HTTP Request node
Telegram node
X (Formerly Twitter) node
+5

Automate Crypto News Posting to X & Telegram with AI Summarization

Published 5 days ago

Template description

Automate Crypto News Posting to X & Telegram with AI Summarization

This n8n template automates the process of curating and sharing the latest cryptocurrency news on X (formerly Twitter) and Telegram. By leveraging AI for content summarization, this workflow allows you to effortlessly maintain an active social media presence, keeping your audience informed about the dynamic crypto market without manual effort.

Who is this for?

This template is ideal for:

  • Content Creators & Marketers: Aiming to consistently share valuable news and engage their audience without manual content curation.
  • Crypto Influencers & Educators: Looking to provide timely news updates to their followers across multiple platforms.
  • Crypto Communities & DAOs: Seeking to automate news dissemination within their Telegram channels and wider X audience.
  • Anyone interested in automated news monitoring and sharing.

What problem is this workflow solving?

Manually tracking, summarizing, and posting crypto news across different social media platforms is time-consuming and requires constant effort. This workflow eliminates these manual tasks, allowing users to:

  • Save Time & Effort: Automate the entire news curation and posting process.
  • Maintain Consistent Presence: Ensure a regular flow of valuable crypto news updates on X and Telegram.
  • Increase Audience Engagement: Provide timely and summarized news to keep your audience informed and engaged.
  • Focus on Strategy: Free up time to focus on broader content strategy and audience growth instead of repetitive manual posting.

What this workflow does:

This workflow automates the following key steps:

  1. Scheduled News Retrieval: Uses a Schedule Trigger to run every 90 minutes (configurable), initiating the news gathering process.
  2. Real-time Crypto News Aggregation: Fetches the latest cryptocurrency news from the CryptoPanic API.
  3. Recent News Filtering: Filters news articles to include only those published within the last 30 minutes, ensuring timely updates.
  4. Content Extraction from News URLs: Visits individual news URLs and extracts the full article content.
  5. AI-Powered Content Summarization: Leverages GPT or other LLMs to extract the core content from news articles.
  6. Content Aggregation: Merges content from multiple news articles into a single input for summarization.
  7. AI-Driven Social Media Content Generation: Utilizes GPT or other LLMs to summarize the aggregated news and create two distinct outputs:
    • Concise & Engaging X Post: Optimized for Twitter's character limit, designed to be attention-grabbing.
    • Detailed Telegram Report: A more comprehensive summary suitable for a Telegram channel or group.
  8. Automated Posting to X (Twitter): Automatically posts the generated X summary to your connected Twitter account.
  9. Automated Delivery to Telegram: Automatically sends the detailed Telegram report to your specified Telegram chat ID.

Setup:

To get started, you will need to configure the following services and credentials:

  1. CryptoPanic API Token:

    • Obtain a free API token from the CryptoPanic website: https://cryptopanic.com/
    • In n8n, navigate to the "HTTP Request" node (named "HTTP Request").
    • In the node parameters, locate the "URL" field and replace "YOURTOKEN" in the URL with your obtained CryptoPanic API token.
  2. OpenAI API Key:

    • Obtain an API key from OpenAI: https://platform.openai.com/
    • For Content Extraction: In n8n, connect your OpenAI account to the "ContentExtraction GPT3.5" node (named "ContentExtraction GPT3.5"). Use your OpenAI API key for the credentials.
    • For News Summarization & Social Media Content Generation: In n8n, connect your OpenAI account to the "Summary news GPT" node (named "Summary news GPT"). Use your OpenAI API key for the credentials.
  3. X (Twitter) Developer Credentials:

    • Create a developer account and project on the X Developer Portal: https://developer.twitter.com/
    • Obtain the necessary API keys and tokens for your X app.
    • In n8n, connect your X Developer account credentials to the "X" node (named "X").
  4. Telegram Bot and Chat ID:

    • Create a Telegram bot using BotFather on Telegram. Obtain your bot's API token.
    • Obtain the Chat ID of the Telegram chat where you want to send news reports.
    • In n8n, connect your Telegram Bot API token to the "Telegram" node (named "Telegram").
    • In the "Telegram" node parameters, replace "YOUR_TELEGRAM_CHAT_ID" with your Telegram Chat ID.

How to customize this workflow:

  • Adapt to ANY Topic: Change the "HTTP Request" node to use a news API for your desired topic (AI, Sports, World News, etc.). Critically, adjust the prompts in the "Summary news GPT" node to be relevant to your chosen topic so the AI generates appropriate summaries and social media content.
  • Adjust Scheduling Frequency: Modify the "Schedule Trigger" node to change how often the workflow runs and posts news.
  • Adjust Scheduling Frequency: Modify the "Schedule Trigger" node to change the frequency of news updates (e.g., change the interval from 90 minutes to a different value).
  • Modify News Filtering: Customize the Python code in the "Extract Meta" node to adjust the news filtering criteria. You can change the time window (currently 30 minutes) or filter based on other criteria from the CryptoPanic API response.
  • Experiment with GPT Models: In the "Summary news GPT" node, try different OpenAI models (e.g., gpt-4, gpt-3.5-turbo-16k) to see how they affect the summarization quality and output. Note that more advanced models may incur higher API costs.
  • Customize AI Prompts: Fine-tune the system and user prompts in the "Summary news GPT" node to alter the tone, style, or format of the generated X and Telegram content. You can adjust the persona of the AI blogger, the desired level of detail in summaries, or specific keywords to include.
  • Extend to Other Platforms: Add nodes to post to other social media platforms like LinkedIn, Facebook, or Discord by adapting the "Summary news GPT" prompts and integrating relevant n8n nodes for those platforms.

Category:

Marketing, Social Media, AI, News Automation, Content Creation

Workflow by: Tianyi (muzi)
n8n Creators Profile: https://n8n.io/creators/muzi/

Share Template

More Finance workflow templates

HTTP Request node
Google Drive node
Google Calendar node
+9

Actioning Your Meeting Next Steps using Transcripts and AI

This n8n workflow demonstrates how you can summarise and automate post-meeting actions from video transcripts fed into an AI Agent. Save time between meetings by allowing AI handle the chores of organising follow-up meetings and invites. How it works This workflow scans for the calendar for client or team meetings which were held online. * Attempts will be made to fetch any recorded transcripts which are then sent to the AI agent. The AI agent summarises and identifies if any follow-on meetings are required. If found, the Agent will use its Calendar Tool to to create the event for the time, date and place for the next meeting as well as add known attendees. Requirements Google Calendar and the ability to fetch Meeting Transcripts (There is a special OAuth permission for this action!) OpenAI account for access to the LLM. Customising the workflow This example only books follow-on meetings but could be extended to generate reports or send emails.
jimleuk
Jimleuk
Google Sheets node
HTTP Request node
Merge node
+10

Invoice data extraction with LlamaParse and OpenAI

This n8n workflow automates the process of parsing and extracting data from PDF invoices. With this workflow, accounts and finance people can realise huge time and cost savings in their busy schedules. Read the Blog: https://blog.n8n.io/how-to-extract-data-from-pdf-to-excel-spreadsheet-advance-parsing-with-n8n-io-and-llamaparse/ How it works This workflow will watch an email inbox for incoming invoices from suppliers It will download the attached PDFs and processing them through a third party service called LlamaParse. LlamaParse is specifically designed to handle and convert complex PDF data structures such as tables to markdown. Markdown is easily to process for LLM models and so the data extraction by our AI agent is more accurate and reliable. The workflow exports the extracted data from the AI agent to Google Sheets once the job complete. Requirements The criteria of the email trigger must be configured to capture emails with attachments. The gmail label "invoice synced" must be created before using this workflow. A LlamaIndex.ai account to use the LlamaParse service. An OpenAI account to use GPT for AI work. Google Sheets to save the output of the data extraction process although this can be replaced for whatever your needs. Customizing this workflow This workflow uses Gmail and Google Sheets but these can easily be swapped out for equivalent services such as Outlook and Excel. Not using Excel? Simple redirect the output of the AI agent to your accounting software of choice.
jimleuk
Jimleuk
Webhook node
Respond to Webhook node
OpenAI node

Analyze tradingview.com charts with Chrome extension, N8N and OpenAI

This flow is supported by a Chrome plugin created with Cursor AI. The idea was to create a Chrome plugin and a backend service in N8N to do chart analytics with OpenAI. It's a good sample on how to submit a screenshot from the browser to N8N. Who is it for? N8N developers who want to learn about using a Chrome plugin, an N8N webhook and OpenAI. What opportunity does it present? This sample opens up a whole range of N8N connected Chrome extensions that can analyze screenshots by using OpenAI. What this workflow does? The workflow contains: a webhook trigger an OpenAI node with GPT-4O-MINI and Analyze Image selected a response node to send back the Text that was created after analysing the screenshot. All this is needed to talk to the Chrome extension which is created with Cursor AI. The idea is to visit the tradingview.com crypto charts, click the Chrome plugin and get back analytics about the shown chart in understandable language. This is driven by the N8N flow. With the new image analytics capabilities of OpenAI this opens up a world of opportunities. Requirements/setup OpenAI API key Cursor AI installed The Chrome extension. Download The N8N JSON code. Download How to customize it to your needs? Both the Chrome extension and N8N flow can be adapted to use on other websites. You can consider: analyzing a financial screen and ask questions about the data shown analyzing other charts extending the N8N workflow with other AI nodes With AI and image analytics the sky is the limit and in some cases it saves you from creating complex API integrations. Download Chrome extension
thingsio
Hans Blaauw
HTTP Request node
+11

Build a Financial Documents Assistant using Qdrant and Mistral.ai

This n8n workflow demonstrates how to manage your Qdrant vector store when there is a need to keep it in sync with local files. It covers creating, updating and deleting vector store records ensuring our chatbot assistant is never outdated or misleading. Disclaimer This workflow depends on local files accessed through the local filesystem and so will only work on a self-hosted version of n8n at this time. It is possible to amend this workflow to work on n8n cloud by replacing the local file trigger and read file nodes. How it works A local directory where bank statements are downloaded to is monitored via a local file trigger. The trigger watches for the file create, file changed and file deleted events. When a file is created, its contents are uploaded to the vector store. When a file is updated, its previous records are replaced. When the file is deleted, the corresponding records are also removed from the vector store. A simple Question and Answer Chatbot is setup to answer any questions about the bank statements in the system. Requirements A self-hosted version of n8n. Some of the nodes used in this workflow only work with the local filesystem. Qdrant instance to store the records. Customising the workflow This workflow can also work with remote data. Try integrating accounting or CRM software to build a managed system for payroll, invoices and more. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/189F1fNOiw6naNSlSwnyLVEm_Ho_IFfdM/view?usp=sharing
jimleuk
Jimleuk
HTTP Request node
Google Drive node
+7

Transcribing Bank Statements To Markdown Using Gemini Vision AI

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditiona OCR because: It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow here: https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, well have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data) which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLMs ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Requirements Google Gemini API for Multimodal LLM. Google Drive access for document storage. Stirling PDF instance for PDF to Image conversion Customising the workflow At time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini however you can switch to other multimodal LLMs such as OpenAI GPT or Antrophic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements any time soon? This template also works for Invoices, inventory lists, contracts, legal documents etc.
jimleuk
Jimleuk
Google Sheets node
Mindee node

Extract expenses from emails and add to Google Sheets

This workflow will check a mailbox for new emails and if the Subject contains Expenses or Reciept it will send the attachment to Mindee for processing then it will update a Google sheet with the values. To use this node you will need to set the Email Read node to use your mailboxes credentials and configure the Mindee and Google Sheets nodes to use your credentials.
jon-n8n
Jonathan

More AI workflow templates

OpenAI Chat Model node
SerpApi (Google Search) node

AI agent chat

This workflow employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions. To use this template, you need to be on n8n version 1.50.0 or later.
n8n-team
n8n Team
HTTP Request node
Merge node
+7

Scrape and summarize webpages with AI

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.
n8n-team
n8n Team
HTTP Request node
Markdown node
+5

AI agent that can scrape webpages

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url** – an address of the page to fetch method** = full / simplified maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recude the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.
eduard
Eduard
HTTP Request node
WhatsApp Business Cloud node
+10

Building Your First WhatsApp Chatbot

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.
jimleuk
Jimleuk
Merge node
Telegram node
Telegram Trigger node
+2

Telegram AI Chatbot

The workflow starts by listening for messages from Telegram users. The message is then processed, and based on its content, different actions are taken. If it's a regular chat message, the workflow generates a response using the OpenAI API and sends it back to the user. If it's a command to create an image, the workflow generates an image using the OpenAI API and sends the image to the user. If the command is unsupported, an error message is sent. Throughout the workflow, there are additional nodes for displaying notes and simulating typing actions.
eduard
Eduard
Google Drive node
Binary Input Loader node
Embeddings OpenAI node
OpenAI Chat Model node
+5

Ask questions about a PDF using AI

The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response. Note that to use this template, you need to be on n8n version 1.19.4 or later.
davidn8n
David Roberts

Implement complex processes faster with n8n

red icon yellow icon red icon yellow icon