nextflight

A Python library to scrape and parse Next.js (App Router) pages — decode React Server Components ("Flight") payloads and self.__next_f.push() data into clean, searchable JSON.

nextflight parses the React Server Components ("Flight") payloads that Next.js embeds in server-rendered HTML — the <script>self.__next_f.push([...])</script> blocks, or the raw RSC response you get back from a request sent with an RSC: 1 header — and turns them into clean, searchable Python dicts and lists. It works on any Next.js 13+ App Router site out of the box, with no per-site configuration, which makes it the go-to Python library for scraping Next.js websites, web scraping, crawling, and structured data extraction with Scrapy, requests, httpx, or the stdlib alone.

Next.js pages don't put their data in one obvious place — it's spread across dozens of numbered chunks, cross-referenced with $-sigils, and reshuffled every time the site redeploys. Hardcoding array paths like data[3]["children"][0][3]... breaks the moment that happens. nextflight resolves those references for you and lets you search for the shape of data you want instead — page.find_by_keys({"price", "title"}) instead of a brittle index chain.

If you've ever searched for "how to scrape a Next.js site", "parse self.__next_f.push in Python", or "extract JSON from Next.js NEXT_DATA / Flight payload" — this is that tool.

Install

pip install nextflight

No required dependencies — stdlib only, so it drops into any existing Scrapy/Zyte project without touching your dependency tree. A few optional extras unlock extra features automatically if installed; see Optional dependencies.

Quick start

Two steps: see what's on the page, then fetch the shape of data you want.

from nextflight import extract

page = extract(html_text)      # a string, bytes, or response object

page.keys()                    # ['0', '1', '3f', '20', ...] -- what's here
page["3f"]                     # the resolved JSON for one specific chunk

# In practice, chunk ids are arbitrary per build (they change on
# redeploy), so search for the shape of data you want instead:
listing = page.find_by_keys({"price", "title"})       # first match
listings = page.find_all_by_keys({"price", "title"})  # every match
products = page.find_by_type("Product")               # by @type
everything = page.resolve_all()                        # everything, dereferenced

Usage

In a Scrapy spider

import scrapy
from nextflight import extract

class MySpider(scrapy.Spider):
    name = "my_spider"

    def parse(self, response):
        page = extract(response.text)
        for item in page.find_all_by_keys({"price", "title"}):
            yield {
                "title": item.get("title"),
                "price": item.get("price"),
                "url": response.url,
            }

For a more idiomatic setup — a lazy response.flight on every response, settings-driven strict/repair/dedupe defaults, an optional item pipeline, and per-domain version-hint logging — see docs/scrapy.md:

# settings.py
DOWNLOADER_MIDDLEWARES = {"nextflight.scrapy_middleware.FlightMiddleware": 543}

def parse(self, response):
    listing = response.flight.find_by_keys({"price", "title"})

Fetching a URL directly (no Scrapy needed)

from nextflight import FlightExtractor

page = FlightExtractor.from_url("https://example.com/product/123")
product = page.find_by_keys({"price", "title"})

from_url uses only the stdlib, for quick exploration or lightweight crawling. For anything needing retries, proxies, JS rendering, or robots.txt handling, fetch the page with your own HTTP client and pass response.text to extract(...) instead.

Raw RSC fetches (no HTML at all)

Sending a request with an RSC: 1 header — the way Next.js's own client-side navigation does — returns the raw Flight row stream directly as the response body, with no HTML wrapper. extract() detects and parses this automatically, same as the HTML-embedded form:

from nextflight import FlightExtractor

# Sets RSC:1 and Next-Url for you, and best-effort auto-discovers a
# build-specific _rsc=<id> from the page's own prefetch links
page = FlightExtractor.from_rsc_url("https://example.com/car/search?page=2")

# Or bring your own client:
import requests
resp = requests.get(
    "https://example.com/car/search",
    params={"page": "2", "_rsc": "1p28d"},   # a build-specific cache key
    headers={"RSC": "1", "Next-Url": "/en/car/search"},
)
page = FlightExtractor(resp.text)

If a site also requires a Next-Router-State-Tree header, grab it once from a real browser's network tab and reuse it — it's stable for every request to the same route regardless of query params, so it doesn't need to be regenerated per request.

Pages Router support

Older or mixed Next.js deployments use the Pages Router's __NEXT_DATA__ blob instead of Flight — already plain JSON, no $-refs to resolve:

from nextflight import extract, find_next_data, detect_next_router

router = detect_next_router(html_text)   # "app" | "pages" | "both" | "unknown"

if router == "app":
    data = extract(html_text).find_by_keys({"price", "title"})
else:
    data = find_next_data(html_text)["props"]["pageProps"]

Monitoring a page over time

diff_pages compares two crawls of the same URL and reports what changed — handy for a price or stock watcher:

from nextflight import FlightExtractor, diff_pages

old_page = FlightExtractor.from_url(url)
# ...re-fetch later...
new_page = FlightExtractor.from_url(url)

diff_pages(old_page, new_page)
# {"added": {...}, "removed": {...}, "changed": {"path.to.price": (100, 90)}}

For a list of records with a stable id, pass id_key — otherwise inserting one new item shifts every later index and makes everything after it look changed even though it didn't:

diff_pages(old_page, new_page, id_key="listing_id")
# {"changed": {"items[listing_id=7165546].price": (929900, 899900)}, ...}

Or from the command line, polling continuously (--rsc for the lighter-weight RSC payload instead of full HTML each poll):

nextflight https://example.com/product/123 --watch 60
nextflight https://example.com/car/search --rsc --watch 60

Exporting to a DataFrame or CSV

page = extract(html_text)

df = page.to_dataframe(required_keys={"id", "price"})       # requires pandas
page.to_csv("listings.csv", required_keys={"id", "price"})  # works either way

Command line

nextflight page.html --keys sections,meta
nextflight https://example.com/product/123 --type Product
nextflight page.html --tree                 # shape summary, no full values
nextflight page.html --all > everything.json

API reference

`extract(html) -> FlightExtractor`

Shorthand constructor. html accepts a plain string, bytes, or a response-like object (Scrapy's Response, requests.Response, etc.) — pass response straight from a parse() method.

`FlightExtractor(html, *, strict=False, repair=False)`

strict=True raises FlightParseError on a row that's neither valid JSON nor a recognizable $-reference, instead of keeping it as a raw string. Useful while developing a new scraper; leave off in production so a handful of odd rows never take down extraction of everything else.

repair=True goes a step further for JSON rows that fail to decode outright (not just bare markers) — typically a payload truncated mid-chunk by a proxy/CDN cutting a response short. It heuristically closes unbalanced brackets/quotes and retries the decode, so a page with one truncated chunk doesn't lose that chunk's data entirely. Mutually exclusive with strict=True. Check how much of a repaired page was actually salvaged with .parse_confidence().

Exploring a page

Method	Returns	What it does
`.keys()`	`list[str]`	Every chunk id on the page, in order
`.kind(chunk_id)`	`str \| None`	Row kind: `"json"`, `"text"`, `"module"`, `"preload"`
`.json_keys()`	`list[str]`	Chunk ids holding structured JSON (dict/list)
`.html_keys()`	`list[str]`	Text-row chunk ids that look like HTML fragments
`.text_keys()`	`list[str]`	All text-row chunk ids, HTML-looking or not
`.shape(chunk_id=None, max_depth=3)`	structure summary	Key names + value types, not values — get a feel for a new site fast
`.stats()`	`dict`	Chunk count, row-kind breakdown, page size
`.parse_confidence()`	`dict`	Score + breakdown of cleanly-parsed vs. raw-fallback vs. repaired chunks
`.next_version_hint()`	`dict`	Best-effort guess at which Next.js version range produced this payload

Resolving data (dereferencing $-refs)

Method	Returns	What it does
`page["id"]` / `.resolve_chunk("id")`	resolved value	One chunk, fully dereferenced (`page[...]` raises `KeyError` if missing)
`.resolve_all()`	`dict`	Every chunk, fully dereferenced
`.resolve_json()` / `.resolve_html()` / `.resolve_text()`	`dict`	Only one kind of chunk — cheaper than `resolve_all()` when you don't need everything
`.iter_resolved()`	iterator	Like `resolve_all()` but lazy, one chunk at a time
`.get("path.to.value", default=None)`	value	Tolerant dotted-path lookup (dict keys, list indices, and React element `"props"`)
`.select(*paths, default=None)`	`dict`	Resolve just the named paths, e.g. `page.select("3f.props.price", "3f.props.title")`

"3f" in page and for k in page also work, like a dict.

Searching (schema-free, works across redeploys)

Method	Returns	What it does
`.find_by_keys(required_keys, root=None)`	dict or `None`	First dict containing all of `required_keys`
`.find_all_by_keys(required_keys, root=None, dedupe=False)`	`list`	Every matching dict — for repeated cards/listings. `dedupe=True` collapses the same object serialized twice at different tree positions
`.find_any_keys(any_keys, root=None)`	`list`	Every dict containing any of `any_keys`
`.find_by_key_pattern(pattern, root=None)`	`list`	Every dict with a key matching a regex, e.g. `r"^price_"`
`.find_by_type(type_value, key="@type", root=None)`	`list`	Every dict whose `key` field equals `type_value`
`.find_text(pattern, root=None)`	`list`	Distinct string values matching a regex (emails, SKUs, ...)
`.find_all(predicate, root=None, max_results=None)`	`list`	Fully custom predicate over every node
`.find_one(predicate, root=None)`	value or `None`	Like `find_all` but just the first match
`.suggest_similar_keys(required_keys)`	`dict`	When a `find_*` call comes back empty, fuzzy-match against keys actually present, e.g. `{"titl": ["title"]}`
`.extract_as(Model, root=None)`	instance or `None`	Find the first dict matching `Model`'s fields and coerce it into a dataclass or pydantic model

Pass include_source=True on any find_* method to get (node, chunk_id) tuples instead of bare nodes, so you can trace a match back to where it came from. find_all/find_one/find_by_keys resolve chunks lazily and stop the moment max_results is hit — they don't pay to resolve chunks after a match is already found.

Fetching

Classmethod	What it does
`.from_url(url, timeout=15.0, headers=None)`	Fetch and parse a URL, stdlib only
`.from_url_async(url, ...)`	Async version for `asyncio.gather(...)` crawls — requires `httpx`. `AsyncFlightExtractor` is a discoverable alias for the same class
`.from_rsc_url(url, headers=None, cookies=None, auto_discover=True)`	Fetch the raw RSC payload instead of full HTML — see "Raw RSC fetches" above
`.from_stream(chunks)`	Incrementally parse an iterable of HTML fragments/bytes, yielding `(chunk_id, value)` as rows complete — see "Streaming a response" below
`.from_page(playwright_page)`	Build from a Playwright page's fully-rendered HTML — for sites that only populate later chunks after client-side JS runs. Requires `playwright` (not bundled in any extra)

from_url/from_rsc_url transparently decompress gzip/deflate/br responses even if the server ignores the default Accept-Encoding: identity request.

Diffing and exporting

Method	Returns	What it does
`.diff(other_page, id_key=None)`	`dict`	Compare against another crawl — see "Monitoring a page over time"
`.to_json(path=None, indent=2)`	`str \| None`	Dump the fully resolved page to a file, or return as a string
`.to_dataframe(records=None, required_keys=None)`	`DataFrame`	Requires pandas
`.to_csv(path, records=None, required_keys=None)`	—	Falls back to the stdlib `csv` module without pandas

Module-level functions

find_json_ld(html, type_=None) -> list — parse <script type="application/ld+json"> blocks, optionally filtered by @type. Often more stable across redesigns than Flight data — worth trying first for product/article/breadcrumb structured data.
find_next_data(html) -> dict | None — parse a Pages Router __NEXT_DATA__ blob. None if the page doesn't have one.
detect_next_router(html) -> str — "app", "pages", "both", or "unknown". Run this first if you're not sure which extractor to use.
diff_pages(old, new, id_key=None) -> dict — module-level form of .diff().
normalize_price(value) -> dict | None — parse a messy price string (currency symbols, thousands separators, either comma or period as the decimal point) into {"amount": float, "currency": str | None}.
clean_text(value) -> str | None — decode HTML entities and collapse whitespace.
parse_date(value) -> datetime | None — best-effort parse against a conservative list of common date formats (ISO 8601, MM/DD/YYYY, "January 5, 2024", ...).

Command-line reference

nextflight <file-or-url>
  [--keys a,b | --all-by-keys a,b | --any-keys a,b
   | --type Product | --text PATTERN | --get path.to.value
   | --json-keys | --html-keys | --tree
   | --router | --next-data | --stats | --watch SECONDS | --all]
  [--rsc] [--redact] [--save out.json]

--tree prints a .shape() summary instead of full values.
--router / --next-data cover Pages Router pages.
--rsc fetches the raw RSC payload instead of full HTML (URL sources only) — lighter weight, also works with --watch.
--watch SECONDS polls a URL and prints only what changed since the last poll.
--redact best-effort scrubs email/phone-shaped strings from output, for sharing debug dumps.

Performance

nextflight parses and resolves a typical product/listing page in the low single-digit milliseconds; see docs/performance.md for the profiling methodology, before/after benchmark numbers, and why a Rust/pyo3-accelerated tokenizer isn't currently justified (short version: the actual hot spots turned out to be pure-Python inefficiencies with a much cheaper fix). Run python benchmarks/run_benchmarks.py yourself against small/medium/large synthetic pages.

Optional dependencies

Nothing below is required to install or use nextflight — each is used automatically if already present in your environment, and raises a clear ImportError only if you call the one method that needs it.

Package	Unlocks	Install
`orjson`	Faster JSON decoding everywhere	`pip install nextflight[fast]`
`pandas`	`.to_dataframe()`, nicer `.to_csv()`	`pip install nextflight[pandas]`
`httpx`	`.from_url_async()` / `AsyncFlightExtractor`	`pip install nextflight[async]`
`pydantic`	`.extract_as(SomePydanticModel)` (dataclasses work with no extra dependency)	`pip install nextflight[schema]`
`scrapy`	`nextflight.scrapy_middleware.FlightMiddleware`	`pip install nextflight[scrapy]`
`pyyaml`	Not required — `next_version_hint()` reads `known_formats.yaml` via a bundled fallback parser if `pyyaml` isn't installed	(optional, used automatically if present)

Or pip install nextflight[all] for all of the above.

How it works

Flight payloads aren't newline-delimited JSON — text rows (id:T<hexByteLen>,<raw bytes>) are byte-length-prefixed and can contain literal newlines or run straight into the next row with no separator, and module/preload rows (id:I[...], id:HL[...]) need bracket-aware parsing. nextflight implements the actual row grammar rather than splitting on \n, so it holds up on both well-formed pages and payloads truncated mid-chunk (e.g. by a proxy that cuts a response short).

It also doesn't assume one <script>self.__next_f.push(...)</script> call is one complete, self-contained set of rows. On large real-world pages, Next.js's own streaming buffer can flush mid-string, splitting a single row's raw text across two or more separate push() calls with no separator between the pieces — all push() payloads are reassembled into one continuous stream before being split into rows, so this doesn't silently corrupt chunk ids on pages large enough to trigger it (confirmed against production pages where over half of all push() calls turned out to be mid-row continuations).

Chunk ids aren't guaranteed unique, either — Next.js deliberately emits preload (HL) rows with a completely empty id (:HL["/path.css","style"]) since nothing ever needs to $-ref them individually, and real pages have had dozens of these sharing the same empty id. Rather than the later ones silently overwriting the earlier ones, only the first occurrence of a duplicated id keeps its real id; later ones get a synthesized, clearly distinguishable key ("id#2", "id#3", ...) so nothing gets lost.

Parsing and resolution are both designed to scale roughly linearly with page size: rows are split cheaply up front, each chunk's JSON is decoded lazily on first access rather than all at once, and searches (find_one/find_by_keys) stop resolving chunks the moment a match is found instead of resolving the whole page first.

Streaming a response

For very large pages, or use cases where acting on data as it arrives matters more than a single final result (a live progress indicator, stopping a slow crawl early once a wanted field shows up), parse an iterable of HTML fragments/bytes incrementally instead of buffering the whole page first:

from nextflight import FlightExtractor

for chunk_id, value in FlightExtractor.from_stream(response.iter_content()):
    print(chunk_id, value)

$-refs are resolved best-effort as each row arrives — a reference to a chunk that hasn't streamed in yet resolves to None rather than blocking, unlike the regular constructor, which sees the whole page at once. Use the regular constructor when getting every cross-reference exactly right matters more than seeing data as it streams in.

Recovering truncated pages

from nextflight import FlightExtractor

page = FlightExtractor(html_text, repair=True)
page.parse_confidence()
# {"score": 0.92, "total_chunks": 40, "clean_chunks": 37,
#  "raw_string_chunks": 3, "repaired_chunks": 2, "failed_repair_chunks": 1}

Debugging a search that came back empty

page.find_by_keys({"price", "titel"})   # None -- typo, or wrong build?
page.suggest_similar_keys({"price", "titel"})
# {"price": ["price"], "titel": ["title"]}

Typed extraction

from dataclasses import dataclass
from nextflight import extract

@dataclass
class Listing:
    title: str
    price: int

page = extract(html_text)
listing = page.extract_as(Listing)   # or a pydantic BaseModel

Cleaning up messy scraped values

from nextflight import normalize_price, clean_text, parse_date

normalize_price("$1,299.00")       # {"amount": 1299.0, "currency": "USD"}
normalize_price("1.299,00 €")      # {"amount": 1299.0, "currency": "EUR"}
clean_text("  Cozy&nbsp;Studio  ") # "Cozy Studio"
parse_date("January 5, 2024")      # datetime(2024, 1, 5, 0, 0)

Scrapy: automatic `.flight` on every response

Instead of calling extract(response) in every callback, install the downloader middleware once and get a lazily-parsed .flight attribute on every Response:

# settings.py
DOWNLOADER_MIDDLEWARES = {
    "nextflight.scrapy_middleware.FlightMiddleware": 543,
}

def parse(self, response):
    listing = response.flight.find_by_keys({"price", "title"})

Requires Scrapy (pip install nextflight[scrapy], or just have Scrapy installed already, as any project using this necessarily does).

Comparison with other tools

Several tools touch the same "Next.js hides its data in a weird wire format" problem space, but solve different parts of it — pick based on what you're actually trying to do:

Tool	What it's for	What it isn't
`nextflight`	Programmatic extraction in Python: parse, resolve `$`-refs, search, and re-use across a crawl of many pages	Not a browser tool or one-off manual paste target
`njsparser`	Also a Python Flight/RSC parser, closer to the wire format itself (lower-level primitives, less of a search-oriented API)	Doesn't provide `nextflight`'s search-by-shape (`find_by_keys`), diffing, Scrapy integration, or CLI
Burp Suite `nextjs-rsc-parser` extension	Inspecting Flight payloads interactively while proxying traffic through Burp, during manual security testing	Not a library — nothing to `import` or run unattended in a crawl/pipeline
rsc-parser.vercel.app	Pasting one payload in a browser to eyeball its structure	No programmatic access, no batch/crawl use, nothing to search or diff

If you need to look at one payload once, the web tool or Burp extension is faster. If you need to extract structured data reliably, from code, across many pages and over time, that's what nextflight is for. njsparser is the closest direct alternative if you want a lower-level parser and plan to build your own search/traversal layer on top yourself.

FAQ

How do I scrape data from a Next.js website in Python? Fetch the page's HTML (with requests, httpx, Scrapy, or FlightExtractor.from_url()), then pass it to extract(). Search for the data you want by key names (find_by_keys) rather than by guessing DOM structure or array positions — see Quick start above.

What is a Next.js Flight payload / RSC payload? It's the wire format Next.js's App Router uses to serialize React Server Component output — rows of id:json (and a few other row kinds) inside self.__next_f.push([...]) script calls, with $-prefixed references between rows so repeated data isn't duplicated. See How it works for the full grammar.

How do I parse self.__next_f.push in Python? That's exactly what this library does — see Install and Quick start. Hand-rolling a regex against push() calls works until a chunk gets truncated, split across multiple push() calls, or has a duplicated/empty id, all of which happen on real production pages; nextflight implements the actual row grammar so those cases don't silently corrupt your data.

Does this work with the Next.js Pages Router (__NEXT_DATA__)? Yes — see Pages Router support. detect_next_router() tells you which one a page uses, and find_next_data() parses the (already-plain-JSON) Pages Router blob directly.

Why not just use BeautifulSoup/XPath/regex on the raw HTML? Because the data you want usually isn't in the rendered HTML at all — it's serialized separately in the Flight payload for client-side hydration, so selecting DOM elements gets you at most a subset of what's actually on the page. See How it works and Comparison with other tools.

Is scraping Next.js sites with this library legal? nextflight is a parser, not a bot-detection bypass tool or legal opinion — it has no opinion on and provides no help with a target site's terms of service, robots.txt, or applicable law in your jurisdiction, which you're responsible for checking yourself. See docs/anti-bot-cookbook.md for what it can and can't help with technically.

Does it work with Scrapy? Yes, natively — see In a Scrapy spider and the dedicated docs/scrapy.md for the middleware, item pipeline, and spider mixin.

What if Next.js changes this format? It's undocumented and unversioned, so it can change at any release — see A note on maintenance for the disclosure and mitigation (next_version_hint(), parse_confidence(), same-week patch policy).

A note on maintenance

This library's entire value depends on tracking an undocumented, unversioned wire format that Next.js can change in any release. That's a real risk to disclose up front, not just a disclaimer:

New Next.js releases that change the wire format will get a same-week patch release with a clear changelog entry whenever the maintainer becomes aware of the change — see next_version_hint() and known_formats.yaml for the mechanism that tracks this.
Bus factor: currently maintained by one person. If that changes, meaningfully, it'll be reflected here.
If you rely on this in production, pin a version, watch releases, and consider parse_confidence() in your own monitoring — a dropping score over time on pages that used to parse cleanly is an early warning sign the format shifted before an official patch lands.

Roadmap and design proposals

These are scoped as proposals, not shipped code, and are listed here so the direction is visible rather than only living in an issue tracker:

Plugin system for third-party row-type handlers and custom $-reference resolvers, so an unrecognized new row kind doesn't have to block on an official patch.
JS/TS port for Node-based scraping stacks (Playwright/Puppeteer), sharing the same golden-file test corpus as this Python implementation so the parsing "spec" lives in language-agnostic tests rather than being owned by one codebase.
Hosted/self-hostable reference-crawl service: periodically snapshot known sites' Flight formats and alert maintainers the moment Next.js changes something, closing the same-week patch loop automatically instead of relying on manual discovery.
Playwright/Selenium integration: FlightExtractor.from_page(page) to grab fully-rendered HTML (including chunks populated after JS execution) directly from a live browser session.
Anonymized-payload GitHub issue template, with accepted submissions wired into the regression test corpus.

License

MIT

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
benchmarks		benchmarks
docs		docs
src		src
tests		tests
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
PKG-INFO		PKG-INFO
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg

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nextflight

Contents

Install

Quick start

Usage

In a Scrapy spider

Fetching a URL directly (no Scrapy needed)

Raw RSC fetches (no HTML at all)

Pages Router support

Monitoring a page over time

Exporting to a DataFrame or CSV

Command line

API reference

`extract(html) -> FlightExtractor`

`FlightExtractor(html, *, strict=False, repair=False)`

Module-level functions

Command-line reference

Performance

Optional dependencies

How it works

Streaming a response

Recovering truncated pages

Debugging a search that came back empty

Typed extraction

Cleaning up messy scraped values

Scrapy: automatic `.flight` on every response

Comparison with other tools

FAQ

A note on maintenance

Roadmap and design proposals

License

About

Releases

Packages

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

nextflight

Contents

Install

Quick start

Usage

In a Scrapy spider

Fetching a URL directly (no Scrapy needed)

Raw RSC fetches (no HTML at all)

Pages Router support

Monitoring a page over time

Exporting to a DataFrame or CSV

Command line

API reference

extract(html) -> FlightExtractor

FlightExtractor(html, *, strict=False, repair=False)

Module-level functions

Command-line reference

Performance

Optional dependencies

How it works

Streaming a response

Recovering truncated pages

Debugging a search that came back empty

Typed extraction

Cleaning up messy scraped values

Scrapy: automatic .flight on every response

Comparison with other tools

FAQ

A note on maintenance

Roadmap and design proposals

License

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

`extract(html) -> FlightExtractor`

`FlightExtractor(html, *, strict=False, repair=False)`

Scrapy: automatic `.flight` on every response