Convert newsletters into AI podcasts with GPT-4o Mini and ElevenLabs
🎧 Convert Unread Newsletters into Conversational AI Podcasts
Turn email overload into audio insights — automatically.
This workflow transforms unread newsletters sitting in your inbox into engaging, human-like audio conversations between two AI voices. It’s perfect for listening during your commute, workout, or while multitasking.
Inspired by Google's NotebookLM, this automation brings long-form content to life by summarizing dense text into a natural dialogue using OpenAI and generating high-quality voice narration with ElevenLabs. The result? A dynamic audio file sent right back to your inbox — hands-free, screen-free, and stress-free.
💡 What this workflow does
- ✅ Connects to your Gmail inbox to fetch unread newsletters
- 🤖 Uses GPT-4o Mini to summarize and rephrase content as a conversation
- 🗣️ Sends the dialogue to ElevenLabs to generate voice clips (
voice1+voice2) - 🔁 Merges all audio segments into a single podcast-like MP3 using FFmpeg
- 📬 Emails the final audio back to you for easy listening
🛠️ What you'll need
- A Gmail account with IMAP enabled
- An OpenAI API key (GPT-4o Mini recommended for cost/performance)
- An ElevenLabs API key + selected voice IDs
- A self-hosted or local n8n instance with FFmpeg installed
- Basic knowledge of binary data and audio handling in n8n
✨ Use cases
- Convert long newsletters into hands-free listening experiences
- Repurpose Substack or Beehiiv content for podcast-like distribution
- Build an internal voice dashboard for teams who prefer audio updates
🙌 Want to go further?
This workflow is modular and extensible. You can add steps to:
- Upload the final audio to Spotify, SoundCloud, or Telegram
- Publish to a private podcast RSS feed
- Create a daily audio digest from multiple newsletters
📬 Contact & Feedback
Need help customizing it? Have ideas or feedback?
Feel free to reach out:
📩 Luis.acosta@news2podcast.com
If you're building something more advanced with audio + AI, like automated podcast publishing to Spotify — let me know and I’ll figure out how I can help you!
Convert Newsletters into AI Podcasts with GPT-4o-mini and ElevenLabs
This n8n workflow automates the process of converting incoming email newsletters into audio podcast episodes using OpenAI's GPT-4o-mini for summarization and ElevenLabs for text-to-speech. It listens for specific emails, extracts their content, summarizes it, generates an audio file, and then saves the audio to disk.
What it does
- Monitors Gmail: Listens for new emails in your Gmail inbox that match specific criteria (e.g., from a particular sender or containing certain keywords).
- Extracts Email Content: Retrieves the full content of the matched emails.
- Summarizes with OpenAI (GPT-4o-mini): Sends the email content to OpenAI's GPT-4o-mini model to generate a concise summary.
- Generates Audio with ElevenLabs: Uses the summarized text to create an audio file via ElevenLabs' text-to-speech service.
- Saves Audio to Disk: Stores the generated audio file on the local disk where your n8n instance is running.
- Cleans Up: Deletes the original email from your Gmail inbox after successful processing.
Prerequisites/Requirements
- n8n Instance: A running n8n instance.
- Gmail Account: A Gmail account configured as a credential in n8n.
- OpenAI API Key: An OpenAI API key with access to GPT-4o-mini, configured as a credential in n8n.
- ElevenLabs API Key: An ElevenLabs API key, configured as a credential in n8n.
- Local Storage: Access to the local disk for saving audio files (requires the
Read/Write Files from DiskandExecute Commandnodes to be enabled on your n8n instance, which might involve security considerations for self-hosted instances).
Setup/Usage
- Import the Workflow: Import the provided JSON into your n8n instance.
- Configure Credentials:
- Gmail Trigger & Gmail Node: Set up or select your Gmail OAuth2 credential.
- OpenAI Node: Set up or select your OpenAI API Key credential.
- HTTP Request (ElevenLabs): Configure an HTTP Header Auth credential for ElevenLabs using your API key.
- Configure Gmail Trigger:
- Specify the email criteria (e.g., sender email address, subject keywords) that identify newsletters you want to convert.
- Configure OpenAI Node:
- Ensure the model is set to
gpt-4o-mini(or your preferred summarization model). - Adjust the prompt to guide the summarization as needed (e.g., "Summarize the following newsletter for a podcast episode:").
- Ensure the model is set to
- Configure ElevenLabs HTTP Request:
- Verify the ElevenLabs API endpoint and payload for text-to-speech generation.
- Select the desired voice ID and model.
- Configure Read/Write Files from Disk:
- Specify the directory path where you want to save the generated audio files on your n8n host.
- Activate the Workflow: Once configured, activate the workflow to start monitoring your Gmail for new newsletters.
Related Templates
Automate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte
By Wessel BulteAI-powered code review with linting, red-marked corrections in Google Sheets & Slack
Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.
By higashiyama 🎓 How to transform unstructured email data into structured format with AI agent
This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
By Davide