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

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

1. Trigger — Upload File or YouTube Link A form trigger receives a youtube_url or an uploaded file/webhook event.

2. Transcription — Transcription using Supadata.ai Calls the transcription API using the x-api-key header to retrieve the video transcript/text.

3. 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.

4. Metadata extraction — File Name Detector The information extractor parses the analyser output to extract structured attributes such as the Title.

5. Aggregation & Merge Merge/Aggregate nodes combine the analysis and extracted fields into a single payload.

6. 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.

7. 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.

Tags

video-analysis transcription n8n langchain automations google-docs openrouter supadata reporting workflow