Back to Catalog

Create AI dream videos with analysis using Veo3 and Telegram

福壽一貴福壽一貴
226 views
2/3/2026
Official Page

Who is this for?

  • Dream journaling enthusiasts who want to visualize and record their dreams
  • Self-improvement practitioners interested in dream analysis and psychology
  • Content creators looking for unique, AI-generated dream-based content
  • Wellness coaches and therapists who use dream work with clients

What it does

  1. Receives dream descriptions via Telegram bot commands
  2. Parses visual style selection from 8 options (cinematic, ghibli, surreal, vintage, horror, abstract, watercolor, cyberpunk)
  3. Analyzes the dream using AI to extract themes, symbols, and psychological meaning
  4. Generates optimized video prompt tailored to the selected style with audio descriptions
  5. Creates AI video with native audio using Google Veo3 (single API call)
  6. Logs to Google Sheets as a searchable dream journal
  7. Sends video + analysis back to user via Telegram

How to set up

Estimated setup time: 15 minutes

Step 1: Create Telegram Bot

  1. Message @BotFather on Telegram
  2. Send /newbot and follow prompts
  3. Copy the API token

Step 2: Get fal.ai API Key

  1. Sign up at fal.ai
  2. Generate API key from dashboard
  3. In n8n, create Header Auth credential:
    • Name: Authorization
    • Value: Key YOUR_FAL_API_KEY

Step 3: Get OpenRouter API Key

  1. Sign up at openrouter.ai
  2. Generate API key
  3. Add to n8n as OpenRouter credential

Step 4: Set up Google Sheets (Optional)

  1. Create new spreadsheet with columns: Timestamp, Username, Style, Dream, Theme, Emotion, Type, Meaning, Video URL
  2. Connect Google Sheets credential in n8n
  3. Select your document and sheet in the "Log to Google Sheets" node

Step 5: Connect Credentials

  1. Add Telegram credential to all Telegram nodes
  2. Add fal.ai Header Auth to both HTTP Request nodes
  3. Add OpenRouter credential to the LLM node

Requirements

| Service | Purpose | Cost | |---------|---------|------| | Telegram Bot | User interface | Free | | fal.ai (Veo3) | Video + audio generation | ~$0.10-0.15/video | | OpenRouter | LLM for dream analysis | ~$0.01-0.03/request | | Google Sheets | Dream journal storage | Free |

How to customize

  • Change LLM: Replace OpenRouter with OpenAI, Anthropic, or other providers
  • Add styles: Edit the STYLES object in "Parse Dream Command" node
  • Modify analysis: Edit system prompt in "AI Dream Analyzer Agent" node
  • Change video model: Replace Veo3 URL with Kling, Luma, or other fal.ai models
  • Skip logging: Remove or disable the Google Sheets node

Commands

| Command | Description | |---------|-------------| | /dream [text] | Generate video in cinematic style | | /dream [style] [text] | Generate with specific style | | /styles | Show all available styles |

Example

Input: /dream ghibli I was flying over a forest where the trees had glowing leaves

Output: 8-second AI video with magical Ghibli-style visuals, ambient soundtrack, plus psychological analysis of flight symbolism and nature connection themes.

Create AI Dream Videos with Analysis using Veo3 and Telegram

This n8n workflow automates the process of generating AI dream videos, analyzing their content, and delivering results via Telegram. It allows users to initiate dream video creation through a simple Telegram message, then uses AI to interpret the dream and generate a video, which is then recorded and stored.

What it does

  1. Listens for Telegram Messages: The workflow is triggered by an incoming message to a configured Telegram bot.
  2. Filters Messages: It checks if the incoming Telegram message is a command to start the dream video generation.
  3. Initiates Dream Video Generation: If the command is recognized, it makes an HTTP request to an external service (likely Veo3) to create the AI dream video.
  4. Waits for Video Completion: The workflow pauses, waiting for the external service to complete the video generation.
  5. Records Video Information: Once the video is ready, it records details about the generated video (e.g., URL, analysis) into a Google Sheet.
  6. Analyzes Dream Content: It uses an AI Agent (likely powered by a language model like OpenRouter) to analyze the dream's content based on the generated video or associated text.
  7. Prepares Analysis for Output: The AI analysis is then processed and formatted for presentation.
  8. Sends Results to Telegram: Finally, the workflow sends the generated video link and the AI analysis back to the user via Telegram.

Prerequisites/Requirements

  • Telegram Bot: A Telegram bot token and chat ID configured in n8n.
  • Veo3 Account/API: Access to the Veo3 API or a similar service capable of generating AI dream videos via HTTP requests.
  • Google Sheets Account: A Google Sheet configured to store dream video data, with appropriate columns (e.g., for video URLs, analysis results).
  • OpenRouter API Key: An API key for OpenRouter or another compatible large language model service for dream analysis.
  • n8n Instance: An active n8n instance to run the workflow.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Telegram: Set up your Telegram Bot API credential with your bot token.
    • Google Sheets: Configure your Google Sheets credential to allow n8n to write to your specified spreadsheet.
    • OpenRouter Chat Model: Set up your OpenRouter API key as a credential for the AI Agent.
  3. Configure Nodes:
    • Telegram Trigger (Node 50): Ensure your Telegram bot is correctly set up to receive messages and the webhook is active.
    • HTTP Request (Node 19): Update the URL and any necessary headers/body for your Veo3 video generation API endpoint.
    • Google Sheets (Node 18): Specify the Spreadsheet ID and Sheet Name where dream video data will be recorded. Map the input fields to the correct columns in your sheet.
    • AI Agent (Node 1119): Configure the AI Agent with the OpenRouter Chat Model (Node 1281) and ensure it's set up to analyze the relevant dream content from previous nodes.
    • Telegram (Node 49): Ensure the correct Chat ID is used to send messages back to the user.
  4. Activate the Workflow: Once all configurations are complete, activate the workflow.
  5. Initiate from Telegram: Send the designated command (as configured in the "If" node) to your Telegram bot to start the dream video generation process.

Related Templates

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 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

Wessel BulteBy Wessel Bulte
247

🎓 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.

DavideBy Davide
1616