Summarize Entire YouTube Playlists with AI

If you have ever tried to extract actionable knowledge from a 30-video conference playlist or a multi-week online course, you know the manual approach breaks down fast. This guide walks through a complete pipeline: pull every video from a YouTube playlist, fetch all the transcripts in batches, chunk and summarize with an LLM, and produce a clean per-video and whole-playlist digest.

The Use Case

Technical conference talks, online courses, and YouTube learning series all share the same problem: the knowledge is locked inside hours of video. A student working through a Python course, a developer reviewing recorded team retrospectives, or a researcher scanning a conference track all benefit from a machine-generated digest that condenses each video to its core points and rolls those up into one readable summary.

The pipeline has four stages:

1. List every video in the playlist.
2. Batch-fetch transcripts for all of them.
3. Chunk long transcripts and summarize each video.
4. Roll up the per-video summaries into a single playlist digest.

Stage 1: Pull Playlist Items

The first call you need is GET /api/v2/playlist/videos. Pass the playlist ID or full YouTube playlist URL as the playlist query parameter.

curl "https://getyoutubetranscriber.com/api/v2/playlist/videos?playlist=PLrEnWoR732-CN09YykVof2lxdI3MLOZda" \
  -H "Authorization: Bearer ytt_your_key_here"

The response includes a results array with one object per video:

{
  "results": [
    {
      "type": "video",
      "video_id": "dQw4w9WgXcQ",
      "title": "Introduction to Async Python",
      "has_captions": true,
      "length_text": "12:34"
    }
  ],
  "result_count": 25,
  "continuation_token": "4qmFsgJ...",
  "has_more": true,
  "playlist_info": {
    "title": "Python Async Deep Dive",
    "num_videos": "47"
  }
}

Two fields are worth flagging here. has_captions tells you whether transcript data is available before you spend a credit on it. has_more plus continuation_token drive pagination: if the playlist has more videos than one page returns, pass continuation in your next request to fetch the next page.

Here is a Python function that collects all video IDs in a playlist, skipping videos without captions:

import requests

API_KEY = "ytt_your_key_here"
BASE = "https://getyoutubetranscriber.com"

def get_playlist_video_ids(playlist_id: str) -> list[str]:
    ids = []
    params = {"playlist": playlist_id}
    headers = {"Authorization": f"Bearer {API_KEY}"}

    while True:
        r = requests.get(f"{BASE}/api/v2/playlist/videos", params=params, headers=headers)
        r.raise_for_status()
        data = r.json()

        for item in data["results"]:
            if item.get("has_captions"):
                ids.append(item["video_id"])

        if not data.get("has_more"):
            break
        params["continuation"] = data["continuation_token"]

    return ids

For a complete walkthrough of the playlist endpoint, see Pull YouTube Playlist Data via API: Full Walkthrough.

Stage 2: Batch Transcript Calls

With a list of video IDs in hand, call POST /api/v2/transcripts/bulk. The endpoint accepts up to 50 video URLs per request, so split larger playlists into chunks of 50.

import math

def fetch_transcripts_bulk(video_ids: list[str]) -> list[dict]:
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    }
    results = []
    batch_size = 50

    for i in range(0, len(video_ids), batch_size):
        batch = video_ids[i : i + batch_size]
        payload = {
            "video_urls": batch,
            "format": "text",        # plain text is easier to chunk
            "include_timestamp": False,
            "send_metadata": True,
        }
        r = requests.post(
            f"{BASE}/api/v2/transcripts/bulk",
            headers=headers,
            json=payload,
        )
        r.raise_for_status()
        data = r.json()
        results.extend(data["results"])

    return results

The response for each video looks like this:

{
  "video_url": "dQw4w9WgXcQ",
  "video_id": "dQw4w9WgXcQ",
  "status": "success",
  "language": "en",
  "transcript": [
    { "text": "Welcome to the course.", "start": 0, "duration": 2.4 }
  ],
  "metadata": {
    "title": "Introduction to Async Python",
    "author_name": "Tech Educator",
    "thumbnail_url": "https://i.ytimg.com/vi/dQw4w9WgXcQ/hqdefault.jpg"
  }
}

When format is json, each entry includes a text, start, and duration. Using format: "text" collapses the array into a single string, which is simpler to pass to an LLM. Use format: "json" if you need timestamps for later alignment.

Stage 3: Chunking and Per-Video Summaries

Most LLMs have context window limits that can trip you up on long videos. A 90-minute conference talk might produce 15,000 words of transcript. Even models with large context windows can produce weaker recall on content buried in the middle of a very long prompt, a phenomenon sometimes called "lost in the middle" degradation. The practical fix is to chunk first, summarize each chunk, then summarize the summaries.

A safe target is 1,500 words per chunk. According to Deepchecks, 1,500 words maps to roughly 2,048 tokens in English, which fits comfortably inside the context of most current models while leaving room for the system prompt and the output.

def chunk_text(text: str, max_words: int = 1500) -> list[str]:
    words = text.split()
    return [
        " ".join(words[i : i + max_words])
        for i in range(0, len(words), max_words)
    ]

def summarize_video(title: str, transcript_text: str, llm_client) -> str:
    chunks = chunk_text(transcript_text)
    chunk_summaries = []

    for chunk in chunks:
        prompt = (
            f"Summarize the following section of a YouTube video titled '{title}'. "
            f"Focus on the key points, concepts, and any concrete examples.\n\n{chunk}"
        )
        response = llm_client.chat(prompt)
        chunk_summaries.append(response)

    if len(chunk_summaries) == 1:
        return chunk_summaries[0]

    # Reduce multiple chunk summaries into one
    combined = "\n\n".join(chunk_summaries)
    final_prompt = (
        f"The following are section summaries for a video titled '{title}'. "
        f"Combine them into one coherent summary of 150-250 words.\n\n{combined}"
    )
    return llm_client.chat(final_prompt)

This two-pass approach scales to videos of any length without hitting token limits. If you are processing dozens of videos, run summarize_video calls concurrently with asyncio or a thread pool to reduce wall-clock time.

Stage 4: Assembling the Playlist Digest

Once you have a summary for each video, roll them up into a single digest. The structure is straightforward: preserve the playlist order, include the video title, and then append a short overall synthesis at the end.

def build_playlist_digest(
    playlist_title: str,
    video_summaries: list[dict],  # [{"title": ..., "summary": ...}]
    llm_client,
) -> str:
    lines = [f"# {playlist_title}\n"]

    for i, item in enumerate(video_summaries, start=1):
        lines.append(f"## Video {i}: {item['title']}\n")
        lines.append(item["summary"])
        lines.append("")

    # Generate an overall synthesis
    all_summaries = "\n\n".join(
        f"{item['title']}: {item['summary']}" for item in video_summaries
    )
    synthesis_prompt = (
        f"Based on these summaries from the playlist '{playlist_title}', "
        f"write a 200-300 word executive summary covering the main themes, "
        f"recurring concepts, and key takeaways across all videos.\n\n{all_summaries}"
    )
    synthesis = llm_client.chat(synthesis_prompt)

    lines.append("## Playlist Summary\n")
    lines.append(synthesis)

    return "\n".join(lines)

The output is a Markdown document: one H2 per video with its summary, followed by an H2 that synthesizes the whole playlist. You can save this to a file, push it to a Notion page, or serve it through an API endpoint in your own product.

Putting It All Together

Here is the complete orchestration:

def summarize_playlist(playlist_id: str, llm_client) -> str:
    print("Fetching playlist videos...")
    video_ids = get_playlist_video_ids(playlist_id)
    print(f"Found {len(video_ids)} captioned videos.")

    print("Fetching transcripts...")
    transcript_results = fetch_transcripts_bulk(video_ids)

    print("Summarizing each video...")
    video_summaries = []
    for result in transcript_results:
        if result["status"] != "success":
            continue
        title = result["metadata"]["title"]
        # Flatten JSON transcript to plain text
        text = " ".join(seg["text"] for seg in result["transcript"])
        summary = summarize_video(title, text, llm_client)
        video_summaries.append({"title": title, "summary": summary})

    print("Building digest...")
    return build_playlist_digest(playlist_id, video_summaries, llm_client)

digest = summarize_playlist("PLrEnWoR732-CN09YykVof2lxdI3MLOZda", my_llm_client)
print(digest)

This is the backbone. Swap my_llm_client for whichever model you use: OpenAI, Anthropic, Google Gemini, or a local model running via Ollama.

Cost and Rate-Limit Tips for Large Playlists

A conference playlist with 60 videos will burn through credits fast if you are not careful. A few practices that matter:

Filter before you fetch. The has_captions: false filter in Stage 1 prevents you from spending credits on videos that will return errors. Run the filter pass for free before any transcript calls.

Cache transcript responses. If you re-run the pipeline on the same playlist (to update a digest or add new videos), cached responses cost nothing. Store the response JSON keyed by video_id and skip the API call if a local entry already exists.

Use the summary field to track spend. The bulk transcript response includes a summary block:

{
  "summary": {
    "total": 50,
    "succeeded": 48,
    "failed": 2,
    "credits_charged": 48
  }
}

Log this after every batch. For large jobs, track cumulative credits charged and set a budget ceiling before you start.

Batch at the maximum size. Sending 50 videos per request instead of one at a time reduces the number of round trips. For a 150-video playlist, three requests instead of 150 matters for both latency and overhead.

Run transcript fetches and LLM calls on separate loops. Fetch all transcripts first, cache them, then run the LLM summarization loop. That way a timeout or rate limit from the LLM provider does not force you to re-fetch transcripts you already paid for.

If you expect to monitor a playlist for new uploads over time, the approach changes slightly: poll with the continuation token to detect new additions rather than re-fetching the full list. See 10 Things to Check Before Scaling YouTube Transcript Calls for a broader checklist on managing production-scale workloads.

A Note on Transcript Quality

Captions come in two forms: manually created and auto-generated. Auto-generated captions often lack punctuation and proper nouns can be mangled, especially for technical terms. If your LLM prompts ask the model to extract specific terminology or speaker names, mention in the prompt that the input is raw transcript text and may contain transcription artifacts. Most models handle this gracefully with a one-sentence context hint.

For playlists in languages other than English, pass the appropriate lang code in the bulk request body. The API returns the auto-detected language in each result's language field, so you can verify what was returned even when you do not specify a language up front.

Getting Started

Sign up at getyoutubetranscriber.com to get 100 free credits with no credit card required. The docs at getyoutubetranscriber.com/docs have the full parameter reference for both the playlist and bulk transcript endpoints. Start with a short playlist of 5-10 videos to validate your chunking and prompt setup before scaling to a full course or conference track.