ytscrape: One Python Interface for YouTube Data — No Browser, No API Key

What if you could collect YouTube search results, videos, channels, comments, replies, and transcripts through one Python interface?

No browser automation.
No Selenium.
No Playwright.
No YouTube API key.

Just Python and HTTP.

ytscrape is an open-source Python library designed to make collecting YouTube data simple, fast, and consistent.

🚀 Get started

GitHub: https://github.com/vsmutok/ytscrape
PyPI: https://pypi.org/project/ytscrape/

pip install ytscrape

Enter fullscreen mode Exit fullscreen mode

Then:

from ytscrape import YouTube

with YouTube() as yt:
    # Search
    videos = yt.search("python", max_results=10)

    # Video
    video = yt.video("dQw4w9WgXQ")

    # Channel
    channel = yt.channel("@SomeChannel")

    # Comments + replies
    comments = yt.comments(
        "dQw4w9WgXQ",
        include_replies=True,
        sort="newest",
    )

    # Transcript
    transcript = yt.transcript(
        "dQw4w9WgXQ",
        languages=["en"],
    )

Enter fullscreen mode Exit fullscreen mode

One client. One interface. Multiple types of YouTube data.

Why ytscrape?

Working with YouTube data usually means choosing between different approaches.

The official YouTube Data API requires an API key and has quota limits.

Browser automation requires Selenium, Playwright, or another browser stack.

Other tools are focused primarily on downloading media.

But many projects don’t need any of that.

Sometimes you simply need the data:

YouTube
   │
   ├── Search
   ├── Videos
   ├── Channels
   ├── Comments
   ├── Replies
   └── Transcripts

Enter fullscreen mode Exit fullscreen mode

And you want to access all of it from Python.

That’s the problem ytscrape is trying to solve.

One Python interface for collecting YouTube data.

Everything starts with one client

The API is intentionally simple:

from ytscrape import YouTube

with YouTube() as yt:
    ...

Enter fullscreen mode Exit fullscreen mode

The same client gives you access to different parts of YouTube.

Search

for video in yt.search("python", max_results=10):
    print(video.title, video.url)

Enter fullscreen mode Exit fullscreen mode

Video metadata

video = yt.video(video_url)

print(video.title)
print(video.views)
print(video.length_seconds)

Enter fullscreen mode Exit fullscreen mode

Channel metadata

channel = yt.channel("@SomeChannel")

print(channel.title)
print(channel.subscribers)
print(channel.video_count)

Enter fullscreen mode Exit fullscreen mode

Comments

for comment in yt.comments(
    video_url,
    sort="newest",
):
    print(comment.author, comment.text)

Enter fullscreen mode Exit fullscreen mode

Replies

for comment in yt.comments(
    video_url,
    include_replies=True,
):
    print(comment.author, comment.text)

Enter fullscreen mode Exit fullscreen mode

Transcripts

transcript = yt.transcript(
    video_url,
    languages=["en", "uk"],
)

for line in transcript[:5]:
    print(line.text)

Enter fullscreen mode Exit fullscreen mode

Different data — same interface.

Built for data collection

ytscrape is designed around a simple idea:

You shouldn’t have to build a different scraper for every type of YouTube data.

You can combine different operations into a single pipeline.

For example, imagine building a dataset of Python-related YouTube videos.

with YouTube() as yt:

    for video in yt.search(
        "python programming",
        max_results=100,
    ):
        details = yt.video(video.video_id)

        comments = yt.comments(
            video.video_id,
            sort="newest",
        )

        transcript = yt.transcript(
            video.video_id,
            languages=["en"],
        )

Enter fullscreen mode Exit fullscreen mode

Now you can combine:

search → video metadata → comments → transcript

without introducing another library or browser automation framework.

Fast because there is no browser

One of the main design goals is to keep the library lightweight.

There is no need to start:

• Chrome
• Chromium
• Selenium
• Playwright
• a headless browser

ytscrape communicates with YouTube through HTTP and parses the responses into Python objects.

Under the hood, it uses YouTube’s internal InnerTube endpoints — the same family of endpoints used by the YouTube web application.

The basic flow is:

Python
  ↓
ytscrape
  ↓
YouTube InnerTube
  ↓
HTTP response
  ↓
Typed Python model

Enter fullscreen mode Exit fullscreen mode

This makes it possible to work with YouTube data without rendering web pages.

No API key

There is also no need to create a Google Cloud project or configure a YouTube Data API key.

Install:

pip install ytscrape

Enter fullscreen mode Exit fullscreen mode

Start using it:

from ytscrape import YouTube

with YouTube() as yt:
    for video in yt.search("machine learning", max_results=5):
        print(video.title)

Enter fullscreen mode Exit fullscreen mode

There is no API key configuration in your application.

Of course, this comes with a trade-off: ytscrape relies on YouTube’s internal endpoints, so those endpoints can change.

If you need an officially supported API, the official YouTube Data API is still the right choice.

Comments are a first-class feature

One particularly useful part of ytscrape is comment collection.

You can retrieve top-level comments:

for comment in yt.comments(video_url):
    print(comment.text)

Enter fullscreen mode Exit fullscreen mode

Or include replies:

for comment in yt.comments(
    video_url,
    include_replies=True,
):
    print(comment.author, comment.text)

Enter fullscreen mode Exit fullscreen mode

And if you need to collect comments using newest-first ordering:

for comment in yt.comments(
    video_url,
    sort="newest",
):
    print(comment.author, comment.text)

Enter fullscreen mode Exit fullscreen mode

This makes the library useful for:

• NLP
• sentiment analysis
• audience research
• content analysis
• building datasets

Transcripts and subtitles

Video metadata is only part of the data available around a YouTube video.

The actual content of the video can also be useful.

ytscrape supports transcript retrieval:

transcript = yt.transcript(
    video_url,
    languages=["en", "uk"],
)

for line in transcript:
    print(line.start, line.text)

Enter fullscreen mode Exit fullscreen mode

This means a single workflow can combine:

Video
 ├── Metadata
 ├── Comments
 ├── Replies
 └── Transcript

Enter fullscreen mode Exit fullscreen mode

Pagination is handled for you

YouTube results are paginated.

With ytscrape, you don’t have to manually implement continuation-token handling.

results = yt.search("python")

for video in results:
    print(video.title)

Enter fullscreen mode Exit fullscreen mode

The library loads additional pages as you iterate.

The same idea is used for comment collection.

You can also limit the amount of data:

yt.search(
    "python",
    max_results=100,
)

Enter fullscreen mode Exit fullscreen mode

Typed Python models

Another goal of the project is to provide a clean Python API instead of exposing raw, deeply nested YouTube JSON.

The library provides models such as:

• Video
• VideoDetails
• Channel
• ChannelDetails
• Playlist
• Comment

For example:

video.title
video.views
video.channel
video.url

Enter fullscreen mode Exit fullscreen mode

The package is typed and PEP 561 compliant, so tools such as mypy and pyright can understand the models.

What’s already supported?

Today, ytscrape supports:

Feature Status YouTube search ✅ Video metadata ✅ Channel metadata ✅ Playlist search ✅ Shorts / movies search ✅ Comments ✅ Comment replies ✅ Newest-first comments ✅ Transcripts / subtitles ✅ Pagination ✅ Language / region ✅ Typed models ✅ CLI ✅

And this is only the beginning.

What’s coming next?

The long-term goal is to make ytscrape a general-purpose Python interface for YouTube data.

Some things already planned:

• ⚡ Async API
• 📺 Channel videos, Shorts, live, playlists and About tabs
• 🎵 Playlist items
• 🔗 Related videos
• 🔥 Trending
• 🏠 Home feed
• 🗒️ Community posts
• 📚 Dedicated documentation and API reference

So the project is not intended to stop at search + metadata.

The idea is to gradually cover more of the data available through YouTube’s web application while keeping the Python API consistent.

Who is it for?

ytscrape can be useful if you are building:

Data pipelines

Collect structured YouTube data for further processing.

NLP / AI projects

Combine transcripts and comments to create datasets for analysis and research.

Market research tools

Analyze videos, channels, and audience discussions.

Content research tools

Search YouTube and collect information about content at scale.

Internal tools

Add YouTube data collection without introducing a full browser automation stack.

Python experiments

Quickly retrieve YouTube data without setting up an API project.

ytscrape vs. other approaches

Different tools solve different problems.

ytscrape YouTube Data API yt-dlp Browser automation API key ❌ ✅ ❌ ❌ Browser ❌ ❌ ❌ ✅ Search ✅ ✅ ✅ ✅ Video metadata ✅ ✅ ✅ ✅ Channel metadata ✅ ✅ — ✅ Comments ✅ ✅ ✅ ✅ Transcripts ✅ — ✅ ✅ Typed Python models ✅ — — — Media downloads ❌ ❌ ✅ ✅

A simple rule:

Need media downloads? → yt-dlp

Need official API access? → YouTube Data API

Need browser automation? → Playwright / Selenium

Need one lightweight Python interface for YouTube data? → ytscrape

Get started

Install the package:

pip install ytscrape

Enter fullscreen mode Exit fullscreen mode

Then:

from ytscrape import YouTube

with YouTube() as yt:
    for video in yt.search(
        "python",
        max_results=5,
    ):
        print(video.title, video.url)

Enter fullscreen mode Exit fullscreen mode

🚀 Project

GitHub: https://github.com/vsmutok/ytscrape

PyPI: https://pypi.org/project/ytscrape/

If you work with YouTube data in Python, I’d love to hear what you would like to see next.

Issues, ideas, pull requests, and contributions are welcome.

Important note

ytscrape communicates with YouTube’s internal InnerTube endpoints rather than the official YouTube Data API.

These endpoints may change over time, and YouTube may throttle aggressive traffic.

Always make sure your use of the library complies with YouTube’s current Terms of Service and applicable laws.

The project is provided for research and educational purposes, and you are responsible for how you use it.