swoop-flights 0.3.1

Search Google Flights programmatically. Real prices, typed results, no API key.

swoop

Search Google Flights programmatically. Real prices, typed results, no API key.

from swoop import search

results = search("JFK", "LAX", "2026-06-15")
for option in results.results[:3]:
    print(f"${option.price}")
    for leg in option.legs:
        itinerary = leg.itinerary
        airline = ", ".join(itinerary.airline_names) if itinerary else "Unknown"
        print(f"  {leg.origin} -> {leg.destination} — {airline}")

[!NOTE] Swoop is not affiliated with Google. It calls undocumented RPC endpoints that can change without notice.

Swoop calls Google Flights' internal GetShoppingResults and GetBookingResults RPC endpoints — the same ones the web app uses when you search for flights. Requests use TLS fingerprint impersonation via primp to match a real browser session. Responses are deeply nested lists (matching an internal protobuf schema) decoded into typed Python dataclasses.

Perch uses Swoop in production to monitor booked flights for price drops, saving users an average of $247 per trip.

---

Install

pip install swoop-flights

# With CLI (adds `swoop` command)
pip install swoop-flights[cli]

CLI

# Search flights
swoop search JFK LAX 2026-06-15

# Nonstop, sorted by price
swoop search JFK LAX 2026-06-15 --nonstop --sort cheapest

# Roundtrip, business class
swoop search JFK LAX 2026-06-15 -r 2026-06-22 --cabin business

# Official multi-city search
swoop search --leg JFK LAX 2026-06-15 --leg LAX SFO 2026-06-18 --leg SFO SEA 2026-06-21

# Bookable fare for a known flight
swoop price JFK LAX --depart 2026-06-15 DL2300

# Show copy/paste price commands for displayed rows
swoop search JFK LAX 2026-06-15 --show-price-commands

# Script-stable pricing via selector
SELECTOR=$(swoop search JFK LAX 2026-06-15 -o json -q | jq -r '.results[0].selector')
swoop price --selector "$SELECTOR"

More CLI examples

# Roundtrip shorthand pricing
swoop price JFK LAX --depart 2026-06-15 DL2300 --return 2026-06-22 DL2301

# Explicit leg pricing (supports 3+ legs)
swoop price --leg JFK LAX 2026-06-15 DL2300 --leg LAX SFO 2026-06-18 UA544 --leg SFO SEA 2026-06-21 AS331

# CSV for spreadsheets
swoop search JFK LAX 2026-06-15 -o csv -q > flights.csv

# Search JSON for piping
swoop search JFK LAX 2026-06-15 -o json -q | jq '.results[0] | {selector, price_usd, legs}'

# Filter by airline and time window
swoop search JFK LAX 2026-06-15 -a DL -a UA --depart-after 8 --depart-before 14

Run swoop search --help for all options.

[!TIP] Search shows shopping totals for browsing. Use --show-price-commands for copy/paste swoop price --selector ... commands in human output, or use selector from JSON with swoop price --selector ... in scripts.

Python API

One-way search

from swoop import search

results = search("SFO", "JFK", "2026-06-15")

for option in results.results[:3]:
    print(f"${option.price}")
    for leg in option.legs:
        itinerary = leg.itinerary
        if itinerary is None:
            continue
        print(f"  {leg.origin} -> {leg.destination}")
        print(f"  {itinerary.airline_names}, {itinerary.stop_count} stops")
        print(f"  {itinerary.travel_time} min")

print(results.is_complete)

search() and search_legs() return shopping totals. Use check_price(), price_legs(), or price_selector() when you need the bookable fare for one chosen itinerary.

More examples

Price check for a specific flight

from swoop import check_price

result = check_price("DL2300", origin="JFK", destination="LAX", date="2026-06-15")
if result:
    print(f"${result.price}")

result = check_price(
    "DL2300", origin="JFK", destination="LAX", date="2026-06-15",
    return_flight_number="DL2301", return_date="2026-06-22",
)
if result:
    print(f"${result.price} roundtrip — {result.fare_brand}")
    for leg in result.resolved_legs:
        print(f"  {leg.flight_summary} {leg.origin}->{leg.destination} ({leg.selection})")

Price a chosen search result by selector

from swoop import price_selector, search

results = search("JFK", "LAX", "2026-06-15")
option = results.results[0]

price = price_selector(option.selector)
if price:
    print(f"${price.price} — {price.fare_brand}")

Leg-based search and pricing

from swoop import SearchLeg, SelectedLeg, price_legs, search_legs

# Search with explicit legs (official entrypoint for multi-city)
results = search_legs([
    SearchLeg(date="2026-06-15", from_airport="JFK", to_airport="LAX"),
    SearchLeg(date="2026-06-18", from_airport="LAX", to_airport="SFO"),
    SearchLeg(date="2026-06-21", from_airport="SFO", to_airport="SEA"),
])

for option in results.results:
    print(option.selector, option.price)
    for leg in option.legs:
        print(f"  {leg.origin}->{leg.destination}")

# Price with explicit legs
result = price_legs([
    SelectedLeg(flight_number="DL2300", origin="JFK", destination="LAX", date="2026-06-15"),
    SelectedLeg(flight_number="UA544", origin="LAX", destination="SFO", date="2026-06-18"),
    SelectedLeg(flight_number="AS331", origin="SFO", destination="SEA", date="2026-06-21"),
])

Roundtrip search

results = search("SFO", "JFK", "2026-06-15", return_date="2026-06-22")
for option in results.results:
    print(option.price)  # roundtrip total

Cabin class and filters

from swoop import search, SORT_CHEAPEST

results = search(
    "LAX", "NRT", "2026-06-15",
    cabin="business",       # economy, premium-economy, business, first
    max_stops=0,            # nonstop only
    sort=SORT_CHEAPEST,     # cheapest first
    airlines=["NH", "JL"],  # filter to specific carriers
    earliest_departure=8,   # depart after 8am
    latest_departure=14,    # depart before 2pm
)

Booking details (fare options)

from swoop import search, get_booking_results

results = search("JFK", "LAX", "2026-06-15")
option = results.results[0]
itinerary = option.legs[0].itinerary

# Get fare tiers — just pass the itinerary
options = get_booking_results(itinerary)

for opt in options:
    print(f"${opt.price} — {opt.brand_label} ({opt.fare_family})")

[!TIP] Google rate-limits aggressively. All RPC functions default to retries=2 with exponential backoff and jitter. Increase to retries=3 for extra resilience.

How it works

Swoop reverse-engineers the FlightsFrontendService RPC interface that powers Google Flights. Search parameters are encoded as nested JSON arrays matching Google's internal protobuf schema, then sent as HTTP POST requests. The HTTP client uses TLS fingerprint impersonation (via primp) so requests are indistinguishable from a real Chrome session.

Responses arrive as deeply nested list structures — no field names, just positional indices. Swoop's decoder walks these structures and maps them to typed Python dataclasses (Itinerary, Flight, Layover, CarbonEmissions, etc.) with named attributes.

graph LR
    A["search()"] --> B["RPC request"] --> C["Google Flights"] --> D["nested lists"] --> E["typed dataclasses"]

API reference

search(origin, destination, date, **kwargs)

Search Google Flights and return a SearchResult.

Parameter	Type	Default	Description
origin	str	required	Origin IATA code
destination	str	required	Destination IATA code
date	str	required	Departure date (YYYY-MM-DD)
return_date	str | None	None	Return date for roundtrip
cabin	str	"economy"	economy, premium-economy, business, first
adults	int	1	Number of adults
max_stops	int | None	None	None=any, 0=nonstop, 1=1 stop, 2=2 stops
sort	int	SORT_DEPARTURE_TIME	Sort order constant
airlines	list[str] | None	None	Filter by airline codes
flight_number	str | None	None	Filter to a specific flight number; carrier is also added to the first-leg airline filter
include_basic_economy	bool	False	Include basic economy fares (excluded by default so prices reflect Main Cabin)
timeout	int	90	HTTP timeout in seconds
retries	int	2	Retries on HTTP 429 with exponential backoff + jitter

Returns SearchResult. Empty results mean no matches were found. Prices in search results are shopping totals.

search_legs(legs, **kwargs)

Search one or more explicit legs and return a trip-level SearchResult. This is the public multi-city entrypoint.

price_legs(legs, **kwargs)

Price one or more explicit legs and return PriceResult | None.

price_selector(selector, **kwargs)

Price a selected trip row by opaque selector and return PriceResult | None.

search_raw(origin, destination, date, **kwargs)

Low-level single-pass search escape hatch. Returns RawSearchResult with raw best and other itinerary buckets from one RPC pass.

check_price(flight_number, *, origin, destination, date, **kwargs)

Look up the current bookable fare for a specific flight. Optimized for the "what does flight X cost?" use case.

Parameter	Type	Default	Description
flight_number	str	required	Flight number (e.g. "DL2300")
origin	str	required	Origin IATA code
destination	str	required	Destination IATA code
date	str	required	Departure date (YYYY-MM-DD)
return_flight_number	str | None	None	Return flight number for roundtrip
return_date	str | None	None	Return date for roundtrip
cabin	str	"economy"	Cabin class
include_basic_economy	bool	False	Include basic economy fares
timeout	int	90	HTTP timeout in seconds
retries	int	2	Retries on HTTP 429

Returns PriceResult | None. PriceResult has price, fare_brand, is_basic_economy, booking_options, itinerary, resolved_legs, rpc_calls.

get_booking_results(itinerary_or_token, **kwargs)

Get fare options for a specific itinerary. Pass an Itinerary object directly, or a booking token string with explicit origin, destination, date, and selected_legs. Returns list[BookingOption] with price, brand_label, brand_code, fare_family, etc. BookingOption supports both attribute access (opt.price) and dict-style access (opt["price"], opt.get("price")).

Result types

• PriceResult — price: int, fare_brand: str | None, is_basic_economy: bool, booking_options: list[BookingOption], itinerary: Itinerary | None, resolved_legs: list[ResolvedLeg], rpc_calls: int
• ResolvedLeg — flight_summary: str, origin: str, destination: str, date: str, itinerary: Itinerary | None, selection: str
• SelectedLeg — flight_number: str, origin: str, destination: str, date: str
• SearchLeg — date: str, from_airport: str, to_airport: str, max_stops: int | None, airlines: list[str] | None
• SearchResult — results: list[TripOption], price_range: PriceRange | None, is_complete: bool
• TripOption — selector: str, price: int | None, legs: list[TripLeg]
• TripLeg — origin: str, destination: str, date: str, itinerary: Itinerary | None
• RawSearchResult — low-level best: list[Itinerary], other: list[Itinerary], price_range: PriceRange | None
• Itinerary — Full itinerary with price, flights, layovers, travel_time, booking_token, carbon_emissions
• Flight — Segment details: airline, flight_number, aircraft, legroom, co2_grams, amenities
• Layover — Stop info: minutes, airports, is_overnight
• CarbonEmissions — this_flight_grams, typical_for_route_grams, difference_percent

Constants

Constant	Value	Description
SORT_TOP	1	Google's default ranking
SORT_CHEAPEST	2	Cheapest first
SORT_DEPARTURE_TIME	3	By departure time
SORT_ARRIVAL_TIME	4	By arrival time
SORT_DURATION	5	Shortest first

Error handling

All exceptions inherit from SwoopError. Catch SwoopRateLimitError for HTTP 429, SwoopHTTPError for other HTTP failures, and SwoopParseError for response decoding issues.

Contributing

Issues and pull requests welcome at github.com/saraswatayu/swoop.

Testing notes:

• Push and PR CI runs the deterministic offline suite only: python -m pytest tests/ -v -m 'not live'
• Benchmarks are opt-in and stay skipped in normal runs unless you pass --run-benchmarks (or use --benchmark-only).
• Live Google canaries run separately in the live-canary workflow on a weekly schedule or by manual dispatch.
• Mutation testing is available by manual dispatch in the mutation workflow and is scoped to _selection, _booking, decoder, and rpc.
• Real-world bugs should be added to the incident regression bank in tests/incidents/manifest.json with a linked regression test or sanitized fixture.
• When a live canary finds a useful new payload shape, promote it manually into tests/fixtures/contract_corpus_manifest.json and the tracked fixture corpus after review.

License

MIT