🔬 Analyze YouTube videos and auto-generate AI reports in Google Docs with DeepSeek

⚡ 1 views · 🔬 Document Extraction & Analysis

💡 Pro Tip — YouTube’s API quotas can be a bottleneck when you’re pulling data at scale. ScraperNode is a community node with dedicated scrapers for channels, videos, and comments — no quota limits, just structured data.

Description

A compact n8n workflow that accepts a YouTube link or uploaded video, pulls a transcript via Supadata.ai, runs a language-model-based video analysis agent to produce a structured report, extracts a title/metadata, then creates and updates a Google Doc with the analysis. It’s designed to automate transcription → analysis → document creation for fast, repeatable video reviews.

How it works

Trigger — Upload File or YouTube Link
A form trigger receives a youtube_url or an uploaded file/webhook event.
Transcription — Transcription using Supadata.ai
Calls the transcription API using the x-api-key header to retrieve the video transcript/text.
Analysis — Analyser
The transcript is passed to the Analyser LangChain agent which runs a tailored prompt (expert video analyst) and generates a plain-text report.
Metadata extraction — File Name Detector
The information extractor parses the analyser output to extract structured attributes such as the Title.
Aggregation & Merge
Merge/Aggregate nodes combine the analysis and extracted fields into a single payload.
Document Creation
Creating New File creates a Google Docs document using the extracted Title, and Updating Content in File inserts the analyser output into the document.
Optional Follow-ups
Additional nodes can forward the document link, send it to Slack, or store metadata in a database.

Quick Setup Guide

👉 Demo & Setup Video 👉 Course

Nodes of interest

Upload File or YouTube Link
formTrigger (webhook) — Entry point for user-supplied links or files.
Transcription using Supadata.ai
httpRequest — Fetches transcript from https://api.supadata.ai/... and requires the x-api-key header.
OpenRouter Chat Model / OpenRouter Chat Model1
lmChatOpenRouter — Language model nodes connected to the Analyser and File Name Detector using the model
deepseek/deepseek-r1-distill-llama-70b.
Analyser
LangChain agent node that contains the expert analysis prompt and generates a full plain-text report from the transcript.
Configuration includes hasOutputParser: true and retry enabled.
File Name Detector
LangChain information extractor that extracts structured attributes like Title from the analysis output.
Merge / Aggregate
Combines outputs from analysis and extraction into a single payload used for document creation.
Creating New File / Updating Content in File
Google Docs nodes used to create and update documents using googleDocsOAuth2Api credentials.

What you’ll need (credentials)

OpenRouter account
Used by OpenRouter Chat Model nodes. API key stored in the openRouterApi credential.
Supadata.ai API key
Added in the HTTP header x-api-key in the transcription request.
Google Docs OAuth2
googleDocsOAuth2Api credential used for creating and updating Google Docs.
Optional integrations
Slack webhook, Google Drive, or database credentials if adding notifications or persistent storage.

Recommended settings & best practices

Prompt control
Keep the Analyser prompt explicit about required sections, output style, and how to handle missing transcripts.
Retries & timeouts
Enable retries for long-running model or HTTP calls. Configure proper HTTP request timeouts.
Rate limits
Respect transcription and model provider rate limits. Add throttling if needed.
Input validation
Validate the youtube_url before processing and handle transcript failures gracefully.
Chunk transcripts
Split long transcripts into chunks before sending to the LLM to avoid context limit issues.
Logging & audit
Store transcripts, analysis results, and metadata for debugging and traceability.
Security
Store API keys as n8n credentials rather than plaintext.
Document naming
Sanitize the extracted Title to prevent invalid filename characters.
Monitoring
Add error notifications via email or Slack for failed runs.

Customization ideas

Alternative transcription providers
Replace Supadata.ai with AssemblyAI, Whisper (self-hosted), or YouTube captions.
Multiple output formats
Export results to Google Docs, PDF, or JSON metadata.
Speaker diarization
Include speaker labels and timestamps in the analysis.
Summaries & highlights
Add TL;DR summaries and timestamped key moments.
Content classification
Use additional LLM nodes to detect sentiment, category, or compliance issues.
Thumbnail generation
Capture frames from the video to generate thumbnails.
Webhook callbacks
Send the document link to Slack, email, or other systems.
Model routing
Use smaller models for short videos and higher-quality models for long videos.
Human review pipeline
Create a review queue for manual verification before publishing results.