Apify Google Flights Tracking

Google Flights Tracking: Your Own Fare History, With a Baseline in Every Row

Schedule the deals feed, store the rows, and you own a fare record where every observation already knows what that route normally costs.

When to use this skill

• The user wants "flight price tracking" or airfare monitoring from their own data.
• They are building a price-alert product and need a source feed to sit under it.
• They want fare data where each row carries a reference price, not just a number.
• They want to watch several home airports on one schedule.

Not for: a one-shot look at today's bargains (use the companion apify-google-flights-deals skill) or pricing a specific route you have already picked (johnvc/Google-Flights-Data-Scraper-Flight-and-Price-Search). See references/actor-index.md.

What this does and does not do

An alert is two pieces: a repeated observation, and a rule about when to notify. This Actor is the observation half. It returns a snapshot each run, keeps no history, and sends nothing. You schedule it, you store the rows, and you write the rule. Any copy or agent response that implies this Actor notifies the user is wrong.

The payoff for doing it this way is average_price. Because every observation arrives with what that route typically costs, your notify rule can be "below typical" rather than a fixed threshold you guessed at.

What you get

One dataset row per destination, per run. result_type separates deal rows from error rows:

• price and average_price, the observation and its reference
• savings, savings_percent, is_below_typical
• route, departure_airport_code, arrival_airport_code
• outbound_date and return_date for the exact itinerary priced
• airline, stops, flight_duration in minutes, flight_link
• name, country, description, thumbnail for the destination

Key your storage on departure, arrival, and travel date, and add the run timestamp yourself.

Prerequisites

• Apify account (sign up at https://apify.com?fpr=9n7kx3&fp_sid=awesomeskills).
• Authentication via apify login, or an APIFY_TOKEN environment variable (Apify Console, Settings, Integrations).

The Actor

• Store page: https://apify.com/johnvc/google-flights-deals-api?fpr=9n7kx3&fp_sid=awesomeskills
• Actor ID: johnvc/google-flights-deals-api
• Pricing: pay per event; see the cost section below and references/gotchas.md for live-price commands.

Run it with the Apify CLI

One observation across three home airports:

apify actors call "johnvc/google-flights-deals-api" -i '{"departureIds":["JFK","EWR","LGA"],"maxDealsPerAirport":30,"currency":"USD"}' \
  --json \
  --user-agent apify-awesome-skills/apify-google-flights-tracking \
  2>/dev/null

Pin the shape you want to track so successive runs stay comparable:

apify actors call "johnvc/google-flights-deals-api" -i '{"departureId":"SFO","maxStops":"nonstop","travelClass":"economy","maxDealsPerAirport":30}' \
  --json \
  --user-agent apify-awesome-skills/apify-google-flights-tracking \
  2>/dev/null

Confirm live pricing and the input schema before scheduling a sweep:

apify actors info "johnvc/google-flights-deals-api" --json \
  --user-agent apify-awesome-skills/apify-google-flights-tracking \
  2>/dev/null

Every call carries the three flags this repo expects: --json, --user-agent apify-awesome-skills/apify-google-flights-tracking, and 2>/dev/null.

Run it from Claude or another AI agent (MCP)

The Actor is MCP-ready. Add the hosted server URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/google-flights-deals-api

Then ask, for example: "Take today's reading for my three New York airports and tell me which routes are below their typical price." MCP setup docs: https://docs.apify.com/platform/integrations/mcp

Workflow

1. Decide the shape you are tracking: airports, cabin, and stops. Keep it fixed so readings stay comparable.
2. Save it as an Apify task, then attach a schedule. Daily is enough for most fare movement.
3. Append each run's rows to your own store, keyed on departure, arrival, and travel date, stamped with the run time.
4. Write the notify rule against is_below_typical or a savings_percent floor you choose.
5. Watch average_price too, not just price. A baseline that drifts down means the route repriced, which is different from a sale.

Inputs

• departureId (string) or departureIds (array): the airports you watch
• maxDealsPerAirport (integer, default 30, max 30)
• travelClass (enum economy, premium_economy, business, first, default economy)
• maxStops (enum any, nonstop, one_stop, two_stops, default any)
• travelDuration (enum any, one_week, weekend, two_weeks, default any)
• tripType (enum round_trip, one_way, default round_trip)
• outboundDate / returnDate (string): leave blank so the feed keeps finding cheap dates
• maxPrice (integer), includeAirlines / excludeAirlines (string)
• currency (default USD), hl (default en), gl (string)

Cost

Billing is pay per event, plus a negligible platform fee per dataset row. Prices below are the BRONZE tier at the time of writing; confirm live prices with the info command above.

• About $0.0025 per deal returned.
• About $0.001 to start a run.
• One airport at thirty deals is about $0.08 per reading, so roughly $2.30 a month daily.
• Three airports daily is roughly $7 a month.

Suggested confirmation thresholds: warn the user over about $5; get explicit confirmation over about $20. Present cost as "around $X", never a guarantee. Scheduled tracking is recurring spend, so state the monthly figure, not just the per-run one.

Honest limits

• No history and no alerts inside the Actor. Both are yours to build, and copy must not claim otherwise.
• The destination set can change between runs, since it is the cheapest thirty rather than a fixed watchlist. A route can drop out because it got expensive.
• With dates left blank the itinerary moves between runs, so you are tracking the cheapest available trip, not one fixed departure.
• maxDurationMinutes switches off the baseline and with it savings and is_below_typical. Never set it on a tracking schedule.
• Thirty destinations per airport is a hard ceiling.

Troubleshooting

• A route vanished between runs: it left the cheapest thirty. Treat absence as missing data, not a price of zero.
• The baseline disappeared: maxDurationMinutes is set. Remove it and re-run.
• Readings look noisy: the itinerary is floating because dates are blank. Pin outboundDate if you need one fixed departure.
• Costs climbing faster than expected: count airports times deals times runs per month before widening a schedule.

See references/gotchas.md for cost guardrails and error recovery, and references/actor-index.md for the Actor routing table.

Related Actors

• Google Flights Data Scraper: https://apify.com/johnvc/Google-Flights-Data-Scraper-Flight-and-Price-Search?fpr=9n7kx3&fp_sid=awesomeskills
• Google Travel Explore API: https://apify.com/johnvc/google-travel-explore-api?fpr=9n7kx3&fp_sid=awesomeskills
• Google Hotels Search Scraper: https://apify.com/johnvc/google-hotels-search-scraper?fpr=9n7kx3&fp_sid=awesomeskills
• Google Maps Photos API: https://apify.com/johnvc/google-maps-photos-api?fpr=9n7kx3&fp_sid=awesomeskills