# swoop

[![PyPI](https://img.shields.io/pypi/v/swoop-flights)](https://pypi.org/project/swoop-flights/)
[![Python](https://img.shields.io/pypi/pyversions/swoop-flights)](https://pypi.org/project/swoop-flights/)
[![License](https://img.shields.io/github/license/saraswatayu/swoop)](https://github.com/saraswatayu/swoop/blob/main/LICENSE)
[![CI](https://github.com/saraswatayu/swoop/actions/workflows/ci.yml/badge.svg)](https://github.com/saraswatayu/swoop/actions/workflows/ci.yml)

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

```python
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](https://github.com/deedy5/primp) to match a real browser session. Responses are deeply nested lists (matching an internal protobuf schema) decoded into typed Python dataclasses.

[Perch](https://perchtravel.com) uses Swoop in production to monitor booked flights for price drops, saving users an average of $247 per trip.

---

## Install

```bash
pip install swoop-flights

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

## CLI

<p align="center">
  <img src="docs/screenshot.svg" alt="swoop search JFK LAX 2026-06-15" width="750">
</p>

```bash
# 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"
```

<details>
<summary>More CLI examples</summary>

```bash
# 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
```

</details>

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

```python
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.

<details>
<summary>More examples</summary>

### Price check for a specific flight

```python
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

```python
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

```python
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

```python
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

```python
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)

```python
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})")
```

</details>

> [!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](https://github.com/deedy5/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.

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

<details>
<summary>API reference</summary>

### `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.

</details>

## Contributing

Issues and pull requests welcome at [github.com/saraswatayu/swoop](https://github.com/saraswatayu/swoop/issues).

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`](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
