swoop-flights 0.5.0

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} — {', '.join(option.legs[0].itinerary.airline_names)}")

[!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.

Landing page · How I built this

---

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

# Price CSV (one row per booking option, with seller and booking_url)
swoop price JFK LAX --depart 2026-06-15 DL2300 -o csv -q > fares.csv

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

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

# Surface RPC debug logging on stderr (URLs, response sizes, retries)
swoop search JFK LAX 2026-06-15 --verbose

# Discover deals from an airport
swoop deals JFK

# Multi-origin (NYC) — single call, mixed origins
swoop deals JFK,LGA,EWR

# Parallel per-origin fetch, deduped by trip identity
swoop deals JFK,LGA,EWR --per-origin

# Europe-only summer deals, 5-10 day trips, under $700, 40%+ off
swoop deals JFK --region europe \
    --depart-window 2026-06-01,2026-08-31 \
    --trip-length 5-10 --max-price 700 --min-discount 40

# Delta-only deals
swoop deals JFK -a DL

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.

Shell completion

# bash (~/.bashrc)
eval "$(_SWOOP_COMPLETE=bash_source swoop)"

# zsh (~/.zshrc)
eval "$(_SWOOP_COMPLETE=zsh_source swoop)"

# fish (~/.config/fish/config.fish)
_SWOOP_COMPLETE=fish_source swoop | source

After reloading your shell, swoop <TAB>, swoop search --<TAB>, and -o <TAB> will autocomplete.

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.

Each price-check call costs ~2 RPCs (one search, one booking lookup) — including one-way trips, so PriceResult.booking_options is populated and booking_url is available. For high-volume scoring of many fares, expect rate-limit pressure to scale accordingly; increase retries or pace your calls.

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:
    label = opt.seller_name or "airline-direct"
    print(f"${opt.price} — {opt.brand_label} ({opt.fare_family}) via {label}")
    if opt.booking_url:
        print(f"  book at: {opt.booking_url}")

Deals discovery

deals() is the third primitive: instead of "what flights from A to B?" (search()) or "how much for this exact flight?" (check_price()), it answers "where's cheap from here right now?"

from swoop import deals, search_deal, price_deal, Region

# Top 30 deals from JFK (excluding basic economy by default)
result = deals("JFK")
for deal in result.deals[:5]:
    print(f"{deal.destination_city:25s} ${deal.price}  {deal.discount_pct}% off")

# Filter by region, budget, trip length, discount
result = deals(
    "JFK",
    region=Region.EUROPE,
    max_price=700,
    trip_length=(5, 10),
    min_discount_pct=40,
    depart_window=("2026-06-01", "2026-08-31"),
)

# Multi-origin (NYC). Default: one RPC call. per_origin=True parallelizes
# and merges by fingerprint (cheaper variant wins on collision).
nyc = deals(["JFK", "LGA", "EWR"], per_origin=True)

# Bridge to swoop's existing pricing flow — no field-shuffling.
top = nyc.deals[0]
itineraries = search_deal(top)            # search() with the deal's route + dates + carriers
bookable = price_deal(top)                # cheapest matching itinerary, priced

Deals discovery is roundtrip-only — that's an upstream Google Flights product constraint, not a swoop limitation. The server ignores date and time-window slots in the payload; swoop applies those filters client-side over the 30 deals returned. For one-way exploration, use search() with an explicit destination.

Watching deals over time

from swoop import deals, watch_deals, Region

# First run: every deal goes into diff.new and the cache is created.
# Subsequent runs: diff.new / diff.gone / diff.price_changes / diff.unchanged.
result = deals("JFK", region=Region.EUROPE, max_price=700)
diff = watch_deals(result, cache_path=".swoop-deals-cache.json")

for change in diff.price_changes:
    if change.delta < -50:          # $50+ drop from prior run
        d = change.current
        print(f"Price drop! {d.origin}->{d.destination}: "
              f"${change.prior.price} -> ${d.price}")

for new_deal in diff.new:
    print(f"New deal: {new_deal.destination_city} ${new_deal.price}")

Deal.fingerprint (origin + destination + dates + sorted airlines) identifies "the same trip" across runs — a price drop on the same trip shows up as a PriceChange rather than a new+gone pair. examples/deals_watcher.py is a runnable CLI version.

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

Runnable examples

Real-world patterns are in examples/:

• examples/price_drop_watcher.py — Watch a known flight for price drops on a schedule (the pattern Perch uses to save users ~$247/trip).
• examples/multi_city_finder.py — Multi-city / open-jaw search with beam-search tuning knobs.

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, Segment, Layover, CarbonEmissions, etc.) with named attributes.

                                    ┌─────────────────────────────────────┐
                                    │         Google Flights              │
                                    │    FlightsFrontendService RPC       │
                                    └──────────┬──────────────────────────┘
                                               │
                                    protobuf response
                                    (nested arrays, no field names)
                                               │
┌──────────────┐   HTTP POST    ┌──────────────▼──────────────┐   typed    ┌──────────────────┐
│  search()    │──────────────▶ │     TLS fingerprint         │──────────▶│  Itinerary       │
│  price()     │  JSON-in-JSON  │     impersonation (primp)   │  Python   │  Segment         │
│  check_price │  URL-encoded   │                             │  dataclass│  Layover         │
└──────────────┘                └─────────────────────────────┘           │  CarbonEmissions │
                                                                          └──────────────────┘

For the full reverse-engineering story (744 lines of handmade schema, binary protobuf decoding, cabin class debugging), read How I built this.

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
children	int	0	Number of children (2–11)
infants_in_seat	int	0	Number of infants in seat
infants_on_lap	int	0	Number of infants on lap
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
country	str | None	None	Two-letter country code for point of sale (e.g. "GB"). Controls currency and available fares
proxy	str | None	None	Proxy URL for routing requests
max_results	int | None	None	Max trip combinations for beam search (multi-leg only)
beam_width	int | None	None	Candidate prefixes per stage (multi-leg only)
time_budget	int | None	None	Seconds before beam search stops exploring (multi-leg only)

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
adults	int	1	Number of adults
children	int	0	Number of children (2–11)
infants_in_seat	int	0	Number of infants in seat
infants_on_lap	int	0	Number of infants on lap
include_basic_economy	bool	False	Include basic economy fares
timeout	int	90	HTTP timeout in seconds
retries	int	2	Retries on HTTP 429
country	str | None	None	Two-letter country code for point of sale
proxy	str | None	None	Proxy URL for routing requests

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, is_basic, fare_family, rebookability_signal, plus seller fields seller_name, seller_code, booking_url, logo_url, and is_airline_direct for routing users to the actual booking page.

set_country(country_code)

Set the default country code for all subsequent requests. Controls point of sale, currency, and available fares. Pass None to clear.

set_proxy(proxy_url)

Set the default proxy URL for all subsequent requests. Pass None to clear.

Result types

• PriceResult — price: int, currency: str | None, 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, currency: str | None
• TripOption — selector: str, price: int | None, currency: str | 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
• Segment — 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