent The `overview` dataset view is a narrow, token-efficient slice for LLM tool responses. A paired **MCP server** (`mcp-ad-library-intel`) exposes these as agent tools (`search_advertiser_ads`, `get_active_creatives`, `compare_competitors`, `track_new_creatives`, `search_ads_by_keyword`) and is x402 (USDC on Base) + Skyfire ready for token-less agentic payments. ### Features - Cross-platform in one actor: Meta + Google + TikTok, deduped by advertiser/domain - Active-vs-stopped signal + days-running per creative - Creative hook / offer / theme / CTA tagging (no LLM key required) - Per-advertiser rollups + cross-advertiser competitor comparison - New-creative monitor mode with Slack webhook - Anti-bot escalation (curl\_cffi + browser XHR capture) with fail-soft notes - Pay-per-event pricing that undercuts the ~$325/mo ad-spy SaaS stack ### Use cases - **Competitive creative research** - what's your competitor running right now, on which platform, in which format - **"What's working" trend-spotting** - which hooks/offers dominate a niche this week - **Creative refresh tracking** - get alerted when a competitor launches or kills a creative - **Agency reporting** - a cross-platform competitor footprint table per client - **DTC / performance marketing** - mine proven (long-running) creatives for angles to test ### Pricing (Pay Per Event) | Event | Price | When | |---|---|---| | `ad_record` | $0.004 | Per normalized cross-platform creative | | `creative_analysis` | $0.008 | Per creative tagged with hook/offer/theme/CTA | | `advertiser_tracking` | $0.010 | Per advertiser rollup + competitor comparison | | `scheduled_delta_run` | $0.050 | Per scheduled monitor-mode delta digest | A run that returns nothing costs nothing. The free Apify plan covers exploration runs on your $5 platform credit. ### Related actors Part of the Seibs.co intelligence portfolio. Pairs well with `shopify-store-discovery` (find a store -> see its ads), `tiktok-shop-creator-intel`, and the MCP twin `mcp-ad-library-intel`. # Actor input Schema ## `mode` (type: `string`): advertiser\_ads = pull creatives for the given advertisers/brands/domains across the selected platforms. keyword\_search = search the archives by keyword/term (creative discovery). competitor\_set = advertiser\_ads plus a cross-advertiser competitor comparison (active footprint, format lean, shared/unique angles). ## `advertisers` (type: `array`): Brand, Facebook page, or domain names to pull ads for, e.g. \['Nike', 'Allbirds', 'glossier.com']. Used in advertiser\_ads / competitor\_set modes. Hard cap of 50. ## `keywords` (type: `array`): Search terms to discover ads by, e.g. \['free shipping', 'running shoes']. Used in keyword\_search mode. Hard cap of 25. ## `platforms` (type: `array`): Which ad-transparency archives to query: meta (Meta Ad Library / Facebook + Instagram), google (Google Ads Transparency Center), tiktok (TikTok Commercial Content Library / Creative Center). Aliases like 'facebook', 'instagram', 'youtube' are accepted. Leave empty for all three. Pass \['all'] for all. ## `all_platforms` (type: `boolean`): Shortcut to query Meta + Google + TikTok (overrides the platforms list). ## `country` (type: `string`): Two-letter region code scoping the archive query (e.g. US, GB, DE). Meta and Google scope by audience country; TikTok's Commercial Content Library is strongest for EU regions. Default US. ## `active_status` (type: `string`): all = active + stopped creatives (best for the active-vs-stopped signal). active = only currently-running. inactive = only stopped. Applies to Meta; the other archives return what they expose. ## `include_creative_analysis` (type: `boolean`): Tag each creative's hook (question / social-proof / urgency / curiosity / ...), offer (% discount / free shipping / BOGO / lead magnet / ...), themes, and CTA. Adds a creative\_analysis PPE charge per tagged ad. This is the 'what's working now' signal. ## `include_advertiser_tracking` (type: `boolean`): Build a per-advertiser footprint rollup (active vs stopped counts, format mix, spend-band mix, top hooks/offers, longest-running creative). Adds an advertiser\_tracking PPE charge per advertiser rollup. Defaults on for competitor\_set mode. ## `max_results_per_platform` (type: `integer`): Hard cap on ads returned per platform per advertiser/keyword. Default 30. ## `monitor_webhook_url` (type: `string`): When this actor runs under an Apify Schedule (monitor mode), post the change digest (new creatives, newly-stopped creatives) to this Slack-compatible webhook URL. ## `use_apify_proxy` (type: `boolean`): Route archive requests through Apify Proxy. DATACENTER handles the first (httpx) pass; a RESIDENTIAL tier is provisioned for the anti-bot escalation legs (the archives fingerprint datacenter IPs). ## `use_browser_fallback` (type: `boolean`): When an archive serves an anti-bot / token / login challenge (Meta's Ad Library is token-locked; Google's RPC fingerprints TLS), automatically escalate: switch to the RESIDENTIAL proxy and retry with curl\_cffi Chrome TLS impersonation, then a headless browser that captures the archive's own search XHR. Turn off to use plain httpx only (Meta then returns a documented platform\_pending note). ## `browser_cdp_url` (type: `string`): Optional. CDP/WebSocket endpoint of an already-running, anti-detect (UC-mode / real Chrome) browser. When set, the browser tier connects to it (inheriting its session + fingerprint) so it clears Meta's edge + Google's challenge reliably. Without it a plain headless Chromium is launched (works on the apify/actor-python-playwright image; weaker against managed challenges). Can also be set as the BROWSER\_CDP\_URL env var. ## `apify_proxy_groups` (type: `array`): Override the auto-selected proxy group. Leave empty to let the actor pick DATACENTER for the first pass and RESIDENTIAL for escalation. ## `concurrency` (type: `integer`): Parallel archive fetches. The archives are rate-sensitive; default 4. ## Actor input object example ```json { "mode": "advertiser_ads", "advertisers": [ "Allbirds", "Rothy's" ], "keywords": [], "platforms": [ "meta", "google", "tiktok" ], "all_platforms": false, "country": "US", "active_status": "all", "include_creative_analysis": true, "include_advertiser_tracking": false, "max_results_per_platform": 20, "monitor_webhook_url": "", "use_apify_proxy": true, "use_browser_fallback": true, "browser_cdp_url": "", "apify_proxy_groups": [], "concurrency": 4 } ``` # Actor output Schema ## `datasetItems` (type: `string`): Narrow, token-efficient slice of every record. Consumer: LLM agents (Claude, GPT, LangChain tools), MCP hosts, dashboards. Fields: platform, advertiser, format, headline, active-vs-stopped, days running, start date, domain. ## `datasetItemsAds` (type: `string`): All fields for every creative. Consumer: humans browsing the dataset, RAG ingest, full backups. Larger payload - not recommended as a direct LLM tool response. ## `datasetItemsAdvertisers` (type: `string`): Per-advertiser footprint rollups (active vs stopped, format mix, top hooks/offers). Consumer: competitive-intel dashboards, agency reporting. ## `datasetItemsMcp` (type: `string`): First 50 overview records as a clean JSON array. Wrap on the agent side in an MCP tool-call response envelope, e.g. `{ "ok": true, "data": , "meta": { "actor": "ad-library-intel", "count": , "view": "overview" } }`. Consumer: MCP servers, Claude Desktop, Cursor, OpenAI Assistants tool calls. ## `datasetItemsCsv` (type: `string`): Spreadsheet-friendly export of the overview view. Consumer: humans, marketing-ops teams, Excel / Google Sheets users. # 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 = { "mode": "advertiser_ads", "advertisers": [ "Allbirds", "Rothy's" ], "platforms": [ "meta", "google", "tiktok" ], "country": "US", "max_results_per_platform": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/ad-library-intel").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 = { "mode": "advertiser_ads", "advertisers": [ "Allbirds", "Rothy's", ], "platforms": [ "meta", "google", "tiktok", ], "country": "US", "max_results_per_platform": 20, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/ad-library-intel").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 '{ "mode": "advertiser_ads", "advertisers": [ "Allbirds", "Rothy'\''s" ], "platforms": [ "meta", "google", "tiktok" ], "country": "US", "max_results_per_platform": 20 }' | apify call seibs.co/ad-library-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/ad-library-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Cross-Platform Ad Intelligence - Meta + Google + TikTok", "description": "Competitor ad creatives, formats, run-dates, and active-vs-stopped signal across Meta Ad Library + Google Ads Transparency + TikTok, in one schema keyed by advertiser/brand/domain. Creative hook/offer tagging, competitor-set rollups, new-creative monitor mode. For marketers, DTC, agencies.", "version": "0.1", "x-build-id": "1XOD7zQKyUDcMI34J" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~ad-library-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-ad-library-intel", "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/seibs.co~ad-library-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-ad-library-intel", "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/seibs.co~ad-library-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-ad-library-intel", "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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "advertiser_ads", "keyword_search", "competitor_set" ], "type": "string", "description": "advertiser_ads = pull creatives for the given advertisers/brands/domains across the selected platforms. keyword_search = search the archives by keyword/term (creative discovery). competitor_set = advertiser_ads plus a cross-advertiser competitor comparison (active footprint, format lean, shared/unique angles).", "default": "advertiser_ads" }, "advertisers": { "title": "Advertisers / brands / domains", "maxItems": 50, "type": "array", "description": "Brand, Facebook page, or domain names to pull ads for, e.g. ['Nike', 'Allbirds', 'glossier.com']. Used in advertiser_ads / competitor_set modes. Hard cap of 50.", "default": [], "items": { "type": "string" } }, "keywords": { "title": "Keywords (keyword_search mode)", "maxItems": 25, "type": "array", "description": "Search terms to discover ads by, e.g. ['free shipping', 'running shoes']. Used in keyword_search mode. Hard cap of 25.", "default": [], "items": { "type": "string" } }, "platforms": { "title": "Platforms", "maxItems": 3, "uniqueItems": true, "type": "array", "description": "Which ad-transparency archives to query: meta (Meta Ad Library / Facebook + Instagram), google (Google Ads Transparency Center), tiktok (TikTok Commercial Content Library / Creative Center). Aliases like 'facebook', 'instagram', 'youtube' are accepted. Leave empty for all three. Pass ['all'] for all.", "items": { "type": "string", "enum": [ "meta", "google", "tiktok" ] }, "default": [ "meta", "google", "tiktok" ] }, "all_platforms": { "title": "Query all platforms", "type": "boolean", "description": "Shortcut to query Meta + Google + TikTok (overrides the platforms list).", "default": false }, "country": { "title": "Country / region", "type": "string", "description": "Two-letter region code scoping the archive query (e.g. US, GB, DE). Meta and Google scope by audience country; TikTok's Commercial Content Library is strongest for EU regions. Default US.", "default": "US" }, "active_status": { "title": "Active status filter", "enum": [ "all", "active", "inactive" ], "type": "string", "description": "all = active + stopped creatives (best for the active-vs-stopped signal). active = only currently-running. inactive = only stopped. Applies to Meta; the other archives return what they expose.", "default": "all" }, "include_creative_analysis": { "title": "Creative analysis (hook / offer / format tagging)", "type": "boolean", "description": "Tag each creative's hook (question / social-proof / urgency / curiosity / ...), offer (% discount / free shipping / BOGO / lead magnet / ...), themes, and CTA. Adds a creative_analysis PPE charge per tagged ad. This is the 'what's working now' signal.", "default": true }, "include_advertiser_tracking": { "title": "Advertiser tracking + competitor rollups", "type": "boolean", "description": "Build a per-advertiser footprint rollup (active vs stopped counts, format mix, spend-band mix, top hooks/offers, longest-running creative). Adds an advertiser_tracking PPE charge per advertiser rollup. Defaults on for competitor_set mode.", "default": false }, "max_results_per_platform": { "title": "Max ads per platform per query", "minimum": 1, "maximum": 200, "type": "integer", "description": "Hard cap on ads returned per platform per advertiser/keyword. Default 30.", "default": 30 }, "monitor_webhook_url": { "title": "Monitor webhook URL (Slack / email, optional)", "type": "string", "description": "When this actor runs under an Apify Schedule (monitor mode), post the change digest (new creatives, newly-stopped creatives) to this Slack-compatible webhook URL.", "default": "" }, "use_apify_proxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Route archive requests through Apify Proxy. DATACENTER handles the first (httpx) pass; a RESIDENTIAL tier is provisioned for the anti-bot escalation legs (the archives fingerprint datacenter IPs).", "default": true }, "use_browser_fallback": { "title": "Anti-bot escalation (curl_cffi + browser)", "type": "boolean", "description": "When an archive serves an anti-bot / token / login challenge (Meta's Ad Library is token-locked; Google's RPC fingerprints TLS), automatically escalate: switch to the RESIDENTIAL proxy and retry with curl_cffi Chrome TLS impersonation, then a headless browser that captures the archive's own search XHR. Turn off to use plain httpx only (Meta then returns a documented platform_pending note).", "default": true }, "browser_cdp_url": { "title": "Warm browser CDP endpoint (for token-locked surfaces)", "type": "string", "description": "Optional. CDP/WebSocket endpoint of an already-running, anti-detect (UC-mode / real Chrome) browser. When set, the browser tier connects to it (inheriting its session + fingerprint) so it clears Meta's edge + Google's challenge reliably. Without it a plain headless Chromium is launched (works on the apify/actor-python-playwright image; weaker against managed challenges). Can also be set as the BROWSER_CDP_URL env var.", "default": "" }, "apify_proxy_groups": { "title": "Proxy groups (optional override)", "type": "array", "description": "Override the auto-selected proxy group. Leave empty to let the actor pick DATACENTER for the first pass and RESIDENTIAL for escalation.", "default": [], "items": { "type": "string" } }, "concurrency": { "title": "Max concurrent requests", "minimum": 1, "maximum": 8, "type": "integer", "description": "Parallel archive fetches. The archives are rate-sensitive; default 4.", "default": 4 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```