What is YouTube Video Transcripts?
Scavio's YouTube transcript endpoint returns the complete transcript for any public YouTube video as an ordered array of segments, each with the text, a start time in seconds, and a duration. We detect whether the transcript was manually uploaded or auto-generated, return the detected language, and support requesting a specific language when multiple tracks exist. Transcripts are deduplicated and gently cleaned to remove speaker artifacts without altering the wording. For RAG pipelines, summarization agents, and analytics tools, transcripts are the single most information-dense signal you can extract from a video, and getting them via a single API call eliminates the need for fragile YouTube scraping libraries.
Example Response
{
"video_id": "dQw4w9WgXcQ",
"title": "Build an AI Agent in 10 Minutes with LangGraph",
"language": "en",
"auto_generated": false,
"duration_seconds": 642,
"transcript": [
{ "text": "Welcome back to the channel.", "start": 0.0, "duration": 2.4 },
{ "text": "Today we're building a stateful AI agent.", "start": 2.4, "duration": 3.1 },
{ "text": "We'll use LangGraph and Claude Opus 4.6.", "start": 5.5, "duration": 2.8 },
{ "text": "The first step is installing the packages.", "start": 8.3, "duration": 2.6 },
{ "text": "Run pip install langgraph langchain-anthropic.", "start": 10.9, "duration": 3.2 },
{ "text": "Now let's define our graph state.", "start": 14.1, "duration": 2.1 }
],
"available_languages": ["en", "es", "pt", "ja"]
}Use Cases
- Summarizing long tutorials and podcasts for newsletters
- Building searchable video libraries for internal knowledge bases
- Extracting timestamped highlights for video clipping tools
- Grounding LLM answers in source video quotes
- Translation and multilingual subtitle generation
Why YouTube Video Transcripts Matters
YouTube transcripts are the highest-leverage piece of content for any AI application working with video, but the unofficial libraries for pulling them break frequently and do not scale. Scavio's endpoint is a managed, rate-limited, high-throughput alternative with language selection and clean segmentation. Teams use it to run summarization on millions of hours of video, build semantic search over podcasts, and turn creator content into structured knowledge.
LangChain Example
Drop youtube video transcripts data into your LangChain agent in a few lines:
from langchain_scavio import ScavioYouTubeTranscriptTool
from langchain_anthropic import ChatAnthropic
tool = ScavioYouTubeTranscriptTool(api_key="your_scavio_api_key")
llm = ChatAnthropic(model="claude-opus-4-6")
result = tool.invoke({"url": "https://youtube.com/watch?v=dQw4w9WgXcQ"})
full_text = " ".join(seg["text"] for seg in result["transcript"])
summary = llm.invoke(f"Summarize this transcript in 5 bullets:\n\n{full_text}")
print(summary.content)