# Travel Fare & Hotel Price Intel - Flights, Hotels, Drops (`seibs.co/travel-fare-intel`) Actor Structured flight fares + hotel prices into one normalized schema. Google Flights has no API and ITA's was discontinued - this liberates the open result pages. Route summaries, cheapest-per-date grid, and monitor-mode price-drop alerts. For OTAs, fare-drop apps, corporate travel, travel fintech. - **URL**: https://apify.com/seibs.co/travel-fare-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** Travel, Business, 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 flight fare 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 ## Travel Fare & Hotel Price Intel > **TL;DR for OTAs, fare-drop apps, corporate-travel tools, and travel fintech:** Pulls structured flight fares and hotel prices into one normalized schema - origin/destination, dates, carrier, fare, cabin, stops, times (flights); property, dates, total + per-night price, stars, review score (hotels) - from the open, logged-out result pages metasearch sites render. On top of the raw fares it adds the layers a fare-drop product actually needs: **route summaries** (min/median/max fare + carrier spread), a **cheapest-per-date grid**, and **monitor-mode price-drop alerts**. The gate is the whole point: **Google Flights has no public API, the ITA Software API was discontinued, and the airline/GDS APIs (Amadeus, Sabre) are approval- and contract-gated** - so structured fare data has no clean free path. Public data only, polite rate limits, no PII. Free Apify plan covers exploration runs on your $5 platform credit. ### Run it in 30 seconds ```python ## Via the Apify Python SDK from apify_client import ApifyClient client = ApifyClient("") run = client.actor("seibs.co/travel-fare-intel").call(run_input={ "mode": "flight_fares", "routes": [{"origin": "JFK", "destination": "LAX", "departure_date": "2026-07-15"}] }) 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~travel-fare-intel/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"mode": "flight_fares", "routes": [{"origin": "JFK", "destination": "LAX", "departure_date": "2026-07-15"}]}' ``` 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 `access_notes` record up top documenting each provider's access method, proxy needs, anti-bot escalation, and the responsible-use posture - A sample-output preview at [`.actor/sample-output.json`](./.actor/sample-output.json) ### What does Travel Fare Intel do? It queries each selected provider's public result page for your routes/destinations and normalizes every result into one schema. For flights: `origin`, `destination`, `departure_date`, `return_date`, `carrier` (+ name), `fare_amount` (+ `fare_amount_usd`, `currency`), `fare_class` (normalized to `economy` / `premium_economy` / `business` / `first`), `stops`, `duration_minutes`, `departure_time`, `arrival_time`, `booking_url`, and `observed_at`. For hotels: `property_name`, `destination`, `check_in`/`check_out`, `nights`, `price_total` (+ `price_total_usd`), `price_per_night`, `star_rating`, `review_score`, `room_type`. Then it runs the **enrichment layer**: - **Route summaries** (`route_history`) - min / median / max fare, carrier count and spread, and the cheapest carrier across all offers for a route+date+cabin slice. A trend snapshot, not a single price. - **Date grid** - the cheapest fare per departure date across a window, so a fare-drop UI can answer "which day is cheapest?". - **Price-drop alerts** (`price_drop_alert`) - in monitor mode, diffs this run's cheapest fares against the previous scheduled run and flags drops past your USD/percent thresholds. The fare-watch signal. ### Modes | Mode | What it returns | |---|---| | `flight_fares` (default) | Current fares per route from each selected flight provider, plus route summaries. | | `hotel_prices` | Property prices per destination/date window from each selected hotel provider. | | `route_grid` | Cheapest-per-date grid across `date_from`..`date_to` for each route (date-grid enrichment) + route summaries. | | `fare_watch` | Monitor mode: diffs cheapest fares vs the previous scheduled run and emits `price_drop` alerts. Pair with an Apify Schedule. | ### Why this is gated (and the honest coverage tiers) Structured fare data has no clean public API - Google Flights never shipped one, ITA's was discontinued, and the GDS APIs are contract-gated. The open path is the public result pages, which sit behind edge WAFs and consent walls. The actor handles that with the portfolio's three-tier anti-bot escalation and is honest about which providers are wired: **Fully wired** - **google\_flights** - the flagship. Because Google Flights has no API, the actor builds the *same* base64 protobuf `tfs` query string the website itself uses (origin, destination, date, cabin, passengers, trip type) and parses the rendered fare cards off the public results page. No login, no OAuth. **Catalog-registered (anti-bot pipeline wired, provider parser pending -> returns a documented `provider_pending` note)** - Flights: **skyscanner**, **kayak**, **kiwi** - Hotels: **google\_hotels**, **booking**, **hotels\_com** Upgrading a catalog provider to fully-wired is one parser - the orchestration, normalization, grid, and monitor layers are already provider-agnostic. This is the same architecture the portfolio's `business-registry-intel` uses for 50-state coverage. ### Anti-bot escalation ladder Each fetch escalates only when challenged, then fails soft: 1. **httpx** over the DATACENTER proxy - cheapest, tried first. 2. **curl\_cffi** with real Chrome TLS impersonation over the RESIDENTIAL proxy - defeats JA3/TLS-fingerprint WAFs. 3. **Playwright** (patchright stealth) headful Chromium over RESIDENTIAL - for JS/consent interstitials. Optional; set `browser_cdp_url` (or the `BROWSER_CDP_URL` env var) to a warm anti-detect browser to clear the metasearch consent/Cloudflare walls, or run on the `apify/actor-python-playwright` image. 4. **Fail-soft** - if every tier is blocked/unavailable, the provider emits a documented `fetch_error` / `provider_pending` note and the run still finishes SUCCEEDED. ### Legal-safety & responsible use Per the gated-data playbook: **public, logged-out result pages only.** The client paces requests politely (a per-request delay plus a concurrency cap), never logs in, never submits payment, and never carries traveller PII - fares and prices are facts, not personal data. Honor each provider's terms and your own counsel's guidance; use the data for price research and fare monitoring, not abuse. The opt-in browser/CDP tier is provided so an operator can bring their own warm browser deliberately. ### Pricing (pay-per-event) | Event | Price | When | |---|---|---| | `fare_record` | $0.004 | Per normalized flight fare | | `hotel_record` | $0.004 | Per normalized hotel price | | `route_history` | $0.008 | Per route summary (min/median/max + carrier spread) | | `price_drop_alert` | $0.010 | Per route whose cheapest fare dropped vs the previous run | | `scheduled_delta_run` | $0.050 | Once per scheduled monitor/fare-watch run | A run that returns nothing costs nothing. A 5-layer cost-control defense (pre-flight caps, demo-mode soft-fail, a `_RunBudget` guard, in-loop budget checks, and hardcoded PPE prices) prevents runaway compute. ### MCP twin An AI-agent-native wrapper ships as **[mcp-travel-fare-intel](https://apify.com/seibs.co/mcp-travel-fare-intel)** - tools: `search_flight_fares`, `get_route_grid`, `watch_fare`, `search_hotel_prices`. It is x402 (USDC on Base) and Skyfire ready for token-less agentic payments. ### Cross-sell Part of the [seibs.co intel portfolio](https://apify.com/seibs.co). Diversification play into travel; pairs with the other monitor-mode intel actors for recurring, scheduled data feeds. # Actor input Schema ## `mode` (type: `string`): flight\_fares = current fares for each route. hotel\_prices = property prices for each destination/date window. route\_grid = cheapest-per-date grid across a date window (date-grid enrichment). fare\_watch = monitor mode: diff cheapest fares vs the previous scheduled run and emit price-drop alerts. ## `routes` (type: `array`): Routes to price (flight\_fares / route\_grid / fare\_watch). Each item: {"origin": "JFK", "destination": "LAX", "departure\_date": "2026-07-15", "return\_date": "2026-07-22" (optional, makes it round-trip), "cabin": "economy" (optional: economy|premium\_economy|business|first), "max\_stops": 1 (optional)}. Use IATA airport codes. For route\_grid, departure\_date is supplied by date\_from/date\_to. Hard cap of 50. ## `destinations` (type: `array`): Destinations to price (hotel\_prices mode). Each item: {"destination": "Miami", "check\_in": "2026-07-15", "check\_out": "2026-07-18"}. Hard cap of 50. ## `flight_providers` (type: `array`): Flight metasearch providers. 'google\_flights' is fully wired (the open result page parsed via the same protobuf query the site uses). 'skyscanner', 'kayak', 'kiwi' are catalog-registered with the anti-bot pipeline wired (provider parser pending; they return a documented provider\_pending note). Pass \['ALL'] for all; leave empty for google\_flights. ## `hotel_providers` (type: `array`): Hotel price providers: 'google\_hotels', 'booking', 'hotels\_com'. All catalog-registered with the anti-bot pipeline wired (provider parser pending; they return a documented provider\_pending note). Pass \['ALL'] for all; leave empty for google\_hotels. ## `date_from` (type: `string`): Lower bound departure date for route\_grid mode (YYYY-MM-DD). The grid prices every date from date\_from to date\_to per route (capped at 60 days). ## `date_to` (type: `string`): Upper bound departure date for route\_grid mode (YYYY-MM-DD). ## `adults` (type: `integer`): Number of adult passengers to price (1-9). Default 1. ## `min_drop_usd` (type: `integer`): fare\_watch: only flag a price drop when the cheapest fare fell at least this many USD vs the previous run. 0 = any drop. ## `min_drop_pct` (type: `integer`): fare\_watch: only flag a price drop when the cheapest fare fell at least this percent vs the previous run. 0 = any drop. A drop must clear BOTH the USD and percent thresholds. ## `max_results_per_query` (type: `integer`): Hard cap on records returned per (provider, route/destination). Default 25. ## `contact_email` (type: `string`): Optional operator contact added to request headers. Good-citizen practice; not required. ## `monitor_webhook_url` (type: `string`): When this actor runs under an Apify Schedule (monitor mode) or in fare\_watch mode, post the change digest (price drops, new/changed fares) to this Slack-compatible webhook URL. ## `use_apify_proxy` (type: `boolean`): Route requests through Apify Proxy. The DATACENTER tier handles the first (httpx) pass; a RESIDENTIAL tier is provisioned for the anti-bot escalation legs. ## `use_browser_fallback` (type: `boolean`): When a portal serves a Cloudflare/consent/403 challenge, automatically escalate: switch to the RESIDENTIAL proxy and retry with curl\_cffi Chrome TLS impersonation, then (if available) a headless browser. Turn off to use plain httpx only. ## `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 the consent/Cloudflare walls on the metasearch providers (Skyscanner, Kayak, Booking). Without it, a plain headless Chromium is launched (often blocked). Can also be set as the BROWSER\_CDP\_URL env var. ## `apify_proxy_groups` (type: `array`): Override the auto-selected proxy group for the httpx tier. Leave empty to let the actor pick DATACENTER (RESIDENTIAL is always provisioned for escalation legs). ## `concurrency` (type: `integer`): Parallel provider fetches. Fare portals are rate-sensitive; default 4. ## Actor input object example ```json { "mode": "flight_fares", "routes": [ { "origin": "JFK", "destination": "LAX", "departure_date": "2026-07-15" } ], "destinations": [], "flight_providers": [ "google_flights" ], "hotel_providers": [ "google_hotels" ], "date_from": "", "date_to": "", "adults": 1, "min_drop_usd": 0, "min_drop_pct": 0, "max_results_per_query": 10, "contact_email": "", "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, travel dashboards. ## `datasetItemsFares` (type: `string`): Full normalized fares with carrier, times, stops, and booking source. Consumer: OTAs, fare-drop apps, corporate-travel tools, RAG ingest. ## `datasetItemsPriceDrops` (type: `string`): Routes whose cheapest fare fell vs the previous scheduled run. Consumer: fare-drop alert apps, deal newsletters. ## `datasetItemsMcp` (type: `string`): First 50 overview records as a clean JSON array. Wrap on the agent side in an MCP tool-call envelope. Consumer: MCP servers, Claude Desktop, Cursor, OpenAI Assistants tool calls. ## `datasetItemsCsv` (type: `string`): Spreadsheet-friendly export of the overview view. Consumer: travel analysts, 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": "flight_fares", "routes": [ { "origin": "JFK", "destination": "LAX", "departure_date": "2026-07-15" } ], "flight_providers": [ "google_flights" ], "max_results_per_query": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/travel-fare-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": "flight_fares", "routes": [{ "origin": "JFK", "destination": "LAX", "departure_date": "2026-07-15", }], "flight_providers": ["google_flights"], "max_results_per_query": 10, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/travel-fare-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": "flight_fares", "routes": [ { "origin": "JFK", "destination": "LAX", "departure_date": "2026-07-15" } ], "flight_providers": [ "google_flights" ], "max_results_per_query": 10 }' | apify call seibs.co/travel-fare-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/travel-fare-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Travel Fare & Hotel Price Intel - Flights, Hotels, Drops", "description": "Structured flight fares + hotel prices into one normalized schema. Google Flights has no API and ITA's was discontinued - this liberates the open result pages. Route summaries, cheapest-per-date grid, and monitor-mode price-drop alerts. For OTAs, fare-drop apps, corporate travel, travel fintech.", "version": "0.1", "x-build-id": "zvZLGvXSzIcLtPlA0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~travel-fare-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-travel-fare-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~travel-fare-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-travel-fare-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~travel-fare-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-travel-fare-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": [ "flight_fares", "hotel_prices", "route_grid", "fare_watch" ], "type": "string", "description": "flight_fares = current fares for each route. hotel_prices = property prices for each destination/date window. route_grid = cheapest-per-date grid across a date window (date-grid enrichment). fare_watch = monitor mode: diff cheapest fares vs the previous scheduled run and emit price-drop alerts.", "default": "flight_fares" }, "routes": { "title": "Flight routes", "maxItems": 50, "type": "array", "description": "Routes to price (flight_fares / route_grid / fare_watch). Each item: {\"origin\": \"JFK\", \"destination\": \"LAX\", \"departure_date\": \"2026-07-15\", \"return_date\": \"2026-07-22\" (optional, makes it round-trip), \"cabin\": \"economy\" (optional: economy|premium_economy|business|first), \"max_stops\": 1 (optional)}. Use IATA airport codes. For route_grid, departure_date is supplied by date_from/date_to. Hard cap of 50.", "default": [] }, "destinations": { "title": "Hotel destinations", "maxItems": 50, "type": "array", "description": "Destinations to price (hotel_prices mode). Each item: {\"destination\": \"Miami\", \"check_in\": \"2026-07-15\", \"check_out\": \"2026-07-18\"}. Hard cap of 50.", "default": [] }, "flight_providers": { "title": "Flight providers", "maxItems": 12, "type": "array", "description": "Flight metasearch providers. 'google_flights' is fully wired (the open result page parsed via the same protobuf query the site uses). 'skyscanner', 'kayak', 'kiwi' are catalog-registered with the anti-bot pipeline wired (provider parser pending; they return a documented provider_pending note). Pass ['ALL'] for all; leave empty for google_flights.", "default": [ "google_flights" ], "items": { "type": "string" } }, "hotel_providers": { "title": "Hotel providers", "maxItems": 12, "type": "array", "description": "Hotel price providers: 'google_hotels', 'booking', 'hotels_com'. All catalog-registered with the anti-bot pipeline wired (provider parser pending; they return a documented provider_pending note). Pass ['ALL'] for all; leave empty for google_hotels.", "default": [ "google_hotels" ], "items": { "type": "string" } }, "date_from": { "title": "Date from (route_grid)", "type": "string", "description": "Lower bound departure date for route_grid mode (YYYY-MM-DD). The grid prices every date from date_from to date_to per route (capped at 60 days).", "default": "" }, "date_to": { "title": "Date to (route_grid)", "type": "string", "description": "Upper bound departure date for route_grid mode (YYYY-MM-DD).", "default": "" }, "adults": { "title": "Adult passengers", "minimum": 1, "maximum": 9, "type": "integer", "description": "Number of adult passengers to price (1-9). Default 1.", "default": 1 }, "min_drop_usd": { "title": "Minimum price drop (USD) for an alert", "minimum": 0, "type": "integer", "description": "fare_watch: only flag a price drop when the cheapest fare fell at least this many USD vs the previous run. 0 = any drop.", "default": 0 }, "min_drop_pct": { "title": "Minimum price drop (%) for an alert", "minimum": 0, "maximum": 100, "type": "integer", "description": "fare_watch: only flag a price drop when the cheapest fare fell at least this percent vs the previous run. 0 = any drop. A drop must clear BOTH the USD and percent thresholds.", "default": 0 }, "max_results_per_query": { "title": "Max fares/prices per route or destination", "minimum": 1, "maximum": 100, "type": "integer", "description": "Hard cap on records returned per (provider, route/destination). Default 25.", "default": 25 }, "contact_email": { "title": "Contact email (optional)", "type": "string", "description": "Optional operator contact added to request headers. Good-citizen practice; not required.", "default": "" }, "monitor_webhook_url": { "title": "Monitor webhook URL (Slack / email, optional)", "type": "string", "description": "When this actor runs under an Apify Schedule (monitor mode) or in fare_watch mode, post the change digest (price drops, new/changed fares) to this Slack-compatible webhook URL.", "default": "" }, "use_apify_proxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Route requests through Apify Proxy. The DATACENTER tier handles the first (httpx) pass; a RESIDENTIAL tier is provisioned for the anti-bot escalation legs.", "default": true }, "use_browser_fallback": { "title": "Anti-bot escalation (curl_cffi + browser)", "type": "boolean", "description": "When a portal serves a Cloudflare/consent/403 challenge, automatically escalate: switch to the RESIDENTIAL proxy and retry with curl_cffi Chrome TLS impersonation, then (if available) a headless browser. Turn off to use plain httpx only.", "default": true }, "browser_cdp_url": { "title": "Warm browser CDP endpoint (for metasearch providers)", "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 the consent/Cloudflare walls on the metasearch providers (Skyscanner, Kayak, Booking). Without it, a plain headless Chromium is launched (often blocked). 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 for the httpx tier. Leave empty to let the actor pick DATACENTER (RESIDENTIAL is always provisioned for escalation legs).", "default": [], "items": { "type": "string" } }, "concurrency": { "title": "Max concurrent requests", "minimum": 1, "maximum": 8, "type": "integer", "description": "Parallel provider fetches. Fare portals 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 } } } } } } } } } } ```