HumationHuman + Automation
Back to Catalog

Generate complete stories with GPT-4o and save them in Google Drive

Ian DikhtiarIan Dikhtiar
1417 views
2/3/2026
Lead GenerationJotFormAutomationSalesforceCRM
Official Page

AI Story Generator with GPT-4o and Google Drive Integration

Automatically generate complete stories with GPT-4o and seamlessly save them to Google Drive.

Who is this for?

  • Creative writers and authors
  • Marketing and sales professionals
  • Educators and content creators
  • Fan fiction enthusiasts
  • Anyone interested in automating storytelling with AI

What problem is this workflow solving?

Manually creating engaging, structured narratives can be time-consuming. Writers and content creators often struggle to maintain consistency, depth, and engaging storytelling structure. This workflow solves these challenges by automating story creation using advanced AI (GPT-4o) and proven storytelling techniques.

What this workflow does

This n8n automation generates comprehensive stories through an iterative AI-driven process:

Step 1: Provide Your Story Idea Users input a clear description and specify their desired story format (short story, fan fiction, sales email, etc.).

Step 2: AI-Driven Analysis GPT-4o analyzes the provided idea, categorizes the story, selects relevant storytelling frameworks inspired by PipDeck Storyteller Tactics, and determines narrative tone and direction.

Step 3: Story and Character FoundationEstablishes core themes, emotional hooks, and detailed character backgrounds.

Step 4: Initial Story Development Creates a structured plot outline including engaging elements such as hooks, twists, and resolutions.

Step 5: Iterative Enhancement Refines the story through multiple automated prompts, improving narrative depth, character development, dialogue, and realism.

Step 6: Editorial Feedback Generates automated critiques highlighting clichés, weak dialogues, and areas for improvement.

Step 7: Final Polished Version Incorporates editorial feedback to produce a complete, polished, ready-to-use narrative.

Step 8: Instant Google Drive OrganizationAutomatically saves the final story directly to your specified Google Drive folder for easy access and management.

Setup Instructions

Prerequisites:

  • n8n account (cloud or self-hosted)
  • GPT-4o API access via OpenAI
  • Google Drive account

Configure OpenAI Node:

  • Add your GPT-4o API key in the OpenAI node settings.
  • Configure Google Drive Node:
  • Connect your Google Drive account by authenticating with n8n.
  • Specify the folder where generated stories should be saved.

Test Workflow:

Run the workflow with a simple story prompt to ensure proper setup.

How to Customize this Workflow

Adjust Prompt Details: Modify AI prompt instructions to suit your specific story style and audience.

Expand or Narrow Iterations: Change the number of iterations to balance between speed and story complexity.

Customize Feedback Level: Adjust the level of editorial feedback provided.

Dependencies and Requirements

  • GPT-4o API from OpenAI
  • Google Drive integration enabled in n8n

Get Started

Download and deploy this template today to streamline your storytelling process and produce consistently engaging, high-quality content effortlessly.

Generate Complete Stories with GPT-4o and Save Them in Google Drive

This n8n workflow automates the process of generating complete stories using an AI agent (likely GPT-4o, given the directory name context, though the JSON itself specifies an Azure OpenAI Chat Model) and then saving these stories as text files in Google Drive. It's designed to streamline content creation by leveraging AI for narrative generation and integrating with cloud storage for easy access and organization.

What it does

This workflow performs the following steps:

  1. Manual Trigger: Initiates the workflow upon a manual click, allowing for on-demand story generation.
  2. Edit Fields (Set): This node is present in the workflow but not connected, suggesting it might be a placeholder or an unused configuration step.
  3. Loop Over Items (Split in Batches): This node is present but not connected, indicating it's either a placeholder or an unused component for processing multiple items.
  4. AI Agent: This is the core of the story generation. It uses an AI model (configured as an Azure OpenAI Chat Model) to generate narrative content based on prompts or predefined instructions.
  5. Structured Output Parser: This node is present but not connected, suggesting it might be a placeholder or an unused component for parsing structured output from the AI.
  6. Aggregate: This node is present but not connected, indicating it's either a placeholder or an unused component for combining data.
  7. Split Out: This node is present but not connected, suggesting it might be a placeholder or an unused component for splitting data.
  8. Azure OpenAI Chat Model: This node is present but not connected, suggesting it might be a placeholder or an unused component for interacting with Azure OpenAI directly. The AI Agent node likely encapsulates this functionality.
  9. Sentiment Analysis: This node is present but not connected, suggesting it might be a placeholder or an unused component for analyzing the sentiment of the generated text.
  10. Google Drive: This node is present but not connected, indicating it's either a placeholder or an unused component for interacting with Google Drive.

Note: Based on the provided JSON, several nodes (Edit Fields, Loop Over Items, Structured Output Parser, Aggregate, Split Out, Azure OpenAI Chat Model, Sentiment Analysis, Google Drive) are present but not connected to the main flow. This implies that the current workflow, as defined by the connections, only consists of a "Manual Trigger" and an "AI Agent" node. To fully realize the "save to Google Drive" functionality suggested by the directory name, these nodes would need to be connected and configured. The description above reflects the potential intended functionality given the node names, but the actual connected flow is much simpler.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance to import and execute the workflow.
  • Azure OpenAI Account: An Azure OpenAI account with access to a chat model (e.g., GPT-4o) and the necessary API keys/credentials for the "AI Agent" node.
  • Google Drive Account: A Google Drive account and credentials configured in n8n if the Google Drive node were to be connected and used.

Setup/Usage

  1. Import the workflow: Download the provided JSON file and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Azure OpenAI credentials in n8n for the "AI Agent" node. This typically involves providing your API key and endpoint.
    • If you intend to use the Google Drive node, configure your Google Drive credentials in n8n.
  3. Configure the AI Agent:
    • Open the "AI Agent" node and configure its parameters. This will include defining the prompt or instructions for generating stories. You might specify genre, characters, plot points, or length.
  4. Connect Google Drive (Optional, but recommended for full functionality):
    • If you want to save the generated stories to Google Drive, connect the output of the "AI Agent" node to the "Google Drive" node.
    • Configure the "Google Drive" node to specify the file name, folder, and content to be saved (e.g., the output of the AI Agent).
  5. Activate the workflow: Once configured, activate the workflow.
  6. Execute the workflow: Click "Execute Workflow" on the "Manual Trigger" node to run it and generate a story.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942