This article provides a complete, practical walkthrough of the Telegram Schedule Automation Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title:**  
   AI-Driven Nostr #damus Insights with n8n: Automated Reporting via Gmail and Telegram
   
   **Meta Description:**  
   Learn how a custom n8n workflow leverages LLMs, Google Gemini, and messaging platforms like Gmail and Telegram to automate theme extraction and reporting of #damus-tagged threads on Nostr. Discover the power of AI in community monitoring.
   
   **Keywords:**  
   n8n workflow, Nostr, #damus, Damus, Google Gemini, AI summarization, Telegram automation, Gmail automation, LangChain, community reporting, social media automation, LLM-powered workflow, n8n automation, sentiment analysis
   
   ---
   
   **AI-Powered Nostr Monitoring: Unlocking the Narrative Behind #damus**
   
   In the ever-evolving landscape of decentralized social media, Nostr and its mobile app, Damus, have carved out a strong following with users eager to embrace a censorship-resistant platform. But with this freedom comes complexity—how can developers, marketers, and community moderators keep up with conversations and themes developing under hashtags like #damus?
   
   Enter a powerful AI-driven n8n workflow that automatically collects, analyzes, and reports on #damus-tagged threads from Nostr using a combination of scheduling, aggregation, natural language processing (NLP), and automation tools like Gmail and Telegram. With large language models (LLMs) powering the insight engine via Google Gemini, this setup provides comprehensive and intelligent reporting without human intervention.
   
   ### Understanding the Workflow
   
   This specific n8n automation performs end-to-end analysis and reporting of trends, sentiments, and recurring themes from Nostr threads containing the #damus hashtag. Here’s a step-by-step breakdown:
   
   1. **Trigger & Collection**
      - The process is initiated either manually (for testing) or on a set schedule using the Schedule Trigger node.
      - The “Nostr Read #damus” community node (from `n8n-nodes-nostrobots`) scrapes threads from the decentralized social network Nostr that are tagged with #damus.
   
   2. **Data Aggregation**
      - These threads are aggregated using n8n’s built-in data structuring via the Aggregate node. This ensures all content is compiled in a suitable format for AI processing.
   
   3. **AI-Powered Theme Extraction**
      - The aggregated content is sent to Google Gemini 2.0 (via the LangChain integration) with prompts crafted to analyze and return:
        - The central themes of these #damus threads.
        - Highlights and motivations of the community.
        - Key examples or quotes from the content.
        - Suggestions for user engagement improvements.
   
   4. **Markdown to HTML Conversion**
      - Reports and summaries are first written in markdown format by the AI. These are then converted to HTML using the Markdown node to ensure delivery in a visually appealing and properly formatted style.
   
   5. **Dissemination via Email and Telegram**
      - Two different deliveries are triggered:
        - **Gmail Integration:** Sends a detailed report to a specified email address (e.g., joe@example.com) with the subject “#damus.” This could be used for marketing teams or product teams.
        - **Telegram Integration:** Sends HTML-formatted insights directly to a chosen Telegram channel/chat ID—helping community managers, moderators, or users on the go stay informed.
   
   ### Why Use AI for Damus Thread Monitoring?
   
   The n8n workflow utilizes AI not just to summarize text but to go deeper—extract motivations, identify community trends, and even recommend improvements. It accomplishes in seconds what a team of analysts might take hours to do.
   
   By leveraging Google’s Gemini models, known for their reasoning and contextual comprehension, the system produces human-like insights that are truly useful. Additionally, LangChain integration gives flexibility to shape prompts course-correct for different types of outputs, from bullet-point summaries to conversational analyses.
   
   ### Real-Time Community Insight, Delivered Where You Work
   
   The adaptable nature of this automation—whether the report arrives via email or your Telegram feed—means it can fit into almost any workflow. Marketing teams looking to capture user sentiments, product teams researching feedback, or moderators needing an overview of community behavior can all benefit.
   
   Furthermore, by using open-source infrastructure (n8n and Nostr), privacy and customization are prioritized—no vendor lockdown, no data exploitation.
   
   ---
   
   ### APIs and Tools Used
   
   Here are the third-party APIs and services integrated into this advanced automation:
   
   1. **Nostr (via n8n-nodes-nostrobots)** – Reads posts from the decentralized Nostr network filtered by hashtag.
   2. **Google Gemini API (via LangChain)** – Large Language Model used for analyzing and summarizing content.
   3. **Telegram Bot API** – Used to send summaries and insights to a Telegram channel or user.
   4. **Gmail API (OAuth2)** – Sends themed reports directly to designated email inboxes.
   5. **n8n’s Markdown Node** – Converts generated markdown content into HTML for formatting compatibility across channels.
   
   ---
   
   ### Final Thoughts
   
   This workflow exemplifies how AI and automation can simplify community monitoring and content analysis on decentralized platforms like Nostr. By combining LLMs for understanding and summarizing, and the scalable infrastructure of n8n, organizations and individuals can stay close to community sentiment, user concerns, and evolving conversations—all wrapped into instant, formatted reports delivered straight to their inbox or Telegram chats.
   
   Whether you’re a Damus app developer, a Nostr community leader, a social researcher, or simply a curious techie, harnessing workflows like this turns noise into knowledge—autonomously, accurately, and efficiently.
   
   ---
   
   Want to try it yourself? Check out the open-source Damus workflow on the [n8n community](https://github.com/ocknamo/n8n-nodes-nostrobots). Decentralized data, centralized insights—delivered seamlessly.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.