Send YouTube Transcripts to LangChain

Pipe any YouTube video into your LangChain pipeline in seconds

Or just change youtube.com to 2outube.com in your browser

Swap 'youtube.com' to '2outube.com' in any video URL to instantly get the full transcript. Copy it into LangChain as a Document, vector store input, or RAG source — no API keys, no scraping, completely free.

✓ Free✓ No signup✓ Works with any video

The Trick

Before: youtube.com/watch?v=VIDEO_ID

→

After: 2outube.com/watch?v=VIDEO_ID

Just change 'y' to '2'

Works with any YouTube video that has captions

Using Transcripts with LangChain

Get the YouTube video URL

Find any YouTube video you want to process in LangChain — a tutorial, lecture, podcast, interview, or any other content you need to analyze, summarize, or use as a retrieval source.

Swap the domain to 2outube.com

Change 'youtube.com' to '2outube.com' in the URL and load it. You'll see the full plain-text transcript on the page, ready to copy with one click.

Load the transcript into LangChain

Paste the transcript text into a LangChain Document object using the Document class, or pass it directly to a text splitter like RecursiveCharacterTextSplitter to chunk it for embedding and vector store ingestion.

Build your RAG pipeline or chain

Embed the chunked transcript with OpenAIEmbeddings, Cohere, or any LangChain-supported embedder, store it in a vector store like Chroma, FAISS, or Pinecone, then wire up a RetrievalQA chain to query your YouTube content with natural language.

Quick Start

Get the transcript

Find the YouTube video you want to use in LangChain and copy its URL from the browser address bar.

Change youtube to 2outube

In the URL, replace 'youtube.com' with '2outube.com' — for example, youtube.com/watch?v=abc123 becomes 2outube.com/watch?v=abc123. Hit enter and the transcript appears.

Paste into LangChain

Copy the transcript text and load it into a LangChain Document object, then split, embed, and store it for use in your RAG chain or LLM pipeline.

Ready-Made Template

from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.chains import RetrievalQA

# 1. Paste your transcript from 2outube.com here
transcript_text = """
[PASTE TRANSCRIPT FROM 2OUTUBE.COM HERE]
"""

# 2. Wrap in a LangChain Document
doc = Document(
    page_content=transcript_text,
    metadata={"source": "https://2outube.com/watch?v=YOUR_VIDEO_ID", "type": "youtube_transcript"}
)

# 3. Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = splitter.split_documents([doc])

# 4. Embed and store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

# 5. Build RetrievalQA chain
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
llm = ChatOpenAI(model="gpt-4o", temperature=0)
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

# 6. Query your YouTube content
result = qa_chain.invoke({"query": "What are the main points of this video?"})
print(result["result"])

Questions

Does this work with any YouTube video?

Yes, any video with captions.

Is it really free?

Completely free. No account, no limits.

How do I load a 2outube transcript into a LangChain Document?

Copy the transcript text from 2outube.com and wrap it in a LangChain Document object: Document(page_content=transcript_text, metadata={"source": "video_url"}). Then pass it to your text splitter and vector store as usual.

What text splitter works best for YouTube transcripts in LangChain?

RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=100 works well for most transcripts. For longer technical videos, you may want larger chunks (1500–2000 chars) to preserve context within each segment.

Can I use 2outube transcripts with LangChain's FAISS or Chroma vector stores?

Yes. Once you have the transcript as a LangChain Document and split it into chunks, you can embed and store it in any supported vector store — Chroma, FAISS, Pinecone, Weaviate, Qdrant, and others all work the same way.

Is this better than the LangChain YoutubeLoader?

2outube requires no API keys or dependencies — just a URL swap. If you want a quick no-code way to grab a transcript for a one-off pipeline or don't want to manage the youtube-transcript-api package, 2outube is faster to get started.

Can I process multiple YouTube videos into a single LangChain vector store?

Yes. Get transcripts for each video from 2outube.com, create a Document for each with the video URL in the metadata, split them all, and call Chroma.from_documents() or FAISS.from_documents() with the combined list of chunks.

Does 2outube include timestamps in the transcript?

2outube provides the full caption text of the video. If the video's auto-generated captions include timestamps, they will appear in the transcript. You can strip them before ingestion if your LangChain pipeline doesn't need them.

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