How to Scrape YouTube With Python in 2026

If you have ever hit a YouTube Data API v3 quota wall in the middle of a research run, you already know why developers learn how to scrape YouTube directly. The official API is clean and well-documented, but its 10,000-unit daily budget vanishes quickly once you start hitting search.list or pulling deep comment threads. YouTube web scraping fills the gap, and the data is much richer than the API exposes: full comment trees, transcripts, tags, like counts, Shorts, and channel video catalogs.

This guide is built for Python developers, data engineers, growth and SEO analysts, and AI/ML practitioners who need bulk YouTube data for analytics, RAG pipelines, or competitor research. We'll move from a no-API-key quick start to production-grade pulls using yt-dlp, hidden /youtubei/v1/ endpoints, and a managed scraping API. Every section ships runnable Python, and the examples assume Python 3.11 or newer.

By the end you'll have a clear method-selection matrix, working code for the seven most common YouTube scraping jobs, an anti-block layer that survives real-world traffic, and a legal checklist that won't get your project shut down. Let's get into it.

YouTube is the second-largest search engine on the planet and the single largest archive of long-form video commentary in any language. That makes it a goldmine for three jobs the official Data API was never designed to handle at scale.

Creator and competitor analytics. Pulling a competitor's entire upload history with view counts, durations, tags, and publish cadence reveals which formats are actually working, not just which ones the YouTube algorithm surfaces today.

Audience sentiment and product research. Comment threads under product reviews, tutorials, and unboxings are some of the most honest user-generated text on the open web. Sentiment models trained on YouTube comments tend to generalize well because the writing is conversational and opinionated.

SEO, trend, and RAG inputs. Transcripts plus titles plus top comments give you a clean text payload that a retrieval-augmented generation pipeline can chunk and embed without scraping the video file itself. That is the use case that pushed most teams from the Data API toward learning how to scrape YouTube programmatically in the first place.

Whatever the goal, treat this article as a method-by-method playbook rather than a single-tool tutorial. Different jobs want different tools.

There are four realistic ways to learn how to scrape YouTube in Python today, and the wrong choice will burn quota, get you blocked, or simply not return the field you need. Pick first, code second.

Two patterns run through the last three rows: extracting JSON embedded in script tags (ytInitialPlayerResponse, ytInitialData) and replicating the internal XHR calls the YouTube SPA fires. Both return structured data without spinning up a headless browser, which keeps requests fast and cheap. Reach for a real browser only when you have to log in or trigger a UI-driven event.

If you are still asking yourself which one before you write code, the next two subsections are the decision tiebreakers.

The Data API v3 is the safest choice for clean, structured data at low volume. It returns canonical IDs, official statistics, and stable schemas, and you stay on the right side of YouTube's Terms of Service by design.

The catch is the unit math. Per the YouTube Data API documentation, every project starts with a default daily quota (currently around 10,000 units, but verify in the Google Cloud Console because Google adjusts this). A videos.list call costs roughly 1 unit while a search.list call costs roughly 100 units, so a single deep search session can burn a full day's budget in minutes. Reconfirm the exact unit costs against the official docs before you commit to API-only.

Use the API when you need a few thousand video records per day with stable fields and zero anti-bot exposure. Anything beyond that, you scrape.

Switch to web scraping the moment any of these are true:

• You need bulk metadata across thousands of videos per day.
• You need full comment threads with replies, sort order, or the comment author's channel handle.
• You need transcripts or auto-generated captions programmatically.
• You need YouTube Shorts filtered cleanly out of search results.
• You need channel video catalogs deeper than the API's paged response gives you.
• You need any field that simply isn't exposed by the API (tags on most videos, for example).

The decision matrix on this page is intentionally the first answer for anyone learning how to scrape YouTube in 2026, because picking the wrong tool is the most expensive mistake you can make on this platform.

Before any of the YouTube web scraping code below runs, set up a clean Python 3.11+ project so dependencies do not collide with your other tooling.

mkdir youtube-scraper && cd youtube-scraper
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install --upgrade yt-dlp requests beautifulsoup4 parsel \
            jsonpath-ng youtube-transcript-api

That single install covers everything in this guide:

• yt-dlp for video metadata, comments, and subtitles.
• requests + BeautifulSoup4 for HTML pages and the <script> tag trick.
• parsel for CSS/XPath when BeautifulSoup feels too verbose.
• jsonpath-ng for walking deeply nested /youtubei/v1/ responses.
• youtube-transcript-api as a one-call transcript shortcut.

Two terms you will see throughout. Hidden data scraping pulls a JSON blob out of a <script> tag in the page HTML; on YouTube the canonical example is ytInitialPlayerResponse. Hidden API scraping replicates the internal XHR/fetch calls the YouTube single-page app makes, hitting /youtubei/v1/ endpoints directly to receive structured JSON. Both avoid the rendered DOM, which makes scrapers faster and less brittle than parsing visual layout. With those concepts in hand, the no-API-key quick start is only a few lines away.

The fastest no-key win in YouTube web scraping is to fetch a watch page, extract the embedded ytInitialPlayerResponse JSON, and read whatever you need from it. No API project, no OAuth, no headless browser.

import json, re, requests

VIDEO_URL = 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'
HEADERS = {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) '
                  'AppleWebKit/537.36 (KHTML, like Gecko) '
                  'Chrome/124.0 Safari/537.36',
    'Accept-Language': 'en-US,en;q=0.9',
}

html = requests.get(VIDEO_URL, headers=HEADERS, timeout=15).text
match = re.search(r'ytInitialPlayerResponse\s*=\s*(\{.+?\})\s*;', html)
data = json.loads(match.group(1))

details = data['videoDetails']
print(details['title'])
print(details['author'])
print(details['viewCount'])
print(details['lengthSeconds'])

That snippet is roughly 90% of what most readers came here for: a working answer to how to scrape YouTube video metadata without an API key. The regex is targeted at the assignment line YouTube has used for years, but treat it as a moving target. If a request returns blank or the regex fails, YouTube probably hit you with a consent-cookie redirect (more on that later), or it tweaked the script tag's surrounding markup. We harden this in the anti-block section.

ytInitialPlayerResponse is great for a few fields. For the long tail (formats, like count, upload date, every tag, automatic captions, chapter list), reach for yt-dlp. It is a community-maintained fork of youtube-dl with a broader feature set and active updates; consult the yt-dlp GitHub repository for the current option flags and field surface before relying on any specific field name.

yt-dlp's extract_info(download=False) returns a flat Python dictionary with a large number of fields covering title, view and like counts, upload date, tags, thumbnails, and every available media format. The exact field count drifts release to release, so do not hard-code expectations.

import json
import yt_dlp

VIDEO_URL = 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'

ydl_opts = {
    'quiet': True,
    'skip_download': True,
    # 'cookiesfrombrowser': ('chrome',),  # for age-gated content
}

with yt_dlp.YoutubeDL(ydl_opts) as ydl:
    info = ydl.extract_info(VIDEO_URL, download=False)

keys_we_care_about = (
    'id', 'title', 'channel', 'channel_id', 'upload_date',
    'duration', 'view_count', 'like_count', 'tags',
    'categories', 'description',
)
flat = {k: info.get(k) for k in keys_we_care_about}
print(json.dumps(flat, indent=2, ensure_ascii=False))

Two flags you will reach for often:

• 'cookiesfrombrowser': ('chrome',) lets yt-dlp reuse your browser session for age-gated, region-locked, or member-only videos.
• 'extract_flat': 'in_playlist' skips per-video metadata calls when you only need the playlist's video IDs.

To persist the result, dump the dictionary to JSON Lines (one record per line) so you can append new videos without rewriting the file:

with open('videos.jsonl', 'a', encoding='utf-8') as f:
    f.write(json.dumps(flat, ensure_ascii=False) + '\n')

yt-dlp self-throttles and handles a lot of edge cases (live streams, premieres, members-only) without any extra code, which is why it is the default tool when teams ask how to scrape YouTube video data programmatically without standing up a full anti-bot stack.

Comments are the most common reason teams outgrow the Data API. The commentThreads endpoint is heavy on quota and shallow on replies, while yt-dlp will pull a configurable depth in a single call. This is where scrape YouTube comments Python gets real.

import yt_dlp

VIDEO_URL = 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'

ydl_opts = {
    'quiet': True,
    'skip_download': True,
    'getcomments': True,
    'extractor_args': {
        'youtube': {
            'comment_sort': ['top'],   # or 'new'
            'max_comments': ['200', '50', '10', '0'],  # total, per-thread, replies-per-thread, child-replies
        }
    },
}

with yt_dlp.YoutubeDL(ydl_opts) as ydl:
    info = ydl.extract_info(VIDEO_URL, download=False)

for c in info.get('comments', [])[:5]:
    print(c['author'], '|', c['text'][:120])

Three things to know about that response:

• The list is flat. Replies appear with a parent field pointing at their root comment ID, so reconstruct threads in post-processing.
• Sort order is best effort. comment_sort only requests it; YouTube serves what it has cached.
• For deep pagination beyond what max_comments returns reliably, drop down to the /youtubei/v1/next endpoint with the commentContinuationToken extracted from the watch page. You post the token, get a new continuation, and repeat until the response stops returning one.

That hidden-API approach is heavier to maintain because YouTube changes the response shape periodically, but it is the only way to reach the long-tail comment count on viral videos. Plan to wrap it in a 429-aware retry layer (covered later) before you let it run unattended.

Two jobs sit under "channel scraping": pulling the channel's profile fields (name, description, links, country, subscriber count when public) and walking the videos tab end-to-end.

Path 1: parse the channel About section. It is HTML, so a requests + BeautifulSoup pass on /@channelhandle/about gives you the basics. The handful of fields YouTube hides behind a JS-driven dialog (links, business email) are still embedded in the same ytInitialData script blob you used for the quick start.

import json, re, requests
from bs4 import BeautifulSoup

URL = 'https://www.youtube.com/@GoogleDevelopers/about'
html = requests.get(URL, headers={'Accept-Language': 'en-US,en;q=0.9'}).text

soup = BeautifulSoup(html, 'html.parser')
title_tag = soup.find('meta', attrs={'name': 'title'})
channel_title = title_tag['content'] if title_tag else None

init = re.search(r'var ytInitialData\s*=\s*(\{.+?\});', html).group(1)
data = json.loads(init)
# data now contains description, links, subscriberCountText, etc., nested under 'header' / 'metadata'

Path 2: paginate the videos tab through /youtubei/v1/browse. The first batch of videos arrives in ytInitialData, but everything after that ships through continuation tokens. Pull the first token from the initial blob, then post it back to the hidden API for the next page.

import requests

YT_API = 'https://www.youtube.com/youtubei/v1/browse'
CLIENT = {'clientName': 'WEB', 'clientVersion': '2.20240101.00.00'}

def fetch_videos_page(continuation_token: str) -> dict:
    payload = {
        'context': {'client': CLIENT},
        'continuation': continuation_token,
    }
    r = requests.post(YT_API, params={'prettyPrint': 'false'}, json=payload, timeout=20)
    r.raise_for_status()
    return r.json()

def walk(continuation_token: str, max_pages: int = 5):
    for _ in range(max_pages):
        data = fetch_videos_page(continuation_token)
        # videos under: onResponseReceivedActions[*].appendContinuationItemsAction.continuationItems[*]
        yield data
        next_tokens = [
            item['continuationItemRenderer']['continuationEndpoint']['continuationCommand']['token']
            for action in data.get('onResponseReceivedActions', [])
            for item in action.get('appendContinuationItemsAction', {}).get('continuationItems', [])
            if 'continuationItemRenderer' in item
        ]
        if not next_tokens:
            return
        continuation_token = next_tokens[0]

That continuation pattern is the same one used by search results and comments, so it is worth getting comfortable with it once. Use jsonpath-ng or jmespath to keep the path expressions readable instead of chaining .get() calls. For deep historical pulls, run the loop behind a proxy pool, because hitting /youtubei/v1/browse rapidly from one IP is the fastest way to trip YouTube's rate limiter.

YouTube search is the hardest surface to keep working long term, because the SERP layout shifts and the API requires a fresh clientVersion header. Replicate the private XHR you can observe in DevTools (Network tab, filter on youtubei) and you have a stable scraper.

import requests
from jsonpath_ng.ext import parse

YT_SEARCH = 'https://www.youtube.com/youtubei/v1/search'
CONTEXT = {'client': {'clientName': 'WEB', 'clientVersion': '2.20240101.00.00'}}

def search_youtube(query: str) -> list[dict]:
    payload = {'context': CONTEXT, 'query': query}
    r = requests.post(YT_SEARCH, json=payload, timeout=20)
    r.raise_for_status()
    data = r.json()

    expr = parse('$..videoRenderer')
    results = []
    for m in expr.find(data):
        v = m.value
        results.append({
            'id': v.get('videoId'),
            'title': v['title']['runs'][0]['text'],
            'channel': v.get('ownerText', {}).get('runs', [{}])[0].get('text'),
            'views_text': v.get('viewCountText', {}).get('simpleText'),
        })
    return results

for hit in search_youtube('python web scraping')[:5]:
    print(hit)

A few production notes that will save you debugging time:

• The clientVersion value drifts. Pull a fresh one occasionally from https://www.youtube.com/sw.js_data or by inspecting any watch page; pinning a stale version eventually returns empty payloads.
• Pagination uses the same continuation token pattern as channel videos, just sourced from the search response itself.
• videoRenderer is the most common result type, but search also returns channelRenderer, playlistRenderer, and reelItemRenderer (Shorts), so filter explicitly to whichever surface you want.

If you maintain a long-running tool that learns how to scrape YouTube search results in production, expect to refresh the JSON-walking logic every few months. The endpoint shape is stable enough to depend on, but field names inside renderers do shift.

YouTube Shorts use a different player surface than regular videos, but the metadata pipeline is identical. Each Short still has a watch URL of the form https://www.youtube.com/shorts/<videoId> that resolves to a regular watch page server-side, and the page still embeds ytInitialPlayerResponse. That means everything from the quick-start section works on Shorts with no code changes.

import json, re, requests
URL = 'https://www.youtube.com/shorts/<videoId>'
html = requests.get(URL, headers={'Accept-Language': 'en-US'}).text
data = json.loads(re.search(r'ytInitialPlayerResponse\s*=\s*(\{.+?\})\s*;', html).group(1))
print(data['videoDetails']['title'], data['videoDetails']['viewCount'])

To isolate Shorts inside search results, filter for the reelItemRenderer (or shortsLockupViewModel on newer responses) keys instead of videoRenderer when walking the JSON. To list a creator's Shorts, hit the channel's /shorts tab through the same /youtubei/v1/browse continuation loop you used for the regular videos tab, just with the channel's Shorts browseId. That covers most of what people mean when they ask how to scrape YouTube Shorts at scale without a custom mobile-app emulator.

Transcripts are the highest-value text payload on the platform and the most fragile one to scrape. There are two practical paths.

Path A: yt-dlp with writesubtitles and writeautomaticsub. This is the most reliable path because yt-dlp generates the signed json3 URL for you. YouTube no longer reliably serves transcripts from a plain api/timedtext?lang=en&v=ID URL without signed parameters, so generating the URL through an extractor is the recommended approach. Verify against the current yt-dlp source if behavior shifts.

import yt_dlp, requests

VIDEO_URL = 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'
ydl_opts = {
    'quiet': True,
    'skip_download': True,
    'writesubtitles': True,
    'writeautomaticsub': True,
    'subtitleslangs': ['en', 'en-US'],
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
    info = ydl.extract_info(VIDEO_URL, download=False)

subs = info.get('subtitles') or info.get('automatic_captions') or {}
for lang, tracks in subs.items():
    json3 = next((t['url'] for t in tracks if t.get('ext') == 'json3'), None)
    if json3:
        captions = requests.get(json3, timeout=20).json()
        text = ' '.join(seg['utf8'] for ev in captions['events']
                        for seg in ev.get('segs', []) if 'utf8' in seg)
        print(lang, text[:200])
        break

Path B: youtube-transcript-api shortcut. When you only need the text and timestamps, this library is the one-liner:

from youtube_transcript_api import YouTubeTranscriptApi

api = YouTubeTranscriptApi()
fetched = api.fetch('dQw4w9WgXcQ', languages=['en', 'en-US', 'en-GB'])
for snippet in fetched:
    print(f"{snippet.start:7.2f}  {snippet.text}")

It returns a FetchedTranscript-style iterable of timestamped snippets with the language code and an is_generated flag that tells you whether captions are auto-generated. Pass an ordered list of language codes for fallback. If you are pulling transcripts at any meaningful volume, route the underlying HTTP through a proxy pool because the timedtext host rate-limits aggressively from cloud IPs.

Every method above scales until YouTube starts noticing. Past a few hundred requests per minute from one IP, you'll see empty /youtubei/v1/ payloads, redirects to consent.youtube.com, or HTTP 429s. Five practices keep a YouTube web scraping pipeline alive in production.

1. Rotate residential IPs for sustained runs. Datacenter proxies still work for low-volume requests calls but get flagged quickly on /youtubei/v1/. A residential pool of 150M+ IPs across 195 countries makes traffic look like ordinary household browsers, which is the difference between a scraper that runs for a week and one that gets blocked in an hour. Geo-targeting also lets you scrape region-locked metadata without a VPN. (See our internal guide on using proxies with Python's requests module for the wiring details.)

2. Randomize headers. A single User-Agent across thousands of requests is a fingerprint. Rotate User-Agent and Accept-Language, and set a sensible Referer (https://www.youtube.com/) so the call looks page-driven.

3. Throttle and back off on 429. Detect rate limiting and pause exponentially:

import time, random, requests

def fetch(url, attempts=5, **kwargs):
    for i in range(attempts):
        r = requests.get(url, timeout=20, **kwargs)
        if r.status_code == 429 or r.status_code >= 500:
            sleep_for = (2 ** i) + random.random()
            time.sleep(sleep_for)
            continue
        r.raise_for_status()
        return r
    raise RuntimeError(f'Gave up after {attempts} attempts')

4. Handle the consent-cookie redirect. EU-region requests often bounce to consent.youtube.com. The cheap fix is to send a CONSENT=YES+cb cookie on every call, which tells YouTube the consent banner has already been dismissed.

requests.get(url, cookies={'CONSENT': 'YES+cb'}, headers=HEADERS)

5. Use a managed scraping API for production. Owning the proxy and retry layer is fine for a side project. For anything customer-facing, our Scraper API handles proxy rotation, header randomization, JavaScript rendering, and CAPTCHA solving behind a single endpoint, so your code stays focused on parsing the YouTube response. You only pay for successful requests, which makes cost forecasting tractable. Pair the techniques above with a proven anti-block playbook and the failure rate drops from "daily firefight" to "check the dashboard once a week".

The reason most teams stitch metadata, comments, and transcripts together is to feed a language model. The flat dictionaries above are easy to convert into a single Markdown payload that an embedding pipeline or a Gemini-style summarizer can ingest directly.

def to_markdown(meta: dict, transcript: str, top_comments: list[dict]) -> str:
    parts = [
        f"# {meta['title']}",
        f"**Channel:** {meta['channel']}  ",
        f"**Published:** {meta.get('upload_date')}  ",
        f"**Views:** {meta.get('view_count')}  ",
        '',
        '## Description',
        meta.get('description', '').strip(),
        '',
        '## Transcript',
        transcript.strip(),
        '',
        '## Top comments',
    ]
    for c in top_comments[:25]:
        parts.append(f"- **{c['author']}**: {c['text']}")
    return '\n'.join(parts)

Once you have a Markdown payload per video, two small tweaks make it production-friendly for RAG:

• Chunk for context windows. Even with long-context models, send 2,000-4,000 token chunks with a 200-token overlap so retrieval can pull the right slice without losing surrounding context.
• Embed structured metadata alongside text. Store videoId, channelId, publishedAt, and language as separate columns in your vector store. Filtering on those at query time is cheaper and more accurate than relying on semantic similarity alone.

This pattern is why teams that already know how to scrape YouTube tend to fold it into the same pipeline as their podcast and webinar ingestion: the output schema lines up, and the same chunker/embedder reuses cleanly across sources.

Nothing in this guide is legal advice; treat it as a developer's checklist, and consult counsel before any commercial deployment. With that out of the way, four boundaries matter when you scrape YouTube.

Terms of Service. YouTube's Terms of Service restrict automated access in ways that change periodically; review the current YouTube Terms of Service directly rather than trusting any third-party summary. Violating them can lead to IP blocking, account suspension, or legal action against the operator.

Copyright. Video files, thumbnails, and original creator metadata are protected by copyright in most jurisdictions. Storing or redistributing that content beyond fair-use, research, or transformative use cases without authorization can amount to infringement. Linking and analytical use of public metadata sit on much safer ground.

Privacy law. Comments and channel handles often qualify as personal data. The EU GDPR text sets out lawful-basis, minimization, and retention requirements; California's CCPA imposes parallel duties on a different set of actors. If you're collecting comments from EU or California users, document a lawful basis, minimize what you keep, and offer a deletion path.

Etiquette. Respect robots.txt even where it isn't strictly binding, throttle aggressively when you detect 429s, identify your scraper in a clearly attributable User-Agent for first-party reach-outs, and stop the moment you see a written objection from a creator or YouTube itself.

• Pick the method first. The Data API v3 is fine for low-volume structured pulls; everything else (comments, transcripts, Shorts, deep search) belongs in a scraping pipeline.
• The ytInitialPlayerResponse regex trick is the fastest no-API-key route to title, view count, channel, and length, and it works on regular videos and Shorts alike.
• yt-dlp is the default workhorse for video metadata, comments, and transcripts because it self-throttles and ships with signed-URL handling for captions.
• Hidden /youtubei/v1/ endpoints unlock search results, channel video pagination via continuation tokens, and deep comment threads, but they need a proxy pool and a 429-aware retry layer to stay healthy.
• Treat anti-bot defense, ToS compliance, and GDPR/CCPA hygiene as production requirements, not afterthoughts.

Public YouTube metadata can usually be collected for analytical and research purposes, but doing so may still violate YouTube's Terms of Service, which restrict automated access in ways that change periodically. Copyright still attaches to videos and thumbnails, and personal data in comments triggers GDPR or CCPA duties. Review the current ToS, document a lawful basis, and consult counsel before any commercial use.

Skip the Data API entirely. yt-dlp's getcomments=True option pulls thread text, authors, like counts, and parent IDs in a single call with no quota and no API key. For deeper threads on viral videos, replicate the /youtubei/v1/next XHR with the commentContinuationToken from the watch page and paginate until the response stops returning a new continuation.

Install youtube-transcript-api and call YouTubeTranscriptApi().fetch(video_id, languages=['en','en-US','en-GB']). It returns timestamped snippets, the language code, and an is_generated flag indicating auto-captions. Pass an ordered languages list for graceful fallback. For signed-URL edge cases (live captions, member-only content), drop down to yt-dlp with writesubtitles=True and read the json3 URL it generates.

Three usual suspects. You're hitting the same IP too often, so rotate to residential proxies and add exponential backoff on 429s. You're being redirected to consent.youtube.com, so set a CONSENT=YES+cb cookie. Or your clientVersion header is stale on /youtubei/v1/ calls, so refresh it from a current watch page or sw.js_data once per run.

Endpoint paths and top-level wrappers stay stable for months at a time, but renderer field names inside (videoRenderer, reelItemRenderer, comment continuation paths) drift every few weeks. Defend against drift by parsing through jsonpath-ng or jmespath expressions that are easy to update, monitoring response shape with schema snapshots, and writing integration tests that fail loudly when a critical field disappears.

If you only remember one thing from this guide, let it be the decision matrix: knowing how to scrape YouTube starts with picking the right tool, not writing code. The Data API for low-volume structured calls, yt-dlp for per-video depth, hidden /youtubei/v1/ endpoints for search and pagination, and a managed API when production is on the line.

Before you ship a YouTube scraper, run three production checks. First, confirm your proxy pool rotates often enough to keep per-IP request rates in the conservative zone. Second, verify your retry policy treats 429s, 5xx errors, and consent redirects as distinct failure modes with different backoff curves. Third, set up monitoring that alerts on response-shape changes, not just HTTP failures, so a silent schema drift on /youtubei/v1/ does not corrupt a week of data.

Pair channel scraping with comment sentiment analysis, transcript chunking, or competitor cadence dashboards and you have a real intelligence pipeline. When you'd rather skip the proxy and retry plumbing entirely, our team at WebScrapingAPI offers a Scraper API that returns clean HTML or JSON from any YouTube surface with anti-bot handling baked in, so you can keep the parsing code and swap out the fetch layer with one HTTP call.