Back to Catalog

Multi-modal personal AI assistant with Telegram & Google Gemini for productivity tools

Anshul ChauhanAnshul Chauhan
1838 views
2/3/2026
Official Page

Automate Your Life: The Ultimate AI Assistant in Telegram (Powered by Google Gemini)

Transform your Telegram messenger into a powerful, multi-modal personal or team assistant. This n8n workflow creates an intelligent agent that can understand text, voice, images, and documents, and take action by connecting to your favorite tools like Google Calendar, Gmail, Todoist, and more.

At its core, a powerful Manager Agent, driven by Google Gemini, interprets your requests, orchestrates a team of specialized sub-agents, and delivers a coherent, final response, all while maintaining a persistent memory of your conversations.

Key Features

🧠 Intelligent Automation: Uses Google Gemini as a central "Manager Agent" to understand complex requests and delegate tasks to the appropriate tool.

πŸ—£οΈ Multi-Modal Input: Interact naturally by sending text, voice notes, photos, or documents directly into your Telegram chat.

πŸ”Œ Integrated Toolset: Comes pre-configured with agents to manage your memory, tasks, emails, calendar, research, and project sheets.

πŸ—‚οΈ Persistent Memory: Leverages Airtable as a knowledge base, allowing the assistant to save and recall personal details, company information, or past conversations for context-rich interactions.

βš™οΈ Smart Routing: Automatically detects the type of message you send and routes it through the correct processing pipeline (e.g., voice is transcribed, images are analyzed).

πŸ”„ Conversational Context: Utilizes a window buffer to maintain short-term memory, ensuring follow-up questions and commands are understood within the current conversation.

How It Works

  1. The Telegram Trigger node acts as the entry point, receiving all incoming messages (text, voice, photo, document).

  2. A Switch node intelligently routes the message based on its type:

  • Voice: The audio file is downloaded and transcribed into text using a voice-to-text service.

  • Photo: The image is downloaded, converted to a base64 string, and prepared for visual analysis.

  • Document: The file is routed to a document handler that extracts its text content for processing.

  • Text: The message is used as-is.

  1. A Merge node gathers the processed input into a unified prompt.

  2. The Manager Agent receives this prompt. It analyzes the user's intent and orchestrates one or more specialized agents/tools:

  • memory_base (Airtable): For saving and retrieving information from your long-term knowledge base.

  • todo_and_task_manager (Todoist): To create, assign, or check tasks.

  • email_agent (Gmail): To compose, search, or send emails.

  • calendar_agent (Google Calendar): To schedule events or check your agenda.

  • research_agent (Wikipedia/Web Search): To look up information.

  • project_management (Google Sheets): To provide updates on project trackers.

  1. After executing the required tasks, the Manager Agent formulates a final response and sends it back to you via the Telegram node.

Setup Instructions

Follow these steps to get your AI assistant up and running.

  1. Telegram Bot:
  • Create a new bot using the BotFather in Telegram to get your Bot Token.
  • In the n8n workflow, configure the Telegram Trigger node's webhook.
  • Add your Bot Token to the credentials in all Telegram nodes.
  • For proactive messages, replace the chatId placeholders with your personal Telegram Chat ID.
  1. Google Gemini AI:
  • In the Google Gemini nodes, add your credentials by providing your Google Gemini API key.
  1. Airtable Knowledge Base:
  • Set up an Airtable base to act as your assistant's long-term memory.
  • In the memory_base nodes (Airtable nodes), configure the credentials and provide the Base ID and Table ID.
  1. Google Workspace APIs:
  • Connect your Google account credentials for Gmail, Google Calendar, and Google Sheets.
  • In the relevant nodes, specify the Document/Sheet IDs you want the assistant to manage.
  1. Connect Other Tools:
  • Add your credentials for Todoist and any other integrated tool APIs.
  1. Configure Conversational Memory:
  • This workflow is designed for multi-user support. Verify that the Session Key in the "Window Buffer Memory" nodes is correctly set to a unique user identifier from Telegram (e.g., {{ $json.chat.id }}). This ensures conversations from different users are kept separate.
  1. Review Schedule Triggers:
  • Check any nodes designed to run on a schedule (e.g., "At a regular time"). Adjust their cron expressions, times, and timezone to fit your needs (e.g., for daily summaries).
  1. Test the Workflow:
  • Activate the workflow.
  • Send a text message to your bot (e.g., "Hello!").

Estimated Setup Time

  • 30–60 minutes: If you already have your API keys, account credentials, and service IDs (like Sheet IDs) ready.

  • 2–3 hours: For a complete, first-time setup, which includes creating API keys, setting up new spreadsheets or Airtable bases, and configuring detailed permissions.

Multi-Modal Personal AI Assistant with Telegram & Google Gemini for Productivity Tools

This n8n workflow creates a powerful, multi-modal personal AI assistant that integrates with Telegram and leverages Google Gemini for intelligent responses and productivity tools. It allows users to interact with an AI agent directly from Telegram, providing a conversational interface to various functionalities.

What it does

This workflow orchestrates the following key steps:

  1. Listens for Telegram Messages: It continuously monitors a Telegram bot for incoming messages from users.
  2. Initializes AI Agent: When a message is received, it prepares the context for an AI agent.
  3. Manages Conversational Memory: It maintains a short-term memory of the conversation to provide context-aware responses.
  4. Processes User Input with AI Agent: The AI agent, powered by Google Gemini, analyzes the user's message.
  5. Utilizes Tools for Enhanced Capabilities: The AI agent can decide to use specific tools based on the user's request. Currently, it includes:
    • Wikipedia Search: To retrieve information from Wikipedia.
  6. Generates Responses: The Google Gemini Chat Model generates a natural language response based on the AI agent's processing.
  7. Sends Response to Telegram: The generated response is sent back to the user via Telegram.
  8. Schedules Regular Tasks (Optional): A schedule trigger is included, suggesting the potential for the AI assistant to perform routine tasks or send proactive messages.
  9. Allows for Field Editing: An "Edit Fields (Set)" node is present, which could be used for data transformation or setting specific values within the workflow.
  10. Includes Code Execution: A "Code" node allows for custom JavaScript logic to be executed, offering flexibility for advanced functionalities.
  11. Provides Flow Control: A "Switch" node enables conditional branching, allowing the workflow to take different paths based on specific criteria (e.g., message content, AI agent's decision).
  12. Facilitates Workflow Documentation: Sticky notes are included for internal documentation and explanations within the workflow.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance (self-hosted or cloud).
  • Telegram Bot: A Telegram Bot Token obtained from BotFather.
  • Google Gemini API Key: Access to the Google Gemini API.
  • Wikipedia: While not requiring an API key, the Wikipedia tool relies on external access to Wikipedia.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Telegram Credentials:
    • In the "Telegram Trigger" node, select or create a new Telegram API credential. You'll need your Telegram Bot Token.
    • In the "Telegram" node, select the same Telegram API credential.
  3. Configure Google Gemini Credentials:
    • In the "Google Gemini Chat Model" node, select or create a new Google Gemini API credential. You'll need your Google Gemini API Key.
    • In the "Google Gemini" node, select the same Google Gemini API credential.
  4. Activate the Workflow: Once all credentials are set up, activate the workflow.

Your AI assistant will now be live and respond to messages sent to your configured Telegram bot! You can interact with it by sending messages to your Telegram bot, and it will use its AI capabilities and tools to respond.

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 β€” Manually start the workflow or schedule it to run periodically. Input Setup β€” Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) β€” 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 β€” Clean and convert GPT output into JSON format. Data Storage (Google Sheets) β€” 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 β€œSet 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 β†’ Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection β†’ Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report β†’ Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization β†’ Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs β†’ 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 chatβ€”turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. πŸ“‹ 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 πŸ”§ 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 πŸ”‘ Required Credentials OpenAI API Setup Go to platform.openai.com β†’ API keys (sidebar) Click "Create new secret key" β†’ 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 β†’ Dashboard β†’ API Keys Generate a new API key β†’ 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) βš™οΈ 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 workflowβ€”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 β†’ 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 β†’ Download File From Google Download File From Google β†’ Extract from File β†’ Update File to One Drive Extract from File β†’ Message a model Message a model β†’ Create or update an account Create or update an account β†’ Create an opportunity Create an opportunity β†’ Build SOQL Build SOQL β†’ Query PricebookEntries Query PricebookEntries β†’ Code in JavaScript Code in JavaScript β†’ Create Opportunity Line Items --- Quick setup checklist πŸ” Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. πŸ“‚ 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 β†’ Account upsert fields Invoice code/date/amount β†’ Opportunity fields Product name must equal your Product2.ProductCode in SF. βœ… Test: Drop a sample PDF β†’ 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 β€œReturn 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